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Positional and Match Action Profiles of EliteWomen’s Field Hockey Players in Relationship tothe 2015 FIH Rule ChangesHeather A. AbbottEast Tennessee State University

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1

Positional and Match Action Profiles of Elite Women’s Field Hockey Players in

Relationship to the 2015 FIH Rule Changes

_______________

A dissertation

presented to

the faculty of the Department of Exercise and Sport Sciences

East Tennessee State University

In partial fulfillment

of the requirements for the degree

Doctor of Physiology in Sport Physiology and Performance

_______________

by

Heather Anne Abbott

August 2016

_______________

Kimitake Sato, Ph.D., Chair

Michael H. Stone, Ph.D.

Satoshi Mizuguchi, Ph.D.

Dave Hamilton, MS.

Keywords: Global Positioning System, Time Motion Analysis, Team Sport,

Substitutions, Field Sports, Time on Pitch

2

ABSTRACT

Positional and Match Action Profiles of Elite Women’s Field Hockey Players in

Relationship to the 2015 FIH Rule Changes

by

Heather A. Abbott

The objective of this dissertation was to examine the action profiles of elite field hockey

players in relationship to the 2015 FIH rule change. The following are major findings of

the dissertation:

Study 1 – Relative action profiles before the rule change revealed that defenders work at

a lower meter per minute (m/min) when compared with all other positions, and that

forwards, midfielders, and screens perform similar m/min during a game. Examination of

pre rule change difference from the 1st to the 2nd half play showed that elite level field

hockey players are able maintain high-intensity actions in zone 6 throughout the game by

increasing actions in zones 1 and 2, and decreasing actions in zones 4 and 5.

Study 2 – Action profiles after the rule formatting change revealed the team was unable

to match the percent of distance covered in zones 4 and 5 during the 1st quarter all in

subsequent quarters. The low intensity actions in zone 1 and 2 gradually increased, while

m/min gradually declined. However the percent of distance covered in zone 6 showed no

statistically significant change. When positional differences were examined forwards

covered the greatest percent of distance in zones 5 and 6, followed by midfielders,

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screens, and defenders. This pattern varies for zone 4, within which the midfielders

possesses the greatest percent distance covered.

Study 3 – Relative action profile comparisons for the team, pre to post the 2015 rule

change did not indicate a significant change in zones 2, 3, 4, 5, and 6. However zone 1

experience a statistically significant decrease. Positional analysis showed statistically

significant changes for midfielders only. The changes were a decrease in zone 1, and

increase in zone 5 and 6 during the first half of the game, and decrease in zone 1 and

m/min during the second half of the game.

A major focus of the US Women’s National Team is to develop the athletes’ physical

capacity to maintain and repeat high intensity actions. The combination of physical

preparation and tactical strategies allow the team to express high m/min and numerous

high intensity actions throughout a match.

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Copyright 2016 by Heather A. Abbott

All Rights Reserved

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DEDICATION

This dissertation is dedicated to my family. This journey would not have been

possible without your love, support and encouragement.

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ACKNOWLEDGEMENTS

Dr. Kimi Sato - for his advice and endless patience throughout this project. It

would not have been possible without his support and assistance in the analysis and

subsequent write-ups.

Dr. Michael Stone – for his willingness to share his knowledge and experience, I

am forever grateful.

Dr. Satoshi Mizuguchi – for his constructive criticism, and for tell me what I

needed to hear instead of what I wanted to hear.

Dave Hamilton - for allowing me the opportunity to work with the US Women’s

National Field Hockey Team, and providing me with endless advice on GPS match

analysis.

The US Women’s National Field Hockey Team – for volunteering to participate

in this research. Without their participation, none of this would have been possible.

My colleagues at East Tennessee State University – for motivating me during my

educational pursuits.

Thank you

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TABLE OF CONTENTS

Page

ABSTRACT ...............................................................................................................2

DEDICATION ...........................................................................................................5

ACKNOWLEDGEMNTS .........................................................................................6

LIST OF TABLES .....................................................................................................11

LIST OF FIGURES ...................................................................................................13

Chapters

1. INTRODUCTION ................................................................................................14

Rational for Research/Statement of Problem .....................................................17

Objective of Research ........................................................................................17

Significance ........................................................................................................18

Operational Definitions ......................................................................................20

2. COMPREHENSIVE LITATURE REVIEW .........................................................21

Introduction ........................................................................................................21

Existing Literature .............................................................................................21

Field Hockey Rules ............................................................................................22

Time Motion Analysis History ..........................................................................24

Global Positioning System History ....................................................................25

Global Positioning System Validity and Reliability ..........................................26

Field Hockey Specific Global Positioning System Validation ..........................30

Global Positioning System and Micro Technology Metrics .............................34

Distance ......................................................................................................34

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Velocity ......................................................................................................35

Inertial Movement Analysis .......................................................................38

Player Load and Metabolic Power .............................................................39

Global Positioning System Action Profiles in Field Hockey .............................40

Action Profile Comparison ........................................................................41

Monitoring .................................................................................................42

Game vs. Practice .......................................................................................43

Fatigue ........................................................................................................45

Problems/Considerations/Match Variability ..............................................47

Summary ............................................................................................................47

3. STUDY 1: Positional and Match Action Profiles of Elite Women’s Field Hockey

Players ........................................................................................................................49

Abstract ..............................................................................................................50

Introduction ........................................................................................................51

Methods..............................................................................................................53

Experimental Approach to the Problem .....................................................53

Athletes ......................................................................................................53

Procedures ..................................................................................................................54

Statistical Analysis .....................................................................................................55

Terms/Definitions .....................................................................................56

Results ................................................................................................................57

Descriptors .................................................................................................57

Position Differences ...................................................................................61

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1st vs. 2nd Half ............................................................................................64

Discussion ..........................................................................................................66

Practical Application ..........................................................................................68

References ..........................................................................................................69

4. STUDY 2 International Women’s Field Hockey Action Profiles Post 2015 Rule

Change .......................................................................................................................75

Abstract ..............................................................................................................76

Introduction ........................................................................................................77

Methods..............................................................................................................78

Athletes Subjects ........................................................................................78

Data Collection ..........................................................................................78

Data Analysis .............................................................................................81

Statistical Analysis .............................................................................................82

Results ................................................................................................................83

Descriptive .................................................................................................83

Team Quarter Comparison .........................................................................85

Quarter Comparison by Position ................................................................89

Discussion ..........................................................................................................99

Conclusion .........................................................................................................100

References ..........................................................................................................101

5. STUDY 3 – Changes in Elite Field Hockey Player Action Profile due to 2015

Rule Change ...............................................................................................................104

Abstract ..............................................................................................................105

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Introduction ........................................................................................................106

Methods..............................................................................................................107

Athletes Subjects ........................................................................................107

Data Collection ..........................................................................................107

Data Analysis .............................................................................................110

Statistical Analysis .............................................................................................111

Results ................................................................................................................112

Team Full Game Pre vs. Post Rule Change ...............................................112

Team Pre 1st vs. Post 1st and Pre 2nd vs. Post 2nd ...........................................................112

Position Pre 1st vs. Post 1st and Pre 2nd vs. Post 2nd ....................................................112

Discussion ..........................................................................................................121

Conclusion .........................................................................................................122

References ..........................................................................................................123

6. SUMMARY AND FUTURE INVESTIGATIONS ..............................................127

REFERENCES ..........................................................................................................147

APPENDIX:ETSU Institutional Review Board Approval Form ...............................148

VITA ........................................................................................................................149

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LIST OF TABLES

Table Page

2.1 Validation GPS: devices, sampling frequency, and criterion measures ..............33

2.2 GPS Velocity Zones .............................................................................................37

3.1 Tournament Schedule and Outcome ....................................................................58

3.2 Positional Playing Time .......................................................................................58

3.3 Champions Challenge and World Cup Player and Position Action Profiles .......69

3.4 Absolute Number of efforts, Maximum and Average Distance Covered in Top

nkl3 Velocity Zones ...................................................................................................60

3.5 Descriptive Data for Relative Velocity Zones .....................................................62

3.6 ANOVA Main Effect for Position .......................................................................62

3.7 Relative Percent Distance covered in Velocity Zones Between Positions ..........63

3.8 Team Changes Relative Percent Distance covered from 1st to 2nd half ...............65

4.1 Tournament Schedule and Outcome ....................................................................78

4.2 Absolute Positional Action Profiles for the Full Game .......................................82

4.3 Relative Action Profiles for the Full Game .........................................................82

4.4 Team Relative Action Profiles by Quarter ...........................................................84

4.5 Team Relative Action Profiles Between Quarters ...............................................84

4.6a Forwards Relative Action Profiles by Quarter ...................................................89

4.6b Midfielder Relative Action Profiles by Quarter .................................................89

4.6c Screens Relative Action Profiles by Quarter .....................................................90

4.6d Defenders Relative Action Profiles by Quarter .................................................90

4.7a. Forwards effect size across Quarter ..................................................................91

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4.7b. Midfielders effect size across Quarter ..............................................................91

4.7c. Screens effect size across Quarter .....................................................................92

4.7d. Defenders effect size across Quarter .................................................................92

5.1 Tournament Schedule ..........................................................................................106

5.2 Paired t-test for Team Full Game Action Profiles Pre vs. Post ..........................111

5.3a Paired t-test for Team Full Game Action Profiles Pre1 vs. Post 1 .....................112

5.3b Paired t-test for Team Full Game Action Profiles Pre 2 vs. Post 2 ...................113

5.4a Forwards Pre 1 vs. Post 1 & Pre 2 vs. Post 2 Wilcoxon Signed Rank Tests ....114

5.4b Midfielders Pre 1 vs. Post 1 & Pre 2 vs. Post 2 Wilcoxon Signed Rank Tests .115

5.4c Screens Pre 1 vs. Post 1 & Pre 2 vs. Post 2 Wilcoxon Signed Rank Tests ........116

5.4d Defenders Pre 1 vs. Post 1 & Pre 2 vs. Post 2 Wilcoxon Signed Rank Tests ....117

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LIST OF FIGURES

Figure Page

4.1 Team m/min by quarter ........................................................................................85

4.2 Team Relative Percent Distance ..........................................................................86

4.3a Forwards Relative Percent Distance ..................................................................93

4.3b Midfielders Relative Percent Distance ...............................................................94

4.3c Screens Relative Percent Distance .....................................................................95

4.3d Defenders Relative Percent Distance .................................................................96

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CHAPTER 1

INTRODUCTION

Field hockey is an international sport played by both men and women, which has

undergone rapid and radical change within the past ten years. The arrival of synthetic

playing surface (Malhotra, Ghosh, & Khanna, 1983), modernized sticks (Allen, Foster,

Carré, & Choppin, 2012), and rule alterations such as the self-start (Tromp & Holmes,

2011), have changed the physiological requirements of the game. Most recently the

match formatting has been changed from two thirty-five minute halves with a ten minute

half time, into four fifteen minute quarters with two, two minute rest periods, and one ten

minute half time (FIH, 2014).

The sporting actions in field hockey consist of multiple interspersed high intensity

activities, which are multi directional in nature. It requires significant levels of aerobic

and anaerobic capacity, strength and power (Reilly & Borrie, 1992). In field hockey the

number of players on the field at one time is eleven. Normally a team plays with ten field

players, and one goalie (Mitchell-Taverner, 2005). Seven players are available on the

bench for rolling substitutions; expect for the Olympics and Pan American games, in

which only five players are allowed on the bench. The interchanging of players can occur

at any moment, creating the opportunity to maintain high levels of intensity throughout

the game. Various factors such as player position (Gabbett, 2010) (Field Hockey), style

of play (Bradley et al., 2011) (Soccer), level of play (Jennings, Cormack, Coutts, &

Aughey, 2012b) (Field Hockey), age of players (Vescovi, 2014) (Field Hockey), gender

of the athletes (Bradley, Dellal, Mohr, Castellano, & Wilkie, 2014) (Soccer), level of

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opposition (Lago, 2009) (Soccer), and match status (Lago-Penas, 2012) (Soccer) can

have an impact on the action profiles of players.

Time motion analysis (TMA) techniques have provided valuable information

regarding the action profiles of field players, in numerous sports including field hockey

(Jennings, Cormack, Coutts, & Aughey, 2012a; Jennings et al., 2012b; MacLeod,

Bussell, & Sunderland, 2007; Macutkiewicz & Sunderland, 2011; Spencer, Lawrence, et

al., 2004; Spencer, Rechichi, et al., 2005; Vescovi & Frayne, 2015; White & MacFarlane,

2014; Wyldea, Yonga, Azizb, Mukherjeec, & Chiac, 2014). It has proved to be a useful

tool to quantify the physical output of players during matches (Bloomfield, Polman, &

O'Donoghue, 2007) through either the manual or computer analysis of film. However, the

use of time motion analysis systems has been restricted from most teams due to its

expensive and time intensive nature. The recent introduction of sport specific Global

Positioning System (GPS) technology for TMA has allowed the monitoring of action

profiles by a greater number of field sport teams due to its less expensive nature, and

computerized analysis programs.

The evolution of the sports GPS technology allowed coaches and sports scientist

to determine and compare player’s movements during the games or training. The newer

higher sampling rate has created a more valid tool for measuring distance and velocity

(Castellano, Casamichana, Calleja-Gonzalez, Roman, & Ostojic, 2011; Johnston,

Watsford, Kelly, Pine, & Spurrs, 2014; Portas, Harley, Barnes, & Rush, 2010). However,

using inappropriate analysis procedures can alter the perceived action profiles of field

hockey players (Wehbe, Hartwig, & Duncan, 2014). It has been previously noted that

discrepancies exist when reporting locomotor activities (e.g. walking, jogging, running)

16

with TMA (Gabbett, 2010), and making a comparison between studies is difficult.

Inconsistencies in data processing also create discrepancies in reported measures,

specifically when comparing full game time to the time on pitch. Furthermore, different

GPS analysis procedures can also influence the time-dependent measures reported by

GPS.

The current practice for GPS analysis has essentially been driven by soccer

literature. Normally soccer players are on the pitch for a full game or do not reenter the

game after substitution. However, in field hockey a high number of substitutions are

allowed. Unlimited substitutions allow for offensive and defensive pressure to be applied

throughout the match. With this understanding it is important to acknowledge the need

for different analysis strategies, and the difficulty to generalize women’s soccer data to

field hockey. Full game versus time on pitch analysis alters the description of the work to

rest ratio and physical demands of elite field hockey players (White & MacFarlane,

2013).

It is also important to note that TMA system such as GPS do not measure the

demands of competition rather the outputs of players. Data from GPS systems can be

easily misinterpreted. For example, total distance expressed without the minutes of

match play can be misleading, because total distance covered in related to time on pitch.

The expression of distance per minute of match time is important in sports with unlimited

substitutions like field hockey. However, even the use of distance per minute can be

misleading high-intensity movements are lumped together with low-intensity movements.

Therefore it has led to the implementation of distances being measured in varying

velocity zones. This allows practitioners to locate intense action during matches.

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However velocity zones can be arbitrarily set leading to the misrepresentation of data.

Regardless, GPS has provided coaches, trainers and sports scientist with a better

understanding of the action profiles of athletes during games and training. This has led to

the ability to tailor and adapt training and recovery programs to individual athlete

profiles.

Rationale for Research/Statement of Problem

New match formatting and clock stoppages during corners and after goals,

implemented by the International Hockey Federation (FIH), are believed to provide teams

the ability to maintain and produce more high intensity actions. The new rule reads:

“…Match Periods (4 x 15 Minute Quarters and time stoppages for the award of Penalty

Corners and Goals), the Penalty Corner Countdown Clock and Video Umpire, which will

only be used at FIH World level Tournaments…” (FIH, 2014). The author suspects that

the implementation of these rules has affected the action profiles of field hockey players,

by allowing for more high-intensity actions, resulting from the additional rest periods.

The available action profiles for women’s field hockey in relationship to rule

changes is scarce. For example only one study exists examining the effect of the 2009,

free-hit rule on the game (Tromp & Holmes, 2011). To the authors knowledge no data

exists on the effects of the 2015 rule changes on women’s field hockey.

