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Report from Dagstuhl Seminar 15382
Modeling and Simulation of Sport Games SportMovements and Adaptations to TrainingEdited byRicardo Duarte1 Bjoumlrn Eskofier2 Martin Rumpf3 andJosef Wiemeyer4
1 University of Lisbon PT rduartefmhulisboapt2 Universitaumlt Erlangen-Nuumlrnberg DE bjoerneskofierfaude3 Universitaumlt Bonn DE martinrumpfinsuni-bonnde4 TU Darmstadt DE wiemeyersporttu-darmstadtde
AbstractThis report documents the program and the outcomes of Dagstuhl Seminar 15382 ldquoModelingand Simulation of Sport Games Sport Movements and Adaptations to Trainingrdquo The primarygoal of the seminar was the continuation of the interdisciplinary and transdisciplinarity researchin sports and computer science with the emphasis on modeling and simulation technologies Inthis seminar experts on modeling and simulation from computer science sport science andindustry were invited to discuss recent developments problems and future tasks in these fieldsFor instance computational models are applied in motor control and learning biomechanicsgame analysis training science sport psychology and sport sociology However for these modelsto be adequate accurate and fully utilized to their potential major inputs from both computerand sports scientists are required To bridge the potential disconnect between the skill setsof both sets of experts the major challenge is to equip both computer and sports scientistswith a common language and skill sets where both parties can communicate effectively Theseminar focused on three application areas sport games sport movements and adaptationsto training In conclusion the seminar showed that the different application areas face closelyrelated problems The disciplines could mutually benefit from each other combing the knowledgeof domain experts in eg computer vision biomechanics and match theory
Seminar September 13ndash16 2015 ndash httpwwwdagstuhlde153821998 ACM Subject Classification I63 Simulation and Modeling ndash ApplicationsKeywords and phrases Modeling Simulation Machine Learning Sports Science Biomechanics
Sport Games Training AdaptationDigital Object Identifier 104230DagRep5938Edited in cooperation with Eva Dorschky
1 Executive Summary
Josef WiemeyerRicardo DuarteBjoumlrn EskofierMartin Rumpf
License Creative Commons BY 30 Unported licensecopy Josef Wiemeyer Ricardo Duarte Bjoumlrn Eskofier and Martin Rumpf
Computational modeling and simulation are essential to analyze human motion and interactionin sport science sport practice and sport industry Applications range from game analysis
Except where otherwise noted content of this report is licensedunder a Creative Commons BY 30 Unported license
Modeling and Simulation of Sport Games Sport Movements and Adaptations to Training Dagstuhl ReportsVol 5 Issue 9 pp 38ndash56Editors Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer
Dagstuhl ReportsSchloss Dagstuhl ndash Leibniz-Zentrum fuumlr Informatik Dagstuhl Publishing Germany
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 39
issues in exercising like training load-adaptation relationship motor control and learning tobiomechanical analysis New challenges appear due to the rapid development of informationand communication technologies (ICT) as well as the enormous amount of data beingcaptured within training and competition domains The motivation of this seminar was toenable an interdisciplinary exchange between sports and computer scientists as well as sportpractice and industry to advance modeling and simulation technologies in selected fields ofapplications sport games sport movements and adaptations to trainingFrom September 13 to September 16 2015 about 29 representatives of science practice andindustry met at the Leibniz-Zentrum fuumlr Informatik in Schloss Dagstuhl to discuss selectedissues of modelling and simulation in the application fields of sport games sport movementsand adaptation to training This seminar was the fifth in a series of seminars addressingcomputer science in sport starting in 2006 Based on previously selected issues four mainstreams were identified
Validation and model selectionSensing and trackingSubject-specific modellingTraining and sport games
The talks addressing these four topics are summarized in this report They have beenarranged according to the three main application fields sport games sport movements andadaptations to training In addition generic comments on modeling in industry and scienceare presented Moreover the final discussion is summarized and a conclusion of the seminaris drawn
15382
40 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
2 Table of Contents
Executive SummaryJosef Wiemeyer Ricardo Duarte Bjoumlrn Eskofier and Martin Rumpf 38
Overview of Talks Sport GamesOn the Use of Tracking Data to Support Coaches in Professional FootballJohn Komar 42
The ldquoPractical Impact Debaterdquo in Performance AnalysisMartin Lames 42
Performance Analysis in Soccer Based on Knowledge Discovery ApproachRoland Leser 44
Individual Ball Possession in SoccerDaniel Link 45
Understanding Actions in a Sports ContextJim Little 45
Performance Analysis in Soccer Based on Knowledge Discovery ApproachBernhard Moser 46
Covered Distances of Handball Players Obtained by an Automatic Tracking MethodTiago Guedes Russomanno 46
Data Requirements in the Sports Data IndustryMalte Siegle 47
Factors that Influence Scoring Dynamics in Low-Scoring and High-Scoring TeamGamesAnna Volossovitch 48
Overview of Talks Sport MovementPredicting Human Responses to Environmental ChangesEva Dorschky 48
Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier 49
What is the Right ModelKaren Roemer 50
Model-Based Tracking of Human MotionAntonie van den Bogert 50
Overview of Talks Adaptions to TrainingModelling Speed-HR Relation using PerPotStefan Endler 51
Performance Adaptions to Football Training Is More Always BetterHugo Folgado 51
Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann 52
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 41
Model Design and Validation for Oxygen DynamicsDietmar Saupe 53
CommentsResearch PerspectiveAnne Danielle Koelewijn 53
Industry PerspectiveMalte Siegle 54
Discussion 54
Conclusion 55
Participants 56
15382
42 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
3 Overview of Talks Sport Games
31 On the Use of Tracking Data to Support Coaches in ProfessionalFootball
John Komar (Prozone Sports Ltd ndash Leeds GB)
License Creative Commons BY 30 Unported licensecopy John Komar
A new era in sports sciences is emerging with the advent of new digital tools for the analysis ofathletesrsquo training performance and health However massive technological changes emergingover the past few years have led to a new issue captured in the term Big Data A clearchallenge for sports scientists and practitioners is to understand which data matter andhow to interpret them The real challenge for the professional world is now to move fromdata-driven decisions to data-informed decisions From this perspective my talk addressesthe question of investigating tracking data in football (ie both events data and playersposition in xyt coordinate) in order to derive meaningful and functional metrics Broadlyspeaking the idea is to overcome traditional generic statistics (eg number of passes donenumber of shots number of tackles percentage of ball possession) by combining them intomeaningful items for coaches and practitioners (ie information that can help them to makeinformed choices for recruitment injury prevention or match analysis) More specifically partof the work presented looked at the number of goals one could expect from a player basedon the location of the shots he took [1] A model of expected goals per field position wasderived from previous seasons and then compared to the actual number of goals scored by aspecific player This comparison can thus inform about the ability of this player to under- oroverachieve in shots success Looking at probability of scoring goals during a season coachescan then be informed about conversion rate of a player but rather than a raw goalsshotsratio this goal expectancy metrics gives more context to the conversion ability (eg it cantake into account the position of the shot the defensive density during the shots) Combinedto other measures like ball movement effectiveness this kind of metrics can feed models ofoffensive contribution in professional football
References1 H Ruiz PJ Lisboa PJ Neilson and W Gregson ldquoMeasuring scoring efficiency through
goal expectancy estimationrdquo in Proc ESANNrsquo15 Bruges 2015
32 The ldquoPractical Impact Debaterdquo in Performance AnalysisMartin Lames (TU Muumlnchen DE)
License Creative Commons BY 30 Unported licensecopy Martin Lames
Problem
Mainstream research in Performance Analysis (PA) applies traditional linear methods to dataanalysis eg ANOVA In this context complex interactions in game sports are modelledquite poorly eg quality of opponent is introduced as another static variable in a linearmodel rank in final table of league [8 9] Fluctuations in behaviour typically a constitutivepattern of game sports are treated as measurement error and sought to be controlled by
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 43
enlarging sample size [3] On the other hand practical support is considered to be the mainpurpose of PA [6] McGarry (2009) sees advancing the understanding of sports mainly withregard to improving future outcomes In this situation a review of Drust and Green (2013)gave rise to the ldquoPractical Impact Debaterdquo The authors stated ldquo it can be suggested thatthe influence of the scientific information that is available has a relatively small influence onthe day-to-day activities within the ldquoreal worldrdquo of footballrdquo (p 1382)
The ldquoPractical Impact Debaterdquo
This statement was supported by a review of Mackenzie and Cushion (2013) They analysedexisting PA-research and found widespread methodological problems as well as a missingresearch strategy for practical support Carling et al (2014) replied to this paper and ndash amongother issues ndash defended analysts working in practice against some methodological criticismby the demands of conducting research in practical settings He frequently referred to hisearlier paper with the telling header ldquoShould we be more pragmatic in our approachrdquo [1]
Remarks on the ldquoPractical Impact Debaterdquo
The problem addressed has its root in a lacking distinction between applied and basic researchThis issue is mentioned by Mackenzie and Cushion (2013) without drawing consequences forresearch strategies to be applied In the eyes of the author it is helpful to distinguish betweenpractical PA (PPA) and theoretical PA (TPA) [5] TPA aims at clarifying the general structureof sports It looks for general rules appropriate models (dynamical systems modelling) andneeds large representative samples PPA is in some respects the opposite It may be definedas PA activities conducted in practice ie analysing training and competition to supporta team or a player PPA is interested in any information that provides practical supportand typically works with and for a single case the own team or player Moreover practicalconsequences for training may not be found algorithmically from data collected but need athorough interpretation with a background of in-depth knowledge from the many sources ofinformation available in a professional football club (medical physiotherapy fitness trainingare only the most important ones) So in PPA ndash whether with or without methodologicalawareness ndash qualitative research methodology is used which may be considered as a typicalfeature as well With a basic distinction between TPA and PPA researchers analysing thegeneral structure of performances are relieved from demonstrating an immediate practicaluse of their results and analysts working in practice shouldnrsquot feel obliged any more to refutecriticism from the point of view of basic research on their pragmatic solutions Neverthelessthere is a tight connection between both areas Measures in practice should be in agreementwith general findings about the nature of the game and in the other direction findings inpractice can give rise to new hypotheses on its structure
Agenda for computer science in PA
What are consequences of the ldquoPractical Impact Debaterdquo for the interdisciplinary researchfield of computer science in sports It becomes clear that there are different agendas for itworking in either TPA or PPA Nevertheless both areas depend on data on the matchesmeaning that there remains a general agenda including the detection of positions and actionsThe automatisation of action detection will be a prominent future task as well as drawinginferences on higher level from action and position data like analysing constructs not availablebefore like availability or more complex tactical behaviours like passing style or pressingSpecifically for TPA the introduction of more appropriate models will determine a future
15382
44 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
agenda As interaction and dynamics are constitutive for game sports these features shouldbe included in any future approach What PPA is concerned challenges lie in improvinginformational service for coaches and athletes We will see information systems that combinethe different sources of information by machine learning technologies to arrive at advancedversions of automated data mining for example driven by assumptions on the nature ofinformation needed and driven by query habits of the user All in all there is a challengingbut also promising future for computer science in PA to be expected
References1 C Carling C Wright LN Nelson and PS Bradley ldquoComment on Performance analysis
in football A critical review and implications for future researchrdquo J Sport Sci vol 32pp 2ndash7 2014
2 B Drust and M Green ldquoScience and football evaluating the influence of science onperformancerdquo J Sport Sci vol 31 pp 1377ndash1382 2013
3 M Hughes S Evans and J Wells ldquoEstablishing normative profiles in performance ana-lysisrdquo Int J Perform Anal Sport vol 1 pp 4ndash27 2001
4 M Lames and G Hansen ldquoDesigning observational systems to support top-level teams ingame sportsrdquo Int J Perform Anal Sport vol 1 pp 85ndash91 2001
5 M Lames and T McGarry ldquoOn the search for reliable performance indicators in gamesportsrdquo Int J Perform Anal Sport vol 7 no 1 pp 62ndash79 2007
6 R Mackenzie and C Cushion ldquoPerformance analysis in football A critical review andimplications for future researchrdquo J Sport Sci vol 31 pp 639ndash676 2013
7 T McGarry DI Anderson SA Wallace MD Hughes and IM Franks ldquoSport compet-ition as a dynamical self-organizing systemrdquo J Sport Sci vol 20 pp 771ndash781 2002
8 P OrsquoDonoghue ldquoInteracting Performances Theoryrdquo Int J Perform Anal Sport vol 9pp 26ndash46 2009
9 A Tenga and E Sigmundstad ldquoCharacteristics of goal-scoring possessions in open-playComparing the top in-between and bottom teams from professional soccer leaguerdquo Int JPerform Anal Sport vol 11 pp 545ndash552 2011
33 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Roland Leser (Universitaumlt Wien AT)
License Creative Commons BY 30 Unported licensecopy Roland Leser
Joint work of Leser Roland Moser Bernhard Hoch Thomas Baca Arnold
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games this
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 45
approach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
34 Individual Ball Possession in SoccerDaniel Link (TU Muumlnchen DE)
License Creative Commons BY 30 Unported licensecopy Daniel Link
This paper describes models for detecting individual and team ball possession in soccer basedon position data The types of ball possession are classified as Individual Ball Possession(IBC) Individual Ball Action (IBA) Individual Ball Control (IBC) Team Ball Possession(TBP) Team Ball Control (TBC) und Team Playmaking (TPM) according to the type ofball control involved The machine learning approach used is able to determine how longthe ball spends in the sphere of influence of a player based on the distance between theplayers and the ball together with their direction of motion speed and the acceleration ofthe ball The degree of ball control exhibited during this phase is classified based on thespatio-temporal configuration of the player controlling the ball the ball itself and opposingplayers using a Bayesian networkThe evaluation and illustrative application of this approachuses data taken from a game in a top European league When applied to error-corrected rawdata the algorithm showed an accuracy of 92 (IBA) 86 (IBP) and 92 (IBC) using atolerance of 06 s This is well above the accuracy achieved manually by the competitioninformation providers of 52 (TBC) There were 1291 phases involving ball control (IBC)totalling 2939 min with a gross game time of 9012 min and a net game time of 5756 minThis initial analysis of ball possession at the player level indicates IBC times of between022 and 318 min The shortest ball control times are observed for the centre forwards ofeach team (09 s) and the full backs of the losing team (07 s) and the longest for the losingteamrsquos goalkeeper (29 s) This can be interpreted as a tendency for the defenders to try andclear the ball as quickly as possible and the goalkeeper attempting to slow the pace of game
35 Understanding Actions in a Sports ContextJim Little (University of British Columbia ndash Vancouver CA)
License Creative Commons BY 30 Unported licensecopy Jim Little
Understanding human action is critical to surveillance monitoring and situation under-standing Sports present events where the types of actions are limited and depend on roleslocations and situations Understanding the actions activity and performance of the playersinterests many Action understanding in broadcast video has led to progress in trackingrecognition camera rectification pose and multi-view action recognition
15382
46 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
36 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Bernhard Moser (Software Competence Center ndash Hagenberg AT)
License Creative Commons BY 30 Unported licensecopy Bernhard Moser
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games thisapproach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
37 Covered Distances of Handball Players Obtained by an AutomaticTracking Method
Tiago Guedes Russomanno (University of Brasilia BR)
License Creative Commons BY 30 Unported licensecopy Tiago Guedes Russomanno
Joint work of Russomanno Tiago Guedes Misuta Milton Shoiti Menezes Rafael Pombo Brandatildeo BrunoCedraz Figueroa Pascual Jovino Leite Neucimar Jeronimo Goldenstein Siome Klein BarrosRicardo Machado Leite
Main reference TG Russomanno M S Misuta R PMenezes BC Brandatildeo P J Figueroa N J Leite SKGoldstein RM L Barros ldquoCovered distances of handball players obtained by an automatictracking methodrdquo in Proc of the 25th Intrsquol Symp on Biomechanics in Sports (ISBSrsquo07)pp 324ndash327 University of Konstanz 2007
URL httpsojsubuni-konstanzdecpaarticleview473
Tracking players in sports events is still a topic of discussion in sport science and the dataprovided by this tracking is useful for team staff to evaluate team performance Therefore theaim of this work was to obtain the distances covered by handball players and their velocitiesduring a match using a new approach based on automatic tracking method described inFigueroa et al [1 2] and the Adaboost detector [5] A whole game of a Brazilian regionalhandball championship for players under age of 21 was recorded Applying the mentionedautomatic tracking the accumulated covered distances and the velocities were calculated forall the players The results of average covered distances (plusmnSD) in the 1st and 2nd halveswere 2199 (plusmn230) and 2453 (plusmn214) The results of covered distances and the velocities allowindividual and collective analyses of the players by the team staff The proposed methodrevealed to be a powerful tool to improve physical analysis of the handball players
References1 P J Figueroa N J Leite and RM L Barros ldquoTracking soccer players aiming their kin-
ematical motion analysisrdquo Comput Vis Image Underst vol 101 no 2 pp 122-135 2006
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 47
2 P J Figueroa N J Leite and RM L Barros ldquoBackground recovering in outdoor imagesequences An example of soccer players segmentationrdquo Image Vision Comput vol 24 no4 pp 363-374 2006
3 M Kristan J Pers J M Perse M Bon and S Kovacic ldquoMultiple interacting targetstracking with application to team sportsrdquo in Proc ISAP Zagreb 2005 pp 322-327
4 MS Misuta R P Menezes P J Figueroa SA Cunha and RML Barros ldquoRepresent-ation and analysis of soccer player trajectoriesrdquo in Proc ISB Cleveland 2005 vol 415
5 K Okuma A Taleghani N de Freitas J Little and D Lowe ldquoA boosted particle filtermultitarget detection and trackingrdquo in Proc ECCV Prague 2004 pp 28-38
6 P Viola and M J Jones ldquoRapid Object Detection using a Boosted Cascade of SimpleFeaturesrdquo in Proc CVPR Kauai 2001 vol 1 pp 511-518
38 Data Requirements in the Sports Data IndustryMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
Different markets do have different requirements concerning data depth data delivery speedand data quality Here is a short overview about three markets (1) Professional Sports (2)Media and (3) Bookmakers Betting1 Professional Sports (Teams Clubs Leagues)
Strong need for deep dataDelivery speed not too important as most teams use the data post-match Moreoverlive-match analyses are sometimes prohibitedData quality is important Performance analysis based on imprecise data could causewrong conclusions
2 MediaStrong need for deep data (eg for story telling)Delivery speed Not too important as there is a broadcasting latency anywaysData quality is not important
3 Bookmakers BettingData depth is not important as there is a market limit anyways If you would offertoo many bets you would cannibalize your own marketDelivery speed is a must have and very importantJust as data quality is Wrong data could cause a of lot trouble and punters wouldclaim their right to get their money back
Consequently for a company like Sportradar it is very important to fulfill all differentrequirements This results in the claim to be able to provide fast highly accurate and deepdata
15382
48 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
39 Factors that Influence Scoring Dynamics in Low-Scoring andHigh-Scoring Team Games
Anna Volossovitch (University of Lisbon PT)
License Creative Commons BY 30 Unported licensecopy Anna Volossovitch
Joint work of Volossovitch Anna Pratas Joseacute Dumangane Montezuma Rosati NicolettaMain reference M Dumangane N Rosati A Volossovitch ldquoDeparture from independence and stationarity in a
handball matchrdquo Journal of Applied Statistics 36(7)723ndash741 2009URL httpdxdoiorg10108002664760802499329
Research in match analysis frequently attempts to establish causal relationships betweenisolated performance variables and goal scoring or game outcome This approach reduces thecomplexity of performance by presenting it in overly descriptive and regular ways which donot reflect properly the curse of the game The purpose of the presentation was to discuss twoexamples of the analysis of factors which could influence the scoring dynamic during a matchin handball and in football In the first example the influence of teams past performanceon the present teams performance was evaluated throughout the handball match using themodel with time-varying parameters This model estimates the probability of scoring as afunction of the past performance of the opposing team and the current match result Thisassessment considers the specific context of the game situation the teams rankings thematch equilibrium and the number of ball possessions per match In the second example theperformance indicators which had a significant effect on the time of the first goal scoringin football has been identified using Cox time-dependent proportional hazard model Thesurvival analysis is suggested to be a suitable tool to identify which and how performanceindicators influence the time of the first ball is scored in different competitive context
References1 J Pratas AVolossovitch and A I Carita ldquoWhat performance indicators influence the
time of the first goal in the matchrdquo In Proc World Congress of Performance Analysis ofSport X Opatija 2014 p 102
2 A Volossovitch M Dumangane and N Rosati ldquoThe influence of the pace of match onthe dynamic of handball gamerdquo Int J Sports Psychol vol 41 no 4 pp 117ndash118 2010
3 M Dumangane N Rosati and A Volossovitch ldquoDeparture from independence and sta-tionarity in a handball matchrdquo J Appl Stat vol 36 no 7 pp 723ndash741 2009
4 Overview of Talks Sport Movement
41 Predicting Human Responses to Environmental ChangesEva Dorschky (Universitaumlt Erlangen ndash Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Eva Dorschky
Joint work of Dorschky Eva van den Bogert Antonie J Schlarb Heiko Eskofier BjoumlrnMain reference E Dorschky et al ldquoPredictive Musculoskeletal Simulation of Uphill and Downhill Runningrdquo in
Proc ECSS Malmouml 2015 p 126
Predicting human responses to environmental changes is necessary for biomechanical analysisand sports product design If case studies environmental conditions or prototypes cannot berealized modeling and simulation can be used instead The aim of this work was to evaluatea method of predictive musculoskeletal simulation [1] for uphill and downhill running A
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 49
study was simulated by randomizing the modelrsquos muscle parameters The predicted energycosts for running at different slopes were compared to literature [2] Future work includes apersonalization of biomechanical models to represent individual athletes as well as populationgroups The sensitivity of simulation results to model parameters will be studied to ensurerobust simulation results
References1 A J van den Bogert M Hupperets H Schlarb and B KrabbeldquoPredictive musculoskeletal
simulation using optimal control effects of added limb mass on energy cost and kinematicsof walking and runningrdquo in Proc Inst Mech Eng Part P J Sports Eng Technol vol 226pp 123-133 2012
2 AE Minetti C Moia G S Roi D Susta and G Ferretti ldquoEnergy cost of walking andrunning at extreme uphill and downhill slopesrdquo J Appl Physiol vol 93 no 3 pp 1039-10462002
42 Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier (Universitaumlt Erlangen-Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Bjoumlrn Eskofier
Wearable computing systems play an increasingly important role in recreational and elitesports They comprise of two important parts The first are sensors embedded into clothesand equipment that are used for physiological (ECG EMG) and biomechanical (accelerometergyroscope) data recording The second are signal processing and data mining algorithmsimplemented on wearable computers (smartphones watches) that are used for analysis of therecorded data Wearable computing systems can provide support real-time feedback andcoaching advice to sportsmen of all performance levels In order to implement these systemsseveral challenges have to be addressed Our work focusses on four of the most prevalent ofthese
Integration sensors and microprocessors have to be embedded unobtrusively and have torecord a variety of signalsCommunication sensors and microprocessors have to communicate in body-area-networksin a secure safe and energy-saving mannerInterpretation physiological and biomechanical data have to be interpreted using signalprocessing and machine learning methodsSimulation and modeling understanding of sensor data is needed to model processes insports more accurately simulation methodologies help here to provide basic informationto drive those models
15382
50 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
43 What is the Right ModelKaren Roemer (Central Washington University ndash Ellensburg US)
License Creative Commons BY 30 Unported licensecopy Karen Roemer
Investigating human movements requires using biomechanical models to perform kinematicand kinetic analyses Depending on the available motion analysis system or software packagesanthropometric models tracking models joint models etc are applied to quantify kinematicand kinetic variables Two standard biomechanical models (OpenSim and Visual3D) wereused to analyze a simple stepping task Similarities and differences of kinematic and kineticresults for both models were discussed
44 Model-Based Tracking of Human MotionAntonie van den Bogert (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Antonie van den Bogert
Two sensing modalities are in use for field studies of human movement inertial sensing andvideo While both are suitable for analysis of human movement during sports events dataquality is usually far inferior to those encountered during laboratory conditions Inspiredby the classical Kalman filtering concept we consider using a dynamic model to improvethe estimates of the state trajectory of the system This approach presents the estimationproblem as an optimal control problem find state and control trajectories that satisfy systemdynamics and minimize a cost function For tracking of sensor data the cost function consistsof the sum of squared errors between simulated and measured sensor signals When using amusculoskeletal model an extra term representing muscular effort must be added to ensurea unique solution This is due to the classical load sharing redundancy humans have moremuscles than strictly necessary to produce movementEfficient solution methods have been developed to solve this optimal control problem andthe approach was successfully used to obtain a detailed analysis of a landing movementduring skiing based on low-quality video data [1] The same approach was used to performa full dynamic gait analysis including muscle force estimation with body-mounted accelero-meters [2] The accelerometer-based analysis was not good enough for clinical applicationsbecause it was too sensitive to the unmodeled dynamics (damped vibrations) of the accelero-meter attachments Improvement is expected when the instrumentation is supplemented bygyroscopic angular velocity sensorsModel-based state estimation will reduce the sensitivity to measurement error and thereare additional advantages The estimation process includes estimation of the full state ofthe system including variables such as muscle forces which could not be directly obtainedfrom sensor signals The raw data is then not only filtered but also enriched by the dynamicmodel A second advantage is that a dynamically consistent simulation is obtained as anadditional result Such simulations can be useful to explore ldquowhat ifrdquo scenarios such assports injuries that are caused by unfavorable landing postures [3]
References1 A J van den Bogert D Blana and D Heinrich ldquoImplicit methods for efficient musculo-
skeletal simulation and optimal controlrdquo Procedia IUTAM vol 2 pp 297-316 2011
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 51
2 O Nwanna ldquoValidation of an Accelerometry Based Method of Human Gait Analysisrdquo MSthesis Cleveland State University 2014 httpraveohiolinkeduetdcviewacc_num=csu1400424346
3 D Heinrich A J van den Bogert and W Nachbauer ldquoRelationship between jump landingkinematics and peak ACL force during a jump in downhill skiing A simulation studyrdquoScand J Med Sci Sports vol 24 no 3 pp 180-187 2013
5 Overview of Talks Adaptions to Training
51 Modelling Speed-HR Relation using PerPotStefan Endler (Universitaumlt Mainz DE)
License Creative Commons BY 30 Unported licensecopy Stefan Endler
Joint work of Endler Stefan Perl JuumlrgenMain reference S Endler J Perl ldquoOptimizing practice and competition in marathon running by means of the
meta-modelrdquo in Proc of 2012 pre-Olympic Congress on Sports Science and Computer Science inSport (IACSSrsquo12) pp 127ndash131 World Academic Union 2012
A model which represents the heart rate (HR) process based on a speed process has tomodel three apects1 Increasing HR after increasing speed2 Decreasing HR after decreasing speed3 Break down after exhaustionThe performance potential metamodell (PerPot) models all these behaviors It was adapted tothe environment of endurance running Once the model is calibrated to the individual athleteby a graded incremental test it can be used for simulation of eg competitions Simulationscan help unexperienced athletes particularly to avoid overloading and underperforming
52 Performance Adaptions to Football Training Is More AlwaysBetter
Hugo Folgado (University of Evora PT)
License Creative Commons BY 30 Unported licensecopy Hugo Folgado
Joint work of Folgado Hugo Sampaio Jaime
Traditionally performance in sports is measured by magnitude based indicators summedup by the Olympic moto ndash Citius Altius Fortius ndash Faster Higher Stronger Howeverin several sport domains and particularly in team sports this idea has been challengedby recent research In football the physical analysis of matches in different competitiveleagues have showed that players in higher level contexts tend to run less and at lowerintensities than players involved in lower leagues [1] In other approach studying the effectsof congested fixtures in players physical performance showed no differences in the amount ofdistance covered and distance covered at different displacement intensities [2] So it may bespeculated that the amount of displacement is not related to greater levels of performance infootball Based in these approaches we measured players physical and tactical performancedevelopment during the preseason evaluated during sided-games Our results showed thatplayers tend to reduce the amount of distance covered during these situation has the preseason
15382
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 39
issues in exercising like training load-adaptation relationship motor control and learning tobiomechanical analysis New challenges appear due to the rapid development of informationand communication technologies (ICT) as well as the enormous amount of data beingcaptured within training and competition domains The motivation of this seminar was toenable an interdisciplinary exchange between sports and computer scientists as well as sportpractice and industry to advance modeling and simulation technologies in selected fields ofapplications sport games sport movements and adaptations to trainingFrom September 13 to September 16 2015 about 29 representatives of science practice andindustry met at the Leibniz-Zentrum fuumlr Informatik in Schloss Dagstuhl to discuss selectedissues of modelling and simulation in the application fields of sport games sport movementsand adaptation to training This seminar was the fifth in a series of seminars addressingcomputer science in sport starting in 2006 Based on previously selected issues four mainstreams were identified
Validation and model selectionSensing and trackingSubject-specific modellingTraining and sport games
The talks addressing these four topics are summarized in this report They have beenarranged according to the three main application fields sport games sport movements andadaptations to training In addition generic comments on modeling in industry and scienceare presented Moreover the final discussion is summarized and a conclusion of the seminaris drawn
15382
40 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
2 Table of Contents
Executive SummaryJosef Wiemeyer Ricardo Duarte Bjoumlrn Eskofier and Martin Rumpf 38