Objective of Research

The primary objective of this dissertation was to establish the changes in GPS

game action profiles for the relative variables, m/min, and percent of the total distance

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covered in zones 1 through 6 from the US Women’s National Field Hockey team players,

for the team and by position, caused by the evolution of game formatting, specifically the

rules adapting play at World level tournaments by the FIH (FIH, 2014).

The secondary objectives of this dissertation included:

1. Reporting the absolute action profiles including total distance, and total

distance covered in zones 1, 2, 3, 4, 5, 6, pre 2015 rule change for team,

player, and position, and post the 2015 rule change for team, and

position.

2. Determine the changes in team, and positional relative action profiles

before the 2015 rule change from the 1st to 2nd half of the game.

3. Determine the team and positional relative action profiles after the 2015

rule change in across the four quarters of a game.

Significance

This type of research is not novel; GPS research has provided useful information

in many field sports such as field hockey (Gabbett, 2010), soccer (Wehbe et al., 2014),

rugby (Sirotic, Knowles, Catterick, & Coutts, 2011), and Australian football (Varley,

Gabbett, & Aughey, 2014), and various studies have investigated the changes in rules on

different sports such as Rugby League (Eaves, Lamb, & Hughes, 2008; Meir, Colla, &

Milligan, 2001), Rugby Union (Williams, Hughes, & O'Donoghue, 2005), Beach

Volleyball (Giatsis, 2003; Ronglan & Grydeland, 2006), and Netball (O'Donoghue,

2012). However, for this dissertation the population, sample size, consistency of team

members, and investigation of the 2015 rule change are innovative. This dissertation uses

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the US women’s national team, who currently represent an elite standard of women’s

field hockey. The team finished with a World Ranking of 8th in December 2014, and 7th

in December 2015. The data included in this dissertation span a period of four

international tournaments: the Champions Challenge 2014, Rabobank Hockey World Cup

2014, World League 3 Semi Finals, and 2015 Pan American Games. Two international

tournaments were played before to the rule change, and two international tournaments

were played after the rule change. The significance of this research is also strengthened

by the high percentage of returning players allowing for a comparison in almost same

sample. The team members remained constant between the Champions Challenge 2014

and Rabobank Hockey World Cup 2014. Two player changes were made for World

League 3 in comparison to the 2015 roster, and one player change was made for the Pan

American Games compare to the 2015 roster. In addition to the retention of players

across games, the large number of files, and the evaluation of multiple tournaments is

uncommon. Analysis with many observations of the same players are more likely to

reduce discrepancies between matches due to different opponents and tactics and to

reveal underlying commonalities. To the author’s knowledge the effects of 2015 rule

change on games action profiles has never been studied. It is important to define the new

match play action profiles of the team and of each position in order to better prepare

players. This will allow for the continued development of attributes specific to the new

style of play. Knowledge of the progression of the game, and the ongoing changes in

physiological demands will be beneficial for strength and conditioning practitioners when

designing training programs for the development of athletes.

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Operational Definitions

FIH International Hockey Federation – is the international governing body of field hockey.

GPS Global position system – is a satellite based navigation system, that provided location and time information

TMA Time-Motion Analysis – varying techniques used to quantify the action profiles of players during competitions.

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CHAPTER 2

COMPREHENSIVE LITERATURE REVIEW

Introduction

The purpose of this review is to examine the current literature surrounding the

methods used to determine GPS action profiles for team sports. The primary focus is field

hockey. However other team-based sports are discussed in the literature review.

Existing Literature

The existing literature on field hockey is sparse. Podgórski and Pawlak (2011)

published “A Half Century of Scientific Research in Field Hockey” with the goal of

illuminating the status of research field hockey publications from 1960 to 2010. The

number of citations for field hockey (7,459) pales in comparison to other field based team

sports such as lacrosse (22,700), cricket (182,759), soccer (258,943), and football

(1,025,038). The research publications were obtained from PubMed and EBSCO host

databases, and www.scholar.google.pl, by entering the term “FIELD HOCKEY”. Upon

further analysis the authors deduced that only 208 of the 7,459 citations directly related to

field hockey, with the majority of the peer-reviewed research examining: the injuries

occurring during training and games, skill development, tactics and match play, as well as

the anthropometric and physical characteristics of players.

There is a lack of research examining the specific game and practice demands of

field hockey from elite population. Several studies claim to involve elite populations, but

the use of the word elite can be often be misrepresented (Dwyer & Gabbett, 2012;

Wassmer & Mookerjee, 2002). The minuscule amount of research involving elite field

22

hockey players presents with methodological limitations. Issues include lack of

information on the reliability of the analysis method, low sample size, generalizing one

players action profile for a whole positional group, and varying team rosters. Podgórski

and Pawlak (2011) showed an increasing trend in the number of field hockey related

publications. However the numbers of papers are still inferior to other field sports.

Field Hockey Rules

Field hockey is an internationally played sport, with the two largest tournaments

based competitions being the Olympics and the World Cup. It is played between two

teams of eleven players, normally ten field players, and a goalkeeper (Mitchell-Taverner,

2005). The objective of the game is to score a spherical ball, with a circumference

between 224 and 235 mm, and a weight between 156 and 163 grams in into the

opponents 3.66 meters wide by 2.14 meters high goal (FIH, 2014). The game is played at

the international level on 91.4 meters by 55 meters’ artificial water based turf. Field

hockey shares similar tactical and structural concepts with soccer. However aspects of

field hockey are unique to the sport, and must be taken into consideration when analyzing

field hockey specific research (Hodun, Clarke, De Ste Croix, & Hughes, 2016). The

unique aspect of field hockey includes: body positions due to the use of sticks (Reilly &

Borrie, 1992; Wdowski & Gittoes, 2013), rolling substitutions (Tromp & Holmes, 2011),

penalty corners (Vinson et al., 2013), and the drag flick (de Subijana, Juarez, Mallo, &

Navarro, 2011; Laird & Sutherland, 2003).

Historically field hockey has been an intermittent high intensity sport consisting

of two 35 minute halves (FIH, 2012). Numerous rule changes, implemented by the

23

International Hockey Federation (FIH) over the years have transformed the game of field

hockey with some having more impact than others. Some rules have had more noticeable

effects on the game. In 1975 when the FIH developed a common set of rules for men and

women, in 1992 unlimited substitutions were allowed, in 1998 the off sides rule was

removed from the game, in 2009 self-pass rule was implemented, and most recently in

2015 the time parameters of the game were changed (FIH, 2014). The action demands of

the sport are in transition due to the most recent rule change with international matches

being played in four 15 minute quarters. In addition to decreasing the game time from 70

minutes to 60 minutes, clock stoppages have been introduced. The game clock is stopped

during corners, and a countdown clock, independent from the game clock, of 40 seconds

is started. The offensive team has 40 seconds to complete the awarded corner. The game

clock is also stopped after a goal is scored. The clock is stopped, and a 40 second

countdown clock independent of the game clock is started, this gives the team scoring

team time to celebrate. The rule change effects the official game time, but with the ability

to stop the clock, the actual length of a game can vary. For example, if three corners

occurred in the first quarter of a game the official game time maybe 15 minute. However,

the length of play would be 17 minutes. International players, coaches, and sport

scientists have recognized the impact of the game formatting rule change on substitution

tactics, and game intensity. However to the author’s knowledge no empirical evidence of

the effect of the 2015 game formatting rule change has been presented.

Time motion analysis is commonly used in team sports to track player

movements, and create player action profiles (Gabbett, 2010; Jennings et al., 2012a;

Macutkiewicz & Sunderland, 2011; Vescovi & Frayne, 2015; White & MacFarlane,

24

2014; Wyldea et al., 2014). Global positioning systems specifically allow for the

movement patterns such as total distance, distances covered at various speeds, and time

on pitch. With the ever-evolving rules in field sports, performance analysis, and player

action profiles provided by GPS can be a helpful tool in assessing the effects of different

rules.

Time Motion Analysis History

Time motion analysis is a method used for quantifying the action profiles

performed by athletes. It involves tracking the movements of athletes, through either the

manual or computer analysis of film, or GPS. Video based TMA has proved to be a

useful tool to quantify the physical output of players during field based sporting events

(Bloomfield et al., 2007; Rienzi, Drust, Reilly, Carter, & Martin, 2000). Early research

involving TMA utilized video cameras, and could only captured the data of a single

player (Carling, Bloomfield, Nelsen, & Reilly, 2008). The nature of time and labor

intensive analysis made it difficult for use in the team environment. However, it was

shown to be reliable for examining movements of individual players (Duthie, Pyne, &

Hooper, 2003). The introductions of sophisticated computer analysis systems have

reduced the time-intensive nature of video analysis (Carling et al., 2008; Gray & Jenkins,

2010). However, the systems are expensive, require costly stadium based instillation, and

require intensive calibrations. Recently sporting GPS technology and combined GPS and

accelerometer systems have become popular in training and match quantification. The

relatively inexpensive, and portable GPS technology has allowed performance tracking to

be used by a greater number of field sports.

25

Global Position Systems History

Current GPS technology uses satellites to construct the geographical location and

movements of a receiver, which allows for the tracking of individual players. However,

until the 1980s GPS technology was restricted for military use only. When the technology

was finally made available for civilian use, the system was created with deliberate error.

This made precision measurement impossible. In 2000, the US Department of Defense

reduced the deliberate error in the system allowing for an increase in accuracy of non-

differential GPS technology (Terrier, Ladetto, Merminod, & Schutz, 2000). This change

allowed for lighter, smaller and cheaper units to become available to the general public.

Specific sports units were then developed to withstand heat, moisture, and sporting

collisions. Current units are light and compact enough to wear under an athletic jersey in

specially designed harnesses. Specialized GPS units and associated computer analysis

provided the ability to both collect and analyze data relevant to the performance of

athletes. The growth of the sports focused GPS industry resulted in GPS technology

integrated with accelerometers and gyroscopes to allow for the collection of physical

performance markers by individual athletes (Coutts & Duffield, 2010; D. Jennings, S.

Cormack, A. J. Coutts, L. J. Boyd, & R. J. Aughey, 2010b; Rampinini et al., 2007;

Varley, Fairweather, & Aughey, 2012).

The application of GPS technology in the context of team sport has overcome

many of the limitations of video-based TMA. The evolution manual video analysis to

GPS allows coaches and sports scientist to determine and compare player’s movements

during real time games or training. The low cost of GPS technology and portability of the

system allow for data to be collected in many outdoor settings.

26

Global Positioning System Validity and Reliability

The validity and reliability of GPS systems when assessing movement demands

of team sports are influenced by several factors: GPS sampling frequency, speed,

distance, and path of movements. The current trend in team sports is 10 Hz non-

differential GPS unit integrated with a 100 Hz tri-axial accelerometer, gyroscope, and

magnetometer. However the above mention measures must be considered when using

any GPS units, and especially when examining previous publications.

Sampling frequency also known as sampling rate is the number of samples

obtained during one second of collection. The sampling frequency of the technology

influences the accuracy, reliability, and validity of data. Early GPS units operated at 1

Hz, while newer GPS units sample at frequencies of 5, 10, and even 15 Hz (Cummins,

Orr, O'Connor, & West, 2013). As the sampling rates of GPS signals have increased, the

accuracy of the measuring has also increased. Studies that compared units operating at 5

Hz to older units operating at 1 Hz found that the increased sampling frequency increased

the accuracy of measurements (Coutts & Duffield, 2010; Jennings, Cormack, Coutts,

Boyd, & Aughey, 2010a; MacLeod, Morris, Nevill, & Sunderland, 2009; Petersen, Pyne,

Portus, & Dawson, 2009; Portas et al., 2010; Randers et al., 2010).

Jennings et al. (2010a) assessed the validity and reliability of distance data

measured by GPS units sampling at 1 Hz and 5 Hz during movement patterns common to

team sports. They found that the accuracy decreased as speed of locomotion increased in

straight line and the change of direction (COD) running. When comparing the accuracy

of distance collected between a 1 Hz and a 5 Hz GPS, the standard error of estimate was

calculated as the percent differences between the know distance and the GPS measured

27

distance. For a standing start, 10-m sprint was the percent difference between the known

and GOPS measured was 32.4 % for units collecting at 1 Hz and 30.9 % for units

collecting at 5 Hz. The increased sampling rate improved the GPS devices standard error.

However, the 5 Hz systems were still limited in its ability to assess short, high speed,

linear and COD running. Given that the most common sprint distance in team sports such

as soccer, rugby, and field hockey are between 10-20 meters (Spencer, Bishop, Dawson,

& Goodman, 2005), using 5 Hz GPS to measure distance traveled at very high velocities

may be inappropriate, especially if older units that sample at low frequencies are used for

data collection.

Studies comparing GPS units sampling at 5 Hz and 10 Hz found superior

accuracy for units sampling at 10 Hz when measuring velocity during straight-line

running, accelerations, and decelerations, when compared to a tripod mounted laser-

based criterion measure (Rampinini et al., 2015; Varley et al., 2012). The superiority of

10 Hz units over a linear distance of 15 and 30 meters is shown by Castellano et al.

(2011). The researchers used video and timing gates to show accurate distance

measurements taken by a unit sampling at 10 Hz. They noted an increase in accuracy as

the distances increased. The intra-device reliability was greater over 30 meters than 15

meters. The inter-device reliability showed CV = 1.3%for 15 meters 30 meters and CV =

0.7% 30 meters. They found that the accuracy of the units improved with increased

distance, and the standard error of measure between the known distance and distance

measured by the GPS units was 10.9% when running 15 meters, but it was only 5.1% for

the 30 meters trails. Regardless of the sampling frequency used, accuracy is reduced as

movement velocity increases and distances covered decrease. Portas et al. (2010)

28

reported measurement error between the know distance and GPS estimated distance to be

lowest during walking, which was measured at 1.8 meters per seconds, and produced of

0.7 % and highest during running at 5.6 %. Jennings et al. (2010a) showed that the

measurement accuracy of both 1 Hz and 5 Hz GPS systems improved as distances

covered increases during straight-line movements, COD, and a team sport running

simulation circuit. The difference between criterion and GPS measured distance ranged

from 9.0% to 32.4%. Total distance over the simulated running circuit exhibited the

lowest variation (CV 3.6%) while sprinting over 10 meters demonstrated the highest (CV

77.2% at 1 Hz). These studies provide justification for the use of newer, higher

frequency, GPS units in intermittent high intensity team sports.

In addition to sampling frequency the movements used during validity and

reliability testing must be considered. A study by Rampinini et al. (2015) examined a

team sport specific running courses varying intensities. The courses required the

participants to perform acceleration and deceleration movements. The authors examined

two GPS units, sampling at 5 Hz and 10 Hz. The authors found that when compared to

radar gun sampling at 32 Hz the 10 Hz GPS unit showed a 1.9% error for total distance

and a 4.7% error for high speed running.

Steps have even been taken to examine specific field sport conditioning. For

example, the convergent validity and the test–retest reliability of GPS devices for

measuring repeated sprint ability was studied by Barbero-Álvarez, Coutts, Granda,

Barbero-Álvarez, and Castagna (2010). Results showed a strong correlation between peak

speed measures with the GPS device and repeated sprint ability test performance

measured against timing gates (r=0.87 for 15 meters, and r= 0.94 for 30 meters) These

29

data supports the use of 1Hz GPS for measuring repeated sprint ability performance

testing in field sports, suggesting that GPS can be used in place of timing gates for RSA

performance testing.

When examining the validity and reliability of GPS units it is also import to

consider the unit’s manufacture, and model. In a study by Randers et al. (2010) the

authors compared four different TMA systems, a video-based time-motion analysis

system, a semi-automatic multiple-camera system, and two GPS systems, one sampling a

5 Hz and one sampling at 1 Hz. The total distance covered during a soccer match was

different between a semi-automatic multiple-camera system (10.8 ± 0.8 km), a video-

based time–motion analysis system (9.5 ± 0.7 km), a 5 Hz GPS device (10.7 ± 0.7 km)

and a 1 Hz GPS device (9.5 ± 0.9 km). The distance covered in high-intensity running

was 2.7 ± 0.5, 1.6 ± 0.4, 2.0 ± 0.6 and 1.7 ± 0.4 km for the semi-automatic multiple-

camera system, video-based system, 5 Hz GPS and 1 Hz GPS, respectively. Sprinting

distance during the match was different between the four systems, with recorded values

of 0.4 ± 0.2, 0.4 ± 0.2, 0.4 ± 0.2 and 0.2 ± 0.2 km for the semi-automatic multiple-

camera system, video-based system, 5 Hz GPS and 1 Hz GPS, respectively. These results

suggest that absolute distances taken from different time-motion analysis technologies

should not be used interchangeably.

When examining the previously presented information it is difficult to compare

the validation studies due to the multiple criterion measures used to validate the GPS

units. Therefore, the validation of GPS has methodological limitations that must be taken

into account when assessing results. Criterion measures ranged from: electronic timing

gates (Barbero-Álvarez et al., 2010; Castellano et al., 2011; Coutts & Duffield, 2010;

30

Jennings et al., 2010a; MacLeod et al., 2009; Petersen et al., 2009; Portas et al., 2010),

radar gun (Rampinini et al., 2015), Tripod-mounted laser (Varley et al., 2012), VICON

(Duffield, Reid, Baker, & Spratford, 2010),Theodolite (Gray & Jenkins, 2010) and video-

based time-motion analysis system, and a semi-automatic multiple-camera system

(Randers et al., 2010). The use of trundle wheel or tape measure, and timing gates can

introduce error. For example, there is error in the ability of a trundle wheel to accurately

measure distance, and in the timing gates to accurately measure time (Barbero-Álvarez et

al., 2010; Castellano et al., 2011; Coutts & Duffield, 2010; Jennings et al., 2010a;

MacLeod et al., 2009; Petersen et al., 2009; Portas et al., 2010). The methodological

limitations of the trundle wheel or tape measure, and timing gates are most apparent

when the research by Duffield et al. (2010) is examined. They used VICON to validate

two 1 Hz GPS units and two 5Hz GPS units for total distance and speed. Both devices

showed statically significant differences to the VICON for distance and speed. The inter-

unit reliability ranged from r=0.10-0.70, and CV was 4% for 1 Hz units, and 25% for 5

Hz units. When a precision system was used to examine GPS validity it showed that both

devices underestimate distance and speed. It also showed inter-unit error exist,

establishing the need for player to wear the same units across data collection.

Field Hockey Specific Global Positioning System Validation

Various research has been done to validate GPS for general field sports, including field

hockey (MacLeod et al., 2009). MacLeod et al. (2009) examined the validity of non-

differential GPS specifically for movement patterns used during field hockey. They

conducted a TMA of international field hockey players to determine the movement

31

patterns expressed during games. With the TMA information, the authors created a circuit

including a T-shaped shuttle, a straight-line shuttle, a straight sprint, and a zigzag shuttle

to replicated players’ game movements. Each shuttle was measured against timing gates

for mean speed and total distance was measured with a trundle wheel. All participants

completed 14 laps with the intent of the total distance covered in an international match.

A limitation of the validation is in the available equipment; the GPS units only collected

at 1 Hz, and the accelerometer at 100 Hz. The Pearson correlation for the mean speed

recorded by the timing gates and the GPS for the shuttle movements is r = 0.99. However

the GPS overestimated distances for the T-shuttle, straight-line shuttle and straight-line

shuttle sprint by 0.1 meters, 0.2 meters and 0.1 meters respectively. The mean distance

covered reported by the GPS was 6820.5 meters, whereas, the actual distance covered

was 6818 meters reported by the trundle wheel. The authors concluded that the non-

differential GPS system used in the study provided a valid measurement tool for

assessing player movement patterns typically seen in field hockey. However the

previously noted practical limitations of sampling frequency affect the potential precision

of the data. With all of the previously mentioned validation studies the inherent error of

the criterion measure must be taken into consideration. There is human error in the ability

of the trundle wheel to actually measure the distance covered, and determining the exact

starting point with GPS software can be difficult. In addition to this the timing gates are

not a gold standard of measurement and carry inherent error. A summary of the GPS

devices, sampling frequency, and criterion measure used for validation can be found in

Table 2.1.

32

With the current information we know that the velocity and distance of a task

directly influence the validity of the GPS measurements, and that GPS units with a higher

sampling rate enhance the validity of the data. While the addition of high intensity short

duration, and COD movement patterns decrease the validity of GPS results. The validity

of GPS units is task and time dependent, and must be considered when presenting the

results of data collection. With all the above taken into consideration we can then assume

that studies conducted with 1 Hz GPS units and even 5 Hz GPS units provide a poor

representation of sport action profiles, especially high intensity interval sports, in which

short distances are covered at high intensities

33

Table 2.1 Validation GPS: devices, sampling frequency, and criterion measures

Study GPS Device Frequency Task Measure Parameter

MacLeod, Morris, Nevill, & Sunderland, 2009 SPI Elite 1 HZ Field Hockey SC ETT, TW Distance, Speed

Petersen, Pyne, Portus, & Dawson, 2009 MinimaxX, SPI-10, SPI-Pro 5 Hz, 1 Hz Cricket SC ETG Distance, Speed

Barbero-Álvarez, Coutts, Granda, Barbero-Álvarez, & Castagna, 2010 SpiElite 1 Hz LR ETG Peak Velocity

Coutts & Duffield, 2010 SPI-10, SpieElite WiSPI 1 Hz Team Sport SC ETG Distance, Peak Velocity

Jennings, S. Cormack, Coutts,Boyd, & Aughey, 2010 MinimaxX 1 Hz, 5 Hz LR, COD, Team Sport SC ETG Distance, Speed

Jennings, Cormack, Coutts, Boyd, & Aughey, 2010 MinimaxX v2.5 5 Hz LR, COD, Team Sport SC GPS Unit Between Unit Duffield, Reid, Baker, & Spratford, 2010 MinimaxX v2.0, SpiElite 1 Hz, 5 Hz Tennis SC VICON Distance, Velocity Portas, Harley, Barnes, & Rush, 2010 MinimaxX Version 2.5, 1 Hz, 5Hz LR, COD, Soccer SC TW Distance Gray, Jenkins, Andrews, Taaffe, & Glover, 2010 SpiElite 1 HZ LR Theodolite Distance Randers et al., 2010 MinimaxX v2.0, SpiElite 1 Hz, 5Hz Soccer Match Running Video Distance Castellano, Casamichana, Calleja-Gonzalez, Roman, & Ostojic, 2011 MinimaxX v4.0 10 Hz LR ETG, Video Distance

Varley, Fairweather, & Aughey, 2012 V2.0 vs. MinimaxX 5 Hz, 10 Hz LR Acc, Dec ETG Instantaneous Velocity

Waldron, Worsfold, Twist, & Lamb, 2011 SPI-Pro 5 Hz LR ETG Distance, Peak Velocity

Johnston et al., 2012 MinimaxX 5 Hz Team Sport SC Rader Distance, Peak Velocity

Rampinini et al., 2015 SPI-Pro and MinimaxX 5 Hz, 10 Hz LR, Acc, Dec Radar Distance, Speed, Metabolic Power

Johnston, Watsford, Kelly, Pine, & Spurrs, 2014

MinimaxX S4 vs. SPI-ProX 10 Hz, 15 Hz Team Sport SC ETG Distance, Speed

Notes: LR=Linear Runs, SC=Simulation Circuit, Acc=Acceleration, Dec=Deceleration, ETG= Electronic Timing Gates, TW=Trundle Wheel

34

Global Positioning System and Micro Technology Metrics

It is important to note that GPS does not measure the physical demands of a

competition; rather it measures the output of a player. Therefor action profile should be

used when refereeing to the metrics reported by GPS. There is extensive information on

the action profiles of athletes from GPS, accelerometer, gyroscope, and magnetometer

combined systems. The information produced by these systems includes total distance,

distance per minute, distance and time in velocity zones, time on pitch, COD,

acceleration, and deceleration. These metrics have been applied to detect fatigue in

matches, identify periods of most intense play, and differentiate action profiles by

position, competition level, and sport.

Distance

Total distance covered is a useful metric in steady state scenarios, but team sports

are intermittent in nature. In team sports with unlimited substitutions reporting total

distance travel can be especially misleading. The expression of distance per minute of

match time is more useful for individual athletes, because it provides a reference to the

time the athlete spent in game. Total distance can be misleading when examining athlete

profiles because high-intensity movements are lumped together with low-intensity

movements. The logical progression of total distance measures is to break movement into

distance per minute and then further into minutes spent in different velocity zones.

35

Velocity

Velocity zones measure the distances covered at a velocity. The velocity zones

can be manipulated within the analysis systems of the GPS. Therefore the definition of

the velocity band for standing, walking, jogging, running and sprinting often differs

between researchers. Recent research suggests the number of high-intensity activities

performed, and the time spent in each high-intensity zone, and the total distances covered

in each high-intensity zone are important metrics (Cummins et al., 2013). It has been

recommended that sport scientist use velocity ranges and sprint definition previously

published in the literature, but large variations in the definitions and inability to agree on

set definitions have resulted in confusion among researchers. Although recommendations

for reporting GPS data for field hockey have been presented there is generally a wide

range of reporting methods employed in the literature (Dwyer & Gabbett, 2012) (Table

2.2) A recent trend in the field hockey literature has been to simplify the velocity zones

into low, moderate, high, and very high intensity running classifications (Vescovi &

Frayne, 2015; White & MacFarlane, 2014) or into low and high intensity classifications

(Jennings et al., 2012a, 2012b), but these classifications also vary greatly. Regardless it

seems that the existing velocity ranges used in publications have been arbitrarily

determined. Several prominent field hockey specific publication (Lythe & Kilding, 2011;

White & MacFarlane, 2013) derive their velocity zones from Men’s International Soccer

Video based time motion analysis (Di Salvo et al., 2007).

For the purpose of this dissertation velocity zones from previously published

international women’s field hockey will be used (Macutkiewicz & Sunderland, 2011).

The velocity zones used will be: zone 1 standing (0-.16 m/s), zone 2 walking (.19-1.6

36

m/s), zone 3 jogging (1.69-3.05 m/s), zone 4 running (3.08-4.16 m/s), zone 5 fast running

(4.19-5.27 m/s), zone 6 sprinting (>5.27 m/s). The velocity zones, designating the action

profiles were originally adapted from definitions provided by Bangsbo and Lindquist

(1992) and Lothian and Farrally (1992). These studies were performed on soccer and

field hockey populations, respectively (Table 2.2).

37

Table 2.2 GPS Velocity Zones

Study Gender Type Population Velocity Zones (m/s)

Zone 1 Zone 2 Zone 3 Zone 4 Zone 5 Zone 6

Gabbett, 2010 W GPS Australian FH League Low 0 - 1 Mod 1 - 5 High 5 - 7

Jennings, Cormack, Coutts, & Aughey, 2012 M GPS

National and International

FH Low .1 - 4.17 High > 4.17

Jennings, Cormack, Coutts, & Aughey, 2012 M GPS Australian FH Low .1 - 4.17 High > 4.17

Vescovi & Frayne, 2015 W GPS DI FH Low 0-2.22 Mod 2.25-4.44

High 4.47-5.55

Max 5.58-8.88

Lythe & Kilding, 2011 M GPS National New Zealand Stand 0-0.16 Walk .19-1.6 Jog 1.69-3.05 Run 3.08-

4.16 Fast Run 4.19-5.27

Sprint >5.27

Di Salvo et al., 2007) M TMA International Soccer Stand 0-0.16 Walk .19-1.6 Jog 1.69-3.05 Run 3.08-

4.16 Fast Run 4.19-5.27

Sprint >5.27

Dwyer & Gabbett, 2012 W GPS Australian State league FH Stand 0-.1 Walk .2-1.7 Jog 1.8-3.6 Run 3.7-5.3 Sprint >5.4

Macutkiewicz & Sunderland, 2011 W GPS International

FH Stand 0-0.16 Walk .19-1.6 Jog 1.69-3.05 Run 3.08-4.16

Fast Run 4.19-5.27

Sprint >5.27

White & MacFarlane, 2014 M GPS Scottish

National FH Low 2.77 -

4.16 Mod 4.16-

5.27 High Speed 5.27- 6.38

Sprint > 6.38

Wyldea, Yonga, Azizb, Mukherjeec, & Chiac, 2014

M GPS U-18 Asia Cup FH Low > 4.17 High < 4.17

Spencer et al., 2005 M TMA Australian FH NA

Polglaze, Dawson, Hiscock, & Peeling, 2014

M GPS Australian FH NA

McManus & Stevenson, 2007 M&W TMA Non-elite FH NA

Lothian & Farrally, 1992 W TMA National League FH NA

Bangsbo & Lindquist, 1992 TMA International

Soccer NA

Notes: FH=Field Hockey M=Male W=Female

38

Inertial Movement Analysis

Some research suggests that distances covered, whether total or at varying

velocities, neglects to consider stresses imposed through acceleration, deceleration, and

impact forces (McLellan, Lovell, & Gass, 2011; Varley et al., 2012). The addition of

inertial sensors, sometimes referred to as micro technology, in GPS units has allowed for

the measuring of jumps, accelerations, deceleration, COD, and levels of impact. Due to

the high number of changes of direction involved in team sports it has been suggested

that these forces be included when analyzing and compiling the action profiles of field

sport athletes. The integration GPS with accelerometer, gyroscopes and magnetometers

has made the estimation of COD effects possible. Accelerometers are motion sensors that

detect linear acceleration, while gyroscopes are motion sensors that detect angular

velocity. Accelerometers and gyroscopes are often used together because they

complement each other’s ability to provide precise acceleration, deceleration, and COD

data. Magnetometers have a sensor that detects the Earth’s magnetic field and measure

the orientation relative to magnetic North. The combination of these micro sensors allows

for the inertial movement analysis metrics.

Cummins et al. (2013) suggests that in addition to reporting distances traveled at

various velocities and user-defined velocity zones the total number of accelerations and

decelerations of the athletes should be reported. However this information can also be

misleading because uses can set the threshold for what constitutes as an acceleration and

deceleration. Similar to the velocity zones, practitioners are able to set thresholds for low,

medium, high, and very high accelerations and decelerations. The lack of universal

definitions must be taken into consideration when reviewing research.

39

Player Load and Metabolic Power

In addition to the breakdown of IMA data, several GPS analysis companies have

created algorithms that combine accelerometer and gyroscopic data into a single value.

Catapult Sports has named their instantaneous sum of the three axes of acceleration

derived from the combination of accelerometer and gyroscopic data “Player Load”.

Player Load is derived from a mathematical formula that accounts for accelerations and

decelerations in all three dimensions. Player Load has demonstrated a strong relationship

to heart rate, blood lactate levels and rating of perceived exertion (Casamichana,

Castellano, Calleja-Gonzalez, San Roman, & Castagna, 2013; Scott, Lockie, Knight,

Clark, & Janse de Jonge, 2013). Metabolic Power is calculated by an algorithm using

known measured oxygen uptake and accelerometer data to describe energy expenditure

of whole body movements (Walker, McAinch, Sweeting, & Aughey, 2016). The

reliability of metabolic power estimated from GPS based algorithms in field sports is

poor with the typical error of the estimate between GPS derived metabolic power and a

VO2 portable gas analyzer was 19.8% (Buchheit, Manouvrier, Cassirame, & Morin,

2015). Previously, energy expenditure estimations in soccer have been done by video

match analysis, but more research is needed for GPS applications (Osgnach, Poser,

Bernardini, Rinaldo, & di Prampero, 2010). More research is needed to improve the

algorithms used for Player Load and Metabolic Power. For the purpose of this

dissertation, Player Load and Metabolic Power are dependent on individual player

attributes, and therefore not generalizable to position profiling.