Overview of Talks Sport GamesOn the Use of Tracking Data to Support Coaches in Professional FootballJohn Komar 42
The ldquoPractical Impact Debaterdquo in Performance AnalysisMartin Lames 42
Performance Analysis in Soccer Based on Knowledge Discovery ApproachRoland Leser 44
Individual Ball Possession in SoccerDaniel Link 45
Understanding Actions in a Sports ContextJim Little 45
Performance Analysis in Soccer Based on Knowledge Discovery ApproachBernhard Moser 46
Covered Distances of Handball Players Obtained by an Automatic Tracking MethodTiago Guedes Russomanno 46
Data Requirements in the Sports Data IndustryMalte Siegle 47
Factors that Influence Scoring Dynamics in Low-Scoring and High-Scoring TeamGamesAnna Volossovitch 48
Overview of Talks Sport MovementPredicting Human Responses to Environmental ChangesEva Dorschky 48
Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier 49
What is the Right ModelKaren Roemer 50
Model-Based Tracking of Human MotionAntonie van den Bogert 50
Overview of Talks Adaptions to TrainingModelling Speed-HR Relation using PerPotStefan Endler 51
Performance Adaptions to Football Training Is More Always BetterHugo Folgado 51
Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann 52
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 41
Model Design and Validation for Oxygen DynamicsDietmar Saupe 53
CommentsResearch PerspectiveAnne Danielle Koelewijn 53
Industry PerspectiveMalte Siegle 54
Discussion 54
Conclusion 55
Participants 56
15382
42 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
3 Overview of Talks Sport Games
31 On the Use of Tracking Data to Support Coaches in ProfessionalFootball
John Komar (Prozone Sports Ltd ndash Leeds GB)
License Creative Commons BY 30 Unported licensecopy John Komar
A new era in sports sciences is emerging with the advent of new digital tools for the analysis ofathletesrsquo training performance and health However massive technological changes emergingover the past few years have led to a new issue captured in the term Big Data A clearchallenge for sports scientists and practitioners is to understand which data matter andhow to interpret them The real challenge for the professional world is now to move fromdata-driven decisions to data-informed decisions From this perspective my talk addressesthe question of investigating tracking data in football (ie both events data and playersposition in xyt coordinate) in order to derive meaningful and functional metrics Broadlyspeaking the idea is to overcome traditional generic statistics (eg number of passes donenumber of shots number of tackles percentage of ball possession) by combining them intomeaningful items for coaches and practitioners (ie information that can help them to makeinformed choices for recruitment injury prevention or match analysis) More specifically partof the work presented looked at the number of goals one could expect from a player basedon the location of the shots he took [1] A model of expected goals per field position wasderived from previous seasons and then compared to the actual number of goals scored by aspecific player This comparison can thus inform about the ability of this player to under- oroverachieve in shots success Looking at probability of scoring goals during a season coachescan then be informed about conversion rate of a player but rather than a raw goalsshotsratio this goal expectancy metrics gives more context to the conversion ability (eg it cantake into account the position of the shot the defensive density during the shots) Combinedto other measures like ball movement effectiveness this kind of metrics can feed models ofoffensive contribution in professional football
References1 H Ruiz PJ Lisboa PJ Neilson and W Gregson ldquoMeasuring scoring efficiency through
goal expectancy estimationrdquo in Proc ESANNrsquo15 Bruges 2015
32 The ldquoPractical Impact Debaterdquo in Performance AnalysisMartin Lames (TU Muumlnchen DE)
License Creative Commons BY 30 Unported licensecopy Martin Lames
Problem
Mainstream research in Performance Analysis (PA) applies traditional linear methods to dataanalysis eg ANOVA In this context complex interactions in game sports are modelledquite poorly eg quality of opponent is introduced as another static variable in a linearmodel rank in final table of league [8 9] Fluctuations in behaviour typically a constitutivepattern of game sports are treated as measurement error and sought to be controlled by
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 43
enlarging sample size [3] On the other hand practical support is considered to be the mainpurpose of PA [6] McGarry (2009) sees advancing the understanding of sports mainly withregard to improving future outcomes In this situation a review of Drust and Green (2013)gave rise to the ldquoPractical Impact Debaterdquo The authors stated ldquo it can be suggested thatthe influence of the scientific information that is available has a relatively small influence onthe day-to-day activities within the ldquoreal worldrdquo of footballrdquo (p 1382)
The ldquoPractical Impact Debaterdquo
This statement was supported by a review of Mackenzie and Cushion (2013) They analysedexisting PA-research and found widespread methodological problems as well as a missingresearch strategy for practical support Carling et al (2014) replied to this paper and ndash amongother issues ndash defended analysts working in practice against some methodological criticismby the demands of conducting research in practical settings He frequently referred to hisearlier paper with the telling header ldquoShould we be more pragmatic in our approachrdquo [1]
Remarks on the ldquoPractical Impact Debaterdquo
The problem addressed has its root in a lacking distinction between applied and basic researchThis issue is mentioned by Mackenzie and Cushion (2013) without drawing consequences forresearch strategies to be applied In the eyes of the author it is helpful to distinguish betweenpractical PA (PPA) and theoretical PA (TPA) [5] TPA aims at clarifying the general structureof sports It looks for general rules appropriate models (dynamical systems modelling) andneeds large representative samples PPA is in some respects the opposite It may be definedas PA activities conducted in practice ie analysing training and competition to supporta team or a player PPA is interested in any information that provides practical supportand typically works with and for a single case the own team or player Moreover practicalconsequences for training may not be found algorithmically from data collected but need athorough interpretation with a background of in-depth knowledge from the many sources ofinformation available in a professional football club (medical physiotherapy fitness trainingare only the most important ones) So in PPA ndash whether with or without methodologicalawareness ndash qualitative research methodology is used which may be considered as a typicalfeature as well With a basic distinction between TPA and PPA researchers analysing thegeneral structure of performances are relieved from demonstrating an immediate practicaluse of their results and analysts working in practice shouldnrsquot feel obliged any more to refutecriticism from the point of view of basic research on their pragmatic solutions Neverthelessthere is a tight connection between both areas Measures in practice should be in agreementwith general findings about the nature of the game and in the other direction findings inpractice can give rise to new hypotheses on its structure
Agenda for computer science in PA
What are consequences of the ldquoPractical Impact Debaterdquo for the interdisciplinary researchfield of computer science in sports It becomes clear that there are different agendas for itworking in either TPA or PPA Nevertheless both areas depend on data on the matchesmeaning that there remains a general agenda including the detection of positions and actionsThe automatisation of action detection will be a prominent future task as well as drawinginferences on higher level from action and position data like analysing constructs not availablebefore like availability or more complex tactical behaviours like passing style or pressingSpecifically for TPA the introduction of more appropriate models will determine a future
15382
44 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
agenda As interaction and dynamics are constitutive for game sports these features shouldbe included in any future approach What PPA is concerned challenges lie in improvinginformational service for coaches and athletes We will see information systems that combinethe different sources of information by machine learning technologies to arrive at advancedversions of automated data mining for example driven by assumptions on the nature ofinformation needed and driven by query habits of the user All in all there is a challengingbut also promising future for computer science in PA to be expected
References1 C Carling C Wright LN Nelson and PS Bradley ldquoComment on Performance analysis
in football A critical review and implications for future researchrdquo J Sport Sci vol 32pp 2ndash7 2014
2 B Drust and M Green ldquoScience and football evaluating the influence of science onperformancerdquo J Sport Sci vol 31 pp 1377ndash1382 2013
3 M Hughes S Evans and J Wells ldquoEstablishing normative profiles in performance ana-lysisrdquo Int J Perform Anal Sport vol 1 pp 4ndash27 2001
4 M Lames and G Hansen ldquoDesigning observational systems to support top-level teams ingame sportsrdquo Int J Perform Anal Sport vol 1 pp 85ndash91 2001
5 M Lames and T McGarry ldquoOn the search for reliable performance indicators in gamesportsrdquo Int J Perform Anal Sport vol 7 no 1 pp 62ndash79 2007
6 R Mackenzie and C Cushion ldquoPerformance analysis in football A critical review andimplications for future researchrdquo J Sport Sci vol 31 pp 639ndash676 2013
7 T McGarry DI Anderson SA Wallace MD Hughes and IM Franks ldquoSport compet-ition as a dynamical self-organizing systemrdquo J Sport Sci vol 20 pp 771ndash781 2002
8 P OrsquoDonoghue ldquoInteracting Performances Theoryrdquo Int J Perform Anal Sport vol 9pp 26ndash46 2009
9 A Tenga and E Sigmundstad ldquoCharacteristics of goal-scoring possessions in open-playComparing the top in-between and bottom teams from professional soccer leaguerdquo Int JPerform Anal Sport vol 11 pp 545ndash552 2011
33 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Roland Leser (Universitaumlt Wien AT)
License Creative Commons BY 30 Unported licensecopy Roland Leser
Joint work of Leser Roland Moser Bernhard Hoch Thomas Baca Arnold
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games this
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 45
approach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
34 Individual Ball Possession in SoccerDaniel Link (TU Muumlnchen DE)
License Creative Commons BY 30 Unported licensecopy Daniel Link
This paper describes models for detecting individual and team ball possession in soccer basedon position data The types of ball possession are classified as Individual Ball Possession(IBC) Individual Ball Action (IBA) Individual Ball Control (IBC) Team Ball Possession(TBP) Team Ball Control (TBC) und Team Playmaking (TPM) according to the type ofball control involved The machine learning approach used is able to determine how longthe ball spends in the sphere of influence of a player based on the distance between theplayers and the ball together with their direction of motion speed and the acceleration ofthe ball The degree of ball control exhibited during this phase is classified based on thespatio-temporal configuration of the player controlling the ball the ball itself and opposingplayers using a Bayesian networkThe evaluation and illustrative application of this approachuses data taken from a game in a top European league When applied to error-corrected rawdata the algorithm showed an accuracy of 92 (IBA) 86 (IBP) and 92 (IBC) using atolerance of 06 s This is well above the accuracy achieved manually by the competitioninformation providers of 52 (TBC) There were 1291 phases involving ball control (IBC)totalling 2939 min with a gross game time of 9012 min and a net game time of 5756 minThis initial analysis of ball possession at the player level indicates IBC times of between022 and 318 min The shortest ball control times are observed for the centre forwards ofeach team (09 s) and the full backs of the losing team (07 s) and the longest for the losingteamrsquos goalkeeper (29 s) This can be interpreted as a tendency for the defenders to try andclear the ball as quickly as possible and the goalkeeper attempting to slow the pace of game
35 Understanding Actions in a Sports ContextJim Little (University of British Columbia ndash Vancouver CA)
License Creative Commons BY 30 Unported licensecopy Jim Little
Understanding human action is critical to surveillance monitoring and situation under-standing Sports present events where the types of actions are limited and depend on roleslocations and situations Understanding the actions activity and performance of the playersinterests many Action understanding in broadcast video has led to progress in trackingrecognition camera rectification pose and multi-view action recognition
15382
46 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
36 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Bernhard Moser (Software Competence Center ndash Hagenberg AT)
License Creative Commons BY 30 Unported licensecopy Bernhard Moser
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games thisapproach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
37 Covered Distances of Handball Players Obtained by an AutomaticTracking Method
Tiago Guedes Russomanno (University of Brasilia BR)
License Creative Commons BY 30 Unported licensecopy Tiago Guedes Russomanno
Joint work of Russomanno Tiago Guedes Misuta Milton Shoiti Menezes Rafael Pombo Brandatildeo BrunoCedraz Figueroa Pascual Jovino Leite Neucimar Jeronimo Goldenstein Siome Klein BarrosRicardo Machado Leite
Main reference TG Russomanno M S Misuta R PMenezes BC Brandatildeo P J Figueroa N J Leite SKGoldstein RM L Barros ldquoCovered distances of handball players obtained by an automatictracking methodrdquo in Proc of the 25th Intrsquol Symp on Biomechanics in Sports (ISBSrsquo07)pp 324ndash327 University of Konstanz 2007
URL httpsojsubuni-konstanzdecpaarticleview473
Tracking players in sports events is still a topic of discussion in sport science and the dataprovided by this tracking is useful for team staff to evaluate team performance Therefore theaim of this work was to obtain the distances covered by handball players and their velocitiesduring a match using a new approach based on automatic tracking method described inFigueroa et al [1 2] and the Adaboost detector [5] A whole game of a Brazilian regionalhandball championship for players under age of 21 was recorded Applying the mentionedautomatic tracking the accumulated covered distances and the velocities were calculated forall the players The results of average covered distances (plusmnSD) in the 1st and 2nd halveswere 2199 (plusmn230) and 2453 (plusmn214) The results of covered distances and the velocities allowindividual and collective analyses of the players by the team staff The proposed methodrevealed to be a powerful tool to improve physical analysis of the handball players
References1 P J Figueroa N J Leite and RM L Barros ldquoTracking soccer players aiming their kin-
ematical motion analysisrdquo Comput Vis Image Underst vol 101 no 2 pp 122-135 2006
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 47
2 P J Figueroa N J Leite and RM L Barros ldquoBackground recovering in outdoor imagesequences An example of soccer players segmentationrdquo Image Vision Comput vol 24 no4 pp 363-374 2006
3 M Kristan J Pers J M Perse M Bon and S Kovacic ldquoMultiple interacting targetstracking with application to team sportsrdquo in Proc ISAP Zagreb 2005 pp 322-327
4 MS Misuta R P Menezes P J Figueroa SA Cunha and RML Barros ldquoRepresent-ation and analysis of soccer player trajectoriesrdquo in Proc ISB Cleveland 2005 vol 415
5 K Okuma A Taleghani N de Freitas J Little and D Lowe ldquoA boosted particle filtermultitarget detection and trackingrdquo in Proc ECCV Prague 2004 pp 28-38
6 P Viola and M J Jones ldquoRapid Object Detection using a Boosted Cascade of SimpleFeaturesrdquo in Proc CVPR Kauai 2001 vol 1 pp 511-518
38 Data Requirements in the Sports Data IndustryMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
Different markets do have different requirements concerning data depth data delivery speedand data quality Here is a short overview about three markets (1) Professional Sports (2)Media and (3) Bookmakers Betting1 Professional Sports (Teams Clubs Leagues)
Strong need for deep dataDelivery speed not too important as most teams use the data post-match Moreoverlive-match analyses are sometimes prohibitedData quality is important Performance analysis based on imprecise data could causewrong conclusions
2 MediaStrong need for deep data (eg for story telling)Delivery speed Not too important as there is a broadcasting latency anywaysData quality is not important
3 Bookmakers BettingData depth is not important as there is a market limit anyways If you would offertoo many bets you would cannibalize your own marketDelivery speed is a must have and very importantJust as data quality is Wrong data could cause a of lot trouble and punters wouldclaim their right to get their money back
Consequently for a company like Sportradar it is very important to fulfill all differentrequirements This results in the claim to be able to provide fast highly accurate and deepdata
15382
48 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
39 Factors that Influence Scoring Dynamics in Low-Scoring andHigh-Scoring Team Games
Anna Volossovitch (University of Lisbon PT)
License Creative Commons BY 30 Unported licensecopy Anna Volossovitch
Joint work of Volossovitch Anna Pratas Joseacute Dumangane Montezuma Rosati NicolettaMain reference M Dumangane N Rosati A Volossovitch ldquoDeparture from independence and stationarity in a
handball matchrdquo Journal of Applied Statistics 36(7)723ndash741 2009URL httpdxdoiorg10108002664760802499329
Research in match analysis frequently attempts to establish causal relationships betweenisolated performance variables and goal scoring or game outcome This approach reduces thecomplexity of performance by presenting it in overly descriptive and regular ways which donot reflect properly the curse of the game The purpose of the presentation was to discuss twoexamples of the analysis of factors which could influence the scoring dynamic during a matchin handball and in football In the first example the influence of teams past performanceon the present teams performance was evaluated throughout the handball match using themodel with time-varying parameters This model estimates the probability of scoring as afunction of the past performance of the opposing team and the current match result Thisassessment considers the specific context of the game situation the teams rankings thematch equilibrium and the number of ball possessions per match In the second example theperformance indicators which had a significant effect on the time of the first goal scoringin football has been identified using Cox time-dependent proportional hazard model Thesurvival analysis is suggested to be a suitable tool to identify which and how performanceindicators influence the time of the first ball is scored in different competitive context
References1 J Pratas AVolossovitch and A I Carita ldquoWhat performance indicators influence the
time of the first goal in the matchrdquo In Proc World Congress of Performance Analysis ofSport X Opatija 2014 p 102
2 A Volossovitch M Dumangane and N Rosati ldquoThe influence of the pace of match onthe dynamic of handball gamerdquo Int J Sports Psychol vol 41 no 4 pp 117ndash118 2010
3 M Dumangane N Rosati and A Volossovitch ldquoDeparture from independence and sta-tionarity in a handball matchrdquo J Appl Stat vol 36 no 7 pp 723ndash741 2009
4 Overview of Talks Sport Movement
41 Predicting Human Responses to Environmental ChangesEva Dorschky (Universitaumlt Erlangen ndash Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Eva Dorschky
Joint work of Dorschky Eva van den Bogert Antonie J Schlarb Heiko Eskofier BjoumlrnMain reference E Dorschky et al ldquoPredictive Musculoskeletal Simulation of Uphill and Downhill Runningrdquo in
Proc ECSS Malmouml 2015 p 126
Predicting human responses to environmental changes is necessary for biomechanical analysisand sports product design If case studies environmental conditions or prototypes cannot berealized modeling and simulation can be used instead The aim of this work was to evaluatea method of predictive musculoskeletal simulation [1] for uphill and downhill running A
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 49
study was simulated by randomizing the modelrsquos muscle parameters The predicted energycosts for running at different slopes were compared to literature [2] Future work includes apersonalization of biomechanical models to represent individual athletes as well as populationgroups The sensitivity of simulation results to model parameters will be studied to ensurerobust simulation results
References1 A J van den Bogert M Hupperets H Schlarb and B KrabbeldquoPredictive musculoskeletal
simulation using optimal control effects of added limb mass on energy cost and kinematicsof walking and runningrdquo in Proc Inst Mech Eng Part P J Sports Eng Technol vol 226pp 123-133 2012
2 AE Minetti C Moia G S Roi D Susta and G Ferretti ldquoEnergy cost of walking andrunning at extreme uphill and downhill slopesrdquo J Appl Physiol vol 93 no 3 pp 1039-10462002
42 Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier (Universitaumlt Erlangen-Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Bjoumlrn Eskofier
Wearable computing systems play an increasingly important role in recreational and elitesports They comprise of two important parts The first are sensors embedded into clothesand equipment that are used for physiological (ECG EMG) and biomechanical (accelerometergyroscope) data recording The second are signal processing and data mining algorithmsimplemented on wearable computers (smartphones watches) that are used for analysis of therecorded data Wearable computing systems can provide support real-time feedback andcoaching advice to sportsmen of all performance levels In order to implement these systemsseveral challenges have to be addressed Our work focusses on four of the most prevalent ofthese
Integration sensors and microprocessors have to be embedded unobtrusively and have torecord a variety of signalsCommunication sensors and microprocessors have to communicate in body-area-networksin a secure safe and energy-saving mannerInterpretation physiological and biomechanical data have to be interpreted using signalprocessing and machine learning methodsSimulation and modeling understanding of sensor data is needed to model processes insports more accurately simulation methodologies help here to provide basic informationto drive those models
15382
50 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
43 What is the Right ModelKaren Roemer (Central Washington University ndash Ellensburg US)
License Creative Commons BY 30 Unported licensecopy Karen Roemer
Investigating human movements requires using biomechanical models to perform kinematicand kinetic analyses Depending on the available motion analysis system or software packagesanthropometric models tracking models joint models etc are applied to quantify kinematicand kinetic variables Two standard biomechanical models (OpenSim and Visual3D) wereused to analyze a simple stepping task Similarities and differences of kinematic and kineticresults for both models were discussed
44 Model-Based Tracking of Human MotionAntonie van den Bogert (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Antonie van den Bogert
Two sensing modalities are in use for field studies of human movement inertial sensing andvideo While both are suitable for analysis of human movement during sports events dataquality is usually far inferior to those encountered during laboratory conditions Inspiredby the classical Kalman filtering concept we consider using a dynamic model to improvethe estimates of the state trajectory of the system This approach presents the estimationproblem as an optimal control problem find state and control trajectories that satisfy systemdynamics and minimize a cost function For tracking of sensor data the cost function consistsof the sum of squared errors between simulated and measured sensor signals When using amusculoskeletal model an extra term representing muscular effort must be added to ensurea unique solution This is due to the classical load sharing redundancy humans have moremuscles than strictly necessary to produce movementEfficient solution methods have been developed to solve this optimal control problem andthe approach was successfully used to obtain a detailed analysis of a landing movementduring skiing based on low-quality video data [1] The same approach was used to performa full dynamic gait analysis including muscle force estimation with body-mounted accelero-meters [2] The accelerometer-based analysis was not good enough for clinical applicationsbecause it was too sensitive to the unmodeled dynamics (damped vibrations) of the accelero-meter attachments Improvement is expected when the instrumentation is supplemented bygyroscopic angular velocity sensorsModel-based state estimation will reduce the sensitivity to measurement error and thereare additional advantages The estimation process includes estimation of the full state ofthe system including variables such as muscle forces which could not be directly obtainedfrom sensor signals The raw data is then not only filtered but also enriched by the dynamicmodel A second advantage is that a dynamically consistent simulation is obtained as anadditional result Such simulations can be useful to explore ldquowhat ifrdquo scenarios such assports injuries that are caused by unfavorable landing postures [3]
References1 A J van den Bogert D Blana and D Heinrich ldquoImplicit methods for efficient musculo-
skeletal simulation and optimal controlrdquo Procedia IUTAM vol 2 pp 297-316 2011
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 51
2 O Nwanna ldquoValidation of an Accelerometry Based Method of Human Gait Analysisrdquo MSthesis Cleveland State University 2014 httpraveohiolinkeduetdcviewacc_num=csu1400424346
3 D Heinrich A J van den Bogert and W Nachbauer ldquoRelationship between jump landingkinematics and peak ACL force during a jump in downhill skiing A simulation studyrdquoScand J Med Sci Sports vol 24 no 3 pp 180-187 2013
5 Overview of Talks Adaptions to Training
51 Modelling Speed-HR Relation using PerPotStefan Endler (Universitaumlt Mainz DE)
License Creative Commons BY 30 Unported licensecopy Stefan Endler
Joint work of Endler Stefan Perl JuumlrgenMain reference S Endler J Perl ldquoOptimizing practice and competition in marathon running by means of the
meta-modelrdquo in Proc of 2012 pre-Olympic Congress on Sports Science and Computer Science inSport (IACSSrsquo12) pp 127ndash131 World Academic Union 2012
A model which represents the heart rate (HR) process based on a speed process has tomodel three apects1 Increasing HR after increasing speed2 Decreasing HR after decreasing speed3 Break down after exhaustionThe performance potential metamodell (PerPot) models all these behaviors It was adapted tothe environment of endurance running Once the model is calibrated to the individual athleteby a graded incremental test it can be used for simulation of eg competitions Simulationscan help unexperienced athletes particularly to avoid overloading and underperforming
52 Performance Adaptions to Football Training Is More AlwaysBetter
Hugo Folgado (University of Evora PT)
License Creative Commons BY 30 Unported licensecopy Hugo Folgado
Joint work of Folgado Hugo Sampaio Jaime
Traditionally performance in sports is measured by magnitude based indicators summedup by the Olympic moto ndash Citius Altius Fortius ndash Faster Higher Stronger Howeverin several sport domains and particularly in team sports this idea has been challengedby recent research In football the physical analysis of matches in different competitiveleagues have showed that players in higher level contexts tend to run less and at lowerintensities than players involved in lower leagues [1] In other approach studying the effectsof congested fixtures in players physical performance showed no differences in the amount ofdistance covered and distance covered at different displacement intensities [2] So it may bespeculated that the amount of displacement is not related to greater levels of performance infootball Based in these approaches we measured players physical and tactical performancedevelopment during the preseason evaluated during sided-games Our results showed thatplayers tend to reduce the amount of distance covered during these situation has the preseason
15382
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
40 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
2 Table of Contents
Executive SummaryJosef Wiemeyer Ricardo Duarte Bjoumlrn Eskofier and Martin Rumpf 38
Overview of Talks Sport GamesOn the Use of Tracking Data to Support Coaches in Professional FootballJohn Komar 42
The ldquoPractical Impact Debaterdquo in Performance AnalysisMartin Lames 42
Performance Analysis in Soccer Based on Knowledge Discovery ApproachRoland Leser 44
Individual Ball Possession in SoccerDaniel Link 45
Understanding Actions in a Sports ContextJim Little 45
Performance Analysis in Soccer Based on Knowledge Discovery ApproachBernhard Moser 46
Covered Distances of Handball Players Obtained by an Automatic Tracking MethodTiago Guedes Russomanno 46
Data Requirements in the Sports Data IndustryMalte Siegle 47
Factors that Influence Scoring Dynamics in Low-Scoring and High-Scoring TeamGamesAnna Volossovitch 48
Overview of Talks Sport MovementPredicting Human Responses to Environmental ChangesEva Dorschky 48
Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier 49
What is the Right ModelKaren Roemer 50
Model-Based Tracking of Human MotionAntonie van den Bogert 50
Overview of Talks Adaptions to TrainingModelling Speed-HR Relation using PerPotStefan Endler 51
Performance Adaptions to Football Training Is More Always BetterHugo Folgado 51
Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann 52
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 41
Model Design and Validation for Oxygen DynamicsDietmar Saupe 53
CommentsResearch PerspectiveAnne Danielle Koelewijn 53
Industry PerspectiveMalte Siegle 54
Discussion 54
Conclusion 55
Participants 56
15382
42 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
3 Overview of Talks Sport Games
31 On the Use of Tracking Data to Support Coaches in ProfessionalFootball
John Komar (Prozone Sports Ltd ndash Leeds GB)
License Creative Commons BY 30 Unported licensecopy John Komar
A new era in sports sciences is emerging with the advent of new digital tools for the analysis ofathletesrsquo training performance and health However massive technological changes emergingover the past few years have led to a new issue captured in the term Big Data A clearchallenge for sports scientists and practitioners is to understand which data matter andhow to interpret them The real challenge for the professional world is now to move fromdata-driven decisions to data-informed decisions From this perspective my talk addressesthe question of investigating tracking data in football (ie both events data and playersposition in xyt coordinate) in order to derive meaningful and functional metrics Broadlyspeaking the idea is to overcome traditional generic statistics (eg number of passes donenumber of shots number of tackles percentage of ball possession) by combining them intomeaningful items for coaches and practitioners (ie information that can help them to makeinformed choices for recruitment injury prevention or match analysis) More specifically partof the work presented looked at the number of goals one could expect from a player basedon the location of the shots he took [1] A model of expected goals per field position wasderived from previous seasons and then compared to the actual number of goals scored by aspecific player This comparison can thus inform about the ability of this player to under- oroverachieve in shots success Looking at probability of scoring goals during a season coachescan then be informed about conversion rate of a player but rather than a raw goalsshotsratio this goal expectancy metrics gives more context to the conversion ability (eg it cantake into account the position of the shot the defensive density during the shots) Combinedto other measures like ball movement effectiveness this kind of metrics can feed models ofoffensive contribution in professional football
References1 H Ruiz PJ Lisboa PJ Neilson and W Gregson ldquoMeasuring scoring efficiency through
goal expectancy estimationrdquo in Proc ESANNrsquo15 Bruges 2015
32 The ldquoPractical Impact Debaterdquo in Performance AnalysisMartin Lames (TU Muumlnchen DE)
License Creative Commons BY 30 Unported licensecopy Martin Lames
Problem
Mainstream research in Performance Analysis (PA) applies traditional linear methods to dataanalysis eg ANOVA In this context complex interactions in game sports are modelledquite poorly eg quality of opponent is introduced as another static variable in a linearmodel rank in final table of league [8 9] Fluctuations in behaviour typically a constitutivepattern of game sports are treated as measurement error and sought to be controlled by
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 43
enlarging sample size [3] On the other hand practical support is considered to be the mainpurpose of PA [6] McGarry (2009) sees advancing the understanding of sports mainly withregard to improving future outcomes In this situation a review of Drust and Green (2013)gave rise to the ldquoPractical Impact Debaterdquo The authors stated ldquo it can be suggested thatthe influence of the scientific information that is available has a relatively small influence onthe day-to-day activities within the ldquoreal worldrdquo of footballrdquo (p 1382)
The ldquoPractical Impact Debaterdquo
This statement was supported by a review of Mackenzie and Cushion (2013) They analysedexisting PA-research and found widespread methodological problems as well as a missingresearch strategy for practical support Carling et al (2014) replied to this paper and ndash amongother issues ndash defended analysts working in practice against some methodological criticismby the demands of conducting research in practical settings He frequently referred to hisearlier paper with the telling header ldquoShould we be more pragmatic in our approachrdquo [1]
Remarks on the ldquoPractical Impact Debaterdquo