40

Global Positioning System Action Profiles in Field Hockey

The GPS and micro technology derived metrics presented above have been used

to quantify the physical output of players during matches (Bloomfield et al., 2007). This

technology has been utilized in team sports to advance the understanding of a variety of

field sports demands including: Soccer (Buchheit, Mendez-Villanueva, Simpson, &

Bourdon, 2010; Wehbe et al., 2014), Rugby Union (Cunniffe, Proctor, Baker, & Davies,

2009), Rugby League (Gabbett, Jenkins, & Abernethy, 2012; McLellan & Lovell, 2013;

McLellan et al., 2011; Sirotic et al., 2011), Rugby Sevens (Granatelli et al., 2014),

Australian Football (Coutts, Quinn, Hocking, Castagna, & Rampinini, 2010; Gray &

Jenkins, 2010; Young, Hepner, & Robbins, 2012), and Field Hockey (Jennings et al.,

2012a; Lythe & Kilding, 2011; Macutkiewicz & Sunderland, 2011; Vescovi & Frayne,

2015; White & MacFarlane, 2014). GPS application through the analyses of action

profiles has enhanced our understanding of the intermittent nature that characterizes team

sports (Spencer, Bishop, et al., 2005). GPS has been used to compare athletes between

positions, age groups and levels of performance. It has also been used to monitor loads

acquired during games and training, in addition to monitoring, comparing games to

practices, and examining the time course of games and tournaments. Before examining

the extensive uses of GPS in field hockey a standard for data collection and analysis must

be defined.

The current practice for GPS analysis has essentially been driven by soccer

literature. Normally soccer players are on the pitch for a full game or do not reenter the

game after substitution. However, in field hockey a high number of substitutions are

allowed. This tactic allows for offensive and defensive pressure to be applied throughout

41

the match, and is used at the elite levels because the strategy relies on an even

distribution of talent throughout team. Rolling substitutions allow players additional

ability to recover off the field, before returning to play. Full game versus time on pitch

analysis alters the description of the work to rest ratio and other time demands of elite

field hockey players (White & MacFarlane, 2013). It has been suggested that elite field

hockey is played at a higher intensity than other team sports with the same relative

distances (Aughey, 2011a), and all previously reported data represents field hockey as an

intermittent high intensity sport (Boyle, Mahoney, & Wallace, 1994; Gabbett, 2010;

Jennings et al., 2012b; Lythe & Kilding, 2011; Polglaze, Dawson, Hiscock, & Peeling,

2014; Spencer, Lawrence, et al., 2004; Spencer, Rechichi, et al., 2005; White &

MacFarlane, 2014). With this understanding it is important to acknowledge the need for

different analysis strategies, and the inability to generalize women’s soccer data to field

hockey.

Action Profile Comparisons

The production of different profiles within sports has drawn attention to the

different running patterns observed at varying levels of the same sport (Jennings et al.,

2012b), throughout a competitive season (Spencer, Bishop, & Lawrence, 2004), through

a tournament scenario (Higham, Pyne, Anson, & Eddy, 2012) and between genders

(Bradley et al., 2014). This information has led to a better understating of long-term

athlete development, and the action profile traits need to perform at a high level within

the sport. To the authors knowledge only one study has compared different levels of field

hockey player’s specifically male national-level Australian player to their international

42

counterparts (Jennings et al., 2012b). The authors determined that international players

cover 13.9% more total distance and 42.0% more distance at high speed running (>15

km/hr) then national players. Within the international population the midfielders

performed the greatest total distance, and achieve the greatest high intensity running

distance when compared to forwards and defenders. The authors also noted a decrease in

total distances from the first to second half of the game for both national and international

players. Macutkiewicz and Sunderland (2011) reported the action profiles of 25 female

field hockey players over 13 international matches, in respect to positional demands.

They attributed the differences in demands to the substitution tactics used by the team.

Macutkiewicz and Sunderland (2011), Spencer, Lawrence, et al. (2004) and MacLeod et

al. (2007) all reported that defender spend more time in low-intensity actions then

midfielder, and forwards. However the exact distances covered are hard to define

between the studies due to the variability in methods used, and the differences in

movement classifications. Comparisons between studies are difficult due to the varying

definitions.

Monitoring

Perhaps the most common, and daily use for GPS within teams sports is for

monitoring purposes. Results from studies using GPS devices to monitor team sport

players during competitions have provided clearer guidelines for training practices, and

the monitoring of practices has led to a better understanding of player movement needs.

The GPS devices also allow for team (Gabbett et al., 2012) and positional (Bloomfield et

al., 2007) based monitoring during competition and practice. The ability to monitor

43

individuals allows sport scientists to examine the influence of training volume, intensity,

and frequency on athletic performance, specifically as an external marker. Generally

performance abilities improve as training volume, intensity, and frequency increase, but

negative adaptations to exercise training are dose related. High incidence of illness and

injury occurring when training loads is too high, resulting in overtraining. GPS

monitoring can be used to assist in proper training prescriptions (Casamichana et al.,

2013; Scott et al., 2013). The use of GPS as a monitoring tool has provided insight into

weekly or day- by-day external load management (Scott et al., 2013). This information

can then be used to improved periodization, and optimize the performance in training and

games, and may help the detection of injury, fatigue and overtraining (Cummins et al.,

2013).

Game vs. Practice

Monitoring procedures have developed into comparing action profiles of games,

practices, and drills within practices. Gabbett et al. (2012) examined the demands of

rugby league competitions and compared those demands to tradition rugby league

practices. They found that the high intensity physical demands of competition were not

well matched by those observed in traditional conditioning, repeated high-intensity effort

exercise, and skills training activities. Individual drills and small-sided games have been

examined to provide a better understanding of a player’s actions under different

constraints. Gabbett and Mulvey (2008) investigated the movement patterns of small-

sided games used in soccer training and compared them to different levels of competition.

They found that while small-sided games offer players opportunities to engage in

44

movement patterns similar to those in games they often lack the high-intensity repeated

sprints performed in games. Compiling the available drill, and small-sided game research

produced by GPS has enabled coaches to design and implement conditioning practices

that stress the appropriate physical requirements of the sport. This information provides

insight for coaches on the modifications needed for training action’s to better prepare

rugby players.

Similar small game vs. practice research has been done with men’s and women’s

field hockey drills with the intent of providing coaches with a better understanding of the

activities occurring during game based training. Gabbett (2010) examined 19 training

appearances and 32 games from an elite population of women’s field hockey players, that

included or members of the Australia women's national field hockey team. The data were

examined for positional profiles during both game based training practices and

competitions. Only game based training session that included minimal coaching was

included. The researchers found that the time spent in specific velocity zones during the

small-sided games training didn’t reflect the action profiles of the matches. With this

information the coaches and athletes examined can then modify the training group size,

drill design, or drill complexity to better simulate the demands of competition. However

without the specific details about the drills performed during the training sessions the

time spent in varying velocity zones is limited in its usefulness for other coaches,

White and MacFarlane (2014) used GPS data from the men’s Scottish National

Team, and 75-competition analysis from 8 games, and 37 training analysis from 4

practice sessions. The practice data ware segmented into four categories: running,

technical, tactical, and small-sided games, providing insight into the training sessions

45

themselves. The researchers also stated the purpose of the drills was technical and tactical

development in preparation for competition, while running was designated as player

conditioning. Similar to Gabbett (2010), White and MacFarlane (2014) attempted to

compensate for the start and stop nature of practice, which is believed to decrease the

overall intensity. Their GPS data breakdown including omitting breaks in-between drills.

They found that even though training duration was longer for practices than matches the

sprinting and high intensity distances were not different between games and practices.

The authors included descriptions of the practices and training competed, and concluded

that training environments can be manipulated to produce similar game action profiles.

When compiling game vs. practice research it is easy to appreciate the multiple

variables in training that can produce large differences in the reported distances covered

and high intensity actions. Without the context of a training plan and the constant

monitoring of training load, the nuisances of the specificity of training are lost. It can

only therefore be generalized that under the right conditions drills can provide an

adaptive stimulus resembling those recorded in games.

Fatigue

GPS has been used to examine the construct of fatigue in field sports, with the

rationale that fatigue manifests as a decrease in performance. Researchers approached the

concept of in game fatigue by splitting the data into distinct time frames to establish

whether work rate varies with time or different tasks. Segments have been broken down

with in games for first and second halves (Bradley & Noakes, 2013; Granatelli et al.,

2014), by quarters (Coutts et al., 2010), and into 5 minute (Bradley & Noakes, 2013), and

46

1 minute (Granatelli et al., 2014) segments. Researchers have also examined the changes

in action profiles across tournaments (Higham et al., 2012; Spencer, Rechichi, et al.,

2005). The premises are based on the comparison of the overall work rate under different

time constraints and the correlations of work rate with indices of the fatigue.

However data with different teams have shown confounding results. Individual

teams and players pace differently (Black & Gabbett, 2014), while substitution strategies

(Bradley & Noakes, 2013), styles of play (Rampinini, Impellizzeri, Castagna, Coutts, &

Wisloff, 2009), opposition (Aughey, 2011b; Bradley & Noakes, 2013), match score

(Black & Gabbett, 2014; Bradley & Noakes, 2013), and level of player (Rampinini et al.,

2009), impact work rate. Assuming fluctuations in work rate are due to fatigue is

extremely misleading. It has recently been shown that pacing may occur in prolonged

high-intensity intermittent team sports (Duffield, Coutts, & Quinn, 2009). Which could

skew the interpretation of GPS-based fatigue. The introduction of substitutions must also

be considered, due to the various rules within different sports. The information available

on pacing measured by GPS comes from sports with limited substitutions like rugby

(Black & Gabbett, 2014) and soccer (Gabbett, Wiig, & Spencer, 2013), and therefore

might not be practical to apply to an unlimited substitution sport like field hockey. With

the above research taken into consideration this dissertation plans to only report GPS data

as athlete profile data, or simply the actions performed by the position or athlete during

the game.

Spencer, Rechichi, et al. (2005) examined an international men’s tournament

consisting of three games player within four days. Across the games the time spent

standing increased, and the time spent in the jogging decreased. The authors concluded

47

that the time between games was to short too allow for recovery, and the subsequent

games were negativity impacted. The authors used a positional based analysis and

accounted for time of pitch. The author note that substitution strategy, playing style of

opposition, changes in tactic, and environmental conditions could have influenced the

results. It is also important to note that each game was won or lost by a one-point goal

differential. Regardless residual fatigue could be a factor in tournament scenarios for

international field hockey players, and recovery techniques should be utilized to

encourage the dissipation of fatigue. This is in contrast to other studies, which show no

changes across a tournament. However direct comparison between studies is difficult.

Problems/Considerations/Match Variability

It is important to note the match-to-match variability that occurs for action

profiles (Gregson, Drust, Atkinson, & Salvo, 2010). There are numerous factors that may

contribute to the variations reported within GPS team sport data. Previously mentioned

factors include: substitution strategies (Bradley & Noakes, 2013), styles of play and

tactics (Rampinini et al., 2009), opposition (Aughey, 2011b; Bradley & Noakes, 2013),

match score (Black & Gabbett, 2014; Bradley & Noakes, 2013), and level of player

(Rampinini et al., 2009), impact work rate. This is combined with the fact that past

studies could be outdated, and lack relevance to the current game.

Summary

The goal of this chapter was to provide a theoretical basis for the experimental

chapters of this dissertation. It provided an overview of the existing TMA literature,

48

specifically GPS. GPS literature conducted on field hockey players in reference to

positions, age groups and levels of performance, games and practices were examined.

Sport specific GPS units provide coaches and sport scientists with action profiles

produced by athletes. The validity and reliability of GPS systems when assessing

movement demands of team sports are influenced by several factors: GPS sampling

frequency, speed, distance, and path of movements, which must be taken into

consideration when examining and conducting research. Information in produced from

GPS, accelerometer, gyroscope, and magnetometer combined systems. The information

gained from these systems includes total distance, distance per minute, distance and time

in velocity zones, time on pitch, COD, acceleration, and deceleration.

The intent of this dissertation was to use GPS derived metrics to examine the

action profiles produced by elite level field hockey players before and after a game

formatting rule change. The purpose was to provide coaches and sport scientist with a

depiction of the changes caused by the 2015 FIH game formatting rule change.

49

CHAPTER 3

POSITIONAL AND MATCH ACTION PROFILES OF ELITE WOMEN’S FIELD

HOCKEY PLAYERS

Authors: Heather A Abbott1, Kimitake Sato1, Satoshi Mizuguchi1, Dave Hamilton2,

Michael H. Stone1

Affiliations: Center of Excellence for Sport Science and Coach Education Department of

Exercise and Sport Sciences, East Tennessee State University, Johnson City, TN, USA1

USA Field Hockey, Manheim, PA2

Prepared for Submission to Journal of Strength and Conditioning Research

50

ABSTRACT

The purpose of this study was to examine the action profiles of the US Women’s

National Field Hockey Team recorded with GPS during the 2014 Champions Challenge

and World Cup. Eighteen female international field hockey players (age 25.5 ± 0.17

years, body mass 61.1 ± 4.7 kg) were tracked over thirteen international matches, giving

a total of 195-player match analysis. The descriptive data (playing time, odometer,

absolute distances and relative percent distances covered zone 1 standing (0-.16 m/s),

zone 2 walking (.19-1.6 m/s), zone 3 jogging (1.69-3.05 m/s), zone 4 running (3.08-4.16

m/s), zone 5 fast running (4.19-5.27 m/s), zone 6 sprinting (>5.27 m/s), and velocity zone

efforts, maximum effort distance, and average effort distance in zones 4,5, and 6) in

reference to both player and position are presented as the mean ± standard deviation

(SD). A series of ANOVAs were used to compare the relative percent distances covered

between positions, and a paired sample t-test was used to compare the relative percent

distances for the team between each half. The findings revealed that defenders work at a

lower meter per minute (m/min) when compared with all other positions, and that

forwards, midfielders, and screens perform similar m/min during a game. It is possible

for elite level field hockey players to maintain high-intensity actions in zone 6 from the

1st half to 2nd half of the game by increasing actions in zones 1 and 2, and decreasing

actions in zones 4 and 5.

KEY WORDS: Global Positioning System, Time Motion Analysis, Team Sports,

Substitutions, Field Sports

51

INTRODUCATION

Field hockey is a multifactorial team sport, with interspersed high-intensity activities that

are multi-directional in nature; it requires significant levels of aerobic and anaerobic

power, strength, and power (5, 8, 24). Field Hockey has undergone fairly rapid and

radical changes within the past ten years. For example, the arrival of synthetic playing

surfaces (19), modernized sticks (1), and new rule alterations such as the self-start (28),

have changed the physiological requirements of the game.

Time-motion analysis techniques, such as global positioning systems (GPS), have

provided valuable information regarding the action profiles of field hockey players (7, 8,

10, 11, 17, 18, 20, 31, 33, 34). It is important to note that GPS does not measure the

demands of competition, but rather the outputs of players. GPS units have been shown to

be reliable and valid for measuring distance and velocity with the accuracy improving as

sampling frequency increases (12, 21, 29). A high sampling frequency is especially

important for intermittent high-intensity sports such as field hockey, which involve

numerous high-speed and short distance actions.

Early field hockey publications might not accurately represent the actions of field players

due to the recent rule changes (28), and varying analysis procedures used (32). It has

been previously noted that discrepancies exist when reporting locomotor activities (eg.

walking, jogging, running) with TMA (8), and making a comparison between studies is

difficult. Moreover, inconsistencies in data processing also create discrepancies in

reported measures, specifically when comparing full game time to the time on pitch.

52

Different GPS analysis procedures can influence the time-dependent measures reported

by GPS. This is specifically evident within sports with rolling substitutions, such as field

hockey. The tactic of rolling substitutions creates offensive and defensive pressure

throughout the match and is utilized at the elite level. This strategy relies on an even

distribution of talent throughout the team. The efficient use of this strategy affects an

athlete’s ability to produce repeat high-intensity actions. Previous research that uses full

game GPS rather than time on pitch analysis, resulting in a misrepresentation of the time

dependent factors provided by GPS. Time on pitch analysis produce higher relative

distance results then analysis of full games (124 m/min vs. 78 m/min, p ≤ .0001)

(32). However, absolute measures such as distances covered, the number of efforts and

distance of efforts are unaffected (32). In addition to varying analysis methods, the level

of players (11), level of competition (14), age of players (30), gender of players (6),

match status (15), and tactical style (23) utilized by the team has the potential to affect

the reported positional profiles. Therefore, it is important to characterize the style of play

implemented at the highest level in the US.

To the author’s knowledge, the unique and aggressive style of play used by the US

Women’s National Team has not been profiled. Therefore, the aim of this study was to

assess the action profiles of elite women’s field hockey players in the US by team, player,

and position across a game, and to determine if action profiles differed between the 1st

and 2nd halves of a match.

53

METHODS

EXPERIMENTAL APPROCACH TO THE PROBLEM

GPS technology allowed for the measurement of absolute variables (total distance

covered, distance covered in zones 1,2,3,4,5, 6, and velocity zone efforts, maximum

effort distance, and average effort distance in zones 4,5, and 6), and relative variables

(m/min, percent of the total distance covered in zones 1, 2, 3, 4, 5, and 6) from the US

Women’s National Field Hockey Team. The relative variables were used to determine the

differences in action profiles because not all players received the same amount of playing

time. The relative action profiles were analyzed for differences between positions across

a game, and between 1st and the 2nd half for the team.

ATHLETES

Eighteen members of the US Women’s National Field Hockey Team age (25.54 ± .17

years, body mass 61.12 ± 4.66 kg) participated in this study. The team finished with a

World Ranking of 8th in December 2014. Goalies were removed from GPS analysis, but

included in physical player profiles. GPS recordings were collected from the 2014

Champions Challenge and 2014 World Cup (Table 1). The team members remained

constant between the two international tournaments. The US Women’s National Team

finished the 2014 Champions Challenge with the 1st ranking, sustaining 5 wins, and 1

loss. During the 2014 World Cup, the team finished with a 4th place ranking, after

winning 4, tying 1, and losing 2 games. The East Tennessee State University Institutional

review board approved this study and each athlete read and signed a written informed

consent document.

54

PROCEDURES

All GPS data collection was performed on outdoor water based turf. Data was collected

during the 2014 Champion’s Challenge in Glasgow, Scotland and the 2014 Rabobank

Hockey World Cup in The Hague, Netherlands, giving a total of 195 player-match files

for analyses from thirteen games. All GPS data was acquired using Catapult Minimax X

(Catapult Innovations, Melbourne, Australia), which samples at 10 Hz. This device had

previously been shown to be valid for distance and velocity during linear and sport

specific movements (12, 13, 21, 29). The GPS units were worn within a specifically

designed bib with a neoprene pouch. The bibs hold the units between the shoulder blades

of the athlete. Each player wore the same device across all matches. All players were

acclimated to wearing the GPS units as a result of previous practice, and game

monitoring.

The GPS data was downloaded using the manufacturer-supplied software (Logan Plus,

version 5.1.7). The downloaded data was edited to only include time spent on the field of

play, during the regulation game time. The data were exported to Microsoft Excel for

further analysis. Individual player action profiles were identified and players were

categorized into four positions: forwards, midfielders, screens, and defenders. Similar

player categorization for field hockey has only been utilized in one prior study (20).

Players were substituted into the same positions; therefore, each play only contributes to

one positional group. Time on the substitution bench was not analyzed or included in any

calculations of rest or low-intensity exercise. The velocity zones were chosen from

previously published international field hockey research (stand 0-.16 m/s, walk .19-1.6

55

m/s, jog 1.69-3.05 m/s, run 3.08-4.16 m/s, fast run 4.19-5.27 m/s, and sprint>5.27 m/s)

(17, 18). The velocity zones were originally adapted from definitions provided by

Bangsbo and Lindquist (2) and Lothian and Farrally (16). The studies consisted of video

based time motion analysis performed on soccer (2) and field hockey (16) populations.

STASTICAL ANAYLSIS

Each individual athlete’s data files were combined across games, and the mean ± SD was

calculated for absolute and relative measures. Absolute data was constructed using two

varying methods: a player method, and a positional method. The player absolute data

represents the mean ± SD for the total number of players used in that positional group.

The absolute position data represents the work performed by the positional group

regardless of the number of players substituting into that position. The players were

separated into four different positions: forwards, midfielders, screens, and defenders, and

player and positional representation were constructed. The benefit of positional reporting

is that it negates the substitution strategy used by the US Women’s National Team, and

presents the mean ± SD of the position regardless of how many players played in that

positional group during the game. Reporting total distances is in the context of player or

position is limited in its usefulness because distance covered is directly related to the

amount of time a player spent on the pitch. Therefore, relative percent distances covered

were used to examine the differences between positions and the team changes across the

game. Total distance covered was considered relative to the total number of minutes

played for each athlete (meters per minute), and the total distance covered in each

velocity zone (1-6) was considered relative to the total distance covered, resulting in the

56

relative percentage distance covered for each velocity zone.

The number of efforts, maximum, and average distance covered in velocity zones 4, 5,

and 6 are presented as mean ± SD. A paired sample t-test was run to compare 1st vs. 2nd

half relative data. The difference between playing positions across a game was analyzed

using a series of 1-way ANOVAs, with post hoc Bonferroni tests. All statistical

procedures were performed with SPSS (IBM, New York, NY). Statistical significance

was set at p - value <0.05, and all data is reported as mean ± SD. The effect size was

calculated from the ratio of the mean difference to the SD. Cohen’s d effect sizes (d)

were assessed using the following criteria: trivial <. 2, small .2-.6, moderate .61-1.20,

large 1.21-2.00 and very large 2.01-4.0 (9).

TERMS/DEFINATIONS

Player = Absolute action profiles divided by the total number of players (team data were

divided by 16, forwards were divided by 4, midfielders were divided by 5, screens were

divided by 3, and defenders were divided by 4)

Position = Absolute action profiles divided by the number of positions on the field (team

data were divided by 10, forwards were divided by 2, midfielders were divided by 3,

screens were divided by 2, and defenders were divided by 3)

Relative = m/min, Percent of distance covered in velocity zones 1, 2, 3, 4, 5, and 6

relative to the total distance covered.

57

RESULTS

DESCRIPTORS

Playing time varied based on the player, and position (Table 2). No inferential statistics

were applied. However, simple numerical comparisons revealed defenders and screens

appear to average the highest playing time per match, followed by the midfielders, and

forwards. The absolute action profile characteristics of the games are presented in Table

3. Action profiles were reported in both the context of position and player. Player profiles

represent the absolute distances covered by the members of the US Women’s National

Team. Player data is specific to the teams’ personnel, tactics, substitution strategy, and

opposition. A positional breakdown of the data provides coaches with the absolute total

distance covered by each position on the team. This information is still specific to the

formation that the US Women’s National Team uses (2 forwards, 3 midfielders, 2

screens, and 3 defenders).

58

Table 1. Tournament Schedule and Outcome

Tournament Finish Location Date/Opponent/Score Champions

Challenge I 2014 1st Glasgow,

Scotland 4/27/2014

ESP 4/28/2014

RSA 4/30/2014

IRL 5/1/2014 INDIA

5/3/2014 ESP

5/4/2015 IRL

3 to 1 1 to 2 3 to 0 1 to 0 3 to 1 3 to 1 Rabobank Hockey World Cup 2014

4th The Hague, Netherlands

6/1/2014 ENG

6/3/2014 ARG

6/6/2014 CHN

6/8/2014 GER

6/10/2014 RSA

6/12/2014 AUS

6/14/2014 ARG

2 to 1 2 to 2 5 to 0 4 to 1 4 to 2 2 to 2 1 to 2

Table 2. Positional Playing time Position N Average Minutes SD Forwards 4 38.25 1.26

Midfielders 5 41.40 8.26 Screens 3 57.00 3.00

Defenders 4 57.50 11.41

59

Table 3. Champions Challenge and World Cup Player and Position Action Profiles

N Odometer (m) Zone 1 Stand 0-.16 m/s

Zone 2 Walk .19-1.6 m/s

Zone 3 Jog 1.69-3.05 m/s

Zone 4 Run 3.08-4.16 m/s

Zone 5 Fast Run 4.19-5.27 m/s

Zone 6 Sprint >5.27 m/s

m/ min

Player 16 3701 ± 1675 36 ± 22 891 ± 443 1270 ± 604 923 ± 442 432 ± 191 151 ± 77 117 ± 9 Position 10 5921 ± 1675 58 ± 22 1426 ± 443 2032 ± 604 1477 ± 442 691 ± 191 241 ± 77 188 ± 9 Forward Player 4 3091 ± 1094 28 ± 12 716 ± 258 1008 ± 350 782 ± 238 414 ± 175 151 ± 120 117 ± 11 Forward Position 2 6181 ± 1094 57 ± 12 1431 ± 258 2017 ± 350 1565 ± 238 827 ± 175 301 ± 120 234 ± 11 Midfielder Player 5 3264 ± 985 31 ± 13 802 ± 291 1119 ± 355 783 ± 232 378 ± 141 151 ± 83 120 ± 11 Midfielder Position 3 5441 ± 985 51 ± 13 1337 ± 291 1865 ± 355 1305 ± 232 630 ± 141 252 ± 83 200 ± 11 Screen Player 3 2293 ± 701 21 ± 9 563 ± 254 804 ± 324 543 ± 136 260 ± 27 107 ± 31 118 ± 11 Screen Position 2 3439 ± 701 32 ± 9 844 ± 254 1206 ± 324 814 ± 136 389 ± 27 160 ± 31 177 ± 11 Defender Player 4 5912 ± 1378 61 ± 26 1425 ± 467 2071 ± 511 1523 ± 390 648 ± 165 183 ± 49 114 ± 9 Defender Position 3 7882 ± 1378 82 ± 26 1901 ± 467 2761 ± 511 2031 ± 390 864 ± 165 244 ± 49 152 ± 9

Data Presented as mean ± SD

60

Table 4. Absolute Number of efforts, Maximum and Average Distance covered in Top 3 Velocity Zones

Velocity Zone 4 Velocity Zone 5 Velocity Zone 6

Velocity Zone

Efforts

Max Effort

Distance (m)

Average Effort

Distance (m)

Velocity Zone

Efforts

Max Effort

Distance (m)

Average Effort

Distance (m)

Velocity Zone

Efforts

Max Effort

Distance (m)

Average Effort

Distance (m)

Team 104 ± 24 50 ± 6 13 ± 1 50 ± 13 37 ± 5 13 ± 1 15 ± 5 38 ± 6 16 ± 1 Forwards 91 ± 4 49 ± 5 13 ± 1 54 ± 3 36 ± 2 12 ± 1 17 ± 1 42 ± 4 17 ± 1 Midfielders 97 ± 27 53 ± 8 14 ± 1 52 ± 16 39 ± 4 13 ± 1 16 ± 5 40 ± 5 17 ± 1 Screens 132 ± 12 54 ± 0 14 ± 0 62 ± 8 44 ± 2 14 ± 1 16 ± 4 40 ± 4 17 ± 1 Defenders 104 ± 30 46 ± 5 12 ± 1 37 ± 11 32 ± 2 12 ± 1 8 ± 3 29 ± 2 15 ± 0

61

POSITION DIFFERENCE

Main effect data is represented in table 6, and the p valve and d for all the interactions is

presented in Table 7. Results of the ANOVAs showed statistically significant differences

in zones 2, 3, 5, and 6 and m/min (Table 6). The post hoc tests indicated that forwards

and midfielders did not had any statistically significant differences in relative percent

distance covered in each zone and m/min (Table 7). Screens had only one statistical

difference when compared to forwards, in which screens covered statistically less relative

percent distance covered in zone 6 than forwards (Tables 5 and 7). On the other hand,

defenders covered statistically greater relative distance in zones 3 and 2 than forwards

and midfielders, respectively, but statistically shorter relative percent distance covered in

zones 5 and 6 and m/min than forwards and midfielders. Defenders also covered shorter

relative percent distance in zone 5 and m/min than screens.

62

Table 5. Descriptive Data for Relative Zones

Relative Zones Team Forwards Midfielders Screens Defenders Zone 1 0 -.16m/s 1.06 ± 0.31 0.91 ± 0.13 0.96 ± 0.31 1.04 ± 0.15 1.37 ± 0.43 Zone 2 .17 - 1.68 m/s 24.12 ± 4.83 22.62 ± 2.00 21.39 ± 4.22 22.77 ± 1.43 30.03 ± 4.87 Zone 3 1.69 - 3.07 m/s 33.11 ± 3.61 29.94 ± 1.72 32.36 ± 1.65 32.73 ± 4.99 37.49 ± 1.46 Zone 4 3.08 - 4.18 m/s 24.89 ± 3.23 25.53 ± 1.94 26.12 ± 2.57 26.78 ± 2.35 21.28 ± 3.46 Zone 5 4.19 -5.27 m/s 12.04 ± 3.04 14.00 ± 0.42 13.68 ± 1.35 12.65 ± 2.33 7.58 ± 1.89 Zone 6 > 5.27 m/s 4.75 ± 2.04 6.96 ± 0.76 5.47 ± 1.16 3.98 ± 1.42 2.21 ± 0.72 m/min 120.58 ± 10.25 125.75 ± 3.86 125.20 ± 10.89 123.67 ± 1.53 107.25 ± 5.74 Note: All data is mean +/- SD

Table 6. ANOVA Main Effect for Position

Relative Zones Df F sig. Zone 1 0 -.16m/s (3,12) 2.038 0.162 Zone 2 .17 - 1.68 m/s (3,12) 4.832 0.020 Zone 3 1.69 - 3.07 m/s (3,12) 6.329 0.008 Zone 4 3.08 - 4.18 m/s (3,12) 3.398 0.054 Zone 5 4.19 -5.27 m/s (3,12) 14.901 0.000 Zone 6 > 5.27 m/s (3,12) 15.691 0.000 m/min (3,12) 6.119 0.009

63

Table 7. Relative Percent Distance covered in Velocity Zones Between Positions

Forward vs. Mid

Forward vs. Screen

Forward vs. Defender

Midfielder vs. Screen

Midfielder vs. Defender

Screen vs. Defender

Relative Zones P ES P ES P ES P ES P ES P ES Zone 1 0 -.16m/s 1.000 0.214 1.000 -0.921 0.279 -1.450 1.000 -0.320 0.356 -1.094 0.983 1.031 Zone 2 .17 - 1.68 m/s 1.000 0.372 1.000 -0.084 0.082 -1.991 1.000 -0.436 *0.024 -1.896 0.135 -2.024 Zone 3 1.69 - 3.07 m/s 1.000 -1.436 1.000 -0.749 *0.007 -4.730 1.000 -0.102 0.062 -3.293 0.175 -1.293 Zone 4 3.08 - 4.18 m/s 1.000 -0.260 1.000 -0.580 0.259 1.514 1.000 -0.267 0.133 1.586 0.114 1.857 Zone 5 4.19 -5.27 m/s 1.000 0.317 1.000 0.807 *0.001 4.697 1.000 0.542 *0.000 3.717 *0.007 2.390 Zone 6 > 5.27 m/s 0.313 1.516 *0.016 2.610 *0.000 6.443 0.424 1.148 *0.003 3.391 0.258 1.578 m/min 1.000 0.067 1.000 0.709 *0.021 3.783 1.000 0.197 *0.018 2.062 *0.069 3.910 * Significance, ES = Effect Size, P= p-value

64

1st VS. 2nd HALF

The team differences determined as the relative percent distance cover in each velocity

zone were used to determine if the team expressed any changes in action profile from the

1st to the 2nd half of the game. The results showed a moderate increase in the percent of

distance cover in zones 1 and 2 and a moderate decrease in the percent distance covered

in zones 4 and 5 from the 1st half to the 2nd half (Table 8). Trivial decreases were

produced in zones 3 and 6, and a moderate decrease was noted in m/min. However, no

relative data showed statistically significance difference between the 1st and 2nd half of

the game.