The problem addressed has its root in a lacking distinction between applied and basic researchThis issue is mentioned by Mackenzie and Cushion (2013) without drawing consequences forresearch strategies to be applied In the eyes of the author it is helpful to distinguish betweenpractical PA (PPA) and theoretical PA (TPA) [5] TPA aims at clarifying the general structureof sports It looks for general rules appropriate models (dynamical systems modelling) andneeds large representative samples PPA is in some respects the opposite It may be definedas PA activities conducted in practice ie analysing training and competition to supporta team or a player PPA is interested in any information that provides practical supportand typically works with and for a single case the own team or player Moreover practicalconsequences for training may not be found algorithmically from data collected but need athorough interpretation with a background of in-depth knowledge from the many sources ofinformation available in a professional football club (medical physiotherapy fitness trainingare only the most important ones) So in PPA ndash whether with or without methodologicalawareness ndash qualitative research methodology is used which may be considered as a typicalfeature as well With a basic distinction between TPA and PPA researchers analysing thegeneral structure of performances are relieved from demonstrating an immediate practicaluse of their results and analysts working in practice shouldnrsquot feel obliged any more to refutecriticism from the point of view of basic research on their pragmatic solutions Neverthelessthere is a tight connection between both areas Measures in practice should be in agreementwith general findings about the nature of the game and in the other direction findings inpractice can give rise to new hypotheses on its structure
Agenda for computer science in PA
What are consequences of the ldquoPractical Impact Debaterdquo for the interdisciplinary researchfield of computer science in sports It becomes clear that there are different agendas for itworking in either TPA or PPA Nevertheless both areas depend on data on the matchesmeaning that there remains a general agenda including the detection of positions and actionsThe automatisation of action detection will be a prominent future task as well as drawinginferences on higher level from action and position data like analysing constructs not availablebefore like availability or more complex tactical behaviours like passing style or pressingSpecifically for TPA the introduction of more appropriate models will determine a future
15382
44 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
agenda As interaction and dynamics are constitutive for game sports these features shouldbe included in any future approach What PPA is concerned challenges lie in improvinginformational service for coaches and athletes We will see information systems that combinethe different sources of information by machine learning technologies to arrive at advancedversions of automated data mining for example driven by assumptions on the nature ofinformation needed and driven by query habits of the user All in all there is a challengingbut also promising future for computer science in PA to be expected
References1 C Carling C Wright LN Nelson and PS Bradley ldquoComment on Performance analysis
in football A critical review and implications for future researchrdquo J Sport Sci vol 32pp 2ndash7 2014
2 B Drust and M Green ldquoScience and football evaluating the influence of science onperformancerdquo J Sport Sci vol 31 pp 1377ndash1382 2013
3 M Hughes S Evans and J Wells ldquoEstablishing normative profiles in performance ana-lysisrdquo Int J Perform Anal Sport vol 1 pp 4ndash27 2001
4 M Lames and G Hansen ldquoDesigning observational systems to support top-level teams ingame sportsrdquo Int J Perform Anal Sport vol 1 pp 85ndash91 2001
5 M Lames and T McGarry ldquoOn the search for reliable performance indicators in gamesportsrdquo Int J Perform Anal Sport vol 7 no 1 pp 62ndash79 2007
6 R Mackenzie and C Cushion ldquoPerformance analysis in football A critical review andimplications for future researchrdquo J Sport Sci vol 31 pp 639ndash676 2013
7 T McGarry DI Anderson SA Wallace MD Hughes and IM Franks ldquoSport compet-ition as a dynamical self-organizing systemrdquo J Sport Sci vol 20 pp 771ndash781 2002
8 P OrsquoDonoghue ldquoInteracting Performances Theoryrdquo Int J Perform Anal Sport vol 9pp 26ndash46 2009
9 A Tenga and E Sigmundstad ldquoCharacteristics of goal-scoring possessions in open-playComparing the top in-between and bottom teams from professional soccer leaguerdquo Int JPerform Anal Sport vol 11 pp 545ndash552 2011
33 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Roland Leser (Universitaumlt Wien AT)
License Creative Commons BY 30 Unported licensecopy Roland Leser
Joint work of Leser Roland Moser Bernhard Hoch Thomas Baca Arnold
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games this
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 45
approach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
34 Individual Ball Possession in SoccerDaniel Link (TU Muumlnchen DE)
License Creative Commons BY 30 Unported licensecopy Daniel Link
This paper describes models for detecting individual and team ball possession in soccer basedon position data The types of ball possession are classified as Individual Ball Possession(IBC) Individual Ball Action (IBA) Individual Ball Control (IBC) Team Ball Possession(TBP) Team Ball Control (TBC) und Team Playmaking (TPM) according to the type ofball control involved The machine learning approach used is able to determine how longthe ball spends in the sphere of influence of a player based on the distance between theplayers and the ball together with their direction of motion speed and the acceleration ofthe ball The degree of ball control exhibited during this phase is classified based on thespatio-temporal configuration of the player controlling the ball the ball itself and opposingplayers using a Bayesian networkThe evaluation and illustrative application of this approachuses data taken from a game in a top European league When applied to error-corrected rawdata the algorithm showed an accuracy of 92 (IBA) 86 (IBP) and 92 (IBC) using atolerance of 06 s This is well above the accuracy achieved manually by the competitioninformation providers of 52 (TBC) There were 1291 phases involving ball control (IBC)totalling 2939 min with a gross game time of 9012 min and a net game time of 5756 minThis initial analysis of ball possession at the player level indicates IBC times of between022 and 318 min The shortest ball control times are observed for the centre forwards ofeach team (09 s) and the full backs of the losing team (07 s) and the longest for the losingteamrsquos goalkeeper (29 s) This can be interpreted as a tendency for the defenders to try andclear the ball as quickly as possible and the goalkeeper attempting to slow the pace of game
35 Understanding Actions in a Sports ContextJim Little (University of British Columbia ndash Vancouver CA)
License Creative Commons BY 30 Unported licensecopy Jim Little
Understanding human action is critical to surveillance monitoring and situation under-standing Sports present events where the types of actions are limited and depend on roleslocations and situations Understanding the actions activity and performance of the playersinterests many Action understanding in broadcast video has led to progress in trackingrecognition camera rectification pose and multi-view action recognition
15382
46 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
36 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Bernhard Moser (Software Competence Center ndash Hagenberg AT)
License Creative Commons BY 30 Unported licensecopy Bernhard Moser
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games thisapproach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
37 Covered Distances of Handball Players Obtained by an AutomaticTracking Method
Tiago Guedes Russomanno (University of Brasilia BR)
License Creative Commons BY 30 Unported licensecopy Tiago Guedes Russomanno
Joint work of Russomanno Tiago Guedes Misuta Milton Shoiti Menezes Rafael Pombo Brandatildeo BrunoCedraz Figueroa Pascual Jovino Leite Neucimar Jeronimo Goldenstein Siome Klein BarrosRicardo Machado Leite
Main reference TG Russomanno M S Misuta R PMenezes BC Brandatildeo P J Figueroa N J Leite SKGoldstein RM L Barros ldquoCovered distances of handball players obtained by an automatictracking methodrdquo in Proc of the 25th Intrsquol Symp on Biomechanics in Sports (ISBSrsquo07)pp 324ndash327 University of Konstanz 2007
URL httpsojsubuni-konstanzdecpaarticleview473
Tracking players in sports events is still a topic of discussion in sport science and the dataprovided by this tracking is useful for team staff to evaluate team performance Therefore theaim of this work was to obtain the distances covered by handball players and their velocitiesduring a match using a new approach based on automatic tracking method described inFigueroa et al [1 2] and the Adaboost detector [5] A whole game of a Brazilian regionalhandball championship for players under age of 21 was recorded Applying the mentionedautomatic tracking the accumulated covered distances and the velocities were calculated forall the players The results of average covered distances (plusmnSD) in the 1st and 2nd halveswere 2199 (plusmn230) and 2453 (plusmn214) The results of covered distances and the velocities allowindividual and collective analyses of the players by the team staff The proposed methodrevealed to be a powerful tool to improve physical analysis of the handball players
References1 P J Figueroa N J Leite and RM L Barros ldquoTracking soccer players aiming their kin-
ematical motion analysisrdquo Comput Vis Image Underst vol 101 no 2 pp 122-135 2006
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 47
2 P J Figueroa N J Leite and RM L Barros ldquoBackground recovering in outdoor imagesequences An example of soccer players segmentationrdquo Image Vision Comput vol 24 no4 pp 363-374 2006
3 M Kristan J Pers J M Perse M Bon and S Kovacic ldquoMultiple interacting targetstracking with application to team sportsrdquo in Proc ISAP Zagreb 2005 pp 322-327
4 MS Misuta R P Menezes P J Figueroa SA Cunha and RML Barros ldquoRepresent-ation and analysis of soccer player trajectoriesrdquo in Proc ISB Cleveland 2005 vol 415
5 K Okuma A Taleghani N de Freitas J Little and D Lowe ldquoA boosted particle filtermultitarget detection and trackingrdquo in Proc ECCV Prague 2004 pp 28-38
6 P Viola and M J Jones ldquoRapid Object Detection using a Boosted Cascade of SimpleFeaturesrdquo in Proc CVPR Kauai 2001 vol 1 pp 511-518
38 Data Requirements in the Sports Data IndustryMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
Different markets do have different requirements concerning data depth data delivery speedand data quality Here is a short overview about three markets (1) Professional Sports (2)Media and (3) Bookmakers Betting1 Professional Sports (Teams Clubs Leagues)
Strong need for deep dataDelivery speed not too important as most teams use the data post-match Moreoverlive-match analyses are sometimes prohibitedData quality is important Performance analysis based on imprecise data could causewrong conclusions
2 MediaStrong need for deep data (eg for story telling)Delivery speed Not too important as there is a broadcasting latency anywaysData quality is not important
3 Bookmakers BettingData depth is not important as there is a market limit anyways If you would offertoo many bets you would cannibalize your own marketDelivery speed is a must have and very importantJust as data quality is Wrong data could cause a of lot trouble and punters wouldclaim their right to get their money back
Consequently for a company like Sportradar it is very important to fulfill all differentrequirements This results in the claim to be able to provide fast highly accurate and deepdata
15382
48 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
39 Factors that Influence Scoring Dynamics in Low-Scoring andHigh-Scoring Team Games
Anna Volossovitch (University of Lisbon PT)
License Creative Commons BY 30 Unported licensecopy Anna Volossovitch
Joint work of Volossovitch Anna Pratas Joseacute Dumangane Montezuma Rosati NicolettaMain reference M Dumangane N Rosati A Volossovitch ldquoDeparture from independence and stationarity in a
handball matchrdquo Journal of Applied Statistics 36(7)723ndash741 2009URL httpdxdoiorg10108002664760802499329
Research in match analysis frequently attempts to establish causal relationships betweenisolated performance variables and goal scoring or game outcome This approach reduces thecomplexity of performance by presenting it in overly descriptive and regular ways which donot reflect properly the curse of the game The purpose of the presentation was to discuss twoexamples of the analysis of factors which could influence the scoring dynamic during a matchin handball and in football In the first example the influence of teams past performanceon the present teams performance was evaluated throughout the handball match using themodel with time-varying parameters This model estimates the probability of scoring as afunction of the past performance of the opposing team and the current match result Thisassessment considers the specific context of the game situation the teams rankings thematch equilibrium and the number of ball possessions per match In the second example theperformance indicators which had a significant effect on the time of the first goal scoringin football has been identified using Cox time-dependent proportional hazard model Thesurvival analysis is suggested to be a suitable tool to identify which and how performanceindicators influence the time of the first ball is scored in different competitive context
References1 J Pratas AVolossovitch and A I Carita ldquoWhat performance indicators influence the
time of the first goal in the matchrdquo In Proc World Congress of Performance Analysis ofSport X Opatija 2014 p 102
2 A Volossovitch M Dumangane and N Rosati ldquoThe influence of the pace of match onthe dynamic of handball gamerdquo Int J Sports Psychol vol 41 no 4 pp 117ndash118 2010
3 M Dumangane N Rosati and A Volossovitch ldquoDeparture from independence and sta-tionarity in a handball matchrdquo J Appl Stat vol 36 no 7 pp 723ndash741 2009
4 Overview of Talks Sport Movement
41 Predicting Human Responses to Environmental ChangesEva Dorschky (Universitaumlt Erlangen ndash Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Eva Dorschky
Joint work of Dorschky Eva van den Bogert Antonie J Schlarb Heiko Eskofier BjoumlrnMain reference E Dorschky et al ldquoPredictive Musculoskeletal Simulation of Uphill and Downhill Runningrdquo in
Proc ECSS Malmouml 2015 p 126
Predicting human responses to environmental changes is necessary for biomechanical analysisand sports product design If case studies environmental conditions or prototypes cannot berealized modeling and simulation can be used instead The aim of this work was to evaluatea method of predictive musculoskeletal simulation [1] for uphill and downhill running A
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 49
study was simulated by randomizing the modelrsquos muscle parameters The predicted energycosts for running at different slopes were compared to literature [2] Future work includes apersonalization of biomechanical models to represent individual athletes as well as populationgroups The sensitivity of simulation results to model parameters will be studied to ensurerobust simulation results
References1 A J van den Bogert M Hupperets H Schlarb and B KrabbeldquoPredictive musculoskeletal
simulation using optimal control effects of added limb mass on energy cost and kinematicsof walking and runningrdquo in Proc Inst Mech Eng Part P J Sports Eng Technol vol 226pp 123-133 2012
2 AE Minetti C Moia G S Roi D Susta and G Ferretti ldquoEnergy cost of walking andrunning at extreme uphill and downhill slopesrdquo J Appl Physiol vol 93 no 3 pp 1039-10462002
42 Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier (Universitaumlt Erlangen-Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Bjoumlrn Eskofier
Wearable computing systems play an increasingly important role in recreational and elitesports They comprise of two important parts The first are sensors embedded into clothesand equipment that are used for physiological (ECG EMG) and biomechanical (accelerometergyroscope) data recording The second are signal processing and data mining algorithmsimplemented on wearable computers (smartphones watches) that are used for analysis of therecorded data Wearable computing systems can provide support real-time feedback andcoaching advice to sportsmen of all performance levels In order to implement these systemsseveral challenges have to be addressed Our work focusses on four of the most prevalent ofthese
Integration sensors and microprocessors have to be embedded unobtrusively and have torecord a variety of signalsCommunication sensors and microprocessors have to communicate in body-area-networksin a secure safe and energy-saving mannerInterpretation physiological and biomechanical data have to be interpreted using signalprocessing and machine learning methodsSimulation and modeling understanding of sensor data is needed to model processes insports more accurately simulation methodologies help here to provide basic informationto drive those models
15382
50 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
43 What is the Right ModelKaren Roemer (Central Washington University ndash Ellensburg US)
License Creative Commons BY 30 Unported licensecopy Karen Roemer
Investigating human movements requires using biomechanical models to perform kinematicand kinetic analyses Depending on the available motion analysis system or software packagesanthropometric models tracking models joint models etc are applied to quantify kinematicand kinetic variables Two standard biomechanical models (OpenSim and Visual3D) wereused to analyze a simple stepping task Similarities and differences of kinematic and kineticresults for both models were discussed
44 Model-Based Tracking of Human MotionAntonie van den Bogert (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Antonie van den Bogert
Two sensing modalities are in use for field studies of human movement inertial sensing andvideo While both are suitable for analysis of human movement during sports events dataquality is usually far inferior to those encountered during laboratory conditions Inspiredby the classical Kalman filtering concept we consider using a dynamic model to improvethe estimates of the state trajectory of the system This approach presents the estimationproblem as an optimal control problem find state and control trajectories that satisfy systemdynamics and minimize a cost function For tracking of sensor data the cost function consistsof the sum of squared errors between simulated and measured sensor signals When using amusculoskeletal model an extra term representing muscular effort must be added to ensurea unique solution This is due to the classical load sharing redundancy humans have moremuscles than strictly necessary to produce movementEfficient solution methods have been developed to solve this optimal control problem andthe approach was successfully used to obtain a detailed analysis of a landing movementduring skiing based on low-quality video data [1] The same approach was used to performa full dynamic gait analysis including muscle force estimation with body-mounted accelero-meters [2] The accelerometer-based analysis was not good enough for clinical applicationsbecause it was too sensitive to the unmodeled dynamics (damped vibrations) of the accelero-meter attachments Improvement is expected when the instrumentation is supplemented bygyroscopic angular velocity sensorsModel-based state estimation will reduce the sensitivity to measurement error and thereare additional advantages The estimation process includes estimation of the full state ofthe system including variables such as muscle forces which could not be directly obtainedfrom sensor signals The raw data is then not only filtered but also enriched by the dynamicmodel A second advantage is that a dynamically consistent simulation is obtained as anadditional result Such simulations can be useful to explore ldquowhat ifrdquo scenarios such assports injuries that are caused by unfavorable landing postures [3]
References1 A J van den Bogert D Blana and D Heinrich ldquoImplicit methods for efficient musculo-
skeletal simulation and optimal controlrdquo Procedia IUTAM vol 2 pp 297-316 2011
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 51
2 O Nwanna ldquoValidation of an Accelerometry Based Method of Human Gait Analysisrdquo MSthesis Cleveland State University 2014 httpraveohiolinkeduetdcviewacc_num=csu1400424346
3 D Heinrich A J van den Bogert and W Nachbauer ldquoRelationship between jump landingkinematics and peak ACL force during a jump in downhill skiing A simulation studyrdquoScand J Med Sci Sports vol 24 no 3 pp 180-187 2013
5 Overview of Talks Adaptions to Training
51 Modelling Speed-HR Relation using PerPotStefan Endler (Universitaumlt Mainz DE)
License Creative Commons BY 30 Unported licensecopy Stefan Endler
Joint work of Endler Stefan Perl JuumlrgenMain reference S Endler J Perl ldquoOptimizing practice and competition in marathon running by means of the
meta-modelrdquo in Proc of 2012 pre-Olympic Congress on Sports Science and Computer Science inSport (IACSSrsquo12) pp 127ndash131 World Academic Union 2012
A model which represents the heart rate (HR) process based on a speed process has tomodel three apects1 Increasing HR after increasing speed2 Decreasing HR after decreasing speed3 Break down after exhaustionThe performance potential metamodell (PerPot) models all these behaviors It was adapted tothe environment of endurance running Once the model is calibrated to the individual athleteby a graded incremental test it can be used for simulation of eg competitions Simulationscan help unexperienced athletes particularly to avoid overloading and underperforming
52 Performance Adaptions to Football Training Is More AlwaysBetter
Hugo Folgado (University of Evora PT)
License Creative Commons BY 30 Unported licensecopy Hugo Folgado
Joint work of Folgado Hugo Sampaio Jaime
Traditionally performance in sports is measured by magnitude based indicators summedup by the Olympic moto ndash Citius Altius Fortius ndash Faster Higher Stronger Howeverin several sport domains and particularly in team sports this idea has been challengedby recent research In football the physical analysis of matches in different competitiveleagues have showed that players in higher level contexts tend to run less and at lowerintensities than players involved in lower leagues [1] In other approach studying the effectsof congested fixtures in players physical performance showed no differences in the amount ofdistance covered and distance covered at different displacement intensities [2] So it may bespeculated that the amount of displacement is not related to greater levels of performance infootball Based in these approaches we measured players physical and tactical performancedevelopment during the preseason evaluated during sided-games Our results showed thatplayers tend to reduce the amount of distance covered during these situation has the preseason
15382
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 41
Model Design and Validation for Oxygen DynamicsDietmar Saupe 53
CommentsResearch PerspectiveAnne Danielle Koelewijn 53
Industry PerspectiveMalte Siegle 54
Discussion 54
Conclusion 55
Participants 56
15382
42 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
3 Overview of Talks Sport Games
31 On the Use of Tracking Data to Support Coaches in ProfessionalFootball
John Komar (Prozone Sports Ltd ndash Leeds GB)
License Creative Commons BY 30 Unported licensecopy John Komar
A new era in sports sciences is emerging with the advent of new digital tools for the analysis ofathletesrsquo training performance and health However massive technological changes emergingover the past few years have led to a new issue captured in the term Big Data A clearchallenge for sports scientists and practitioners is to understand which data matter andhow to interpret them The real challenge for the professional world is now to move fromdata-driven decisions to data-informed decisions From this perspective my talk addressesthe question of investigating tracking data in football (ie both events data and playersposition in xyt coordinate) in order to derive meaningful and functional metrics Broadlyspeaking the idea is to overcome traditional generic statistics (eg number of passes donenumber of shots number of tackles percentage of ball possession) by combining them intomeaningful items for coaches and practitioners (ie information that can help them to makeinformed choices for recruitment injury prevention or match analysis) More specifically partof the work presented looked at the number of goals one could expect from a player basedon the location of the shots he took [1] A model of expected goals per field position wasderived from previous seasons and then compared to the actual number of goals scored by aspecific player This comparison can thus inform about the ability of this player to under- oroverachieve in shots success Looking at probability of scoring goals during a season coachescan then be informed about conversion rate of a player but rather than a raw goalsshotsratio this goal expectancy metrics gives more context to the conversion ability (eg it cantake into account the position of the shot the defensive density during the shots) Combinedto other measures like ball movement effectiveness this kind of metrics can feed models ofoffensive contribution in professional football
References1 H Ruiz PJ Lisboa PJ Neilson and W Gregson ldquoMeasuring scoring efficiency through
goal expectancy estimationrdquo in Proc ESANNrsquo15 Bruges 2015
32 The ldquoPractical Impact Debaterdquo in Performance AnalysisMartin Lames (TU Muumlnchen DE)
License Creative Commons BY 30 Unported licensecopy Martin Lames
Problem
Mainstream research in Performance Analysis (PA) applies traditional linear methods to dataanalysis eg ANOVA In this context complex interactions in game sports are modelledquite poorly eg quality of opponent is introduced as another static variable in a linearmodel rank in final table of league [8 9] Fluctuations in behaviour typically a constitutivepattern of game sports are treated as measurement error and sought to be controlled by
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 43
enlarging sample size [3] On the other hand practical support is considered to be the mainpurpose of PA [6] McGarry (2009) sees advancing the understanding of sports mainly withregard to improving future outcomes In this situation a review of Drust and Green (2013)gave rise to the ldquoPractical Impact Debaterdquo The authors stated ldquo it can be suggested thatthe influence of the scientific information that is available has a relatively small influence onthe day-to-day activities within the ldquoreal worldrdquo of footballrdquo (p 1382)
The ldquoPractical Impact Debaterdquo
This statement was supported by a review of Mackenzie and Cushion (2013) They analysedexisting PA-research and found widespread methodological problems as well as a missingresearch strategy for practical support Carling et al (2014) replied to this paper and ndash amongother issues ndash defended analysts working in practice against some methodological criticismby the demands of conducting research in practical settings He frequently referred to hisearlier paper with the telling header ldquoShould we be more pragmatic in our approachrdquo [1]
Remarks on the ldquoPractical Impact Debaterdquo
The problem addressed has its root in a lacking distinction between applied and basic researchThis issue is mentioned by Mackenzie and Cushion (2013) without drawing consequences forresearch strategies to be applied In the eyes of the author it is helpful to distinguish betweenpractical PA (PPA) and theoretical PA (TPA) [5] TPA aims at clarifying the general structureof sports It looks for general rules appropriate models (dynamical systems modelling) andneeds large representative samples PPA is in some respects the opposite It may be definedas PA activities conducted in practice ie analysing training and competition to supporta team or a player PPA is interested in any information that provides practical supportand typically works with and for a single case the own team or player Moreover practicalconsequences for training may not be found algorithmically from data collected but need athorough interpretation with a background of in-depth knowledge from the many sources ofinformation available in a professional football club (medical physiotherapy fitness trainingare only the most important ones) So in PPA ndash whether with or without methodologicalawareness ndash qualitative research methodology is used which may be considered as a typicalfeature as well With a basic distinction between TPA and PPA researchers analysing thegeneral structure of performances are relieved from demonstrating an immediate practicaluse of their results and analysts working in practice shouldnrsquot feel obliged any more to refutecriticism from the point of view of basic research on their pragmatic solutions Neverthelessthere is a tight connection between both areas Measures in practice should be in agreementwith general findings about the nature of the game and in the other direction findings inpractice can give rise to new hypotheses on its structure
Agenda for computer science in PA
What are consequences of the ldquoPractical Impact Debaterdquo for the interdisciplinary researchfield of computer science in sports It becomes clear that there are different agendas for itworking in either TPA or PPA Nevertheless both areas depend on data on the matchesmeaning that there remains a general agenda including the detection of positions and actionsThe automatisation of action detection will be a prominent future task as well as drawinginferences on higher level from action and position data like analysing constructs not availablebefore like availability or more complex tactical behaviours like passing style or pressingSpecifically for TPA the introduction of more appropriate models will determine a future
15382
44 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
agenda As interaction and dynamics are constitutive for game sports these features shouldbe included in any future approach What PPA is concerned challenges lie in improvinginformational service for coaches and athletes We will see information systems that combinethe different sources of information by machine learning technologies to arrive at advancedversions of automated data mining for example driven by assumptions on the nature ofinformation needed and driven by query habits of the user All in all there is a challengingbut also promising future for computer science in PA to be expected
References1 C Carling C Wright LN Nelson and PS Bradley ldquoComment on Performance analysis
in football A critical review and implications for future researchrdquo J Sport Sci vol 32pp 2ndash7 2014
2 B Drust and M Green ldquoScience and football evaluating the influence of science onperformancerdquo J Sport Sci vol 31 pp 1377ndash1382 2013
3 M Hughes S Evans and J Wells ldquoEstablishing normative profiles in performance ana-lysisrdquo Int J Perform Anal Sport vol 1 pp 4ndash27 2001
4 M Lames and G Hansen ldquoDesigning observational systems to support top-level teams ingame sportsrdquo Int J Perform Anal Sport vol 1 pp 85ndash91 2001
5 M Lames and T McGarry ldquoOn the search for reliable performance indicators in gamesportsrdquo Int J Perform Anal Sport vol 7 no 1 pp 62ndash79 2007
6 R Mackenzie and C Cushion ldquoPerformance analysis in football A critical review andimplications for future researchrdquo J Sport Sci vol 31 pp 639ndash676 2013
7 T McGarry DI Anderson SA Wallace MD Hughes and IM Franks ldquoSport compet-ition as a dynamical self-organizing systemrdquo J Sport Sci vol 20 pp 771ndash781 2002
8 P OrsquoDonoghue ldquoInteracting Performances Theoryrdquo Int J Perform Anal Sport vol 9pp 26ndash46 2009
9 A Tenga and E Sigmundstad ldquoCharacteristics of goal-scoring possessions in open-playComparing the top in-between and bottom teams from professional soccer leaguerdquo Int JPerform Anal Sport vol 11 pp 545ndash552 2011
33 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Roland Leser (Universitaumlt Wien AT)
License Creative Commons BY 30 Unported licensecopy Roland Leser
Joint work of Leser Roland Moser Bernhard Hoch Thomas Baca Arnold
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games this
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 45
approach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
34 Individual Ball Possession in SoccerDaniel Link (TU Muumlnchen DE)
License Creative Commons BY 30 Unported licensecopy Daniel Link
This paper describes models for detecting individual and team ball possession in soccer basedon position data The types of ball possession are classified as Individual Ball Possession(IBC) Individual Ball Action (IBA) Individual Ball Control (IBC) Team Ball Possession(TBP) Team Ball Control (TBC) und Team Playmaking (TPM) according to the type ofball control involved The machine learning approach used is able to determine how longthe ball spends in the sphere of influence of a player based on the distance between theplayers and the ball together with their direction of motion speed and the acceleration ofthe ball The degree of ball control exhibited during this phase is classified based on thespatio-temporal configuration of the player controlling the ball the ball itself and opposingplayers using a Bayesian networkThe evaluation and illustrative application of this approachuses data taken from a game in a top European league When applied to error-corrected rawdata the algorithm showed an accuracy of 92 (IBA) 86 (IBP) and 92 (IBC) using atolerance of 06 s This is well above the accuracy achieved manually by the competitioninformation providers of 52 (TBC) There were 1291 phases involving ball control (IBC)totalling 2939 min with a gross game time of 9012 min and a net game time of 5756 minThis initial analysis of ball possession at the player level indicates IBC times of between022 and 318 min The shortest ball control times are observed for the centre forwards ofeach team (09 s) and the full backs of the losing team (07 s) and the longest for the losingteamrsquos goalkeeper (29 s) This can be interpreted as a tendency for the defenders to try andclear the ball as quickly as possible and the goalkeeper attempting to slow the pace of game
35 Understanding Actions in a Sports ContextJim Little (University of British Columbia ndash Vancouver CA)
License Creative Commons BY 30 Unported licensecopy Jim Little
Understanding human action is critical to surveillance monitoring and situation under-standing Sports present events where the types of actions are limited and depend on roleslocations and situations Understanding the actions activity and performance of the playersinterests many Action understanding in broadcast video has led to progress in trackingrecognition camera rectification pose and multi-view action recognition
15382
46 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
36 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Bernhard Moser (Software Competence Center ndash Hagenberg AT)
License Creative Commons BY 30 Unported licensecopy Bernhard Moser
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games thisapproach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
37 Covered Distances of Handball Players Obtained by an AutomaticTracking Method
Tiago Guedes Russomanno (University of Brasilia BR)
License Creative Commons BY 30 Unported licensecopy Tiago Guedes Russomanno
Joint work of Russomanno Tiago Guedes Misuta Milton Shoiti Menezes Rafael Pombo Brandatildeo BrunoCedraz Figueroa Pascual Jovino Leite Neucimar Jeronimo Goldenstein Siome Klein BarrosRicardo Machado Leite