65

Table 8. Team Changes Relative Percent Distance covered form the 1st to 2nd Half

Relative Zones Periods N Mean ± SD

Std. Error Mean

p -value

Effects Size

Qualitative Descriptor

Zone 1 0 -.16m/s 1st half 16 0.94 ± 0.20 0.05 0.107 -0.59 Moderate 2nd half 16 1.14 ± 0.44 0.11 Zone 2 .17 - 1.68 m/s 1st half 16 22.34 ± 3.48 0.87 0.105 -0.59 Moderate 2nd half 16 25.11 ± 5.66 1.42 Zone 3 1.69 - 3.07 m/s 1st half 16 32.54 ± 2.73 0.68 0.848 0.07 Trivial 2nd half 16 32.30 ± 4.08 1.02 Zone 4 3.08 - 4.18 m/s 1st half 16 25.11 ± 2.43 0.61 0.172 0.49 Moderate 2nd half 16 23.51 ± 3.85 0.96 Zone 5 4.19 -5.27 m/s 1st half 16 12.47 ± 2.71 0.68

0.221 0.44 Moderate 2nd half 16 11.14 ± 3.29 0.82 Zone 6 > 5.27 m/s 1st half 16 4.82 ± 2.01 0.50 0.688 0.14 Trivial 2nd half 16 4.52 ± 2.17 0.54 m/min 1st half 16 121.75 ± 11.50 2.87

0.147 0.53 Moderate 2nd half 16 116.06 ± 10.08 2.52

66

DISCUSSION

This study investigated the action profiles of US Women’s National Team during the

2014 Champions Challenge and 2014 World Cup. In this study, the defenders appear to

have spent greatest time on the pitch (Table 2) and covered the highest absolute distances

by player and position (Table 3). Playing time is important in determining the absolute

distances covered during a match (20). Defenders tactical position allows them to

moderate and control the field. It is the goal of a defender to organize and control space

on the pitch. These positional requirements allow the defenders to recover on the field

resulting in less substitution and more time spent on the pitch. Forwards appear to have

spent the least amount of time on the field (Table 2). This is most likely due to high

intensity and repeated runs performed by the forwards resulting in more frequent

substitutions. Playing time differences for positions have previously been noted by

Macutkiewicz and Sunderland (18). A difference in playing time between positional

groups is a product of the unlimited substitution rule.

The team action profiles of the US Women’s national team showed that players covered a

16.8% of the total distance in zones 5 and 6. This is higher than previously reported

6.1%, for men’s by Lythe and Kilding (17), and 6.4% for women’s by Macutkiewicz and

Sunderland (18) under the same velocity zone definitions. When comparing positional

differences for relative data, the results showed that defenders covered less relative

percent distance than forwards in zones 3, 5, 6 and m/min. Previous studies reported that

defenders cover less distance at higher intensity than forwards, midfielders, and screens

(5, 8, 10, 18), which represent the link of action profiles to the tactical demands specific

67

to positional roles. Previous research (8) has suggested position specific conditioning

based on the differences reported between positions. However, the percent difference in

high intensity velocity zones and variation in significance between zones should be

considered to justify the need for position specific conditioning. When examining the

mean ± SD for relative percent distance covered in zones 3, 5 and 6 between forwards

and midfielders the relative percent distances and m/min are similar. No statistical

differences existed between forwards and midfielders. The lack of significance

contradicts the suggested need for prescription of additional position-specific high-

intensity-type training between forwards, midfielders and screens.

Examination of the average and maximum effort distance covered by the team in velocity

zones 4, 5, 6 suggests that an average effort is between 13 and 16 meters, while a max

effort ranges between 40 and 50 meters (Table 4). Short-duration, high-intensity efforts

replicating match requirements should be used during training (26). The variation of the

percent relative distance covered in the varying velocity zones (Table 5) demonstrates the

need for a balanced development of aerobic and anaerobic capacities for field hockey

players. This agrees with previous research that concluded field hockey to be an

aerobically demanding sport with frequent anaerobic efforts (8, 24). Athletes need to

possess the ability to perform intermittent high-intensity actions and recovery, from those

activities quickly because sprinting is not evenly distributed throughout a game (26).

Increasing an athlete’s top speed can allow them to produce higher relative speeds during

a match, while increasing athletes’ aerobic capacity will allow for a higher critical speed

to be maintained while recovering between intermitted bouts of high-intensity (3, 4, 30).

68

When comparing the 1st to the 2nd half for team relative percent distances no statistical

significance was concluded. More slightly more distance was covered in the second half

in zones 1, 2, and 3 (Table 8). This agrees with the increase in lower work intensity (0-

11km/hr) reported by Spencer, Rechichi, Lawrence, Dawson, Bishop and Goodman (27)

for men’s field hockey. The results also agree with the decrease in zone 4 from the 1st to

2nd half. It seems that players compensate for the high intensity of the game by decreasing

their moderate-intensity actions and increasing their low-intensity actions to allow for the

maintenance of high-intensity actions from the 1st to 2nd half. The increase in low-

intensity actions may be needed to assist in the ability to recover from high-intensity

actions. Optimal utilization of rolling substitutions may cease the increase in low

intensity actions from the 1st to 2nd half by allowing players to recover off the pitch. It has

been suggested that high-intensity running is a much more important indicator of success

than total distances covered (22, 25, 26). It is important to note that the difference in

relative percent distance covered across halves is not considered statistically for any

relative data, this is likely due to the using a high number of rolling substitutions

throughout the match, which is speculated to permit the maintenance of intensity.

PRACTICAL APPLICATION

High-intensity sprints taking place in zone 6 only make up a small portion of match

actions (5-8%) in elite field hockey. Actions performed in zones 4 (12-13%), and 5, (25-

26%), represent a greater portion of the game then zone 6. Due to the intermittent nature

of field hockey, there is a necessity to repeat high-intensity actions throughout a game,

and the ability to perform those actions is recognized as a key determinant of success (3).

69

Unfortunately high-intensity actions create fatigue. Positional differences indicate that

defenders covered less relative percent distance in zones 5 and 6 when compared to

forwards and midfielders. However, the percent difference covered in velocity zones 5

and 6 is not great enough to justify positional specific conditioning.

When comparing the 1st half to the 2nd half of the game, tends in the data show players

increasing their percent distance covered in low-velocity zones, and decreasing their

percent distance covered in moderate-velocity zones, presumably allowing them to

maintain the percent distance covered in the high-velocity zones. However, no

statistically significant results were produced were in any relative velocity zone when

comparing the 1st half to the 2nd half of the game. It is clear that field hockey players

require a high level of aerobic fitness to cover the reported absolute distances (Table 3),

and recover from high-intensity efforts (Table 4 and 5).

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75

CHAPTER 4

WOMEN’S ELITE FIELD HOCKEY ACTION PROFLIES POST 2015 RULE

CHANGE


Michael H. Stone1




Prepared for Submission to International Journal of Sport Science and Coaching

76

Abstract

The object of this paper was to analyze the action profiles of the US Women’s’ National

Field Hockey Team using GPS technology, after the 2015 International Hockey

Federation (FIH) game time formatting change. The purposes were to present coaches

with an elite level standard of the new game, and to examine action profile changes

across the game. Actions were recorded over two international tournaments, during

twelve games, resulting in one hundred forty-eight files for sixteen athletes (age 20.5 ±

2.5 years, body weight 60.6 ± 5.2 kg). The descriptive data was presented for playing

time, odometer, absolute distances covered zone 1 standing (0-.16 m/s), zone 2 walking

(.19-1.6 m/s), zone 3 jogging (1.69-3.05 m/s), zone 4 running (3.08-4.16 m/s), zone 5 fast

running (4.19-5.27 m/s), and zone 6 sprinting. Total distance covered was also considered

relative to the total number of minutes played for each athlete ((meters per minute

(m/min)). A total distance covered in each velocity zone (1-6) was also considered

relative to the total distance covered in percentage. A series of repeated measures

ANOVAs with post hoc tests revealed statistical significance (p>.05) for team data

relative data across quarters. Meters per minute in 1st vs. 3rd and 1st vs. 4th quarters had

large effect sizes (1.2-2.0), and 1st vs. 2nd quarters had moderate (0.61-1.2) effects sizes.

Statistical significance was also found for velocity zones 4 and 5 when examining 1st vs.

2nd, 1st vs. 3rd, and 1st vs. 4th quarters. Another series of repeated measures ANOVAs on

positional data revealed that all positions produce the greatest m/min during quarter 1,

and relative distance covered in zones 1 and 2 statistically increased throughout the game.

Key Words: Global Positioning System, Time Motion Analysis, Team Sports,

Substitutions, Game Formatting

77

Introduction

In the United States, women’s field hockey is played from under twelve developmental

programs to college level, and an athlete’s career can culminate with the highest level of

play on the Women’s National Team. Historically the game has been played during two

thirty-five minute halves, with a ten minute half time, and a continually running clock

(FIH, 2012). New match duration and clock stoppage for corners and goals have been

implemented. The change in the game time requirements and clock management rules

has created a new match format at the international level. The new rule reads: “These

currently include Match Periods (4 x 15 Minute Quarters and time stoppages for the

award of Penalty Corners and Goals), the Penalty Corner Countdown Clock and Video

Umpire, which will only be used at FIH World level Tournaments…” (FIH, 2014)

Athletes are provided with two minutes of rest between quarters 1st and 2nd, and 3rd and

4th, with a ten minutes half time in-between quarters 2nd and 3rd. The rule changes are

expected to impact physiological and tactical demands of the game. Therefore, the

purpose of this study was to provide coaches with a descriptive profile of elite field

hockey players under the new FIH match formatting. Additional analysis was done to

determine the team and positional differences between the four quarters of match play. .

Findings of this study are intended to aid in developments of training methods specific to

the new match formatting

78

Methods

Athletes

Seventeen elite level field hockey players (age 20.5 ± 2.5 years, body weight 60.6 ± 5.2

kg) participated in this study. Goalies were not included in this study. The East Tennessee

State University Institutional Review Board approved this study. Each athlete read and

signed a written informed consent. A total of seventeen field players’ game action

profiles were examined. Sixteen field players participated in the 2015 World League 3

Semi Finals while only fifteen field players participated in the 2015 Pan American

Games. This was due to the limitation on team size during the Pan American games,

because of its designation as an Olympic qualifier, the team carried fifteen field players

and one goalie due to the bench limitation of sixteen. In comparison sixteen field players

and two goalies were carried during the 2015 World League 3 Semi Finals. Roster

changes across the tournaments resulted in the removal of two field player, and one

goalie from the 2015 World League 3 Semi Finals roster, and the addition of one new

field player to the Pan American games roster. This resulted in seventeen different

athletes involvement in data collection. Players’ files were divided into 4 positional

groups: forwards, midfielder, screens, and defenders.

Data Collection

The action profiles of the US Women’s National Field Hockey Team were measured

using GPS sporting technology. Action profile data was collected using Optimye S5 GPS

units (Catapult Innovations, Team S4, Melbourne, Australia), sampling at 10 Hz. This

device has been shown to be valid for distance and velocity during linear and sport

79

specific movements (Johnston, Watsford, Kelly, Pine, & Spurrs, 2014; Rampinini et al.,

2015; Varley, Fairweather, & Aughey, 2012).

Data was collected during the 2015 World League 3 Semi Finals in Valencia, Spain

during 7 matches, and the 2015 Pan American Games in Toronto Canada during six

matches (Table 1). The US Women’s National Team finished 5th during the World

League 3 Semi Finals, and 1st during the 2015 Pan American Games. The team’s

international ranking culminated in a world ranking of 7th during 2015. All matches were

played on a water based, synthetic turf, which was in compliance with FIH field of play

rules (FIH, 2014). GPS units were positioned between the scapulae in a custom built

harness worn under the uniform. Players wore the same units across both tournaments.

All players were acclimated to wearing the GPS units as a result of previous practice, and

game monitoring. GPS data were downloaded and analyzed using proprietary software

(LoganPlus, version 5.1.7). The downloaded data were edited to only include time spent

on the field of play, during the regulation game time. The data was then exported to a

Microsoft Excel spreadsheet for further analysis.

80

Table 1. Schedule

Tournament Finish Location Games/Opponent/Score

World League 3

Semi Finals

5th Valencia, Spain

6/11/2015 URU

6/13/2015 RSA

6/14/2015 GER

6/16/2015 IRL

6/18/2015 ARG

6/20/2015 IRL

6/21/2016 ESP

2 to 0 4 to 1 2 to 2 0 to 2 0 to 3 6 to 1 3 to 1

Pan American

Games

1st Toronto, Canada

7/13/2015 URU

7/15/2015 CHIL

7/17/2015 CUBA

7/20/2015 DOM REP

7/22/2015 CAN

7/24/2015 ARG

5 to 0 2 to 0 12 to 0 15 to 0 3 to 0 2 to 1

81

Data Analysis

One hundred and forty-eight game action profiles were collected over twelve matches.

The velocity zones were set in relationship to previously published international field

hockey research (Macutkiewicz & Sunderland, 2011). The velocity zones used were:

zone 1 stand 0-.16 m/s, zone 2 walk .19-1.6 m/s, zone 3 jog 1.69-3.05 m/s, zone 4 run

3.08-4.16 m/s, zone 5 fast run 4.19-5.27 m/s, and zone 6 sprint>5.27 m/s. These specific

action profiles were originally adapted from definitions provided by Bangsbo and

Lindquist (1992) for Men’s international soccer athletes and Lothian and Farrally (1992)

for Women’s National League field hockey athletes.

Each individual athlete’s data files were averaged across games to obtain absolute and

relative measures. This data processing was chosen to account for several missing files

due to a few corrupted data files. The sample size for this study was seventeen, but each

athlete had a varying number of full game files to produce their sample. Players were

divided into four positional groups: forwards, midfielder, screens, and defenders. Players

were substituted into the same positional group, but not always into the same positional

role (ex: right midfielder vs. center midfielder). In order to provide an estimate of

position’s absolute action profiles , data obtained from all players was average across the

number of players playing the particular position at any point in time on the field (e.g.

forward position). This provided an evaluation for each field position, not the individual

players that play the position. This processing procedure was chosen to account for the

variation of the number of players between the tournaments. Team relative action profiles

and positional relative action profiles were then determined for each quarter. It is

82

important to note that for these analyses, not all players played an equal number of

minutes, and the new match formatting caused variation between quarter lengths.

Therefore, a total distance covered was considered relative to the total number of minutes

played for each athlete (m/min), and total distance covered in each velocity zone (1-6)

was considered relative to the total distance covered in percentage to account for the

difference in time on pitch. Analysis of the relative variables allows for comparisons for

the team across quarters, and for positions across quarters. Team relative action profiles

across the quarters, and positional relative action profiles were determined for each

quarter.

Statistical Analysis

All statistical procedures were performed with SPSS on relative data (IBM, New York,

NY). A series of repeated measures ANOVA’s were used to analyze the differences for

the team between quarters. For team data ANOVA’s statistical significance was set at

p≤0.05, and all data were summarized in mean ± standard deviation (SD) with associated

95% confidence intervals. Cohen’s D effects size were evaluated as trivial <. 2, small .2-

.6, moderate .61-1.20, large 1.21-2.00, and very large 2.01-4.0 (Hopkins, Marshall,

Batterham, & Hanin, 2009). Thesmallsamplesizesforpositionaldatawerenot

deemedappropriateforinferentialstatistics.However,visual representation of

positional action profiles during all quarters is shown in Graphs 3a (forwards), 3b

(midfielders), 3c (screens), and 3d (defenders).

83

Results

Descriptive

The absolute distances covered by positions follow the same descending pattern for

odometers in meters as, the time position time on pitch (Table 2). It has been previously

noted that playing time is important in determining the absolute distances covered during

a match (Jennings et al., 2012). Examination of the relative variables, which account for

the varied playing time, showed that forwards covered the greatest percent distance in

zones 5 and 6, followed by midfielders, screens, and defenders (Table 3). This pattern

varies for zone 4, within which the midfielders possess the greatest percent distance

covered.