Main reference TG Russomanno M S Misuta R PMenezes BC Brandatildeo P J Figueroa N J Leite SKGoldstein RM L Barros ldquoCovered distances of handball players obtained by an automatictracking methodrdquo in Proc of the 25th Intrsquol Symp on Biomechanics in Sports (ISBSrsquo07)pp 324ndash327 University of Konstanz 2007
URL httpsojsubuni-konstanzdecpaarticleview473
Tracking players in sports events is still a topic of discussion in sport science and the dataprovided by this tracking is useful for team staff to evaluate team performance Therefore theaim of this work was to obtain the distances covered by handball players and their velocitiesduring a match using a new approach based on automatic tracking method described inFigueroa et al [1 2] and the Adaboost detector [5] A whole game of a Brazilian regionalhandball championship for players under age of 21 was recorded Applying the mentionedautomatic tracking the accumulated covered distances and the velocities were calculated forall the players The results of average covered distances (plusmnSD) in the 1st and 2nd halveswere 2199 (plusmn230) and 2453 (plusmn214) The results of covered distances and the velocities allowindividual and collective analyses of the players by the team staff The proposed methodrevealed to be a powerful tool to improve physical analysis of the handball players
References1 P J Figueroa N J Leite and RM L Barros ldquoTracking soccer players aiming their kin-
ematical motion analysisrdquo Comput Vis Image Underst vol 101 no 2 pp 122-135 2006
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 47
2 P J Figueroa N J Leite and RM L Barros ldquoBackground recovering in outdoor imagesequences An example of soccer players segmentationrdquo Image Vision Comput vol 24 no4 pp 363-374 2006
3 M Kristan J Pers J M Perse M Bon and S Kovacic ldquoMultiple interacting targetstracking with application to team sportsrdquo in Proc ISAP Zagreb 2005 pp 322-327
4 MS Misuta R P Menezes P J Figueroa SA Cunha and RML Barros ldquoRepresent-ation and analysis of soccer player trajectoriesrdquo in Proc ISB Cleveland 2005 vol 415
5 K Okuma A Taleghani N de Freitas J Little and D Lowe ldquoA boosted particle filtermultitarget detection and trackingrdquo in Proc ECCV Prague 2004 pp 28-38
6 P Viola and M J Jones ldquoRapid Object Detection using a Boosted Cascade of SimpleFeaturesrdquo in Proc CVPR Kauai 2001 vol 1 pp 511-518
38 Data Requirements in the Sports Data IndustryMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
Different markets do have different requirements concerning data depth data delivery speedand data quality Here is a short overview about three markets (1) Professional Sports (2)Media and (3) Bookmakers Betting1 Professional Sports (Teams Clubs Leagues)
Strong need for deep dataDelivery speed not too important as most teams use the data post-match Moreoverlive-match analyses are sometimes prohibitedData quality is important Performance analysis based on imprecise data could causewrong conclusions
2 MediaStrong need for deep data (eg for story telling)Delivery speed Not too important as there is a broadcasting latency anywaysData quality is not important
3 Bookmakers BettingData depth is not important as there is a market limit anyways If you would offertoo many bets you would cannibalize your own marketDelivery speed is a must have and very importantJust as data quality is Wrong data could cause a of lot trouble and punters wouldclaim their right to get their money back
Consequently for a company like Sportradar it is very important to fulfill all differentrequirements This results in the claim to be able to provide fast highly accurate and deepdata
15382
48 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
39 Factors that Influence Scoring Dynamics in Low-Scoring andHigh-Scoring Team Games
Anna Volossovitch (University of Lisbon PT)
License Creative Commons BY 30 Unported licensecopy Anna Volossovitch
Joint work of Volossovitch Anna Pratas Joseacute Dumangane Montezuma Rosati NicolettaMain reference M Dumangane N Rosati A Volossovitch ldquoDeparture from independence and stationarity in a
handball matchrdquo Journal of Applied Statistics 36(7)723ndash741 2009URL httpdxdoiorg10108002664760802499329
Research in match analysis frequently attempts to establish causal relationships betweenisolated performance variables and goal scoring or game outcome This approach reduces thecomplexity of performance by presenting it in overly descriptive and regular ways which donot reflect properly the curse of the game The purpose of the presentation was to discuss twoexamples of the analysis of factors which could influence the scoring dynamic during a matchin handball and in football In the first example the influence of teams past performanceon the present teams performance was evaluated throughout the handball match using themodel with time-varying parameters This model estimates the probability of scoring as afunction of the past performance of the opposing team and the current match result Thisassessment considers the specific context of the game situation the teams rankings thematch equilibrium and the number of ball possessions per match In the second example theperformance indicators which had a significant effect on the time of the first goal scoringin football has been identified using Cox time-dependent proportional hazard model Thesurvival analysis is suggested to be a suitable tool to identify which and how performanceindicators influence the time of the first ball is scored in different competitive context
References1 J Pratas AVolossovitch and A I Carita ldquoWhat performance indicators influence the
time of the first goal in the matchrdquo In Proc World Congress of Performance Analysis ofSport X Opatija 2014 p 102
2 A Volossovitch M Dumangane and N Rosati ldquoThe influence of the pace of match onthe dynamic of handball gamerdquo Int J Sports Psychol vol 41 no 4 pp 117ndash118 2010
3 M Dumangane N Rosati and A Volossovitch ldquoDeparture from independence and sta-tionarity in a handball matchrdquo J Appl Stat vol 36 no 7 pp 723ndash741 2009
4 Overview of Talks Sport Movement
41 Predicting Human Responses to Environmental ChangesEva Dorschky (Universitaumlt Erlangen ndash Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Eva Dorschky
Joint work of Dorschky Eva van den Bogert Antonie J Schlarb Heiko Eskofier BjoumlrnMain reference E Dorschky et al ldquoPredictive Musculoskeletal Simulation of Uphill and Downhill Runningrdquo in
Proc ECSS Malmouml 2015 p 126
Predicting human responses to environmental changes is necessary for biomechanical analysisand sports product design If case studies environmental conditions or prototypes cannot berealized modeling and simulation can be used instead The aim of this work was to evaluatea method of predictive musculoskeletal simulation [1] for uphill and downhill running A
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 49
study was simulated by randomizing the modelrsquos muscle parameters The predicted energycosts for running at different slopes were compared to literature [2] Future work includes apersonalization of biomechanical models to represent individual athletes as well as populationgroups The sensitivity of simulation results to model parameters will be studied to ensurerobust simulation results
References1 A J van den Bogert M Hupperets H Schlarb and B KrabbeldquoPredictive musculoskeletal
simulation using optimal control effects of added limb mass on energy cost and kinematicsof walking and runningrdquo in Proc Inst Mech Eng Part P J Sports Eng Technol vol 226pp 123-133 2012
2 AE Minetti C Moia G S Roi D Susta and G Ferretti ldquoEnergy cost of walking andrunning at extreme uphill and downhill slopesrdquo J Appl Physiol vol 93 no 3 pp 1039-10462002
42 Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier (Universitaumlt Erlangen-Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Bjoumlrn Eskofier
Wearable computing systems play an increasingly important role in recreational and elitesports They comprise of two important parts The first are sensors embedded into clothesand equipment that are used for physiological (ECG EMG) and biomechanical (accelerometergyroscope) data recording The second are signal processing and data mining algorithmsimplemented on wearable computers (smartphones watches) that are used for analysis of therecorded data Wearable computing systems can provide support real-time feedback andcoaching advice to sportsmen of all performance levels In order to implement these systemsseveral challenges have to be addressed Our work focusses on four of the most prevalent ofthese
Integration sensors and microprocessors have to be embedded unobtrusively and have torecord a variety of signalsCommunication sensors and microprocessors have to communicate in body-area-networksin a secure safe and energy-saving mannerInterpretation physiological and biomechanical data have to be interpreted using signalprocessing and machine learning methodsSimulation and modeling understanding of sensor data is needed to model processes insports more accurately simulation methodologies help here to provide basic informationto drive those models
15382
50 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
43 What is the Right ModelKaren Roemer (Central Washington University ndash Ellensburg US)
License Creative Commons BY 30 Unported licensecopy Karen Roemer
Investigating human movements requires using biomechanical models to perform kinematicand kinetic analyses Depending on the available motion analysis system or software packagesanthropometric models tracking models joint models etc are applied to quantify kinematicand kinetic variables Two standard biomechanical models (OpenSim and Visual3D) wereused to analyze a simple stepping task Similarities and differences of kinematic and kineticresults for both models were discussed
44 Model-Based Tracking of Human MotionAntonie van den Bogert (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Antonie van den Bogert
Two sensing modalities are in use for field studies of human movement inertial sensing andvideo While both are suitable for analysis of human movement during sports events dataquality is usually far inferior to those encountered during laboratory conditions Inspiredby the classical Kalman filtering concept we consider using a dynamic model to improvethe estimates of the state trajectory of the system This approach presents the estimationproblem as an optimal control problem find state and control trajectories that satisfy systemdynamics and minimize a cost function For tracking of sensor data the cost function consistsof the sum of squared errors between simulated and measured sensor signals When using amusculoskeletal model an extra term representing muscular effort must be added to ensurea unique solution This is due to the classical load sharing redundancy humans have moremuscles than strictly necessary to produce movementEfficient solution methods have been developed to solve this optimal control problem andthe approach was successfully used to obtain a detailed analysis of a landing movementduring skiing based on low-quality video data [1] The same approach was used to performa full dynamic gait analysis including muscle force estimation with body-mounted accelero-meters [2] The accelerometer-based analysis was not good enough for clinical applicationsbecause it was too sensitive to the unmodeled dynamics (damped vibrations) of the accelero-meter attachments Improvement is expected when the instrumentation is supplemented bygyroscopic angular velocity sensorsModel-based state estimation will reduce the sensitivity to measurement error and thereare additional advantages The estimation process includes estimation of the full state ofthe system including variables such as muscle forces which could not be directly obtainedfrom sensor signals The raw data is then not only filtered but also enriched by the dynamicmodel A second advantage is that a dynamically consistent simulation is obtained as anadditional result Such simulations can be useful to explore ldquowhat ifrdquo scenarios such assports injuries that are caused by unfavorable landing postures [3]
References1 A J van den Bogert D Blana and D Heinrich ldquoImplicit methods for efficient musculo-
skeletal simulation and optimal controlrdquo Procedia IUTAM vol 2 pp 297-316 2011
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 51
2 O Nwanna ldquoValidation of an Accelerometry Based Method of Human Gait Analysisrdquo MSthesis Cleveland State University 2014 httpraveohiolinkeduetdcviewacc_num=csu1400424346
3 D Heinrich A J van den Bogert and W Nachbauer ldquoRelationship between jump landingkinematics and peak ACL force during a jump in downhill skiing A simulation studyrdquoScand J Med Sci Sports vol 24 no 3 pp 180-187 2013
5 Overview of Talks Adaptions to Training
51 Modelling Speed-HR Relation using PerPotStefan Endler (Universitaumlt Mainz DE)
License Creative Commons BY 30 Unported licensecopy Stefan Endler
Joint work of Endler Stefan Perl JuumlrgenMain reference S Endler J Perl ldquoOptimizing practice and competition in marathon running by means of the
meta-modelrdquo in Proc of 2012 pre-Olympic Congress on Sports Science and Computer Science inSport (IACSSrsquo12) pp 127ndash131 World Academic Union 2012
A model which represents the heart rate (HR) process based on a speed process has tomodel three apects1 Increasing HR after increasing speed2 Decreasing HR after decreasing speed3 Break down after exhaustionThe performance potential metamodell (PerPot) models all these behaviors It was adapted tothe environment of endurance running Once the model is calibrated to the individual athleteby a graded incremental test it can be used for simulation of eg competitions Simulationscan help unexperienced athletes particularly to avoid overloading and underperforming
52 Performance Adaptions to Football Training Is More AlwaysBetter
Hugo Folgado (University of Evora PT)
License Creative Commons BY 30 Unported licensecopy Hugo Folgado
Joint work of Folgado Hugo Sampaio Jaime
Traditionally performance in sports is measured by magnitude based indicators summedup by the Olympic moto ndash Citius Altius Fortius ndash Faster Higher Stronger Howeverin several sport domains and particularly in team sports this idea has been challengedby recent research In football the physical analysis of matches in different competitiveleagues have showed that players in higher level contexts tend to run less and at lowerintensities than players involved in lower leagues [1] In other approach studying the effectsof congested fixtures in players physical performance showed no differences in the amount ofdistance covered and distance covered at different displacement intensities [2] So it may bespeculated that the amount of displacement is not related to greater levels of performance infootball Based in these approaches we measured players physical and tactical performancedevelopment during the preseason evaluated during sided-games Our results showed thatplayers tend to reduce the amount of distance covered during these situation has the preseason
15382
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
42 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
3 Overview of Talks Sport Games
31 On the Use of Tracking Data to Support Coaches in ProfessionalFootball
John Komar (Prozone Sports Ltd ndash Leeds GB)
License Creative Commons BY 30 Unported licensecopy John Komar
A new era in sports sciences is emerging with the advent of new digital tools for the analysis ofathletesrsquo training performance and health However massive technological changes emergingover the past few years have led to a new issue captured in the term Big Data A clearchallenge for sports scientists and practitioners is to understand which data matter andhow to interpret them The real challenge for the professional world is now to move fromdata-driven decisions to data-informed decisions From this perspective my talk addressesthe question of investigating tracking data in football (ie both events data and playersposition in xyt coordinate) in order to derive meaningful and functional metrics Broadlyspeaking the idea is to overcome traditional generic statistics (eg number of passes donenumber of shots number of tackles percentage of ball possession) by combining them intomeaningful items for coaches and practitioners (ie information that can help them to makeinformed choices for recruitment injury prevention or match analysis) More specifically partof the work presented looked at the number of goals one could expect from a player basedon the location of the shots he took [1] A model of expected goals per field position wasderived from previous seasons and then compared to the actual number of goals scored by aspecific player This comparison can thus inform about the ability of this player to under- oroverachieve in shots success Looking at probability of scoring goals during a season coachescan then be informed about conversion rate of a player but rather than a raw goalsshotsratio this goal expectancy metrics gives more context to the conversion ability (eg it cantake into account the position of the shot the defensive density during the shots) Combinedto other measures like ball movement effectiveness this kind of metrics can feed models ofoffensive contribution in professional football
References1 H Ruiz PJ Lisboa PJ Neilson and W Gregson ldquoMeasuring scoring efficiency through
goal expectancy estimationrdquo in Proc ESANNrsquo15 Bruges 2015
32 The ldquoPractical Impact Debaterdquo in Performance AnalysisMartin Lames (TU Muumlnchen DE)
License Creative Commons BY 30 Unported licensecopy Martin Lames
Problem
Mainstream research in Performance Analysis (PA) applies traditional linear methods to dataanalysis eg ANOVA In this context complex interactions in game sports are modelledquite poorly eg quality of opponent is introduced as another static variable in a linearmodel rank in final table of league [8 9] Fluctuations in behaviour typically a constitutivepattern of game sports are treated as measurement error and sought to be controlled by
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 43
enlarging sample size [3] On the other hand practical support is considered to be the mainpurpose of PA [6] McGarry (2009) sees advancing the understanding of sports mainly withregard to improving future outcomes In this situation a review of Drust and Green (2013)gave rise to the ldquoPractical Impact Debaterdquo The authors stated ldquo it can be suggested thatthe influence of the scientific information that is available has a relatively small influence onthe day-to-day activities within the ldquoreal worldrdquo of footballrdquo (p 1382)
The ldquoPractical Impact Debaterdquo
This statement was supported by a review of Mackenzie and Cushion (2013) They analysedexisting PA-research and found widespread methodological problems as well as a missingresearch strategy for practical support Carling et al (2014) replied to this paper and ndash amongother issues ndash defended analysts working in practice against some methodological criticismby the demands of conducting research in practical settings He frequently referred to hisearlier paper with the telling header ldquoShould we be more pragmatic in our approachrdquo [1]
Remarks on the ldquoPractical Impact Debaterdquo
The problem addressed has its root in a lacking distinction between applied and basic researchThis issue is mentioned by Mackenzie and Cushion (2013) without drawing consequences forresearch strategies to be applied In the eyes of the author it is helpful to distinguish betweenpractical PA (PPA) and theoretical PA (TPA) [5] TPA aims at clarifying the general structureof sports It looks for general rules appropriate models (dynamical systems modelling) andneeds large representative samples PPA is in some respects the opposite It may be definedas PA activities conducted in practice ie analysing training and competition to supporta team or a player PPA is interested in any information that provides practical supportand typically works with and for a single case the own team or player Moreover practicalconsequences for training may not be found algorithmically from data collected but need athorough interpretation with a background of in-depth knowledge from the many sources ofinformation available in a professional football club (medical physiotherapy fitness trainingare only the most important ones) So in PPA ndash whether with or without methodologicalawareness ndash qualitative research methodology is used which may be considered as a typicalfeature as well With a basic distinction between TPA and PPA researchers analysing thegeneral structure of performances are relieved from demonstrating an immediate practicaluse of their results and analysts working in practice shouldnrsquot feel obliged any more to refutecriticism from the point of view of basic research on their pragmatic solutions Neverthelessthere is a tight connection between both areas Measures in practice should be in agreementwith general findings about the nature of the game and in the other direction findings inpractice can give rise to new hypotheses on its structure
Agenda for computer science in PA
What are consequences of the ldquoPractical Impact Debaterdquo for the interdisciplinary researchfield of computer science in sports It becomes clear that there are different agendas for itworking in either TPA or PPA Nevertheless both areas depend on data on the matchesmeaning that there remains a general agenda including the detection of positions and actionsThe automatisation of action detection will be a prominent future task as well as drawinginferences on higher level from action and position data like analysing constructs not availablebefore like availability or more complex tactical behaviours like passing style or pressingSpecifically for TPA the introduction of more appropriate models will determine a future
15382
44 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
agenda As interaction and dynamics are constitutive for game sports these features shouldbe included in any future approach What PPA is concerned challenges lie in improvinginformational service for coaches and athletes We will see information systems that combinethe different sources of information by machine learning technologies to arrive at advancedversions of automated data mining for example driven by assumptions on the nature ofinformation needed and driven by query habits of the user All in all there is a challengingbut also promising future for computer science in PA to be expected
References1 C Carling C Wright LN Nelson and PS Bradley ldquoComment on Performance analysis
in football A critical review and implications for future researchrdquo J Sport Sci vol 32pp 2ndash7 2014
2 B Drust and M Green ldquoScience and football evaluating the influence of science onperformancerdquo J Sport Sci vol 31 pp 1377ndash1382 2013
3 M Hughes S Evans and J Wells ldquoEstablishing normative profiles in performance ana-lysisrdquo Int J Perform Anal Sport vol 1 pp 4ndash27 2001
4 M Lames and G Hansen ldquoDesigning observational systems to support top-level teams ingame sportsrdquo Int J Perform Anal Sport vol 1 pp 85ndash91 2001
5 M Lames and T McGarry ldquoOn the search for reliable performance indicators in gamesportsrdquo Int J Perform Anal Sport vol 7 no 1 pp 62ndash79 2007
6 R Mackenzie and C Cushion ldquoPerformance analysis in football A critical review andimplications for future researchrdquo J Sport Sci vol 31 pp 639ndash676 2013
7 T McGarry DI Anderson SA Wallace MD Hughes and IM Franks ldquoSport compet-ition as a dynamical self-organizing systemrdquo J Sport Sci vol 20 pp 771ndash781 2002
8 P OrsquoDonoghue ldquoInteracting Performances Theoryrdquo Int J Perform Anal Sport vol 9pp 26ndash46 2009
9 A Tenga and E Sigmundstad ldquoCharacteristics of goal-scoring possessions in open-playComparing the top in-between and bottom teams from professional soccer leaguerdquo Int JPerform Anal Sport vol 11 pp 545ndash552 2011
33 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Roland Leser (Universitaumlt Wien AT)
License Creative Commons BY 30 Unported licensecopy Roland Leser
Joint work of Leser Roland Moser Bernhard Hoch Thomas Baca Arnold
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games this
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 45
approach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
34 Individual Ball Possession in SoccerDaniel Link (TU Muumlnchen DE)
License Creative Commons BY 30 Unported licensecopy Daniel Link
This paper describes models for detecting individual and team ball possession in soccer basedon position data The types of ball possession are classified as Individual Ball Possession(IBC) Individual Ball Action (IBA) Individual Ball Control (IBC) Team Ball Possession(TBP) Team Ball Control (TBC) und Team Playmaking (TPM) according to the type ofball control involved The machine learning approach used is able to determine how longthe ball spends in the sphere of influence of a player based on the distance between theplayers and the ball together with their direction of motion speed and the acceleration ofthe ball The degree of ball control exhibited during this phase is classified based on thespatio-temporal configuration of the player controlling the ball the ball itself and opposingplayers using a Bayesian networkThe evaluation and illustrative application of this approachuses data taken from a game in a top European league When applied to error-corrected rawdata the algorithm showed an accuracy of 92 (IBA) 86 (IBP) and 92 (IBC) using atolerance of 06 s This is well above the accuracy achieved manually by the competitioninformation providers of 52 (TBC) There were 1291 phases involving ball control (IBC)totalling 2939 min with a gross game time of 9012 min and a net game time of 5756 minThis initial analysis of ball possession at the player level indicates IBC times of between022 and 318 min The shortest ball control times are observed for the centre forwards ofeach team (09 s) and the full backs of the losing team (07 s) and the longest for the losingteamrsquos goalkeeper (29 s) This can be interpreted as a tendency for the defenders to try andclear the ball as quickly as possible and the goalkeeper attempting to slow the pace of game
35 Understanding Actions in a Sports ContextJim Little (University of British Columbia ndash Vancouver CA)
License Creative Commons BY 30 Unported licensecopy Jim Little
Understanding human action is critical to surveillance monitoring and situation under-standing Sports present events where the types of actions are limited and depend on roleslocations and situations Understanding the actions activity and performance of the playersinterests many Action understanding in broadcast video has led to progress in trackingrecognition camera rectification pose and multi-view action recognition
15382
46 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
36 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Bernhard Moser (Software Competence Center ndash Hagenberg AT)
License Creative Commons BY 30 Unported licensecopy Bernhard Moser
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games thisapproach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
37 Covered Distances of Handball Players Obtained by an AutomaticTracking Method
Tiago Guedes Russomanno (University of Brasilia BR)
License Creative Commons BY 30 Unported licensecopy Tiago Guedes Russomanno
Joint work of Russomanno Tiago Guedes Misuta Milton Shoiti Menezes Rafael Pombo Brandatildeo BrunoCedraz Figueroa Pascual Jovino Leite Neucimar Jeronimo Goldenstein Siome Klein BarrosRicardo Machado Leite
Main reference TG Russomanno M S Misuta R PMenezes BC Brandatildeo P J Figueroa N J Leite SKGoldstein RM L Barros ldquoCovered distances of handball players obtained by an automatictracking methodrdquo in Proc of the 25th Intrsquol Symp on Biomechanics in Sports (ISBSrsquo07)pp 324ndash327 University of Konstanz 2007
URL httpsojsubuni-konstanzdecpaarticleview473
Tracking players in sports events is still a topic of discussion in sport science and the dataprovided by this tracking is useful for team staff to evaluate team performance Therefore theaim of this work was to obtain the distances covered by handball players and their velocitiesduring a match using a new approach based on automatic tracking method described inFigueroa et al [1 2] and the Adaboost detector [5] A whole game of a Brazilian regionalhandball championship for players under age of 21 was recorded Applying the mentionedautomatic tracking the accumulated covered distances and the velocities were calculated forall the players The results of average covered distances (plusmnSD) in the 1st and 2nd halveswere 2199 (plusmn230) and 2453 (plusmn214) The results of covered distances and the velocities allowindividual and collective analyses of the players by the team staff The proposed methodrevealed to be a powerful tool to improve physical analysis of the handball players
References1 P J Figueroa N J Leite and RM L Barros ldquoTracking soccer players aiming their kin-
ematical motion analysisrdquo Comput Vis Image Underst vol 101 no 2 pp 122-135 2006
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 47
2 P J Figueroa N J Leite and RM L Barros ldquoBackground recovering in outdoor imagesequences An example of soccer players segmentationrdquo Image Vision Comput vol 24 no4 pp 363-374 2006
3 M Kristan J Pers J M Perse M Bon and S Kovacic ldquoMultiple interacting targetstracking with application to team sportsrdquo in Proc ISAP Zagreb 2005 pp 322-327
4 MS Misuta R P Menezes P J Figueroa SA Cunha and RML Barros ldquoRepresent-ation and analysis of soccer player trajectoriesrdquo in Proc ISB Cleveland 2005 vol 415
5 K Okuma A Taleghani N de Freitas J Little and D Lowe ldquoA boosted particle filtermultitarget detection and trackingrdquo in Proc ECCV Prague 2004 pp 28-38
6 P Viola and M J Jones ldquoRapid Object Detection using a Boosted Cascade of SimpleFeaturesrdquo in Proc CVPR Kauai 2001 vol 1 pp 511-518
38 Data Requirements in the Sports Data IndustryMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
Different markets do have different requirements concerning data depth data delivery speedand data quality Here is a short overview about three markets (1) Professional Sports (2)Media and (3) Bookmakers Betting1 Professional Sports (Teams Clubs Leagues)
Strong need for deep dataDelivery speed not too important as most teams use the data post-match Moreoverlive-match analyses are sometimes prohibitedData quality is important Performance analysis based on imprecise data could causewrong conclusions
2 MediaStrong need for deep data (eg for story telling)Delivery speed Not too important as there is a broadcasting latency anywaysData quality is not important
3 Bookmakers BettingData depth is not important as there is a market limit anyways If you would offertoo many bets you would cannibalize your own marketDelivery speed is a must have and very importantJust as data quality is Wrong data could cause a of lot trouble and punters wouldclaim their right to get their money back
Consequently for a company like Sportradar it is very important to fulfill all differentrequirements This results in the claim to be able to provide fast highly accurate and deepdata
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48 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
39 Factors that Influence Scoring Dynamics in Low-Scoring andHigh-Scoring Team Games
Anna Volossovitch (University of Lisbon PT)
License Creative Commons BY 30 Unported licensecopy Anna Volossovitch
Joint work of Volossovitch Anna Pratas Joseacute Dumangane Montezuma Rosati NicolettaMain reference M Dumangane N Rosati A Volossovitch ldquoDeparture from independence and stationarity in a
handball matchrdquo Journal of Applied Statistics 36(7)723ndash741 2009URL httpdxdoiorg10108002664760802499329
Research in match analysis frequently attempts to establish causal relationships betweenisolated performance variables and goal scoring or game outcome This approach reduces thecomplexity of performance by presenting it in overly descriptive and regular ways which donot reflect properly the curse of the game The purpose of the presentation was to discuss twoexamples of the analysis of factors which could influence the scoring dynamic during a matchin handball and in football In the first example the influence of teams past performanceon the present teams performance was evaluated throughout the handball match using themodel with time-varying parameters This model estimates the probability of scoring as afunction of the past performance of the opposing team and the current match result Thisassessment considers the specific context of the game situation the teams rankings thematch equilibrium and the number of ball possessions per match In the second example theperformance indicators which had a significant effect on the time of the first goal scoringin football has been identified using Cox time-dependent proportional hazard model Thesurvival analysis is suggested to be a suitable tool to identify which and how performanceindicators influence the time of the first ball is scored in different competitive context
References1 J Pratas AVolossovitch and A I Carita ldquoWhat performance indicators influence the
time of the first goal in the matchrdquo In Proc World Congress of Performance Analysis ofSport X Opatija 2014 p 102
2 A Volossovitch M Dumangane and N Rosati ldquoThe influence of the pace of match onthe dynamic of handball gamerdquo Int J Sports Psychol vol 41 no 4 pp 117ndash118 2010
3 M Dumangane N Rosati and A Volossovitch ldquoDeparture from independence and sta-tionarity in a handball matchrdquo J Appl Stat vol 36 no 7 pp 723ndash741 2009
4 Overview of Talks Sport Movement
41 Predicting Human Responses to Environmental ChangesEva Dorschky (Universitaumlt Erlangen ndash Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Eva Dorschky
Joint work of Dorschky Eva van den Bogert Antonie J Schlarb Heiko Eskofier BjoumlrnMain reference E Dorschky et al ldquoPredictive Musculoskeletal Simulation of Uphill and Downhill Runningrdquo in
Proc ECSS Malmouml 2015 p 126
Predicting human responses to environmental changes is necessary for biomechanical analysisand sports product design If case studies environmental conditions or prototypes cannot berealized modeling and simulation can be used instead The aim of this work was to evaluatea method of predictive musculoskeletal simulation [1] for uphill and downhill running A
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 49
study was simulated by randomizing the modelrsquos muscle parameters The predicted energycosts for running at different slopes were compared to literature [2] Future work includes apersonalization of biomechanical models to represent individual athletes as well as populationgroups The sensitivity of simulation results to model parameters will be studied to ensurerobust simulation results
References1 A J van den Bogert M Hupperets H Schlarb and B KrabbeldquoPredictive musculoskeletal
simulation using optimal control effects of added limb mass on energy cost and kinematicsof walking and runningrdquo in Proc Inst Mech Eng Part P J Sports Eng Technol vol 226pp 123-133 2012
2 AE Minetti C Moia G S Roi D Susta and G Ferretti ldquoEnergy cost of walking andrunning at extreme uphill and downhill slopesrdquo J Appl Physiol vol 93 no 3 pp 1039-10462002
42 Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier (Universitaumlt Erlangen-Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Bjoumlrn Eskofier
Wearable computing systems play an increasingly important role in recreational and elitesports They comprise of two important parts The first are sensors embedded into clothesand equipment that are used for physiological (ECG EMG) and biomechanical (accelerometergyroscope) data recording The second are signal processing and data mining algorithmsimplemented on wearable computers (smartphones watches) that are used for analysis of therecorded data Wearable computing systems can provide support real-time feedback andcoaching advice to sportsmen of all performance levels In order to implement these systemsseveral challenges have to be addressed Our work focusses on four of the most prevalent ofthese
Integration sensors and microprocessors have to be embedded unobtrusively and have torecord a variety of signalsCommunication sensors and microprocessors have to communicate in body-area-networksin a secure safe and energy-saving mannerInterpretation physiological and biomechanical data have to be interpreted using signalprocessing and machine learning methodsSimulation and modeling understanding of sensor data is needed to model processes insports more accurately simulation methodologies help here to provide basic informationto drive those models
15382
50 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
43 What is the Right ModelKaren Roemer (Central Washington University ndash Ellensburg US)
License Creative Commons BY 30 Unported licensecopy Karen Roemer
Investigating human movements requires using biomechanical models to perform kinematicand kinetic analyses Depending on the available motion analysis system or software packagesanthropometric models tracking models joint models etc are applied to quantify kinematicand kinetic variables Two standard biomechanical models (OpenSim and Visual3D) wereused to analyze a simple stepping task Similarities and differences of kinematic and kineticresults for both models were discussed
44 Model-Based Tracking of Human MotionAntonie van den Bogert (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Antonie van den Bogert
Two sensing modalities are in use for field studies of human movement inertial sensing andvideo While both are suitable for analysis of human movement during sports events dataquality is usually far inferior to those encountered during laboratory conditions Inspiredby the classical Kalman filtering concept we consider using a dynamic model to improvethe estimates of the state trajectory of the system This approach presents the estimationproblem as an optimal control problem find state and control trajectories that satisfy systemdynamics and minimize a cost function For tracking of sensor data the cost function consistsof the sum of squared errors between simulated and measured sensor signals When using amusculoskeletal model an extra term representing muscular effort must be added to ensurea unique solution This is due to the classical load sharing redundancy humans have moremuscles than strictly necessary to produce movementEfficient solution methods have been developed to solve this optimal control problem andthe approach was successfully used to obtain a detailed analysis of a landing movementduring skiing based on low-quality video data [1] The same approach was used to performa full dynamic gait analysis including muscle force estimation with body-mounted accelero-meters [2] The accelerometer-based analysis was not good enough for clinical applicationsbecause it was too sensitive to the unmodeled dynamics (damped vibrations) of the accelero-meter attachments Improvement is expected when the instrumentation is supplemented bygyroscopic angular velocity sensorsModel-based state estimation will reduce the sensitivity to measurement error and thereare additional advantages The estimation process includes estimation of the full state ofthe system including variables such as muscle forces which could not be directly obtainedfrom sensor signals The raw data is then not only filtered but also enriched by the dynamicmodel A second advantage is that a dynamically consistent simulation is obtained as anadditional result Such simulations can be useful to explore ldquowhat ifrdquo scenarios such assports injuries that are caused by unfavorable landing postures [3]
References1 A J van den Bogert D Blana and D Heinrich ldquoImplicit methods for efficient musculo-
skeletal simulation and optimal controlrdquo Procedia IUTAM vol 2 pp 297-316 2011
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 51
2 O Nwanna ldquoValidation of an Accelerometry Based Method of Human Gait Analysisrdquo MSthesis Cleveland State University 2014 httpraveohiolinkeduetdcviewacc_num=csu1400424346
3 D Heinrich A J van den Bogert and W Nachbauer ldquoRelationship between jump landingkinematics and peak ACL force during a jump in downhill skiing A simulation studyrdquoScand J Med Sci Sports vol 24 no 3 pp 180-187 2013
5 Overview of Talks Adaptions to Training
51 Modelling Speed-HR Relation using PerPotStefan Endler (Universitaumlt Mainz DE)
License Creative Commons BY 30 Unported licensecopy Stefan Endler
Joint work of Endler Stefan Perl JuumlrgenMain reference S Endler J Perl ldquoOptimizing practice and competition in marathon running by means of the
meta-modelrdquo in Proc of 2012 pre-Olympic Congress on Sports Science and Computer Science inSport (IACSSrsquo12) pp 127ndash131 World Academic Union 2012
A model which represents the heart rate (HR) process based on a speed process has tomodel three apects1 Increasing HR after increasing speed2 Decreasing HR after decreasing speed3 Break down after exhaustionThe performance potential metamodell (PerPot) models all these behaviors It was adapted tothe environment of endurance running Once the model is calibrated to the individual athleteby a graded incremental test it can be used for simulation of eg competitions Simulationscan help unexperienced athletes particularly to avoid overloading and underperforming
52 Performance Adaptions to Football Training Is More AlwaysBetter
Hugo Folgado (University of Evora PT)
License Creative Commons BY 30 Unported licensecopy Hugo Folgado
Joint work of Folgado Hugo Sampaio Jaime
Traditionally performance in sports is measured by magnitude based indicators summedup by the Olympic moto ndash Citius Altius Fortius ndash Faster Higher Stronger Howeverin several sport domains and particularly in team sports this idea has been challengedby recent research In football the physical analysis of matches in different competitiveleagues have showed that players in higher level contexts tend to run less and at lowerintensities than players involved in lower leagues [1] In other approach studying the effectsof congested fixtures in players physical performance showed no differences in the amount ofdistance covered and distance covered at different displacement intensities [2] So it may bespeculated that the amount of displacement is not related to greater levels of performance infootball Based in these approaches we measured players physical and tactical performancedevelopment during the preseason evaluated during sided-games Our results showed thatplayers tend to reduce the amount of distance covered during these situation has the preseason
15382
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 43
enlarging sample size [3] On the other hand practical support is considered to be the mainpurpose of PA [6] McGarry (2009) sees advancing the understanding of sports mainly withregard to improving future outcomes In this situation a review of Drust and Green (2013)gave rise to the ldquoPractical Impact Debaterdquo The authors stated ldquo it can be suggested thatthe influence of the scientific information that is available has a relatively small influence onthe day-to-day activities within the ldquoreal worldrdquo of footballrdquo (p 1382)
The ldquoPractical Impact Debaterdquo
This statement was supported by a review of Mackenzie and Cushion (2013) They analysedexisting PA-research and found widespread methodological problems as well as a missingresearch strategy for practical support Carling et al (2014) replied to this paper and ndash amongother issues ndash defended analysts working in practice against some methodological criticismby the demands of conducting research in practical settings He frequently referred to hisearlier paper with the telling header ldquoShould we be more pragmatic in our approachrdquo [1]
Remarks on the ldquoPractical Impact Debaterdquo
The problem addressed has its root in a lacking distinction between applied and basic researchThis issue is mentioned by Mackenzie and Cushion (2013) without drawing consequences forresearch strategies to be applied In the eyes of the author it is helpful to distinguish betweenpractical PA (PPA) and theoretical PA (TPA) [5] TPA aims at clarifying the general structureof sports It looks for general rules appropriate models (dynamical systems modelling) andneeds large representative samples PPA is in some respects the opposite It may be definedas PA activities conducted in practice ie analysing training and competition to supporta team or a player PPA is interested in any information that provides practical supportand typically works with and for a single case the own team or player Moreover practicalconsequences for training may not be found algorithmically from data collected but need athorough interpretation with a background of in-depth knowledge from the many sources ofinformation available in a professional football club (medical physiotherapy fitness trainingare only the most important ones) So in PPA ndash whether with or without methodologicalawareness ndash qualitative research methodology is used which may be considered as a typicalfeature as well With a basic distinction between TPA and PPA researchers analysing thegeneral structure of performances are relieved from demonstrating an immediate practicaluse of their results and analysts working in practice shouldnrsquot feel obliged any more to refutecriticism from the point of view of basic research on their pragmatic solutions Neverthelessthere is a tight connection between both areas Measures in practice should be in agreementwith general findings about the nature of the game and in the other direction findings inpractice can give rise to new hypotheses on its structure
Agenda for computer science in PA
What are consequences of the ldquoPractical Impact Debaterdquo for the interdisciplinary researchfield of computer science in sports It becomes clear that there are different agendas for itworking in either TPA or PPA Nevertheless both areas depend on data on the matchesmeaning that there remains a general agenda including the detection of positions and actionsThe automatisation of action detection will be a prominent future task as well as drawinginferences on higher level from action and position data like analysing constructs not availablebefore like availability or more complex tactical behaviours like passing style or pressingSpecifically for TPA the introduction of more appropriate models will determine a future
15382
44 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
agenda As interaction and dynamics are constitutive for game sports these features shouldbe included in any future approach What PPA is concerned challenges lie in improvinginformational service for coaches and athletes We will see information systems that combinethe different sources of information by machine learning technologies to arrive at advancedversions of automated data mining for example driven by assumptions on the nature ofinformation needed and driven by query habits of the user All in all there is a challengingbut also promising future for computer science in PA to be expected
References1 C Carling C Wright LN Nelson and PS Bradley ldquoComment on Performance analysis
in football A critical review and implications for future researchrdquo J Sport Sci vol 32pp 2ndash7 2014
2 B Drust and M Green ldquoScience and football evaluating the influence of science onperformancerdquo J Sport Sci vol 31 pp 1377ndash1382 2013
3 M Hughes S Evans and J Wells ldquoEstablishing normative profiles in performance ana-lysisrdquo Int J Perform Anal Sport vol 1 pp 4ndash27 2001
4 M Lames and G Hansen ldquoDesigning observational systems to support top-level teams ingame sportsrdquo Int J Perform Anal Sport vol 1 pp 85ndash91 2001
5 M Lames and T McGarry ldquoOn the search for reliable performance indicators in gamesportsrdquo Int J Perform Anal Sport vol 7 no 1 pp 62ndash79 2007
6 R Mackenzie and C Cushion ldquoPerformance analysis in football A critical review andimplications for future researchrdquo J Sport Sci vol 31 pp 639ndash676 2013
7 T McGarry DI Anderson SA Wallace MD Hughes and IM Franks ldquoSport compet-ition as a dynamical self-organizing systemrdquo J Sport Sci vol 20 pp 771ndash781 2002
8 P OrsquoDonoghue ldquoInteracting Performances Theoryrdquo Int J Perform Anal Sport vol 9pp 26ndash46 2009
9 A Tenga and E Sigmundstad ldquoCharacteristics of goal-scoring possessions in open-playComparing the top in-between and bottom teams from professional soccer leaguerdquo Int JPerform Anal Sport vol 11 pp 545ndash552 2011
33 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Roland Leser (Universitaumlt Wien AT)
License Creative Commons BY 30 Unported licensecopy Roland Leser
Joint work of Leser Roland Moser Bernhard Hoch Thomas Baca Arnold
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games this
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 45
approach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
34 Individual Ball Possession in SoccerDaniel Link (TU Muumlnchen DE)
License Creative Commons BY 30 Unported licensecopy Daniel Link
This paper describes models for detecting individual and team ball possession in soccer basedon position data The types of ball possession are classified as Individual Ball Possession(IBC) Individual Ball Action (IBA) Individual Ball Control (IBC) Team Ball Possession(TBP) Team Ball Control (TBC) und Team Playmaking (TPM) according to the type ofball control involved The machine learning approach used is able to determine how longthe ball spends in the sphere of influence of a player based on the distance between theplayers and the ball together with their direction of motion speed and the acceleration ofthe ball The degree of ball control exhibited during this phase is classified based on thespatio-temporal configuration of the player controlling the ball the ball itself and opposingplayers using a Bayesian networkThe evaluation and illustrative application of this approachuses data taken from a game in a top European league When applied to error-corrected rawdata the algorithm showed an accuracy of 92 (IBA) 86 (IBP) and 92 (IBC) using atolerance of 06 s This is well above the accuracy achieved manually by the competitioninformation providers of 52 (TBC) There were 1291 phases involving ball control (IBC)totalling 2939 min with a gross game time of 9012 min and a net game time of 5756 minThis initial analysis of ball possession at the player level indicates IBC times of between022 and 318 min The shortest ball control times are observed for the centre forwards ofeach team (09 s) and the full backs of the losing team (07 s) and the longest for the losingteamrsquos goalkeeper (29 s) This can be interpreted as a tendency for the defenders to try andclear the ball as quickly as possible and the goalkeeper attempting to slow the pace of game
35 Understanding Actions in a Sports ContextJim Little (University of British Columbia ndash Vancouver CA)
License Creative Commons BY 30 Unported licensecopy Jim Little
Understanding human action is critical to surveillance monitoring and situation under-standing Sports present events where the types of actions are limited and depend on roleslocations and situations Understanding the actions activity and performance of the playersinterests many Action understanding in broadcast video has led to progress in trackingrecognition camera rectification pose and multi-view action recognition
15382
46 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
36 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Bernhard Moser (Software Competence Center ndash Hagenberg AT)
License Creative Commons BY 30 Unported licensecopy Bernhard Moser
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games thisapproach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
37 Covered Distances of Handball Players Obtained by an AutomaticTracking Method
Tiago Guedes Russomanno (University of Brasilia BR)
License Creative Commons BY 30 Unported licensecopy Tiago Guedes Russomanno
Joint work of Russomanno Tiago Guedes Misuta Milton Shoiti Menezes Rafael Pombo Brandatildeo BrunoCedraz Figueroa Pascual Jovino Leite Neucimar Jeronimo Goldenstein Siome Klein BarrosRicardo Machado Leite
Main reference TG Russomanno M S Misuta R PMenezes BC Brandatildeo P J Figueroa N J Leite SKGoldstein RM L Barros ldquoCovered distances of handball players obtained by an automatictracking methodrdquo in Proc of the 25th Intrsquol Symp on Biomechanics in Sports (ISBSrsquo07)pp 324ndash327 University of Konstanz 2007
URL httpsojsubuni-konstanzdecpaarticleview473
Tracking players in sports events is still a topic of discussion in sport science and the dataprovided by this tracking is useful for team staff to evaluate team performance Therefore theaim of this work was to obtain the distances covered by handball players and their velocitiesduring a match using a new approach based on automatic tracking method described inFigueroa et al [1 2] and the Adaboost detector [5] A whole game of a Brazilian regionalhandball championship for players under age of 21 was recorded Applying the mentionedautomatic tracking the accumulated covered distances and the velocities were calculated forall the players The results of average covered distances (plusmnSD) in the 1st and 2nd halveswere 2199 (plusmn230) and 2453 (plusmn214) The results of covered distances and the velocities allowindividual and collective analyses of the players by the team staff The proposed methodrevealed to be a powerful tool to improve physical analysis of the handball players
References1 P J Figueroa N J Leite and RM L Barros ldquoTracking soccer players aiming their kin-
ematical motion analysisrdquo Comput Vis Image Underst vol 101 no 2 pp 122-135 2006
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 47
2 P J Figueroa N J Leite and RM L Barros ldquoBackground recovering in outdoor imagesequences An example of soccer players segmentationrdquo Image Vision Comput vol 24 no4 pp 363-374 2006
3 M Kristan J Pers J M Perse M Bon and S Kovacic ldquoMultiple interacting targetstracking with application to team sportsrdquo in Proc ISAP Zagreb 2005 pp 322-327
4 MS Misuta R P Menezes P J Figueroa SA Cunha and RML Barros ldquoRepresent-ation and analysis of soccer player trajectoriesrdquo in Proc ISB Cleveland 2005 vol 415
5 K Okuma A Taleghani N de Freitas J Little and D Lowe ldquoA boosted particle filtermultitarget detection and trackingrdquo in Proc ECCV Prague 2004 pp 28-38
6 P Viola and M J Jones ldquoRapid Object Detection using a Boosted Cascade of SimpleFeaturesrdquo in Proc CVPR Kauai 2001 vol 1 pp 511-518
38 Data Requirements in the Sports Data IndustryMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
Different markets do have different requirements concerning data depth data delivery speedand data quality Here is a short overview about three markets (1) Professional Sports (2)Media and (3) Bookmakers Betting1 Professional Sports (Teams Clubs Leagues)
Strong need for deep dataDelivery speed not too important as most teams use the data post-match Moreoverlive-match analyses are sometimes prohibitedData quality is important Performance analysis based on imprecise data could causewrong conclusions
2 MediaStrong need for deep data (eg for story telling)Delivery speed Not too important as there is a broadcasting latency anywaysData quality is not important
3 Bookmakers BettingData depth is not important as there is a market limit anyways If you would offertoo many bets you would cannibalize your own marketDelivery speed is a must have and very importantJust as data quality is Wrong data could cause a of lot trouble and punters wouldclaim their right to get their money back
Consequently for a company like Sportradar it is very important to fulfill all differentrequirements This results in the claim to be able to provide fast highly accurate and deepdata
15382
48 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
39 Factors that Influence Scoring Dynamics in Low-Scoring andHigh-Scoring Team Games
Anna Volossovitch (University of Lisbon PT)
License Creative Commons BY 30 Unported licensecopy Anna Volossovitch
Joint work of Volossovitch Anna Pratas Joseacute Dumangane Montezuma Rosati NicolettaMain reference M Dumangane N Rosati A Volossovitch ldquoDeparture from independence and stationarity in a
handball matchrdquo Journal of Applied Statistics 36(7)723ndash741 2009URL httpdxdoiorg10108002664760802499329
Research in match analysis frequently attempts to establish causal relationships betweenisolated performance variables and goal scoring or game outcome This approach reduces thecomplexity of performance by presenting it in overly descriptive and regular ways which donot reflect properly the curse of the game The purpose of the presentation was to discuss twoexamples of the analysis of factors which could influence the scoring dynamic during a matchin handball and in football In the first example the influence of teams past performanceon the present teams performance was evaluated throughout the handball match using themodel with time-varying parameters This model estimates the probability of scoring as afunction of the past performance of the opposing team and the current match result Thisassessment considers the specific context of the game situation the teams rankings thematch equilibrium and the number of ball possessions per match In the second example theperformance indicators which had a significant effect on the time of the first goal scoringin football has been identified using Cox time-dependent proportional hazard model Thesurvival analysis is suggested to be a suitable tool to identify which and how performanceindicators influence the time of the first ball is scored in different competitive context
References1 J Pratas AVolossovitch and A I Carita ldquoWhat performance indicators influence the
time of the first goal in the matchrdquo In Proc World Congress of Performance Analysis ofSport X Opatija 2014 p 102
2 A Volossovitch M Dumangane and N Rosati ldquoThe influence of the pace of match onthe dynamic of handball gamerdquo Int J Sports Psychol vol 41 no 4 pp 117ndash118 2010
3 M Dumangane N Rosati and A Volossovitch ldquoDeparture from independence and sta-tionarity in a handball matchrdquo J Appl Stat vol 36 no 7 pp 723ndash741 2009
4 Overview of Talks Sport Movement
41 Predicting Human Responses to Environmental ChangesEva Dorschky (Universitaumlt Erlangen ndash Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Eva Dorschky
Joint work of Dorschky Eva van den Bogert Antonie J Schlarb Heiko Eskofier BjoumlrnMain reference E Dorschky et al ldquoPredictive Musculoskeletal Simulation of Uphill and Downhill Runningrdquo in
Proc ECSS Malmouml 2015 p 126
Predicting human responses to environmental changes is necessary for biomechanical analysisand sports product design If case studies environmental conditions or prototypes cannot berealized modeling and simulation can be used instead The aim of this work was to evaluatea method of predictive musculoskeletal simulation [1] for uphill and downhill running A
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 49
study was simulated by randomizing the modelrsquos muscle parameters The predicted energycosts for running at different slopes were compared to literature [2] Future work includes apersonalization of biomechanical models to represent individual athletes as well as populationgroups The sensitivity of simulation results to model parameters will be studied to ensurerobust simulation results
References1 A J van den Bogert M Hupperets H Schlarb and B KrabbeldquoPredictive musculoskeletal
simulation using optimal control effects of added limb mass on energy cost and kinematicsof walking and runningrdquo in Proc Inst Mech Eng Part P J Sports Eng Technol vol 226pp 123-133 2012
2 AE Minetti C Moia G S Roi D Susta and G Ferretti ldquoEnergy cost of walking andrunning at extreme uphill and downhill slopesrdquo J Appl Physiol vol 93 no 3 pp 1039-10462002
42 Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier (Universitaumlt Erlangen-Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Bjoumlrn Eskofier
Wearable computing systems play an increasingly important role in recreational and elitesports They comprise of two important parts The first are sensors embedded into clothesand equipment that are used for physiological (ECG EMG) and biomechanical (accelerometergyroscope) data recording The second are signal processing and data mining algorithmsimplemented on wearable computers (smartphones watches) that are used for analysis of therecorded data Wearable computing systems can provide support real-time feedback andcoaching advice to sportsmen of all performance levels In order to implement these systemsseveral challenges have to be addressed Our work focusses on four of the most prevalent ofthese
Integration sensors and microprocessors have to be embedded unobtrusively and have torecord a variety of signalsCommunication sensors and microprocessors have to communicate in body-area-networksin a secure safe and energy-saving mannerInterpretation physiological and biomechanical data have to be interpreted using signalprocessing and machine learning methodsSimulation and modeling understanding of sensor data is needed to model processes insports more accurately simulation methodologies help here to provide basic informationto drive those models
15382
50 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
43 What is the Right ModelKaren Roemer (Central Washington University ndash Ellensburg US)
License Creative Commons BY 30 Unported licensecopy Karen Roemer
Investigating human movements requires using biomechanical models to perform kinematicand kinetic analyses Depending on the available motion analysis system or software packagesanthropometric models tracking models joint models etc are applied to quantify kinematicand kinetic variables Two standard biomechanical models (OpenSim and Visual3D) wereused to analyze a simple stepping task Similarities and differences of kinematic and kineticresults for both models were discussed
44 Model-Based Tracking of Human MotionAntonie van den Bogert (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Antonie van den Bogert
Two sensing modalities are in use for field studies of human movement inertial sensing andvideo While both are suitable for analysis of human movement during sports events dataquality is usually far inferior to those encountered during laboratory conditions Inspiredby the classical Kalman filtering concept we consider using a dynamic model to improvethe estimates of the state trajectory of the system This approach presents the estimationproblem as an optimal control problem find state and control trajectories that satisfy systemdynamics and minimize a cost function For tracking of sensor data the cost function consistsof the sum of squared errors between simulated and measured sensor signals When using amusculoskeletal model an extra term representing muscular effort must be added to ensurea unique solution This is due to the classical load sharing redundancy humans have moremuscles than strictly necessary to produce movementEfficient solution methods have been developed to solve this optimal control problem andthe approach was successfully used to obtain a detailed analysis of a landing movementduring skiing based on low-quality video data [1] The same approach was used to performa full dynamic gait analysis including muscle force estimation with body-mounted accelero-meters [2] The accelerometer-based analysis was not good enough for clinical applicationsbecause it was too sensitive to the unmodeled dynamics (damped vibrations) of the accelero-meter attachments Improvement is expected when the instrumentation is supplemented bygyroscopic angular velocity sensorsModel-based state estimation will reduce the sensitivity to measurement error and thereare additional advantages The estimation process includes estimation of the full state ofthe system including variables such as muscle forces which could not be directly obtainedfrom sensor signals The raw data is then not only filtered but also enriched by the dynamicmodel A second advantage is that a dynamically consistent simulation is obtained as anadditional result Such simulations can be useful to explore ldquowhat ifrdquo scenarios such assports injuries that are caused by unfavorable landing postures [3]
References1 A J van den Bogert D Blana and D Heinrich ldquoImplicit methods for efficient musculo-
skeletal simulation and optimal controlrdquo Procedia IUTAM vol 2 pp 297-316 2011
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 51
2 O Nwanna ldquoValidation of an Accelerometry Based Method of Human Gait Analysisrdquo MSthesis Cleveland State University 2014 httpraveohiolinkeduetdcviewacc_num=csu1400424346
3 D Heinrich A J van den Bogert and W Nachbauer ldquoRelationship between jump landingkinematics and peak ACL force during a jump in downhill skiing A simulation studyrdquoScand J Med Sci Sports vol 24 no 3 pp 180-187 2013
5 Overview of Talks Adaptions to Training
51 Modelling Speed-HR Relation using PerPotStefan Endler (Universitaumlt Mainz DE)
License Creative Commons BY 30 Unported licensecopy Stefan Endler
Joint work of Endler Stefan Perl JuumlrgenMain reference S Endler J Perl ldquoOptimizing practice and competition in marathon running by means of the
meta-modelrdquo in Proc of 2012 pre-Olympic Congress on Sports Science and Computer Science inSport (IACSSrsquo12) pp 127ndash131 World Academic Union 2012
A model which represents the heart rate (HR) process based on a speed process has tomodel three apects1 Increasing HR after increasing speed2 Decreasing HR after decreasing speed3 Break down after exhaustionThe performance potential metamodell (PerPot) models all these behaviors It was adapted tothe environment of endurance running Once the model is calibrated to the individual athleteby a graded incremental test it can be used for simulation of eg competitions Simulationscan help unexperienced athletes particularly to avoid overloading and underperforming
52 Performance Adaptions to Football Training Is More AlwaysBetter
Hugo Folgado (University of Evora PT)
License Creative Commons BY 30 Unported licensecopy Hugo Folgado
Joint work of Folgado Hugo Sampaio Jaime
Traditionally performance in sports is measured by magnitude based indicators summedup by the Olympic moto ndash Citius Altius Fortius ndash Faster Higher Stronger Howeverin several sport domains and particularly in team sports this idea has been challengedby recent research In football the physical analysis of matches in different competitiveleagues have showed that players in higher level contexts tend to run less and at lowerintensities than players involved in lower leagues [1] In other approach studying the effectsof congested fixtures in players physical performance showed no differences in the amount ofdistance covered and distance covered at different displacement intensities [2] So it may bespeculated that the amount of displacement is not related to greater levels of performance infootball Based in these approaches we measured players physical and tactical performancedevelopment during the preseason evaluated during sided-games Our results showed thatplayers tend to reduce the amount of distance covered during these situation has the preseason
15382
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
44 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
agenda As interaction and dynamics are constitutive for game sports these features shouldbe included in any future approach What PPA is concerned challenges lie in improvinginformational service for coaches and athletes We will see information systems that combinethe different sources of information by machine learning technologies to arrive at advancedversions of automated data mining for example driven by assumptions on the nature ofinformation needed and driven by query habits of the user All in all there is a challengingbut also promising future for computer science in PA to be expected
References1 C Carling C Wright LN Nelson and PS Bradley ldquoComment on Performance analysis
in football A critical review and implications for future researchrdquo J Sport Sci vol 32pp 2ndash7 2014
2 B Drust and M Green ldquoScience and football evaluating the influence of science onperformancerdquo J Sport Sci vol 31 pp 1377ndash1382 2013
3 M Hughes S Evans and J Wells ldquoEstablishing normative profiles in performance ana-lysisrdquo Int J Perform Anal Sport vol 1 pp 4ndash27 2001
4 M Lames and G Hansen ldquoDesigning observational systems to support top-level teams ingame sportsrdquo Int J Perform Anal Sport vol 1 pp 85ndash91 2001
5 M Lames and T McGarry ldquoOn the search for reliable performance indicators in gamesportsrdquo Int J Perform Anal Sport vol 7 no 1 pp 62ndash79 2007
6 R Mackenzie and C Cushion ldquoPerformance analysis in football A critical review andimplications for future researchrdquo J Sport Sci vol 31 pp 639ndash676 2013
7 T McGarry DI Anderson SA Wallace MD Hughes and IM Franks ldquoSport compet-ition as a dynamical self-organizing systemrdquo J Sport Sci vol 20 pp 771ndash781 2002
8 P OrsquoDonoghue ldquoInteracting Performances Theoryrdquo Int J Perform Anal Sport vol 9pp 26ndash46 2009
9 A Tenga and E Sigmundstad ldquoCharacteristics of goal-scoring possessions in open-playComparing the top in-between and bottom teams from professional soccer leaguerdquo Int JPerform Anal Sport vol 11 pp 545ndash552 2011
33 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Roland Leser (Universitaumlt Wien AT)
License Creative Commons BY 30 Unported licensecopy Roland Leser
Joint work of Leser Roland Moser Bernhard Hoch Thomas Baca Arnold
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games this
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 45
approach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
34 Individual Ball Possession in SoccerDaniel Link (TU Muumlnchen DE)
License Creative Commons BY 30 Unported licensecopy Daniel Link
This paper describes models for detecting individual and team ball possession in soccer basedon position data The types of ball possession are classified as Individual Ball Possession(IBC) Individual Ball Action (IBA) Individual Ball Control (IBC) Team Ball Possession(TBP) Team Ball Control (TBC) und Team Playmaking (TPM) according to the type ofball control involved The machine learning approach used is able to determine how longthe ball spends in the sphere of influence of a player based on the distance between theplayers and the ball together with their direction of motion speed and the acceleration ofthe ball The degree of ball control exhibited during this phase is classified based on thespatio-temporal configuration of the player controlling the ball the ball itself and opposingplayers using a Bayesian networkThe evaluation and illustrative application of this approachuses data taken from a game in a top European league When applied to error-corrected rawdata the algorithm showed an accuracy of 92 (IBA) 86 (IBP) and 92 (IBC) using atolerance of 06 s This is well above the accuracy achieved manually by the competitioninformation providers of 52 (TBC) There were 1291 phases involving ball control (IBC)totalling 2939 min with a gross game time of 9012 min and a net game time of 5756 minThis initial analysis of ball possession at the player level indicates IBC times of between022 and 318 min The shortest ball control times are observed for the centre forwards ofeach team (09 s) and the full backs of the losing team (07 s) and the longest for the losingteamrsquos goalkeeper (29 s) This can be interpreted as a tendency for the defenders to try andclear the ball as quickly as possible and the goalkeeper attempting to slow the pace of game