84

Table 2. Absolute Player and Positional Action Profiles for the Full Game

# Odometer (m)

Zone 1 Dist Stand 0-.16 m/s

Zone 2 Dist Walk .19-1.6

m/s

Zone 3 Dist Jog 1.69-3.05

m/s

Zone 4 Dist Run 3.08-4.16 m/s

Zone 5 Dist Fast Run

4.19-5.27 m/s

Zone 6 Dist Sprint >5.27

m/s Position 10 8823 ± 1776 38 ± 29 2141 ± 573 2883 ± 680 2166 ± 479 1134 ± 272 461 ± 140 Forward Position 2 11965 ± 314 46 ± 7 2737 ± 163 3576 ± 174 2911 ± 66 1797 ± 37 898 ± 65 Midfield Position 3 7534 ± 954 29 ± 12 1537 ± 306 2260 ± 374 2029 ± 330 1201 ± 198 478 ± 73

Screen Position 2 8767 ± 1653 54 ± 20 2084 ± 579 2765 ± 567 2365 ± 462 1105 ± 77 393 ± 17 Defender Position 3 8056 ± 972 32 ± 5 2384 ± 493 3121 ± 411 1675 ± 199 646 ± 99 199 ± 64

All Data are represented as mean ± SD

Table 3. Relative Action Profiles for the Full Game

Team Forwards Midfielders Screens Defenders Zone 1 0 -.16m/s 0.42 ± 0.44 0.37 ± 0.12 0.37 ± 0.21 0.59 ± 0.21 0.40 ± 0.04 Zone 2 .17 - 1.68 m/s 22.53 ± 8.29 22.67 ± 2.09 20.61 ± 5.71 23.20 ± 4.34 29.45 ± 4.22 Zone 3 1.69 - 3.07 m/s 29.91 ± 9.75 29.79 ± 2.05 29.81 ± 2.08 31.47 ± 1.68 38.64 ± 1.58 Zone 4 3.08 - 4.18 m/s 22.54 ± 7.28 24.48 ± 1.72 26.81 ± 2.10 26.77 ± 0.57 20.78 ± 1.93 Zone 5 4.19 -5.27 m/s 11.91 ± 5.01 15.16 ± 1.50 15.99 ± 2.33 13.10 ± 3.02 8.22 ± 2.33 Zone 6 > 5.27 m/s 4.98 ± 2.79 7.53 ± 1.36 6.42 ± 0.99 4.88 ± 2.00 2.51 ± 0.98 m/min 108.14 ± 33.40 121.56 ± 6.74 125.28 ± 12.37 118.66 ± 8.84 108.34 ± 5.49

All Data are represented as mean ± SD, Velocity zone data is represented as a % of the total distance covered

85

Team Quarter Comparison

The effect of the game quarter on team m/min was statistically significant for 1st vs. 2nd,

1st vs. 3rd, and 1st vs. 4th; decreases were also statistically significant for 2nd vs. 3rd, and

2nd vs. 4th quarters (Table 5). The large effects size produced when comparing 1st vs. 3rd

and 1st vs. 4th may represent an opportunity for the implementation of recovery

techniques during the 10 minute half time (Table 5). A gradual decrease in m/min is

expressed comparative across all quarters in relationship to the 1st quarter. The changes

for the 3rd vs. 4th quarters were not statistically significant. The changes in m/min across

a game are visually represented in Graph 1.

A statistically significant increase in the percent of distance covered in zone 2 was noted

from 1st vs. 3rd, 1st vs. 4th, 2nd vs. 3rd and 2nd vs. 4th. Increasing the percent of time spent

walking is also indicative of fatigue, but the effect sizes for these changes were small to

trivial. The percent of distance covered in zones 4 and 5 decreased when comparing 1st

vs. 2nd, 1st vs. 3rd and 1st vs. 4th quarters, but no statistical significance occurred when

comparing zone 6 across the game. A visual representation of the team changes across a

game for actions in zones 1, 2, 3, 4, 5, and 6 are presented in Graph 2. The cumulative

effect of this information illuminates the general increase in low-intensity actions such as

walking, and a decrease in running, and fast running in 1st vs. 2nd, 1st vs. 3rd, and 1st vs. 4th

quarters. However, a more in-depth examination of positional changes across the game

was needed to determine the appropriate corrective actions.

86

Table. 4 Team Relative Action Profiles by Quarter

Q1 Q2 Q3 Q4

Mean SD

95% CI

Mean SD

95% CI

Mean SD

95% CI

95% CI

Lower Upper Lower Upper Lower Upper Mean SD Lower Upper Zone 1 0 -.16m/s 0.31 ± 0.18 0.21 0.41 0.34 ± 0.14 0.27 0.42 0.41 ± 0.22 0.30 0.53 0.47 ± 0.29 0.32 0.62

Zone 2 .17 - 1.68 m/s 19.93 ± 4.21 17.69 22.18 21.26 ± 5.49 18.34 24.19 22.25 ± 4.80 19.69 24.81 22.48 ± 5.13 19.74 25.21

Zone 3 1.69 - 3.07 m/s 30.46 ± 4.70 27.95 32.96 30.36 ± 4.93 27.73 32.99 30.75 ± 4.33 28.44 33.05 30.58 ± 4.43 28.22 32.95

Zone 4 3.08 - 4.18 m/s 25.83 ± 2.25 24.63 27.03 24.20 ± 2.69 22.76 25.63 23.61 ± 2.86 22.09 25.14 23.68 ± 2.76 22.21 25.16

Zone 5 4.19 -5.27 m/s 15.31 ± 3.64 13.37 17.25 13.96 ± 3.74 11.96 15.95 13.23 ± 2.68 11.80 14.66 13.00 ± 3.69 11.03 14.97

Zone 6 > 5.27 m/s 6.11 ± 2.36 4.85 7.37 5.70 ± 2.28 4.49 6.92 5.35 ± 2.00 4.29 6.42 5.43 ± 2.01 4.36 6.50

m/min 126.87 ± 8.53 122.33 131.42 119.27 ± 11.11 113.35 125.19 113.43 ± 11.10 107.51 119.34 113.18 ± 10.87 107.39 118.97

*Velocity zone data is represented as a % of the total distance covered

Table 5. Team Relative Action Profiles Between Quarters

1st vs. 2nd * 1st vs. 3rd 1st vs. 4th 2nd vs. 3rd * 2nd vs. 4th 3rd vs. 4th *

p valu

e ES Qual p

value ES Qual p value ES Qual

p valu

e ES Qual p

value ES Qual p value ES Qual

Zone 1 0 -.16m/s 1.00 -0.19 Trivial 0.14 -0.51 Small 0.10 -0.68 Moderate 0.23 -0.39 Small 0.09 -0.58 Small 0.52 -0.24 Small Zone 2 .17 - 1.68 m/s 0.10 -0.27 Small 0.00 -0.51 Small 0.00 -0.54 Small 0.03 -0.19 Trivial 0.05 -0.23 Small 1.00 -0.05 Trivial Zone 3 1.69 - 3.07 m/s

1.00 0.02 Trivial

1.00 0.02 Trivial

1.00 -0.03 Trivial

1.00 -0.08 Trivial

1.00 -0.05 Trivial

1.00 0.04 Trivial

Zone 4 3.08 - 4.18 m/s 0.02 0.66 Moderate 0.00 0.86 Moderate 0.01 0.85 Moderate 0.44 0.21 Small

1.00 0.19 Trivial

1.00 -0.02 Trivial

Zone 5 4.19 -5.27 m/s 0.03 0.37 Small 0.00 0.65 Moderate 0.00 0.63 Moderate 0.61 0.22 Small 0.10 0.26 Small 1.00 0.07 Trivial Zone 6 > 5.27 m/s 1.00 0.17 Trivial 0.12 0.35 Small 0.18 0.31 Small 1.00 0.16 Trivial 1.00 0.13 Trivial 1.00 -0.04 Trivial m/min 0.00 0.77 Moderate 0.00 1.36 Large 0.00 1.40 Large 0.00 0.53 Small 0.00 0.55 Small 1.00 0.02 Trivial

87

Graph 1.

Note: n=16

100.00105.00110.00115.00120.00125.00130.00135.00140.00

Quarter1 Quarter2 Quarter3 Quarter4

m/m

in

Teamm/minbyQuarter

m/min

88

Graph 2.

Note: n=16

0.002.004.006.008.0010.0012.0014.0016.0018.0020.0022.0024.0026.0028.0030.0032.0034.0036.00

Zone10-.16m/s Zone2.17- 1.68m/s

Zone31.69- 3.07m/s

Zone43.08- 4.18m/s

Zone54.19-5.27m/s

Zone6>5.27m/s

PercentofTotalDistance

TeamRelativePercentDistancebyQuarter

Quarter1Quarter2Quarter3Quarter4

89

Quarter Comparison by Position

Figure 6a display the relative mean ± SD and 95% confidence interval for all the action

profiles variables per quarter. Figure 7a displays the effects size and qualitative

descriptors for effect size across quarters. Forwards produced the highest m/min in

quarter 1 compared with all other quarters. Forward’s m/min decreased for 1st vs. 2nd, 1st

vs. 3rd, and 1st vs. 4th quarters. However, when m/min were examined in reference to the

previous quarter the effect sizes gradually decreased, depicting a progressive decline

throughout the game.

Figure 6b display the relative mean ± SD and 95% confidence interval for all the action

profiles variables per quarter. Figure 7b displays the effects size and qualitative

descriptors for effect size across quarters. Similar to forwards, midfielders produced the

highest m/min in the 1st quarter, and the effects sizes were moderate for 1st vs. 2nd, large

for 1st vs. 3rd, and large for 1st vs. 4th. It’s important to note that midfielders showed a

moderate to large effect size in zones 4 and 5 for 1st vs. 2nd, 1st vs. 3rd, and 1st vs. 4th.

Figure 6c display the relative mean ± SD and 95% confidence interval for all the action

profile variables per quarter. Figure 7c displays the effects size and qualitative descriptors

for effect size across quarters and showed a progressive decrease across the game.

Similar to forwards and midfielders, screens produced the highest m/min in the 1st quarter

and the effects sizes were moderate for 1st vs. 2nd, large for 1st vs. 3rd, and large for 1st vs.

4th. They also showed a decline in m/min in subsequent quarters compared to the 1st.

Screens maintained their percent distance covered in zones 5 and 6 across quarters.

90

However, they did express a decrease represented as a large effect size in zone 4 when

comparing 1st vs. 2nd, 1st vs. 3rd and 1st vs. 4th quarters.

Figure 6d displays the relative mean ± SD and 95% confidence interval for all the action

profiles variables per quarter. Figure 7d displays the effects size and qualitative

descriptors for effect size across quarters. Defenders produced the highest m/min in

quarter 1. However, the difference for m/min when comparing the 1st vs. 2nd was trivial.

When comparing 1st vs. 3rd and 1st vs. 4th moderate effect sizes very produced. Defenders

experience a decline in the amount of time spent running when comparing the 1st vs. 4th

quarter. The decrease in running during 1st vs. 4th was accompanied by increases in the

amount of time standing in 2nd vs. 3rd and 2nd vs. 4th.

91

Table 6b. Midfielder Relative Action Profiles by Quarter Q1 Q2 Q3 Q4

Mean SD

95% CI Mean SD 95% CI Mean SD 95% CI Mean SD 95% CI

Midfielders Lower Upper Lower Upper Lower Upper Lower Upper Zone 1 0 -.16m/s 0.41 ± 0.26 0.20 0.61 0.42 ± 0.18 0.28 0.57 0.55 ± 0.31 0.30 0.79 0.71 ± 0.43 0.40 1.01

Zone 2 .17 - 1.68 m/s 21.37 ± 4.67 17.26 25.49 23.04 ± 5.47 17.38 28.69 23.78 ± 3.60 18.94 28.62 24.43 ± 4.90 19.12 29.73

Zone 3 1.69 - 3.07 m/s 28.22 ± 1.37 23.69 32.76 28.59 ± 1.58 23.34 33.85 29.44 ± 2.14 24.54 34.34 28.81 ± 2.53 23.88 33.73

Zone 4 3.08 - 4.18 m/s 27.40 ± 2.20 25.09 29.70 26.11 ± 2.24 23.53 28.70 25.20 ± 1.63 22.44 27.95 25.11 ± 2.18 22.43 27.78

Zone 5 4.19 -5.27 m/s 16.44 ± 3.72 12.54 20.33 14.44 ± 4.04 10.59 18.28 13.31 ± 2.89 10.47 16.16 13.22 ± 3.40 9.06 17.38

Zone 6 > 5.27 m/s 6.16 ± 2.24 3.48 8.84 5.33 ± 1.24 2.94 7.72 5.57 ± 1.86 3.42 7.73 5.68 ± 1.51 3.35 8.01

m/min 129.51 ± 12.14 119.91 139.10 119.72 ± 17.29 106.98 132.46 114.1

3 ± 15.05 101.38 126.88 111.08 ± 15.12 98.64 123.51

Table 6a. Forwards Relative Action Profiles by Quarter Q1 Q2 Q3 Q4

Mean SD

95% CI

Mean SD

95% CI

Mean SD

95% CI

Mean SD

95% CI

Forwards Lower Upper Lower Upper Lower Upper Lower Upper Zone 1 0 -.16m/s 0.28 ± 0.15 0.10 0.47 0.37 ± 0.13 0.24 0.50 0.41 ± 0.18 0.19 0.63 0.44 ± 0.23 0.17 0.71

Zone 2 .17 - 1.68 m/s 20.00 ± 3.09 16.32 23.68 21.98 ± 4.91 16.92 27.03 22.77 ± 4.36 18.44 27.10 23.13 ± 5.20 18.39 27.88

Zone 3 1.69 - 3.07 m/s 32.31 ± 3.78 28.26 36.37 31.34 ± 5.11 26.64 36.04 32.34 ± 5.01 27.96 36.73 31.35 ± 4.95 26.95 35.76

Zone 4 3.08 - 4.18 m/s 26.14 ± 1.83 24.08 28.20 24.60 ± 1.97 22.29 26.91 24.78 ± 2.57 22.32 27.25 24.33 ± 3.24 21.94 26.73

Zone 5 4.19 -5.27 m/s 14.78 ± 3.47 11.30 18.27 13.73 ± 3.45 10.29 17.16 12.94 ± 2.25 10.39 15.48 13.61 ± 5.06 9.88 17.33

Zone 6 > 5.27 m/s 6.49 ± 2.98 4.09 8.89 6.51 ± 2.83 4.38 8.65 5.23 ± 2.35 3.30 7.15 5.55 ± 2.05 3.46 7.64

m/min 129.66 ± 6.04 121.08 138.25 121.31 ± 8.71 109.92 132.70 114.76 ± 11.18 103.35 126.16 116.69 ± 12.09 105.5

7 127.81

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Table 6c. Screens Relative Action Profiles by Quarter Q1 Q2 Q3 Q4

Mean SD

95% CI Mean SD 95% CI Mean SD 95% CI Mean SD 95% CI

Screens Lower Upper Lower Upper Lower Upper Lower Upper Zone 1 0 -.16m/s 0.31 ± 0.13 0.08 0.55 0.33 ± 0.06 0.16 0.50 0.38 ± 0.15 0.10 0.66 0.41 ± 0.12 0.06 0.75

Zone 2 .17 - 1.68 m/s 22.98 ± 3.99 18.23 27.73 24.07 ± 6.09 17.54 30.59 25.05 ± 5.55 19.46 30.65 24.67 ± 5.65 18.54 30.80

Zone 3 1.69 - 3.07 m/s 34.45 ± 7.24 29.21 39.68 34.27 ± 7.33 28.20 40.33 32.20 ± 7.61 26.54 37.86 33.54 ± 7.44 27.85 39.22

Zone 4 3.08 - 4.18 m/s 25.46 ± 3.37 22.80 28.12 21.28 ± 3.28 18.30 24.26 20.63 ± 4.08 17.45 23.81 20.43 ± 1.26 17.34 23.52