35 Understanding Actions in a Sports ContextJim Little (University of British Columbia ndash Vancouver CA)
License Creative Commons BY 30 Unported licensecopy Jim Little
Understanding human action is critical to surveillance monitoring and situation under-standing Sports present events where the types of actions are limited and depend on roleslocations and situations Understanding the actions activity and performance of the playersinterests many Action understanding in broadcast video has led to progress in trackingrecognition camera rectification pose and multi-view action recognition
15382
46 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
36 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Bernhard Moser (Software Competence Center ndash Hagenberg AT)
License Creative Commons BY 30 Unported licensecopy Bernhard Moser
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games thisapproach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
37 Covered Distances of Handball Players Obtained by an AutomaticTracking Method
Tiago Guedes Russomanno (University of Brasilia BR)
License Creative Commons BY 30 Unported licensecopy Tiago Guedes Russomanno
Joint work of Russomanno Tiago Guedes Misuta Milton Shoiti Menezes Rafael Pombo Brandatildeo BrunoCedraz Figueroa Pascual Jovino Leite Neucimar Jeronimo Goldenstein Siome Klein BarrosRicardo Machado Leite
Main reference TG Russomanno M S Misuta R PMenezes BC Brandatildeo P J Figueroa N J Leite SKGoldstein RM L Barros ldquoCovered distances of handball players obtained by an automatictracking methodrdquo in Proc of the 25th Intrsquol Symp on Biomechanics in Sports (ISBSrsquo07)pp 324ndash327 University of Konstanz 2007
URL httpsojsubuni-konstanzdecpaarticleview473
Tracking players in sports events is still a topic of discussion in sport science and the dataprovided by this tracking is useful for team staff to evaluate team performance Therefore theaim of this work was to obtain the distances covered by handball players and their velocitiesduring a match using a new approach based on automatic tracking method described inFigueroa et al [1 2] and the Adaboost detector [5] A whole game of a Brazilian regionalhandball championship for players under age of 21 was recorded Applying the mentionedautomatic tracking the accumulated covered distances and the velocities were calculated forall the players The results of average covered distances (plusmnSD) in the 1st and 2nd halveswere 2199 (plusmn230) and 2453 (plusmn214) The results of covered distances and the velocities allowindividual and collective analyses of the players by the team staff The proposed methodrevealed to be a powerful tool to improve physical analysis of the handball players
References1 P J Figueroa N J Leite and RM L Barros ldquoTracking soccer players aiming their kin-
ematical motion analysisrdquo Comput Vis Image Underst vol 101 no 2 pp 122-135 2006
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 47
2 P J Figueroa N J Leite and RM L Barros ldquoBackground recovering in outdoor imagesequences An example of soccer players segmentationrdquo Image Vision Comput vol 24 no4 pp 363-374 2006
3 M Kristan J Pers J M Perse M Bon and S Kovacic ldquoMultiple interacting targetstracking with application to team sportsrdquo in Proc ISAP Zagreb 2005 pp 322-327
4 MS Misuta R P Menezes P J Figueroa SA Cunha and RML Barros ldquoRepresent-ation and analysis of soccer player trajectoriesrdquo in Proc ISB Cleveland 2005 vol 415
5 K Okuma A Taleghani N de Freitas J Little and D Lowe ldquoA boosted particle filtermultitarget detection and trackingrdquo in Proc ECCV Prague 2004 pp 28-38
6 P Viola and M J Jones ldquoRapid Object Detection using a Boosted Cascade of SimpleFeaturesrdquo in Proc CVPR Kauai 2001 vol 1 pp 511-518
38 Data Requirements in the Sports Data IndustryMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
Different markets do have different requirements concerning data depth data delivery speedand data quality Here is a short overview about three markets (1) Professional Sports (2)Media and (3) Bookmakers Betting1 Professional Sports (Teams Clubs Leagues)
Strong need for deep dataDelivery speed not too important as most teams use the data post-match Moreoverlive-match analyses are sometimes prohibitedData quality is important Performance analysis based on imprecise data could causewrong conclusions
2 MediaStrong need for deep data (eg for story telling)Delivery speed Not too important as there is a broadcasting latency anywaysData quality is not important
3 Bookmakers BettingData depth is not important as there is a market limit anyways If you would offertoo many bets you would cannibalize your own marketDelivery speed is a must have and very importantJust as data quality is Wrong data could cause a of lot trouble and punters wouldclaim their right to get their money back
Consequently for a company like Sportradar it is very important to fulfill all differentrequirements This results in the claim to be able to provide fast highly accurate and deepdata
15382
48 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
39 Factors that Influence Scoring Dynamics in Low-Scoring andHigh-Scoring Team Games
Anna Volossovitch (University of Lisbon PT)
License Creative Commons BY 30 Unported licensecopy Anna Volossovitch
Joint work of Volossovitch Anna Pratas Joseacute Dumangane Montezuma Rosati NicolettaMain reference M Dumangane N Rosati A Volossovitch ldquoDeparture from independence and stationarity in a
handball matchrdquo Journal of Applied Statistics 36(7)723ndash741 2009URL httpdxdoiorg10108002664760802499329
Research in match analysis frequently attempts to establish causal relationships betweenisolated performance variables and goal scoring or game outcome This approach reduces thecomplexity of performance by presenting it in overly descriptive and regular ways which donot reflect properly the curse of the game The purpose of the presentation was to discuss twoexamples of the analysis of factors which could influence the scoring dynamic during a matchin handball and in football In the first example the influence of teams past performanceon the present teams performance was evaluated throughout the handball match using themodel with time-varying parameters This model estimates the probability of scoring as afunction of the past performance of the opposing team and the current match result Thisassessment considers the specific context of the game situation the teams rankings thematch equilibrium and the number of ball possessions per match In the second example theperformance indicators which had a significant effect on the time of the first goal scoringin football has been identified using Cox time-dependent proportional hazard model Thesurvival analysis is suggested to be a suitable tool to identify which and how performanceindicators influence the time of the first ball is scored in different competitive context
References1 J Pratas AVolossovitch and A I Carita ldquoWhat performance indicators influence the
time of the first goal in the matchrdquo In Proc World Congress of Performance Analysis ofSport X Opatija 2014 p 102
2 A Volossovitch M Dumangane and N Rosati ldquoThe influence of the pace of match onthe dynamic of handball gamerdquo Int J Sports Psychol vol 41 no 4 pp 117ndash118 2010
3 M Dumangane N Rosati and A Volossovitch ldquoDeparture from independence and sta-tionarity in a handball matchrdquo J Appl Stat vol 36 no 7 pp 723ndash741 2009
4 Overview of Talks Sport Movement
41 Predicting Human Responses to Environmental ChangesEva Dorschky (Universitaumlt Erlangen ndash Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Eva Dorschky
Joint work of Dorschky Eva van den Bogert Antonie J Schlarb Heiko Eskofier BjoumlrnMain reference E Dorschky et al ldquoPredictive Musculoskeletal Simulation of Uphill and Downhill Runningrdquo in
Proc ECSS Malmouml 2015 p 126
Predicting human responses to environmental changes is necessary for biomechanical analysisand sports product design If case studies environmental conditions or prototypes cannot berealized modeling and simulation can be used instead The aim of this work was to evaluatea method of predictive musculoskeletal simulation [1] for uphill and downhill running A
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 49
study was simulated by randomizing the modelrsquos muscle parameters The predicted energycosts for running at different slopes were compared to literature [2] Future work includes apersonalization of biomechanical models to represent individual athletes as well as populationgroups The sensitivity of simulation results to model parameters will be studied to ensurerobust simulation results
References1 A J van den Bogert M Hupperets H Schlarb and B KrabbeldquoPredictive musculoskeletal
simulation using optimal control effects of added limb mass on energy cost and kinematicsof walking and runningrdquo in Proc Inst Mech Eng Part P J Sports Eng Technol vol 226pp 123-133 2012
2 AE Minetti C Moia G S Roi D Susta and G Ferretti ldquoEnergy cost of walking andrunning at extreme uphill and downhill slopesrdquo J Appl Physiol vol 93 no 3 pp 1039-10462002
42 Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier (Universitaumlt Erlangen-Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Bjoumlrn Eskofier
Wearable computing systems play an increasingly important role in recreational and elitesports They comprise of two important parts The first are sensors embedded into clothesand equipment that are used for physiological (ECG EMG) and biomechanical (accelerometergyroscope) data recording The second are signal processing and data mining algorithmsimplemented on wearable computers (smartphones watches) that are used for analysis of therecorded data Wearable computing systems can provide support real-time feedback andcoaching advice to sportsmen of all performance levels In order to implement these systemsseveral challenges have to be addressed Our work focusses on four of the most prevalent ofthese
Integration sensors and microprocessors have to be embedded unobtrusively and have torecord a variety of signalsCommunication sensors and microprocessors have to communicate in body-area-networksin a secure safe and energy-saving mannerInterpretation physiological and biomechanical data have to be interpreted using signalprocessing and machine learning methodsSimulation and modeling understanding of sensor data is needed to model processes insports more accurately simulation methodologies help here to provide basic informationto drive those models
15382
50 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
43 What is the Right ModelKaren Roemer (Central Washington University ndash Ellensburg US)
License Creative Commons BY 30 Unported licensecopy Karen Roemer
Investigating human movements requires using biomechanical models to perform kinematicand kinetic analyses Depending on the available motion analysis system or software packagesanthropometric models tracking models joint models etc are applied to quantify kinematicand kinetic variables Two standard biomechanical models (OpenSim and Visual3D) wereused to analyze a simple stepping task Similarities and differences of kinematic and kineticresults for both models were discussed
44 Model-Based Tracking of Human MotionAntonie van den Bogert (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Antonie van den Bogert
Two sensing modalities are in use for field studies of human movement inertial sensing andvideo While both are suitable for analysis of human movement during sports events dataquality is usually far inferior to those encountered during laboratory conditions Inspiredby the classical Kalman filtering concept we consider using a dynamic model to improvethe estimates of the state trajectory of the system This approach presents the estimationproblem as an optimal control problem find state and control trajectories that satisfy systemdynamics and minimize a cost function For tracking of sensor data the cost function consistsof the sum of squared errors between simulated and measured sensor signals When using amusculoskeletal model an extra term representing muscular effort must be added to ensurea unique solution This is due to the classical load sharing redundancy humans have moremuscles than strictly necessary to produce movementEfficient solution methods have been developed to solve this optimal control problem andthe approach was successfully used to obtain a detailed analysis of a landing movementduring skiing based on low-quality video data [1] The same approach was used to performa full dynamic gait analysis including muscle force estimation with body-mounted accelero-meters [2] The accelerometer-based analysis was not good enough for clinical applicationsbecause it was too sensitive to the unmodeled dynamics (damped vibrations) of the accelero-meter attachments Improvement is expected when the instrumentation is supplemented bygyroscopic angular velocity sensorsModel-based state estimation will reduce the sensitivity to measurement error and thereare additional advantages The estimation process includes estimation of the full state ofthe system including variables such as muscle forces which could not be directly obtainedfrom sensor signals The raw data is then not only filtered but also enriched by the dynamicmodel A second advantage is that a dynamically consistent simulation is obtained as anadditional result Such simulations can be useful to explore ldquowhat ifrdquo scenarios such assports injuries that are caused by unfavorable landing postures [3]
References1 A J van den Bogert D Blana and D Heinrich ldquoImplicit methods for efficient musculo-
skeletal simulation and optimal controlrdquo Procedia IUTAM vol 2 pp 297-316 2011
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 51
2 O Nwanna ldquoValidation of an Accelerometry Based Method of Human Gait Analysisrdquo MSthesis Cleveland State University 2014 httpraveohiolinkeduetdcviewacc_num=csu1400424346
3 D Heinrich A J van den Bogert and W Nachbauer ldquoRelationship between jump landingkinematics and peak ACL force during a jump in downhill skiing A simulation studyrdquoScand J Med Sci Sports vol 24 no 3 pp 180-187 2013
5 Overview of Talks Adaptions to Training
51 Modelling Speed-HR Relation using PerPotStefan Endler (Universitaumlt Mainz DE)
License Creative Commons BY 30 Unported licensecopy Stefan Endler
Joint work of Endler Stefan Perl JuumlrgenMain reference S Endler J Perl ldquoOptimizing practice and competition in marathon running by means of the
meta-modelrdquo in Proc of 2012 pre-Olympic Congress on Sports Science and Computer Science inSport (IACSSrsquo12) pp 127ndash131 World Academic Union 2012
A model which represents the heart rate (HR) process based on a speed process has tomodel three apects1 Increasing HR after increasing speed2 Decreasing HR after decreasing speed3 Break down after exhaustionThe performance potential metamodell (PerPot) models all these behaviors It was adapted tothe environment of endurance running Once the model is calibrated to the individual athleteby a graded incremental test it can be used for simulation of eg competitions Simulationscan help unexperienced athletes particularly to avoid overloading and underperforming
52 Performance Adaptions to Football Training Is More AlwaysBetter
Hugo Folgado (University of Evora PT)
License Creative Commons BY 30 Unported licensecopy Hugo Folgado
Joint work of Folgado Hugo Sampaio Jaime
Traditionally performance in sports is measured by magnitude based indicators summedup by the Olympic moto ndash Citius Altius Fortius ndash Faster Higher Stronger Howeverin several sport domains and particularly in team sports this idea has been challengedby recent research In football the physical analysis of matches in different competitiveleagues have showed that players in higher level contexts tend to run less and at lowerintensities than players involved in lower leagues [1] In other approach studying the effectsof congested fixtures in players physical performance showed no differences in the amount ofdistance covered and distance covered at different displacement intensities [2] So it may bespeculated that the amount of displacement is not related to greater levels of performance infootball Based in these approaches we measured players physical and tactical performancedevelopment during the preseason evaluated during sided-games Our results showed thatplayers tend to reduce the amount of distance covered during these situation has the preseason
15382
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 45
approach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
34 Individual Ball Possession in SoccerDaniel Link (TU Muumlnchen DE)
License Creative Commons BY 30 Unported licensecopy Daniel Link
This paper describes models for detecting individual and team ball possession in soccer basedon position data The types of ball possession are classified as Individual Ball Possession(IBC) Individual Ball Action (IBA) Individual Ball Control (IBC) Team Ball Possession(TBP) Team Ball Control (TBC) und Team Playmaking (TPM) according to the type ofball control involved The machine learning approach used is able to determine how longthe ball spends in the sphere of influence of a player based on the distance between theplayers and the ball together with their direction of motion speed and the acceleration ofthe ball The degree of ball control exhibited during this phase is classified based on thespatio-temporal configuration of the player controlling the ball the ball itself and opposingplayers using a Bayesian networkThe evaluation and illustrative application of this approachuses data taken from a game in a top European league When applied to error-corrected rawdata the algorithm showed an accuracy of 92 (IBA) 86 (IBP) and 92 (IBC) using atolerance of 06 s This is well above the accuracy achieved manually by the competitioninformation providers of 52 (TBC) There were 1291 phases involving ball control (IBC)totalling 2939 min with a gross game time of 9012 min and a net game time of 5756 minThis initial analysis of ball possession at the player level indicates IBC times of between022 and 318 min The shortest ball control times are observed for the centre forwards ofeach team (09 s) and the full backs of the losing team (07 s) and the longest for the losingteamrsquos goalkeeper (29 s) This can be interpreted as a tendency for the defenders to try andclear the ball as quickly as possible and the goalkeeper attempting to slow the pace of game
35 Understanding Actions in a Sports ContextJim Little (University of British Columbia ndash Vancouver CA)
License Creative Commons BY 30 Unported licensecopy Jim Little
Understanding human action is critical to surveillance monitoring and situation under-standing Sports present events where the types of actions are limited and depend on roleslocations and situations Understanding the actions activity and performance of the playersinterests many Action understanding in broadcast video has led to progress in trackingrecognition camera rectification pose and multi-view action recognition
15382
46 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
36 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Bernhard Moser (Software Competence Center ndash Hagenberg AT)
License Creative Commons BY 30 Unported licensecopy Bernhard Moser
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games thisapproach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
37 Covered Distances of Handball Players Obtained by an AutomaticTracking Method
Tiago Guedes Russomanno (University of Brasilia BR)
License Creative Commons BY 30 Unported licensecopy Tiago Guedes Russomanno
Joint work of Russomanno Tiago Guedes Misuta Milton Shoiti Menezes Rafael Pombo Brandatildeo BrunoCedraz Figueroa Pascual Jovino Leite Neucimar Jeronimo Goldenstein Siome Klein BarrosRicardo Machado Leite
Main reference TG Russomanno M S Misuta R PMenezes BC Brandatildeo P J Figueroa N J Leite SKGoldstein RM L Barros ldquoCovered distances of handball players obtained by an automatictracking methodrdquo in Proc of the 25th Intrsquol Symp on Biomechanics in Sports (ISBSrsquo07)pp 324ndash327 University of Konstanz 2007
URL httpsojsubuni-konstanzdecpaarticleview473
Tracking players in sports events is still a topic of discussion in sport science and the dataprovided by this tracking is useful for team staff to evaluate team performance Therefore theaim of this work was to obtain the distances covered by handball players and their velocitiesduring a match using a new approach based on automatic tracking method described inFigueroa et al [1 2] and the Adaboost detector [5] A whole game of a Brazilian regionalhandball championship for players under age of 21 was recorded Applying the mentionedautomatic tracking the accumulated covered distances and the velocities were calculated forall the players The results of average covered distances (plusmnSD) in the 1st and 2nd halveswere 2199 (plusmn230) and 2453 (plusmn214) The results of covered distances and the velocities allowindividual and collective analyses of the players by the team staff The proposed methodrevealed to be a powerful tool to improve physical analysis of the handball players
References1 P J Figueroa N J Leite and RM L Barros ldquoTracking soccer players aiming their kin-
ematical motion analysisrdquo Comput Vis Image Underst vol 101 no 2 pp 122-135 2006
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 47
2 P J Figueroa N J Leite and RM L Barros ldquoBackground recovering in outdoor imagesequences An example of soccer players segmentationrdquo Image Vision Comput vol 24 no4 pp 363-374 2006
3 M Kristan J Pers J M Perse M Bon and S Kovacic ldquoMultiple interacting targetstracking with application to team sportsrdquo in Proc ISAP Zagreb 2005 pp 322-327
4 MS Misuta R P Menezes P J Figueroa SA Cunha and RML Barros ldquoRepresent-ation and analysis of soccer player trajectoriesrdquo in Proc ISB Cleveland 2005 vol 415
5 K Okuma A Taleghani N de Freitas J Little and D Lowe ldquoA boosted particle filtermultitarget detection and trackingrdquo in Proc ECCV Prague 2004 pp 28-38
6 P Viola and M J Jones ldquoRapid Object Detection using a Boosted Cascade of SimpleFeaturesrdquo in Proc CVPR Kauai 2001 vol 1 pp 511-518
38 Data Requirements in the Sports Data IndustryMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
Different markets do have different requirements concerning data depth data delivery speedand data quality Here is a short overview about three markets (1) Professional Sports (2)Media and (3) Bookmakers Betting1 Professional Sports (Teams Clubs Leagues)
Strong need for deep dataDelivery speed not too important as most teams use the data post-match Moreoverlive-match analyses are sometimes prohibitedData quality is important Performance analysis based on imprecise data could causewrong conclusions
2 MediaStrong need for deep data (eg for story telling)Delivery speed Not too important as there is a broadcasting latency anywaysData quality is not important
3 Bookmakers BettingData depth is not important as there is a market limit anyways If you would offertoo many bets you would cannibalize your own marketDelivery speed is a must have and very importantJust as data quality is Wrong data could cause a of lot trouble and punters wouldclaim their right to get their money back
Consequently for a company like Sportradar it is very important to fulfill all differentrequirements This results in the claim to be able to provide fast highly accurate and deepdata
15382
48 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
39 Factors that Influence Scoring Dynamics in Low-Scoring andHigh-Scoring Team Games
Anna Volossovitch (University of Lisbon PT)
License Creative Commons BY 30 Unported licensecopy Anna Volossovitch
Joint work of Volossovitch Anna Pratas Joseacute Dumangane Montezuma Rosati NicolettaMain reference M Dumangane N Rosati A Volossovitch ldquoDeparture from independence and stationarity in a
handball matchrdquo Journal of Applied Statistics 36(7)723ndash741 2009URL httpdxdoiorg10108002664760802499329
Research in match analysis frequently attempts to establish causal relationships betweenisolated performance variables and goal scoring or game outcome This approach reduces thecomplexity of performance by presenting it in overly descriptive and regular ways which donot reflect properly the curse of the game The purpose of the presentation was to discuss twoexamples of the analysis of factors which could influence the scoring dynamic during a matchin handball and in football In the first example the influence of teams past performanceon the present teams performance was evaluated throughout the handball match using themodel with time-varying parameters This model estimates the probability of scoring as afunction of the past performance of the opposing team and the current match result Thisassessment considers the specific context of the game situation the teams rankings thematch equilibrium and the number of ball possessions per match In the second example theperformance indicators which had a significant effect on the time of the first goal scoringin football has been identified using Cox time-dependent proportional hazard model Thesurvival analysis is suggested to be a suitable tool to identify which and how performanceindicators influence the time of the first ball is scored in different competitive context
References1 J Pratas AVolossovitch and A I Carita ldquoWhat performance indicators influence the
time of the first goal in the matchrdquo In Proc World Congress of Performance Analysis ofSport X Opatija 2014 p 102
2 A Volossovitch M Dumangane and N Rosati ldquoThe influence of the pace of match onthe dynamic of handball gamerdquo Int J Sports Psychol vol 41 no 4 pp 117ndash118 2010
3 M Dumangane N Rosati and A Volossovitch ldquoDeparture from independence and sta-tionarity in a handball matchrdquo J Appl Stat vol 36 no 7 pp 723ndash741 2009
4 Overview of Talks Sport Movement
41 Predicting Human Responses to Environmental ChangesEva Dorschky (Universitaumlt Erlangen ndash Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Eva Dorschky
Joint work of Dorschky Eva van den Bogert Antonie J Schlarb Heiko Eskofier BjoumlrnMain reference E Dorschky et al ldquoPredictive Musculoskeletal Simulation of Uphill and Downhill Runningrdquo in
Proc ECSS Malmouml 2015 p 126
Predicting human responses to environmental changes is necessary for biomechanical analysisand sports product design If case studies environmental conditions or prototypes cannot berealized modeling and simulation can be used instead The aim of this work was to evaluatea method of predictive musculoskeletal simulation [1] for uphill and downhill running A
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 49
study was simulated by randomizing the modelrsquos muscle parameters The predicted energycosts for running at different slopes were compared to literature [2] Future work includes apersonalization of biomechanical models to represent individual athletes as well as populationgroups The sensitivity of simulation results to model parameters will be studied to ensurerobust simulation results
References1 A J van den Bogert M Hupperets H Schlarb and B KrabbeldquoPredictive musculoskeletal
simulation using optimal control effects of added limb mass on energy cost and kinematicsof walking and runningrdquo in Proc Inst Mech Eng Part P J Sports Eng Technol vol 226pp 123-133 2012
2 AE Minetti C Moia G S Roi D Susta and G Ferretti ldquoEnergy cost of walking andrunning at extreme uphill and downhill slopesrdquo J Appl Physiol vol 93 no 3 pp 1039-10462002
42 Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier (Universitaumlt Erlangen-Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Bjoumlrn Eskofier
Wearable computing systems play an increasingly important role in recreational and elitesports They comprise of two important parts The first are sensors embedded into clothesand equipment that are used for physiological (ECG EMG) and biomechanical (accelerometergyroscope) data recording The second are signal processing and data mining algorithmsimplemented on wearable computers (smartphones watches) that are used for analysis of therecorded data Wearable computing systems can provide support real-time feedback andcoaching advice to sportsmen of all performance levels In order to implement these systemsseveral challenges have to be addressed Our work focusses on four of the most prevalent ofthese
Integration sensors and microprocessors have to be embedded unobtrusively and have torecord a variety of signalsCommunication sensors and microprocessors have to communicate in body-area-networksin a secure safe and energy-saving mannerInterpretation physiological and biomechanical data have to be interpreted using signalprocessing and machine learning methodsSimulation and modeling understanding of sensor data is needed to model processes insports more accurately simulation methodologies help here to provide basic informationto drive those models
15382
50 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
43 What is the Right ModelKaren Roemer (Central Washington University ndash Ellensburg US)
License Creative Commons BY 30 Unported licensecopy Karen Roemer
Investigating human movements requires using biomechanical models to perform kinematicand kinetic analyses Depending on the available motion analysis system or software packagesanthropometric models tracking models joint models etc are applied to quantify kinematicand kinetic variables Two standard biomechanical models (OpenSim and Visual3D) wereused to analyze a simple stepping task Similarities and differences of kinematic and kineticresults for both models were discussed
44 Model-Based Tracking of Human MotionAntonie van den Bogert (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Antonie van den Bogert
Two sensing modalities are in use for field studies of human movement inertial sensing andvideo While both are suitable for analysis of human movement during sports events dataquality is usually far inferior to those encountered during laboratory conditions Inspiredby the classical Kalman filtering concept we consider using a dynamic model to improvethe estimates of the state trajectory of the system This approach presents the estimationproblem as an optimal control problem find state and control trajectories that satisfy systemdynamics and minimize a cost function For tracking of sensor data the cost function consistsof the sum of squared errors between simulated and measured sensor signals When using amusculoskeletal model an extra term representing muscular effort must be added to ensurea unique solution This is due to the classical load sharing redundancy humans have moremuscles than strictly necessary to produce movementEfficient solution methods have been developed to solve this optimal control problem andthe approach was successfully used to obtain a detailed analysis of a landing movementduring skiing based on low-quality video data [1] The same approach was used to performa full dynamic gait analysis including muscle force estimation with body-mounted accelero-meters [2] The accelerometer-based analysis was not good enough for clinical applicationsbecause it was too sensitive to the unmodeled dynamics (damped vibrations) of the accelero-meter attachments Improvement is expected when the instrumentation is supplemented bygyroscopic angular velocity sensorsModel-based state estimation will reduce the sensitivity to measurement error and thereare additional advantages The estimation process includes estimation of the full state ofthe system including variables such as muscle forces which could not be directly obtainedfrom sensor signals The raw data is then not only filtered but also enriched by the dynamicmodel A second advantage is that a dynamically consistent simulation is obtained as anadditional result Such simulations can be useful to explore ldquowhat ifrdquo scenarios such assports injuries that are caused by unfavorable landing postures [3]
References1 A J van den Bogert D Blana and D Heinrich ldquoImplicit methods for efficient musculo-
skeletal simulation and optimal controlrdquo Procedia IUTAM vol 2 pp 297-316 2011
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 51
2 O Nwanna ldquoValidation of an Accelerometry Based Method of Human Gait Analysisrdquo MSthesis Cleveland State University 2014 httpraveohiolinkeduetdcviewacc_num=csu1400424346
3 D Heinrich A J van den Bogert and W Nachbauer ldquoRelationship between jump landingkinematics and peak ACL force during a jump in downhill skiing A simulation studyrdquoScand J Med Sci Sports vol 24 no 3 pp 180-187 2013
5 Overview of Talks Adaptions to Training
51 Modelling Speed-HR Relation using PerPotStefan Endler (Universitaumlt Mainz DE)
License Creative Commons BY 30 Unported licensecopy Stefan Endler
Joint work of Endler Stefan Perl JuumlrgenMain reference S Endler J Perl ldquoOptimizing practice and competition in marathon running by means of the
meta-modelrdquo in Proc of 2012 pre-Olympic Congress on Sports Science and Computer Science inSport (IACSSrsquo12) pp 127ndash131 World Academic Union 2012
A model which represents the heart rate (HR) process based on a speed process has tomodel three apects1 Increasing HR after increasing speed2 Decreasing HR after decreasing speed3 Break down after exhaustionThe performance potential metamodell (PerPot) models all these behaviors It was adapted tothe environment of endurance running Once the model is calibrated to the individual athleteby a graded incremental test it can be used for simulation of eg competitions Simulationscan help unexperienced athletes particularly to avoid overloading and underperforming
52 Performance Adaptions to Football Training Is More AlwaysBetter
Hugo Folgado (University of Evora PT)
License Creative Commons BY 30 Unported licensecopy Hugo Folgado
Joint work of Folgado Hugo Sampaio Jaime
Traditionally performance in sports is measured by magnitude based indicators summedup by the Olympic moto ndash Citius Altius Fortius ndash Faster Higher Stronger Howeverin several sport domains and particularly in team sports this idea has been challengedby recent research In football the physical analysis of matches in different competitiveleagues have showed that players in higher level contexts tend to run less and at lowerintensities than players involved in lower leagues [1] In other approach studying the effectsof congested fixtures in players physical performance showed no differences in the amount ofdistance covered and distance covered at different displacement intensities [2] So it may bespeculated that the amount of displacement is not related to greater levels of performance infootball Based in these approaches we measured players physical and tactical performancedevelopment during the preseason evaluated during sided-games Our results showed thatplayers tend to reduce the amount of distance covered during these situation has the preseason
15382
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
46 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
36 Performance Analysis in Soccer Based on Knowledge DiscoveryApproach
Bernhard Moser (Software Competence Center ndash Hagenberg AT)
License Creative Commons BY 30 Unported licensecopy Bernhard Moser
The contribution addresses the development of explanation models for key performanceindices extracted from position and tracking data The goal is to come up with an explanationrather than a black box model which allows the explanation of the performance by meansof behavioral patterns For this purpose a knowledge discovery approach for extractingbehavioral patterns from position measurement data in small-sided soccer games is outlinedThe resulting kinematic feature space of spatial-temporal variables is high-dimensional Inorder to maintain interpretability for coaches therefore the reduction to a reasonable amountof variables is needed To this end the Laplacian Score method is introduced which yieldspromising results This method aims at reducing the dimensionality while keeping thestructure of the data In a further step clusters in this reduced feature space are inducedby taking key performance indicators into account Promisingly for small-sided games thisapproach leads to expressive linguistically interpretable explanation models Our contributionaims at discussing the potential of this approach also for more complex scenarios
37 Covered Distances of Handball Players Obtained by an AutomaticTracking Method
Tiago Guedes Russomanno (University of Brasilia BR)
License Creative Commons BY 30 Unported licensecopy Tiago Guedes Russomanno
Joint work of Russomanno Tiago Guedes Misuta Milton Shoiti Menezes Rafael Pombo Brandatildeo BrunoCedraz Figueroa Pascual Jovino Leite Neucimar Jeronimo Goldenstein Siome Klein BarrosRicardo Machado Leite
Main reference TG Russomanno M S Misuta R PMenezes BC Brandatildeo P J Figueroa N J Leite SKGoldstein RM L Barros ldquoCovered distances of handball players obtained by an automatictracking methodrdquo in Proc of the 25th Intrsquol Symp on Biomechanics in Sports (ISBSrsquo07)pp 324ndash327 University of Konstanz 2007
URL httpsojsubuni-konstanzdecpaarticleview473
Tracking players in sports events is still a topic of discussion in sport science and the dataprovided by this tracking is useful for team staff to evaluate team performance Therefore theaim of this work was to obtain the distances covered by handball players and their velocitiesduring a match using a new approach based on automatic tracking method described inFigueroa et al [1 2] and the Adaboost detector [5] A whole game of a Brazilian regionalhandball championship for players under age of 21 was recorded Applying the mentionedautomatic tracking the accumulated covered distances and the velocities were calculated forall the players The results of average covered distances (plusmnSD) in the 1st and 2nd halveswere 2199 (plusmn230) and 2453 (plusmn214) The results of covered distances and the velocities allowindividual and collective analyses of the players by the team staff The proposed methodrevealed to be a powerful tool to improve physical analysis of the handball players
References1 P J Figueroa N J Leite and RM L Barros ldquoTracking soccer players aiming their kin-
ematical motion analysisrdquo Comput Vis Image Underst vol 101 no 2 pp 122-135 2006
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 47
2 P J Figueroa N J Leite and RM L Barros ldquoBackground recovering in outdoor imagesequences An example of soccer players segmentationrdquo Image Vision Comput vol 24 no4 pp 363-374 2006
3 M Kristan J Pers J M Perse M Bon and S Kovacic ldquoMultiple interacting targetstracking with application to team sportsrdquo in Proc ISAP Zagreb 2005 pp 322-327
4 MS Misuta R P Menezes P J Figueroa SA Cunha and RML Barros ldquoRepresent-ation and analysis of soccer player trajectoriesrdquo in Proc ISB Cleveland 2005 vol 415
5 K Okuma A Taleghani N de Freitas J Little and D Lowe ldquoA boosted particle filtermultitarget detection and trackingrdquo in Proc ECCV Prague 2004 pp 28-38
6 P Viola and M J Jones ldquoRapid Object Detection using a Boosted Cascade of SimpleFeaturesrdquo in Proc CVPR Kauai 2001 vol 1 pp 511-518
38 Data Requirements in the Sports Data IndustryMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
Different markets do have different requirements concerning data depth data delivery speedand data quality Here is a short overview about three markets (1) Professional Sports (2)Media and (3) Bookmakers Betting1 Professional Sports (Teams Clubs Leagues)
Strong need for deep dataDelivery speed not too important as most teams use the data post-match Moreoverlive-match analyses are sometimes prohibitedData quality is important Performance analysis based on imprecise data could causewrong conclusions
2 MediaStrong need for deep data (eg for story telling)Delivery speed Not too important as there is a broadcasting latency anywaysData quality is not important
3 Bookmakers BettingData depth is not important as there is a market limit anyways If you would offertoo many bets you would cannibalize your own marketDelivery speed is a must have and very importantJust as data quality is Wrong data could cause a of lot trouble and punters wouldclaim their right to get their money back
Consequently for a company like Sportradar it is very important to fulfill all differentrequirements This results in the claim to be able to provide fast highly accurate and deepdata
15382
48 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
39 Factors that Influence Scoring Dynamics in Low-Scoring andHigh-Scoring Team Games
Anna Volossovitch (University of Lisbon PT)
License Creative Commons BY 30 Unported licensecopy Anna Volossovitch
Joint work of Volossovitch Anna Pratas Joseacute Dumangane Montezuma Rosati NicolettaMain reference M Dumangane N Rosati A Volossovitch ldquoDeparture from independence and stationarity in a
handball matchrdquo Journal of Applied Statistics 36(7)723ndash741 2009URL httpdxdoiorg10108002664760802499329
Research in match analysis frequently attempts to establish causal relationships betweenisolated performance variables and goal scoring or game outcome This approach reduces thecomplexity of performance by presenting it in overly descriptive and regular ways which donot reflect properly the curse of the game The purpose of the presentation was to discuss twoexamples of the analysis of factors which could influence the scoring dynamic during a matchin handball and in football In the first example the influence of teams past performanceon the present teams performance was evaluated throughout the handball match using themodel with time-varying parameters This model estimates the probability of scoring as afunction of the past performance of the opposing team and the current match result Thisassessment considers the specific context of the game situation the teams rankings thematch equilibrium and the number of ball possessions per match In the second example theperformance indicators which had a significant effect on the time of the first goal scoringin football has been identified using Cox time-dependent proportional hazard model Thesurvival analysis is suggested to be a suitable tool to identify which and how performanceindicators influence the time of the first ball is scored in different competitive context
References1 J Pratas AVolossovitch and A I Carita ldquoWhat performance indicators influence the
time of the first goal in the matchrdquo In Proc World Congress of Performance Analysis ofSport X Opatija 2014 p 102
2 A Volossovitch M Dumangane and N Rosati ldquoThe influence of the pace of match onthe dynamic of handball gamerdquo Int J Sports Psychol vol 41 no 4 pp 117ndash118 2010
3 M Dumangane N Rosati and A Volossovitch ldquoDeparture from independence and sta-tionarity in a handball matchrdquo J Appl Stat vol 36 no 7 pp 723ndash741 2009
4 Overview of Talks Sport Movement
41 Predicting Human Responses to Environmental ChangesEva Dorschky (Universitaumlt Erlangen ndash Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Eva Dorschky
Joint work of Dorschky Eva van den Bogert Antonie J Schlarb Heiko Eskofier BjoumlrnMain reference E Dorschky et al ldquoPredictive Musculoskeletal Simulation of Uphill and Downhill Runningrdquo in
Proc ECSS Malmouml 2015 p 126
Predicting human responses to environmental changes is necessary for biomechanical analysisand sports product design If case studies environmental conditions or prototypes cannot berealized modeling and simulation can be used instead The aim of this work was to evaluatea method of predictive musculoskeletal simulation [1] for uphill and downhill running A
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 49
study was simulated by randomizing the modelrsquos muscle parameters The predicted energycosts for running at different slopes were compared to literature [2] Future work includes apersonalization of biomechanical models to represent individual athletes as well as populationgroups The sensitivity of simulation results to model parameters will be studied to ensurerobust simulation results
References1 A J van den Bogert M Hupperets H Schlarb and B KrabbeldquoPredictive musculoskeletal
simulation using optimal control effects of added limb mass on energy cost and kinematicsof walking and runningrdquo in Proc Inst Mech Eng Part P J Sports Eng Technol vol 226pp 123-133 2012
2 AE Minetti C Moia G S Roi D Susta and G Ferretti ldquoEnergy cost of walking andrunning at extreme uphill and downhill slopesrdquo J Appl Physiol vol 93 no 3 pp 1039-10462002
42 Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier (Universitaumlt Erlangen-Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Bjoumlrn Eskofier
Wearable computing systems play an increasingly important role in recreational and elitesports They comprise of two important parts The first are sensors embedded into clothesand equipment that are used for physiological (ECG EMG) and biomechanical (accelerometergyroscope) data recording The second are signal processing and data mining algorithmsimplemented on wearable computers (smartphones watches) that are used for analysis of therecorded data Wearable computing systems can provide support real-time feedback andcoaching advice to sportsmen of all performance levels In order to implement these systemsseveral challenges have to be addressed Our work focusses on four of the most prevalent ofthese
Integration sensors and microprocessors have to be embedded unobtrusively and have torecord a variety of signalsCommunication sensors and microprocessors have to communicate in body-area-networksin a secure safe and energy-saving mannerInterpretation physiological and biomechanical data have to be interpreted using signalprocessing and machine learning methodsSimulation and modeling understanding of sensor data is needed to model processes insports more accurately simulation methodologies help here to provide basic informationto drive those models
15382
50 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
43 What is the Right ModelKaren Roemer (Central Washington University ndash Ellensburg US)
License Creative Commons BY 30 Unported licensecopy Karen Roemer
Investigating human movements requires using biomechanical models to perform kinematicand kinetic analyses Depending on the available motion analysis system or software packagesanthropometric models tracking models joint models etc are applied to quantify kinematicand kinetic variables Two standard biomechanical models (OpenSim and Visual3D) wereused to analyze a simple stepping task Similarities and differences of kinematic and kineticresults for both models were discussed
44 Model-Based Tracking of Human MotionAntonie van den Bogert (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Antonie van den Bogert
Two sensing modalities are in use for field studies of human movement inertial sensing andvideo While both are suitable for analysis of human movement during sports events dataquality is usually far inferior to those encountered during laboratory conditions Inspiredby the classical Kalman filtering concept we consider using a dynamic model to improvethe estimates of the state trajectory of the system This approach presents the estimationproblem as an optimal control problem find state and control trajectories that satisfy systemdynamics and minimize a cost function For tracking of sensor data the cost function consistsof the sum of squared errors between simulated and measured sensor signals When using amusculoskeletal model an extra term representing muscular effort must be added to ensurea unique solution This is due to the classical load sharing redundancy humans have moremuscles than strictly necessary to produce movementEfficient solution methods have been developed to solve this optimal control problem andthe approach was successfully used to obtain a detailed analysis of a landing movementduring skiing based on low-quality video data [1] The same approach was used to performa full dynamic gait analysis including muscle force estimation with body-mounted accelero-meters [2] The accelerometer-based analysis was not good enough for clinical applicationsbecause it was too sensitive to the unmodeled dynamics (damped vibrations) of the accelero-meter attachments Improvement is expected when the instrumentation is supplemented bygyroscopic angular velocity sensorsModel-based state estimation will reduce the sensitivity to measurement error and thereare additional advantages The estimation process includes estimation of the full state ofthe system including variables such as muscle forces which could not be directly obtainedfrom sensor signals The raw data is then not only filtered but also enriched by the dynamicmodel A second advantage is that a dynamically consistent simulation is obtained as anadditional result Such simulations can be useful to explore ldquowhat ifrdquo scenarios such assports injuries that are caused by unfavorable landing postures [3]
References1 A J van den Bogert D Blana and D Heinrich ldquoImplicit methods for efficient musculo-
skeletal simulation and optimal controlrdquo Procedia IUTAM vol 2 pp 297-316 2011
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 51
2 O Nwanna ldquoValidation of an Accelerometry Based Method of Human Gait Analysisrdquo MSthesis Cleveland State University 2014 httpraveohiolinkeduetdcviewacc_num=csu1400424346
3 D Heinrich A J van den Bogert and W Nachbauer ldquoRelationship between jump landingkinematics and peak ACL force during a jump in downhill skiing A simulation studyrdquoScand J Med Sci Sports vol 24 no 3 pp 180-187 2013
5 Overview of Talks Adaptions to Training
51 Modelling Speed-HR Relation using PerPotStefan Endler (Universitaumlt Mainz DE)
License Creative Commons BY 30 Unported licensecopy Stefan Endler
Joint work of Endler Stefan Perl JuumlrgenMain reference S Endler J Perl ldquoOptimizing practice and competition in marathon running by means of the
meta-modelrdquo in Proc of 2012 pre-Olympic Congress on Sports Science and Computer Science inSport (IACSSrsquo12) pp 127ndash131 World Academic Union 2012
A model which represents the heart rate (HR) process based on a speed process has tomodel three apects1 Increasing HR after increasing speed2 Decreasing HR after decreasing speed3 Break down after exhaustionThe performance potential metamodell (PerPot) models all these behaviors It was adapted tothe environment of endurance running Once the model is calibrated to the individual athleteby a graded incremental test it can be used for simulation of eg competitions Simulationscan help unexperienced athletes particularly to avoid overloading and underperforming
52 Performance Adaptions to Football Training Is More AlwaysBetter
Hugo Folgado (University of Evora PT)
License Creative Commons BY 30 Unported licensecopy Hugo Folgado
Joint work of Folgado Hugo Sampaio Jaime
Traditionally performance in sports is measured by magnitude based indicators summedup by the Olympic moto ndash Citius Altius Fortius ndash Faster Higher Stronger Howeverin several sport domains and particularly in team sports this idea has been challengedby recent research In football the physical analysis of matches in different competitiveleagues have showed that players in higher level contexts tend to run less and at lowerintensities than players involved in lower leagues [1] In other approach studying the effectsof congested fixtures in players physical performance showed no differences in the amount ofdistance covered and distance covered at different displacement intensities [2] So it may bespeculated that the amount of displacement is not related to greater levels of performance infootball Based in these approaches we measured players physical and tactical performancedevelopment during the preseason evaluated during sided-games Our results showed thatplayers tend to reduce the amount of distance covered during these situation has the preseason
15382
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 47
2 P J Figueroa N J Leite and RM L Barros ldquoBackground recovering in outdoor imagesequences An example of soccer players segmentationrdquo Image Vision Comput vol 24 no4 pp 363-374 2006
3 M Kristan J Pers J M Perse M Bon and S Kovacic ldquoMultiple interacting targetstracking with application to team sportsrdquo in Proc ISAP Zagreb 2005 pp 322-327
4 MS Misuta R P Menezes P J Figueroa SA Cunha and RML Barros ldquoRepresent-ation and analysis of soccer player trajectoriesrdquo in Proc ISB Cleveland 2005 vol 415
5 K Okuma A Taleghani N de Freitas J Little and D Lowe ldquoA boosted particle filtermultitarget detection and trackingrdquo in Proc ECCV Prague 2004 pp 28-38
6 P Viola and M J Jones ldquoRapid Object Detection using a Boosted Cascade of SimpleFeaturesrdquo in Proc CVPR Kauai 2001 vol 1 pp 511-518
38 Data Requirements in the Sports Data IndustryMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
Different markets do have different requirements concerning data depth data delivery speedand data quality Here is a short overview about three markets (1) Professional Sports (2)Media and (3) Bookmakers Betting1 Professional Sports (Teams Clubs Leagues)
Strong need for deep dataDelivery speed not too important as most teams use the data post-match Moreoverlive-match analyses are sometimes prohibitedData quality is important Performance analysis based on imprecise data could causewrong conclusions
2 MediaStrong need for deep data (eg for story telling)Delivery speed Not too important as there is a broadcasting latency anywaysData quality is not important
3 Bookmakers BettingData depth is not important as there is a market limit anyways If you would offertoo many bets you would cannibalize your own marketDelivery speed is a must have and very importantJust as data quality is Wrong data could cause a of lot trouble and punters wouldclaim their right to get their money back
Consequently for a company like Sportradar it is very important to fulfill all differentrequirements This results in the claim to be able to provide fast highly accurate and deepdata
15382
48 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
39 Factors that Influence Scoring Dynamics in Low-Scoring andHigh-Scoring Team Games
Anna Volossovitch (University of Lisbon PT)
License Creative Commons BY 30 Unported licensecopy Anna Volossovitch
Joint work of Volossovitch Anna Pratas Joseacute Dumangane Montezuma Rosati NicolettaMain reference M Dumangane N Rosati A Volossovitch ldquoDeparture from independence and stationarity in a
handball matchrdquo Journal of Applied Statistics 36(7)723ndash741 2009URL httpdxdoiorg10108002664760802499329
Research in match analysis frequently attempts to establish causal relationships betweenisolated performance variables and goal scoring or game outcome This approach reduces thecomplexity of performance by presenting it in overly descriptive and regular ways which donot reflect properly the curse of the game The purpose of the presentation was to discuss twoexamples of the analysis of factors which could influence the scoring dynamic during a matchin handball and in football In the first example the influence of teams past performanceon the present teams performance was evaluated throughout the handball match using themodel with time-varying parameters This model estimates the probability of scoring as afunction of the past performance of the opposing team and the current match result Thisassessment considers the specific context of the game situation the teams rankings thematch equilibrium and the number of ball possessions per match In the second example theperformance indicators which had a significant effect on the time of the first goal scoringin football has been identified using Cox time-dependent proportional hazard model Thesurvival analysis is suggested to be a suitable tool to identify which and how performanceindicators influence the time of the first ball is scored in different competitive context
References1 J Pratas AVolossovitch and A I Carita ldquoWhat performance indicators influence the
time of the first goal in the matchrdquo In Proc World Congress of Performance Analysis ofSport X Opatija 2014 p 102
2 A Volossovitch M Dumangane and N Rosati ldquoThe influence of the pace of match onthe dynamic of handball gamerdquo Int J Sports Psychol vol 41 no 4 pp 117ndash118 2010
3 M Dumangane N Rosati and A Volossovitch ldquoDeparture from independence and sta-tionarity in a handball matchrdquo J Appl Stat vol 36 no 7 pp 723ndash741 2009
4 Overview of Talks Sport Movement
41 Predicting Human Responses to Environmental ChangesEva Dorschky (Universitaumlt Erlangen ndash Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Eva Dorschky
Joint work of Dorschky Eva van den Bogert Antonie J Schlarb Heiko Eskofier BjoumlrnMain reference E Dorschky et al ldquoPredictive Musculoskeletal Simulation of Uphill and Downhill Runningrdquo in
Proc ECSS Malmouml 2015 p 126
Predicting human responses to environmental changes is necessary for biomechanical analysisand sports product design If case studies environmental conditions or prototypes cannot berealized modeling and simulation can be used instead The aim of this work was to evaluatea method of predictive musculoskeletal simulation [1] for uphill and downhill running A
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 49
study was simulated by randomizing the modelrsquos muscle parameters The predicted energycosts for running at different slopes were compared to literature [2] Future work includes apersonalization of biomechanical models to represent individual athletes as well as populationgroups The sensitivity of simulation results to model parameters will be studied to ensurerobust simulation results
References1 A J van den Bogert M Hupperets H Schlarb and B KrabbeldquoPredictive musculoskeletal
simulation using optimal control effects of added limb mass on energy cost and kinematicsof walking and runningrdquo in Proc Inst Mech Eng Part P J Sports Eng Technol vol 226pp 123-133 2012
2 AE Minetti C Moia G S Roi D Susta and G Ferretti ldquoEnergy cost of walking andrunning at extreme uphill and downhill slopesrdquo J Appl Physiol vol 93 no 3 pp 1039-10462002
42 Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier (Universitaumlt Erlangen-Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Bjoumlrn Eskofier
Wearable computing systems play an increasingly important role in recreational and elitesports They comprise of two important parts The first are sensors embedded into clothesand equipment that are used for physiological (ECG EMG) and biomechanical (accelerometergyroscope) data recording The second are signal processing and data mining algorithmsimplemented on wearable computers (smartphones watches) that are used for analysis of therecorded data Wearable computing systems can provide support real-time feedback andcoaching advice to sportsmen of all performance levels In order to implement these systemsseveral challenges have to be addressed Our work focusses on four of the most prevalent ofthese
Integration sensors and microprocessors have to be embedded unobtrusively and have torecord a variety of signalsCommunication sensors and microprocessors have to communicate in body-area-networksin a secure safe and energy-saving mannerInterpretation physiological and biomechanical data have to be interpreted using signalprocessing and machine learning methodsSimulation and modeling understanding of sensor data is needed to model processes insports more accurately simulation methodologies help here to provide basic informationto drive those models
15382
50 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
43 What is the Right ModelKaren Roemer (Central Washington University ndash Ellensburg US)
License Creative Commons BY 30 Unported licensecopy Karen Roemer
Investigating human movements requires using biomechanical models to perform kinematicand kinetic analyses Depending on the available motion analysis system or software packagesanthropometric models tracking models joint models etc are applied to quantify kinematicand kinetic variables Two standard biomechanical models (OpenSim and Visual3D) wereused to analyze a simple stepping task Similarities and differences of kinematic and kineticresults for both models were discussed
44 Model-Based Tracking of Human MotionAntonie van den Bogert (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Antonie van den Bogert
Two sensing modalities are in use for field studies of human movement inertial sensing andvideo While both are suitable for analysis of human movement during sports events dataquality is usually far inferior to those encountered during laboratory conditions Inspiredby the classical Kalman filtering concept we consider using a dynamic model to improvethe estimates of the state trajectory of the system This approach presents the estimationproblem as an optimal control problem find state and control trajectories that satisfy systemdynamics and minimize a cost function For tracking of sensor data the cost function consistsof the sum of squared errors between simulated and measured sensor signals When using amusculoskeletal model an extra term representing muscular effort must be added to ensurea unique solution This is due to the classical load sharing redundancy humans have moremuscles than strictly necessary to produce movementEfficient solution methods have been developed to solve this optimal control problem andthe approach was successfully used to obtain a detailed analysis of a landing movementduring skiing based on low-quality video data [1] The same approach was used to performa full dynamic gait analysis including muscle force estimation with body-mounted accelero-meters [2] The accelerometer-based analysis was not good enough for clinical applicationsbecause it was too sensitive to the unmodeled dynamics (damped vibrations) of the accelero-meter attachments Improvement is expected when the instrumentation is supplemented bygyroscopic angular velocity sensorsModel-based state estimation will reduce the sensitivity to measurement error and thereare additional advantages The estimation process includes estimation of the full state ofthe system including variables such as muscle forces which could not be directly obtainedfrom sensor signals The raw data is then not only filtered but also enriched by the dynamicmodel A second advantage is that a dynamically consistent simulation is obtained as anadditional result Such simulations can be useful to explore ldquowhat ifrdquo scenarios such assports injuries that are caused by unfavorable landing postures [3]
References1 A J van den Bogert D Blana and D Heinrich ldquoImplicit methods for efficient musculo-
skeletal simulation and optimal controlrdquo Procedia IUTAM vol 2 pp 297-316 2011
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 51
2 O Nwanna ldquoValidation of an Accelerometry Based Method of Human Gait Analysisrdquo MSthesis Cleveland State University 2014 httpraveohiolinkeduetdcviewacc_num=csu1400424346
3 D Heinrich A J van den Bogert and W Nachbauer ldquoRelationship between jump landingkinematics and peak ACL force during a jump in downhill skiing A simulation studyrdquoScand J Med Sci Sports vol 24 no 3 pp 180-187 2013
5 Overview of Talks Adaptions to Training
51 Modelling Speed-HR Relation using PerPotStefan Endler (Universitaumlt Mainz DE)
License Creative Commons BY 30 Unported licensecopy Stefan Endler
Joint work of Endler Stefan Perl JuumlrgenMain reference S Endler J Perl ldquoOptimizing practice and competition in marathon running by means of the
meta-modelrdquo in Proc of 2012 pre-Olympic Congress on Sports Science and Computer Science inSport (IACSSrsquo12) pp 127ndash131 World Academic Union 2012
A model which represents the heart rate (HR) process based on a speed process has tomodel three apects1 Increasing HR after increasing speed2 Decreasing HR after decreasing speed3 Break down after exhaustionThe performance potential metamodell (PerPot) models all these behaviors It was adapted tothe environment of endurance running Once the model is calibrated to the individual athleteby a graded incremental test it can be used for simulation of eg competitions Simulationscan help unexperienced athletes particularly to avoid overloading and underperforming
52 Performance Adaptions to Football Training Is More AlwaysBetter
Hugo Folgado (University of Evora PT)
License Creative Commons BY 30 Unported licensecopy Hugo Folgado
Joint work of Folgado Hugo Sampaio Jaime
Traditionally performance in sports is measured by magnitude based indicators summedup by the Olympic moto ndash Citius Altius Fortius ndash Faster Higher Stronger Howeverin several sport domains and particularly in team sports this idea has been challengedby recent research In football the physical analysis of matches in different competitiveleagues have showed that players in higher level contexts tend to run less and at lowerintensities than players involved in lower leagues [1] In other approach studying the effectsof congested fixtures in players physical performance showed no differences in the amount ofdistance covered and distance covered at different displacement intensities [2] So it may bespeculated that the amount of displacement is not related to greater levels of performance infootball Based in these approaches we measured players physical and tactical performancedevelopment during the preseason evaluated during sided-games Our results showed thatplayers tend to reduce the amount of distance covered during these situation has the preseason
15382
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
48 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
39 Factors that Influence Scoring Dynamics in Low-Scoring andHigh-Scoring Team Games
Anna Volossovitch (University of Lisbon PT)
License Creative Commons BY 30 Unported licensecopy Anna Volossovitch
Joint work of Volossovitch Anna Pratas Joseacute Dumangane Montezuma Rosati NicolettaMain reference M Dumangane N Rosati A Volossovitch ldquoDeparture from independence and stationarity in a
handball matchrdquo Journal of Applied Statistics 36(7)723ndash741 2009URL httpdxdoiorg10108002664760802499329
Research in match analysis frequently attempts to establish causal relationships betweenisolated performance variables and goal scoring or game outcome This approach reduces thecomplexity of performance by presenting it in overly descriptive and regular ways which donot reflect properly the curse of the game The purpose of the presentation was to discuss twoexamples of the analysis of factors which could influence the scoring dynamic during a matchin handball and in football In the first example the influence of teams past performanceon the present teams performance was evaluated throughout the handball match using themodel with time-varying parameters This model estimates the probability of scoring as afunction of the past performance of the opposing team and the current match result Thisassessment considers the specific context of the game situation the teams rankings thematch equilibrium and the number of ball possessions per match In the second example theperformance indicators which had a significant effect on the time of the first goal scoringin football has been identified using Cox time-dependent proportional hazard model Thesurvival analysis is suggested to be a suitable tool to identify which and how performanceindicators influence the time of the first ball is scored in different competitive context
References1 J Pratas AVolossovitch and A I Carita ldquoWhat performance indicators influence the
time of the first goal in the matchrdquo In Proc World Congress of Performance Analysis ofSport X Opatija 2014 p 102
2 A Volossovitch M Dumangane and N Rosati ldquoThe influence of the pace of match onthe dynamic of handball gamerdquo Int J Sports Psychol vol 41 no 4 pp 117ndash118 2010
3 M Dumangane N Rosati and A Volossovitch ldquoDeparture from independence and sta-tionarity in a handball matchrdquo J Appl Stat vol 36 no 7 pp 723ndash741 2009
4 Overview of Talks Sport Movement
41 Predicting Human Responses to Environmental ChangesEva Dorschky (Universitaumlt Erlangen ndash Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Eva Dorschky
Joint work of Dorschky Eva van den Bogert Antonie J Schlarb Heiko Eskofier BjoumlrnMain reference E Dorschky et al ldquoPredictive Musculoskeletal Simulation of Uphill and Downhill Runningrdquo in
Proc ECSS Malmouml 2015 p 126
Predicting human responses to environmental changes is necessary for biomechanical analysisand sports product design If case studies environmental conditions or prototypes cannot berealized modeling and simulation can be used instead The aim of this work was to evaluatea method of predictive musculoskeletal simulation [1] for uphill and downhill running A
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 49
study was simulated by randomizing the modelrsquos muscle parameters The predicted energycosts for running at different slopes were compared to literature [2] Future work includes apersonalization of biomechanical models to represent individual athletes as well as populationgroups The sensitivity of simulation results to model parameters will be studied to ensurerobust simulation results
References1 A J van den Bogert M Hupperets H Schlarb and B KrabbeldquoPredictive musculoskeletal
simulation using optimal control effects of added limb mass on energy cost and kinematicsof walking and runningrdquo in Proc Inst Mech Eng Part P J Sports Eng Technol vol 226pp 123-133 2012
2 AE Minetti C Moia G S Roi D Susta and G Ferretti ldquoEnergy cost of walking andrunning at extreme uphill and downhill slopesrdquo J Appl Physiol vol 93 no 3 pp 1039-10462002
42 Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier (Universitaumlt Erlangen-Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Bjoumlrn Eskofier
Wearable computing systems play an increasingly important role in recreational and elitesports They comprise of two important parts The first are sensors embedded into clothesand equipment that are used for physiological (ECG EMG) and biomechanical (accelerometergyroscope) data recording The second are signal processing and data mining algorithmsimplemented on wearable computers (smartphones watches) that are used for analysis of therecorded data Wearable computing systems can provide support real-time feedback andcoaching advice to sportsmen of all performance levels In order to implement these systemsseveral challenges have to be addressed Our work focusses on four of the most prevalent ofthese
Integration sensors and microprocessors have to be embedded unobtrusively and have torecord a variety of signalsCommunication sensors and microprocessors have to communicate in body-area-networksin a secure safe and energy-saving mannerInterpretation physiological and biomechanical data have to be interpreted using signalprocessing and machine learning methodsSimulation and modeling understanding of sensor data is needed to model processes insports more accurately simulation methodologies help here to provide basic informationto drive those models
15382
50 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
43 What is the Right ModelKaren Roemer (Central Washington University ndash Ellensburg US)
License Creative Commons BY 30 Unported licensecopy Karen Roemer
Investigating human movements requires using biomechanical models to perform kinematicand kinetic analyses Depending on the available motion analysis system or software packagesanthropometric models tracking models joint models etc are applied to quantify kinematicand kinetic variables Two standard biomechanical models (OpenSim and Visual3D) wereused to analyze a simple stepping task Similarities and differences of kinematic and kineticresults for both models were discussed
44 Model-Based Tracking of Human MotionAntonie van den Bogert (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Antonie van den Bogert
Two sensing modalities are in use for field studies of human movement inertial sensing andvideo While both are suitable for analysis of human movement during sports events dataquality is usually far inferior to those encountered during laboratory conditions Inspiredby the classical Kalman filtering concept we consider using a dynamic model to improvethe estimates of the state trajectory of the system This approach presents the estimationproblem as an optimal control problem find state and control trajectories that satisfy systemdynamics and minimize a cost function For tracking of sensor data the cost function consistsof the sum of squared errors between simulated and measured sensor signals When using amusculoskeletal model an extra term representing muscular effort must be added to ensurea unique solution This is due to the classical load sharing redundancy humans have moremuscles than strictly necessary to produce movementEfficient solution methods have been developed to solve this optimal control problem andthe approach was successfully used to obtain a detailed analysis of a landing movementduring skiing based on low-quality video data [1] The same approach was used to performa full dynamic gait analysis including muscle force estimation with body-mounted accelero-meters [2] The accelerometer-based analysis was not good enough for clinical applicationsbecause it was too sensitive to the unmodeled dynamics (damped vibrations) of the accelero-meter attachments Improvement is expected when the instrumentation is supplemented bygyroscopic angular velocity sensorsModel-based state estimation will reduce the sensitivity to measurement error and thereare additional advantages The estimation process includes estimation of the full state ofthe system including variables such as muscle forces which could not be directly obtainedfrom sensor signals The raw data is then not only filtered but also enriched by the dynamicmodel A second advantage is that a dynamically consistent simulation is obtained as anadditional result Such simulations can be useful to explore ldquowhat ifrdquo scenarios such assports injuries that are caused by unfavorable landing postures [3]
References1 A J van den Bogert D Blana and D Heinrich ldquoImplicit methods for efficient musculo-
skeletal simulation and optimal controlrdquo Procedia IUTAM vol 2 pp 297-316 2011
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 51
2 O Nwanna ldquoValidation of an Accelerometry Based Method of Human Gait Analysisrdquo MSthesis Cleveland State University 2014 httpraveohiolinkeduetdcviewacc_num=csu1400424346
3 D Heinrich A J van den Bogert and W Nachbauer ldquoRelationship between jump landingkinematics and peak ACL force during a jump in downhill skiing A simulation studyrdquoScand J Med Sci Sports vol 24 no 3 pp 180-187 2013
5 Overview of Talks Adaptions to Training
51 Modelling Speed-HR Relation using PerPotStefan Endler (Universitaumlt Mainz DE)
License Creative Commons BY 30 Unported licensecopy Stefan Endler
Joint work of Endler Stefan Perl JuumlrgenMain reference S Endler J Perl ldquoOptimizing practice and competition in marathon running by means of the
meta-modelrdquo in Proc of 2012 pre-Olympic Congress on Sports Science and Computer Science inSport (IACSSrsquo12) pp 127ndash131 World Academic Union 2012
A model which represents the heart rate (HR) process based on a speed process has tomodel three apects1 Increasing HR after increasing speed2 Decreasing HR after decreasing speed3 Break down after exhaustionThe performance potential metamodell (PerPot) models all these behaviors It was adapted tothe environment of endurance running Once the model is calibrated to the individual athleteby a graded incremental test it can be used for simulation of eg competitions Simulationscan help unexperienced athletes particularly to avoid overloading and underperforming
52 Performance Adaptions to Football Training Is More AlwaysBetter
Hugo Folgado (University of Evora PT)
License Creative Commons BY 30 Unported licensecopy Hugo Folgado
Joint work of Folgado Hugo Sampaio Jaime
Traditionally performance in sports is measured by magnitude based indicators summedup by the Olympic moto ndash Citius Altius Fortius ndash Faster Higher Stronger Howeverin several sport domains and particularly in team sports this idea has been challengedby recent research In football the physical analysis of matches in different competitiveleagues have showed that players in higher level contexts tend to run less and at lowerintensities than players involved in lower leagues [1] In other approach studying the effectsof congested fixtures in players physical performance showed no differences in the amount ofdistance covered and distance covered at different displacement intensities [2] So it may bespeculated that the amount of displacement is not related to greater levels of performance infootball Based in these approaches we measured players physical and tactical performancedevelopment during the preseason evaluated during sided-games Our results showed thatplayers tend to reduce the amount of distance covered during these situation has the preseason
15382
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 49
study was simulated by randomizing the modelrsquos muscle parameters The predicted energycosts for running at different slopes were compared to literature [2] Future work includes apersonalization of biomechanical models to represent individual athletes as well as populationgroups The sensitivity of simulation results to model parameters will be studied to ensurerobust simulation results
References1 A J van den Bogert M Hupperets H Schlarb and B KrabbeldquoPredictive musculoskeletal
simulation using optimal control effects of added limb mass on energy cost and kinematicsof walking and runningrdquo in Proc Inst Mech Eng Part P J Sports Eng Technol vol 226pp 123-133 2012
2 AE Minetti C Moia G S Roi D Susta and G Ferretti ldquoEnergy cost of walking andrunning at extreme uphill and downhill slopesrdquo J Appl Physiol vol 93 no 3 pp 1039-10462002
42 Wearable Computing Systems for Recreational and Elite SportsBjoumlrn Eskofier (Universitaumlt Erlangen-Nuumlrnberg DE)
License Creative Commons BY 30 Unported licensecopy Bjoumlrn Eskofier
Wearable computing systems play an increasingly important role in recreational and elitesports They comprise of two important parts The first are sensors embedded into clothesand equipment that are used for physiological (ECG EMG) and biomechanical (accelerometergyroscope) data recording The second are signal processing and data mining algorithmsimplemented on wearable computers (smartphones watches) that are used for analysis of therecorded data Wearable computing systems can provide support real-time feedback andcoaching advice to sportsmen of all performance levels In order to implement these systemsseveral challenges have to be addressed Our work focusses on four of the most prevalent ofthese
Integration sensors and microprocessors have to be embedded unobtrusively and have torecord a variety of signalsCommunication sensors and microprocessors have to communicate in body-area-networksin a secure safe and energy-saving mannerInterpretation physiological and biomechanical data have to be interpreted using signalprocessing and machine learning methodsSimulation and modeling understanding of sensor data is needed to model processes insports more accurately simulation methodologies help here to provide basic informationto drive those models
15382
50 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
43 What is the Right ModelKaren Roemer (Central Washington University ndash Ellensburg US)
License Creative Commons BY 30 Unported licensecopy Karen Roemer
Investigating human movements requires using biomechanical models to perform kinematicand kinetic analyses Depending on the available motion analysis system or software packagesanthropometric models tracking models joint models etc are applied to quantify kinematicand kinetic variables Two standard biomechanical models (OpenSim and Visual3D) wereused to analyze a simple stepping task Similarities and differences of kinematic and kineticresults for both models were discussed
44 Model-Based Tracking of Human MotionAntonie van den Bogert (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Antonie van den Bogert
Two sensing modalities are in use for field studies of human movement inertial sensing andvideo While both are suitable for analysis of human movement during sports events dataquality is usually far inferior to those encountered during laboratory conditions Inspiredby the classical Kalman filtering concept we consider using a dynamic model to improvethe estimates of the state trajectory of the system This approach presents the estimationproblem as an optimal control problem find state and control trajectories that satisfy systemdynamics and minimize a cost function For tracking of sensor data the cost function consistsof the sum of squared errors between simulated and measured sensor signals When using amusculoskeletal model an extra term representing muscular effort must be added to ensurea unique solution This is due to the classical load sharing redundancy humans have moremuscles than strictly necessary to produce movementEfficient solution methods have been developed to solve this optimal control problem andthe approach was successfully used to obtain a detailed analysis of a landing movementduring skiing based on low-quality video data [1] The same approach was used to performa full dynamic gait analysis including muscle force estimation with body-mounted accelero-meters [2] The accelerometer-based analysis was not good enough for clinical applicationsbecause it was too sensitive to the unmodeled dynamics (damped vibrations) of the accelero-meter attachments Improvement is expected when the instrumentation is supplemented bygyroscopic angular velocity sensorsModel-based state estimation will reduce the sensitivity to measurement error and thereare additional advantages The estimation process includes estimation of the full state ofthe system including variables such as muscle forces which could not be directly obtainedfrom sensor signals The raw data is then not only filtered but also enriched by the dynamicmodel A second advantage is that a dynamically consistent simulation is obtained as anadditional result Such simulations can be useful to explore ldquowhat ifrdquo scenarios such assports injuries that are caused by unfavorable landing postures [3]
References1 A J van den Bogert D Blana and D Heinrich ldquoImplicit methods for efficient musculo-
skeletal simulation and optimal controlrdquo Procedia IUTAM vol 2 pp 297-316 2011
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 51
2 O Nwanna ldquoValidation of an Accelerometry Based Method of Human Gait Analysisrdquo MSthesis Cleveland State University 2014 httpraveohiolinkeduetdcviewacc_num=csu1400424346
3 D Heinrich A J van den Bogert and W Nachbauer ldquoRelationship between jump landingkinematics and peak ACL force during a jump in downhill skiing A simulation studyrdquoScand J Med Sci Sports vol 24 no 3 pp 180-187 2013
5 Overview of Talks Adaptions to Training
51 Modelling Speed-HR Relation using PerPotStefan Endler (Universitaumlt Mainz DE)
License Creative Commons BY 30 Unported licensecopy Stefan Endler
Joint work of Endler Stefan Perl JuumlrgenMain reference S Endler J Perl ldquoOptimizing practice and competition in marathon running by means of the
meta-modelrdquo in Proc of 2012 pre-Olympic Congress on Sports Science and Computer Science inSport (IACSSrsquo12) pp 127ndash131 World Academic Union 2012
A model which represents the heart rate (HR) process based on a speed process has tomodel three apects1 Increasing HR after increasing speed2 Decreasing HR after decreasing speed3 Break down after exhaustionThe performance potential metamodell (PerPot) models all these behaviors It was adapted tothe environment of endurance running Once the model is calibrated to the individual athleteby a graded incremental test it can be used for simulation of eg competitions Simulationscan help unexperienced athletes particularly to avoid overloading and underperforming
52 Performance Adaptions to Football Training Is More AlwaysBetter
Hugo Folgado (University of Evora PT)
License Creative Commons BY 30 Unported licensecopy Hugo Folgado
Joint work of Folgado Hugo Sampaio Jaime
Traditionally performance in sports is measured by magnitude based indicators summedup by the Olympic moto ndash Citius Altius Fortius ndash Faster Higher Stronger Howeverin several sport domains and particularly in team sports this idea has been challengedby recent research In football the physical analysis of matches in different competitiveleagues have showed that players in higher level contexts tend to run less and at lowerintensities than players involved in lower leagues [1] In other approach studying the effectsof congested fixtures in players physical performance showed no differences in the amount ofdistance covered and distance covered at different displacement intensities [2] So it may bespeculated that the amount of displacement is not related to greater levels of performance infootball Based in these approaches we measured players physical and tactical performancedevelopment during the preseason evaluated during sided-games Our results showed thatplayers tend to reduce the amount of distance covered during these situation has the preseason
15382
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
50 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
43 What is the Right ModelKaren Roemer (Central Washington University ndash Ellensburg US)
License Creative Commons BY 30 Unported licensecopy Karen Roemer
Investigating human movements requires using biomechanical models to perform kinematicand kinetic analyses Depending on the available motion analysis system or software packagesanthropometric models tracking models joint models etc are applied to quantify kinematicand kinetic variables Two standard biomechanical models (OpenSim and Visual3D) wereused to analyze a simple stepping task Similarities and differences of kinematic and kineticresults for both models were discussed
44 Model-Based Tracking of Human MotionAntonie van den Bogert (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Antonie van den Bogert
Two sensing modalities are in use for field studies of human movement inertial sensing andvideo While both are suitable for analysis of human movement during sports events dataquality is usually far inferior to those encountered during laboratory conditions Inspiredby the classical Kalman filtering concept we consider using a dynamic model to improvethe estimates of the state trajectory of the system This approach presents the estimationproblem as an optimal control problem find state and control trajectories that satisfy systemdynamics and minimize a cost function For tracking of sensor data the cost function consistsof the sum of squared errors between simulated and measured sensor signals When using amusculoskeletal model an extra term representing muscular effort must be added to ensurea unique solution This is due to the classical load sharing redundancy humans have moremuscles than strictly necessary to produce movementEfficient solution methods have been developed to solve this optimal control problem andthe approach was successfully used to obtain a detailed analysis of a landing movementduring skiing based on low-quality video data [1] The same approach was used to performa full dynamic gait analysis including muscle force estimation with body-mounted accelero-meters [2] The accelerometer-based analysis was not good enough for clinical applicationsbecause it was too sensitive to the unmodeled dynamics (damped vibrations) of the accelero-meter attachments Improvement is expected when the instrumentation is supplemented bygyroscopic angular velocity sensorsModel-based state estimation will reduce the sensitivity to measurement error and thereare additional advantages The estimation process includes estimation of the full state ofthe system including variables such as muscle forces which could not be directly obtainedfrom sensor signals The raw data is then not only filtered but also enriched by the dynamicmodel A second advantage is that a dynamically consistent simulation is obtained as anadditional result Such simulations can be useful to explore ldquowhat ifrdquo scenarios such assports injuries that are caused by unfavorable landing postures [3]
References1 A J van den Bogert D Blana and D Heinrich ldquoImplicit methods for efficient musculo-
skeletal simulation and optimal controlrdquo Procedia IUTAM vol 2 pp 297-316 2011
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 51
2 O Nwanna ldquoValidation of an Accelerometry Based Method of Human Gait Analysisrdquo MSthesis Cleveland State University 2014 httpraveohiolinkeduetdcviewacc_num=csu1400424346
3 D Heinrich A J van den Bogert and W Nachbauer ldquoRelationship between jump landingkinematics and peak ACL force during a jump in downhill skiing A simulation studyrdquoScand J Med Sci Sports vol 24 no 3 pp 180-187 2013
5 Overview of Talks Adaptions to Training
51 Modelling Speed-HR Relation using PerPotStefan Endler (Universitaumlt Mainz DE)
License Creative Commons BY 30 Unported licensecopy Stefan Endler
Joint work of Endler Stefan Perl JuumlrgenMain reference S Endler J Perl ldquoOptimizing practice and competition in marathon running by means of the
meta-modelrdquo in Proc of 2012 pre-Olympic Congress on Sports Science and Computer Science inSport (IACSSrsquo12) pp 127ndash131 World Academic Union 2012
A model which represents the heart rate (HR) process based on a speed process has tomodel three apects1 Increasing HR after increasing speed2 Decreasing HR after decreasing speed3 Break down after exhaustionThe performance potential metamodell (PerPot) models all these behaviors It was adapted tothe environment of endurance running Once the model is calibrated to the individual athleteby a graded incremental test it can be used for simulation of eg competitions Simulationscan help unexperienced athletes particularly to avoid overloading and underperforming
52 Performance Adaptions to Football Training Is More AlwaysBetter
Hugo Folgado (University of Evora PT)
License Creative Commons BY 30 Unported licensecopy Hugo Folgado
Joint work of Folgado Hugo Sampaio Jaime
Traditionally performance in sports is measured by magnitude based indicators summedup by the Olympic moto ndash Citius Altius Fortius ndash Faster Higher Stronger Howeverin several sport domains and particularly in team sports this idea has been challengedby recent research In football the physical analysis of matches in different competitiveleagues have showed that players in higher level contexts tend to run less and at lowerintensities than players involved in lower leagues [1] In other approach studying the effectsof congested fixtures in players physical performance showed no differences in the amount ofdistance covered and distance covered at different displacement intensities [2] So it may bespeculated that the amount of displacement is not related to greater levels of performance infootball Based in these approaches we measured players physical and tactical performancedevelopment during the preseason evaluated during sided-games Our results showed thatplayers tend to reduce the amount of distance covered during these situation has the preseason
15382
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 51
2 O Nwanna ldquoValidation of an Accelerometry Based Method of Human Gait Analysisrdquo MSthesis Cleveland State University 2014 httpraveohiolinkeduetdcviewacc_num=csu1400424346
3 D Heinrich A J van den Bogert and W Nachbauer ldquoRelationship between jump landingkinematics and peak ACL force during a jump in downhill skiing A simulation studyrdquoScand J Med Sci Sports vol 24 no 3 pp 180-187 2013
5 Overview of Talks Adaptions to Training
51 Modelling Speed-HR Relation using PerPotStefan Endler (Universitaumlt Mainz DE)
License Creative Commons BY 30 Unported licensecopy Stefan Endler
Joint work of Endler Stefan Perl JuumlrgenMain reference S Endler J Perl ldquoOptimizing practice and competition in marathon running by means of the
meta-modelrdquo in Proc of 2012 pre-Olympic Congress on Sports Science and Computer Science inSport (IACSSrsquo12) pp 127ndash131 World Academic Union 2012
A model which represents the heart rate (HR) process based on a speed process has tomodel three apects1 Increasing HR after increasing speed2 Decreasing HR after decreasing speed3 Break down after exhaustionThe performance potential metamodell (PerPot) models all these behaviors It was adapted tothe environment of endurance running Once the model is calibrated to the individual athleteby a graded incremental test it can be used for simulation of eg competitions Simulationscan help unexperienced athletes particularly to avoid overloading and underperforming
52 Performance Adaptions to Football Training Is More AlwaysBetter
Hugo Folgado (University of Evora PT)
License Creative Commons BY 30 Unported licensecopy Hugo Folgado
Joint work of Folgado Hugo Sampaio Jaime
Traditionally performance in sports is measured by magnitude based indicators summedup by the Olympic moto ndash Citius Altius Fortius ndash Faster Higher Stronger Howeverin several sport domains and particularly in team sports this idea has been challengedby recent research In football the physical analysis of matches in different competitiveleagues have showed that players in higher level contexts tend to run less and at lowerintensities than players involved in lower leagues [1] In other approach studying the effectsof congested fixtures in players physical performance showed no differences in the amount ofdistance covered and distance covered at different displacement intensities [2] So it may bespeculated that the amount of displacement is not related to greater levels of performance infootball Based in these approaches we measured players physical and tactical performancedevelopment during the preseason evaluated during sided-games Our results showed thatplayers tend to reduce the amount of distance covered during these situation has the preseason
15382
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
52 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
progresses However their tactical performance measured as the amount of time players aredisplacing in synchronized manner was higher as the preseason progressed These findingslead to a need for shifting to a more holistic approach were performance indicators need tobe understood within the different interpersonal relations established during the match
References1 P S Bradley C Carling AG Diaz P Hood C Barnes J Ade M Boddy P Krustrup
and M Mohr ldquoMatch performance and physical capacity of players in the top three com-petitive standards of English professional soccerrdquo Hum Movement Sci vol 32 no 4 pp808-821 2013
2 A Dellal C Lago-Penas E Rey K Chamari and E Orhant ldquoThe effects of a congestedfixtures period on physical performance technical activity and injury rate during matchesin a professional soccer teamrdquo Br J Sports Med vol 49 no 6 2013
53 Modeling Individual HR Dynamics to the Change of LoadKatrin Hoffmann (TU Darmstadt DE)
License Creative Commons BY 30 Unported licensecopy Katrin Hoffmann
The success of training in sports and in particular in the application of Exergames isdependent on setting an appropriate training load Modeling the individual HR dynamics tothe change of load in the sub maximal range provides an effective and efficient prediction ofthe individual strain in the human body This enables systematic load control and is essentialfor an individually optimal training This task is not simple Beside the final steady stateHR corresponding to the load the slope of the curve is also essential for a reliable modelingHowever research has shown that this slope can vary in humans depending on a greatamount of influencing factors ie age body weight sex training and resting level and manymore Additionally it can also vary in the same human under apparently similar conditionsFurther research is needed to improve the modeling of HR dynamics inside Exergames1 Additional influencing factors on the HR ie emotion or diseases need to be identified2 Additional load on the human body caused by game control ie body movements need
to be identified and controlled3 The formula for modeling the human HR responses need to be improvement and dynam-
ically adapted
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 53
54 Model Design and Validation for Oxygen DynamicsDietmar Saupe (Universitaumlt Konstanz DE)
License Creative Commons BY 30 Unported licensecopy Dietmar Saupe
Joint work of Artiga Gonzalez A Bertschinger R Brosda F Dahmen T Thumm P Saupe DMain reference AA Gonzalez eR Bertschinger F Brosda T Dahmen P Thumm D Saupe ldquoModeling Oxygen
Dynamics under Variable Work Raterdquo in Proc of the 3rd Intrsquol Congress on Sport SciencesResearch and Technology Support (icSPORTSrsquo15) pp 198ndash207 ScitePress 2015 pre-printavailable from authorrsquos webpage
URL httpdxdoiorg1052200005607701980207URL httpswwwuni-konstanzdemmsppubsyspublishedFilesArBeBr15pdf
Measurements of oxygen uptake and blood lactate content are central to methods for assess-ment of physical fitness and endurance capabilities in athletes Two important parametersextracted from such data of incremental exercise tests are the maximal oxygen uptake andthe critical power A commonly accepted model of the dynamics of oxygen uptake duringexercise at constant work rate comprises a constant baseline oxygen uptake an exponentialfast component and another exponential slow component for heavy and severe work ratesWe generalized this model to variable load protocols by differential equations that naturallycorrespond to the standard model for constant work rate This provides the means for pre-diction of oxygen uptake response to variable load profiles including phases of recovery Themodel parameters were fitted for individual subjects from a cycle ergometer test The modelpredictions were validated by data collected in separate tests Our findings indicate thatoxygen kinetics for variable exercise load can be predicted using the generalized mathematicalstandard model however with an overestimation of the slow component Such models allowfor applications in the field where the constant work rate assumption generally is not valid
6 Comments
61 Research PerspectiveAnne Danielle Koelewijn (Cleveland State University ndash Cleveland US)
License Creative Commons BY 30 Unported licensecopy Anne Danielle Koelewijn
The Dagstuhl seminar was a very interesting and inspiring event for me There were manyopportunities and a good environment to discuss research I would recommend to keep talksshort also in future events to encourage this even more Also the different disciplines thatwere brought together was insightful as well as the different backgrounds of the participantsIt showed in what ways human modelling could be used in other fields of research and alsohow this could be helpful in commercial applications A lot of work is still to be done beforethis can happen for example in personalization of models for specific sports or even specificathletes
15382
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
54 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
62 Industry PerspectiveMalte Siegle (Sportradar AG ndash St Gallen CH)
License Creative Commons BY 30 Unported licensecopy Malte Siegle
For a company it is very important to know about the latest research activities TheDagstuhl seminar is a great opportunity to get the latest insights and discuss with prestigiousrepresentatives from different areas Besides the important fact to extend the network it canresult in concrete research collaborations and even in funded projects I highly recommendto open Dagstuhl even more towards the industry as science needs to be applied in concreteprojects or products Moreover knowing about the requirements of the industry can alsohelp scientists and universities to improve their research programs
7 Discussion
In the seminar the term ldquomodelrdquo was not exactly defined to allow for a broad discussionDuring the discussions a different understanding of ldquomodelsrdquo became apparent dependingon the background of each participant In sport science models are used to understandand predict the behaviour of eg sport games human movement or physical performanceof athletes Models are commonly physically motivated and based on specialist knowledgeHowever such models reach their limits when the underlying process is complex and thereal world cannot be adequately represented In machine learning a model refers to analgorithm describing the dependency of input and output variables Models are commonlydata-driven rather than physics-based Their application as black boxes conceals risks Howdo we know that the results are accurate How can we optimize our results What causesproblems How to interpret the model structure physically As a result from the seminarcomputer scientist should provide more support and expertise to allow for insights into thebehaviour of the model Contrarily the model structure depends on the chosen input andoutput variables Therefore it is essential that sports scientists and industry provide usefulinformation What are meaningful performance indicators ie model input related to theproblem What output variables are of interest eg to give useful feedback to a coach orthe athleteIn the seminar the term ldquosensemakeingrdquo was mentioned in this context As the amount ofcollected data is increasing machine learning methods like unsupervised learning offer newopportunities for joint collaboration The combination of knowledge-based and data-drivenmodels would be another advance For example position data of players during sport gamesis already available using computer vision or local positioning technologies The position dataitself might not offer enough insight to the course of the game In terms of ldquosensemakingrdquo apose estimation of the players would lead to a better behavioral understanding of the athletesThis could be done by tracking a biomechanical model with recorded video or inertial sensordata Moreover physical models like biomechanical models could be used to synthesisetraining data for training neural networks to improve activity recognition based on noisysensor data Finally new methodologies like agent-based modelling and simulation shouldbe applied to sports related problems This implies a close cooperation between computerand sports scientists
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
Ricardo Duarte Bjoumlrn Eskofier Martin Rumpf and Josef Wiemeyer 55
8 Conclusion
The seminar enabled fruitful interdisciplinary discussions concerning the core problems ofmodeling and simulation starting with the acquisition and preprocessing of data the selectionof the appropriate model(s) and ending with the verification and validation of modelsThe seminar also uncovered the different perspectives of science practice and industry onmodeling and simulation as well as the necessity of all parties to communicate about theirviews and mutual expectations Furthermore the discussions revealed the ambivalence ofapplying ICT to modeling and simulations in sports On the one hand added values likeaccuracy speed and complexity as well as convenience were emphasized On the otherhand numerous issues including error identification and correction in the data data qualityin general classification problems and knowledge discovery in ldquobig datardquo were addressedDue to the ldquospirit of Dagstuhlrdquo the schedule was finalized and flexibly adapted during theseminar Some guidelines were suggested to the presenters to establish overarching aspectsfor discussion eg how models were selected and applied to the problem at hand and whichadvantages and disadvantages appeared in the process of modelingThere was a broad agreement that the series of Dagstuhl seminars on computer science insport should be continued The positive results of the seminar evaluation confirmed the highquality of the seminar However some things need to be improved concerning the structureof the seminar as well as the commitment of the participants eg talks more structuredand focused on fundamental issues rather than specific aspects fostering more discussions byshortening the talks as well as a better preparation of the seminar by collecting main topicsin advance (eg three basic issues per participant) The organizers are sure that the nextDagstuhl seminar will be successful in improving the quality beyond the high level alreadyestablished by this seminar
15382
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
56 15382 ndash Modeling and Simulation of Sport Games Sport Movements and
Participants
Arnold BacaUniversitaumlt Wien AT
Eva DorschkyUniv Erlangen ndash Nuumlrnberg DE
Ricardo DuarteUniversity of Lisbon PT
Stefan EndlerUniversitaumlt Mainz DE
Bjoumlrn EskofierUniv Erlangen-Nuumlrnberg DE
Irfan A EssaGeorgia Institute of Technology ndashAtlanta US
Hugo FolgadoUniversity of Evora PT
Katrin HoffmannTU Darmstadt DE
Anne Danielle KoelewijnCleveland State University ndashCleveland US
John KomarProzone Sports Ltd ndash Leeds ampUniversity of Rouen
Martin LamesTU Muumlnchen DE
Roland LeserUniversitaumlt Wien AT
Daniel LinkTU Muumlnchen DE
Jim LittleUniversity of British Columbia ndashVancouver CA
Stuart MorganAustralian Institute of Sport ndashBruce AU
Bernhard MoserSoftware Competence Center ndashHagenberg AT
Juumlrgen PerlUniversitaumlt Mainz DE
Robert ReinDeutsche SporthochschuleKoumlln DE
Karen RoemerCentral Washington University ndashEllensburg US
Martin RumpfUniversitaumlt Bonn DE
Tiago Guedes RussomannoUniversity of Brasilia BR
Dietmar SaupeUniversitaumlt Konstanz DE
Heiko Schlarbadidas AG ndash Herzogenaurach DE
Malte SiegleSportradar AG ndash St Gallen CH
Michael StoumlcklUniversitaumlt Wien AT
Antonie van den BogertCleveland State University ndashCleveland US
Anna VolossovitchUniversity of Lisbon PT
Hendrik WeberDFL GmbH - Frankfurt DE
Josef WiemeyerTU Darmstadt DE