Zone 5 4.19 -5.27 m/s 12.33 ± 4.95 7.83 16.83 10.48 ± 4.24 6.04 14.92 11.21 ± 4.05 7.93 14.49 10.26 ± 3.70 5.46 15.07

Zone 6 > 5.27 m/s 4.46 ± 3.09 1.36 7.55 3.61 ± 2.86 0.86 6.37 3.77 ± 1.89 1.28 6.26 4.24 ± 3.81 1.54 6.93

m/min 124.57 ± 11.95 113.49 135.65 111.91 ± 12.79 97.20 126.62 106.2

2 ± 12.50 91.50 120.94 107.15 ± 6.71 92.79 121.51

93

Table 6d. Defenders Relative Action Profiles by Quarter Q1 Q2 Q3 Q4

Mean SD

95% CI

Mean SD

95% CI Mean SD 95% CI Mean SD 95% CI

Defenders Lower Upper Lower Upper Lower Upper Lower Upper Zone 1 0 -.16m/s 0.24 ± 0.19 0.04 0.45 0.23 ± 0.12 0.08 0.37 0.31 ± 0.19 0.06 0.55 0.33 ± 0.20 0.03 0.63

Zone 2 .17 - 1.68 m/s 16.12 ± 3.43 12.00 20.23 16.49 ± 4.57 10.84 22.15 17.96 ± 4.50 13.12 22.80 18.06 ± 3.69 12.75 23.36

Zone 3 1.69 - 3.07 m/s 27.37 ± 3.67 22.84 31.91 27.98 ± 4.46 22.72 33.23 28.97 ± 2.09 24.06 33.87 29.19 ± 2.43 24.26 34.11

Zone 4 3.08 - 4.18 m/s 24.15 ± 1.01 21.85 26.45 23.97 ± 2.26 21.38 26.55 22.81 ± 1.71 20.05 25.56 23.89 ± 2.08 21.22 26.57

Zone 5 4.19 -5.27 m/s 17.08 ± 2.21 13.19 20.98 16.37 ± 2.36 12.52 20.21 15.04 ± 1.12 12.19 17.88 14.08 ± 1.87 9.92 18.24

Zone 6 > 5.27 m/s 6.83 ± 1.00 4.15 9.50 6.64 ± 1.24 4.25 9.02 6.47 ± 1.57 4.32 8.63 5.93 ± 0.86 3.60 8.26

m/min 122.47 ± 4.39 112.87 132.07 121.78 ± 6.15 109.04 134.52 116.46 ± 7.12 103.71 129.21 115.42 ± 8.21 102.98 127.85

Table 7a. Forwards effects size across Quarter 1st vs. 2nd 1st vs. 3rd 1st vs. 4th 2nd vs. 3rd 2nd vs. 4th 3rd vs. 4th Forwards ES Qual ES Qual ES Qual ES Qual ES Qual ES Qual Zone 1 0 -.16m/s -0.66 Moderate -0.77 Moderate -0.80 Moderate -0.24 Small -0.34 Small -0.13 Trivial Zone 2 .17 - 1.68 m/s -0.48 Small -0.73 Moderate -0.73 Moderate -0.17 Trivial -0.23 Small -0.08 Trivial Zone 3 1.69 - 3.07 m/s 0.22 Small -0.01 Trivial 0.22 Small -0.20 Trivial 0.00 Trivial 0.20 Trivial Zone 4 3.08 - 4.18 m/s 0.81 Moderate 0.61 Small 0.69 Moderate -0.08 Trivial 0.10 Trivial 0.15 Trivial Zone 5 4.19 -5.27 m/s 0.31 Small 0.63 Moderate 0.27 Small 0.27 Small 0.03 Trivial -0.17 Trivial Zone 6 > 5.27 m/s -0.01 Trivial 0.47 Small 0.37 Small 0.50 Small 0.39 Small -0.15 Trivial m/min 1.12 Large 1.66 Large 1.36 Large 0.65 Moderate 0.44 Small -0.17 Trivial

94

Table 7b. Midfields effects effect size across Quarters 1st vs. 2nd 1st vs. 3rd 1st vs. 4th 2nd vs. 3rd 2nd vs. 4th 3rd vs. 4th

Midfielders ES Qual ES Qual ES Qual ES Qual ES Qual ES Qual Zone 1 0 -.16m/s -0.07 Trivial -0.47 Small -0.83 Moderate -0.48 Small -0.86 Moderate -0.42 Small Zone 2 .17 - 1.68 m/s -0.33 Small -0.58 Small -0.64 Moderate -0.16 Trivial -0.27 Small -0.15 Trivial Zone 3 1.69 - 3.07 m/s -0.25 Small -0.68 Moderate -0.29 Small -0.45 Small -0.10 Trivial 0.27 Small Zone 4 3.08 - 4.18 m/s 0.58 Moderate 1.13 Large 1.05 Moderate 0.47 Small 0.46 Small 0.05 Trivial Zone 5 4.19 -5.27 m/s 0.51 Moderate 0.94 Moderate 0.90 Moderate 0.32 Small 0.33 Small 0.03 Trivial Zone 6 > 5.27 m/s 0.46 Small 0.29 Small 0.25 Small -0.15 Trivial -0.25 Small -0.06 Trivial m/min 0.66 Moderate 1.12 Large 1.34 Large 0.34 Small 0.53 Small 0.20 Small

Table 7c. Screens effect size across Quarters 1st vs. 2nd 1st vs. 3rd 1st vs. 4th 2nd vs. 3rd 2nd vs. 4th 3rd vs. 4th Screens ES Qual ES Qual ES Qual ES Qual ES Qual ES Qual Zone 1 0 -.16m/s -0.17 Trivial -0.48 Small -0.75 Moderate -0.43 Small -0.84 Moderate -0.22 Small Zone 2 .17 - 1.68 m/s -0.21 Small -0.43 Small -0.35 Small -0.17 Trivial -0.10 Trivial 0.07 Trivial Zone 3 1.69 - 3.07 m/s 0.02 Trivial 0.30 Small 0.12 Trivial 0.28 Small 0.10 Trivial -0.18 Trivial Zone 4 3.08 - 4.18 m/s 1.26 Large 1.29 Large 1.98 Large 0.18 Trivial 0.34 Small 0.07 Trivial Zone 5 4.19 -5.27 m/s 0.40 Small 0.25 Small 0.47 Small -0.18 Trivial 0.06 Trivial 0.24 Small Zone 6 > 5.27 m/s 0.29 Small 0.27 Small 0.06 Trivial -0.07 Trivial -0.19 Trivial -0.16 Trivial m/min 1.02 Moderate 1.50 Large 1.80 Large 0.45 Small 0.47 Small -0.09 Trivial

95

Table 7d. Defenders effect size across Quarters 1st vs. 2nd 1st vs. 3rd 1st vs. 4th 2nd vs. 3rd 2nd vs. 4th 3rd vs. 4th Defenders ES Qual ES Qual ES Qual ES Qual ES Qual ES Qual Zone 1 0 -.16m/s 0.27 Small -0.33 Small -0.42 Small -4.58 Very Large -4.53 Very Large -0.10 Trivial Zone 2 .17 - 1.68 m/s -0.09 Trivial -0.46 Small -0.54 Small -0.32 Small -0.38 Small -0.02 Trivial Zone 3 1.69 - 3.07 m/s -0.15 Trivial -0.54 Small -0.58 Small -0.28 Small -0.34 Small -0.10 Trivial Zone 4 3.08 - 4.18 m/s 0.10 Trivial 0.95 Moderate 0.16 Trivial 0.58 Small 0.04 Trivial -0.57 Small Zone 5 4.19 -5.27 m/s 0.31 Small 1.16 Moderate 1.47 Large 0.72 Moderate 1.08 Moderate 0.62 Moderate Zone 6 > 5.27 m/s 0.17 Trivial 0.27 Small 0.97 Moderate 0.12 Trivial 0.67 Moderate 0.43 Small m/min 0.13 Trivial 1.02 Moderate 1.07 Moderate 0.80 Moderate 0.88 Moderate 0.14 Trivial

Graph 3a.

Note: n=5

0.005.0010.0015.0020.0025.0030.0035.0040.0045.0050.00

Zone10-.16m/s Zone2.17- 1.68m/s

Zone31.69- 3.07m/s

Zone43.08- 4.18m/s

Zone54.19-5.27m/s

Zone6>5.27m/s


FowardsRelativePercentDistancebyQuarter


96

Graph 3b.

Note: n=4

0.005.0010.0015.0020.0025.0030.0035.0040.0045.0050.00

Zone10-.16m/s Zone2.17- 1.68m/s

Zone31.69- 3.07m/s

Zone43.08- 4.18m/s

Zone54.19-5.27m/s

Zone6>5.27m/sPercentofTotalDistance

MidfieldersRelativePercentDistancebyQuarter


97

Graph 3c.

Note: n=3

0.005.0010.0015.0020.0025.0030.0035.0040.0045.0050.00

Zone10-.16m/s Zone2.17- 1.68m/s

Zone31.69- 3.07m/s

Zone43.08- 4.18m/s

Zone54.19-5.27m/s

Zone6>5.27m/sPercentofTotalDistance

ScreensRelativePercentDistancebyQuarter


98

Graph 3d.

Note: n=4

0.005.0010.0015.0020.0025.0030.0035.0040.0045.0050.00

Zone10-.16m/sZone2.17- 1.68m/sZone31.69- 3.07m/sZone43.08- 4.18m/sZone54.19-5.27m/sZone6>5.27m/s


DefendersRelativePercentDistancebyQuarter


99

Discussion

This study was an investigation of the action profiles of US Women’s National Team

during the 2015 World League 3 Semi-Finals, and the 2015 Pan American games (Table

1). These tournaments represent games played with the 2015 FIH game time formatting

change. It should be noted that disparities exist between the rankings of competition

during the two tournaments. It has also been reported that more high intensity running is

performed when teams play against better opposition (Rampinini, Coutts, Castagna,

Sassi, & Impellizzeri, 2007). Due to the team’s success at the Pan American Games, the

US national team may not have had to perform as much intensity running as the opposing

team. However the opposition during the World League 3 Semi-Finals was considered to

be a high caliber, possibility resulting in more high intensity running.

It is important to note that the official time of a game has changed from 70 minute to 60

minute. However, the newly implemented clock stoppage for corners, and goals can

drastically affect the game length. The new match formatting rules stop the clock for

corners and goals. The official game clock is stopped on a corner, or after a goal, and an

independent running clock of forty seconds begins. Once the forty seconds ends, the

official game clock resumes. The quarter lengths ranged from 15 minutes to 24 minutes.

The actual game time will depend on the number of corners and goals awarded during the

game. For this reason, caution must be used when examining m/min as an indicator of

fatigue, because changes in m/min may be due to the varying durations in quarter length.

The seeming increases in low intensity actions during the clock stoppage could also

decrease the reported m/min. To retain ecological validity corner and goal clock

100

stoppages were not removed when calculating time on pitch. Future research should

examine the effect of removing the additional time from corners and goals on action

profiles.

Previous research has suggested that a large sample size is needed to provide true average

action profiles for elite level athletes (Carling, 2013; Cummins, Orr, O'Connor, & West,

2013). Due to the small sample size p-values were not determined for the positional data

across quarters. While this was a specific limitation, it should be noted that this study is

one of only a few on elite level field hockey players. Thus, this study is a first step in

characterizing the US women’s elite level of play, and allowing for comparisons with

other countries elite athletes. The data reported in this paper is specific to the 2015 US

Women’s National Team during both the World League 3 Semi-Finals, and the Pan

American Games. Using high-level athletes allows for a unique opportunity to examine

elite performance. Although it adds uncontrollable variability due to competitors, team

tactics, injuries, and other environmental factors, ecologically valid measurements are of

value to the sport science research.

Conclusion

To the author’s knowledge, this is the first study to examine the action profiles of

international field hockey player’s during games played under the newly implemented

match duration and clock stoppage rules. The findings demonstrated that the teams low

intensity actions in zone 2 gradually increased, while the percent distance covered in

zones 4 and 5 decreased when comparing 1st vs. 2nd, 1st vs. 3rd and 1st vs. 4th quarters.

101

However, no statistical significance occurred when comparing zone 6 across the game.

The US women’s national team increased the percent distance covered walking and

decreased the distance running and fast running to possibility maintain the percent

distance covered sprinting across a game.

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Carling, C. (2013). Interpreting physical performance in professional soccer match-play:

should we be more pragmatic in our approach? Sports Med, 43(8), 655-663.

Cummins, C., Orr, R., O'Connor, H., & West, C. (2013). Global Positioning Systems

(GPS) and Microtechnology Sensors in Team Sports: A Systematic Review.

Sports Medicine, 43(10), 1025-1042.

FIH. (2012). Rules of Hockey Including Explanations Lausanne, Switzerland


Gabbett, T. J. (2010). GPS analysis of elite women's field hockey training and

competition. J Strength Cond Res, 24(5), 1321-1324.

Hopkins, W. G., Marshall, S. W., Batterham, A. M., & Hanin, J. (2009). Progressive

statistics for studies in sports medicine and exercise science. Med Sci Sports

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Jennings, D., Cormack, S. J., Coutts, A. J., & Aughey, R. J. (2012). GPS analysis of an

international field hockey tournament. Int J Sports Physiol Perform, 7(3), 224-

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Johnston, R. J., Watsford, M. L., Kelly, S. J., Pine, M. J., & Spurrs, R. W. (2014).

Validity and interunit reliability of 10 Hz and 15 Hz GPS units for assessing

athlete movement demands. J Strength Cond Res, 28(6), 1649-1655.

Lothian, F., & Farrally, M. (1992). Estimating the energy cost of womens hockey using

heart rate and video analysis. . Journal of Human Movement Studies, 23, 215-231.

MacLeod, H., Bussell, C., & Sunderland, C. (2007). Time-motion analysis of elite

women's field hockey, with particular reference to maximum intensity movement

patterns. International Journal of Performance Analysis in Sport, 7(2).

Macutkiewicz, D., & Sunderland, C. (2011). The use of GPS to evaluate activity profiles

of elite women hockey players during match-play. Journal of Sports Sciences,

29(9), 967-973.

Rampinini, E., Alberti, G., Fiorenza, M., Riggio, M., Sassi, R., Borges, T. O., et al.

(2015). Accuracy of GPS devices for measuring high-intensity running in field-

based team sports. Int J Sports Med, 36(1), 49-53.

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104

CHAPTER 5

CHANGES IN ELITE FIELD HOCKEY PLAYER ACTION PROFLIES DUE TO THE

2015 RULE CHANGE


Michael H. Stone1




Prepared for Submission International journal of sport Science and Coaching

105

Abstract

The objective of this study was to analyze the effects of the 2015 International

Hockey Federation (FIH) rule changes on the United State Women’s National Field

Hockey Team’s game action profiles. The purpose was to determine the magnitude of

effect of the rule change on game action profiles. Comparisons were made between two

international tournaments before and two international tournaments after the match

formatting rule change, resulting in three hundred and forty-three files. Relative actions

profiles consisted of meters per minute (m/min), and the percent of the total distance

covered in zone 1 standing (0-.16 m/s), zone 2 walking (.19-1.6 m/s), zone 3 jogging

(1.69-3.05 m/s), zone 4 running (3.08-4.16 m/s), zone 5 fast running (4.19-5.27 m/s), and

zone 6 sprinting (>5.27 m/s). The results indicated that team’s relative action profiles

(n=16) across a game were only statistically significant in zone 1 (p=0.0000005) pre to

post rule change. After the rule change, the team’s percent of the total distance in the 1st

half was statically lower in zones 1 (p= 0.0000002), and greater in 5 (p= 0.02) and 6 (p=

0.02), and, in the 2nd half, lower in zone 1 (p= 0.000009). Meters per minute were also

statistically lower in the 2nd half (p= 0.003). Wilcoxon signed ranks tests revealed

statistical significance changes for midfielders in both halves after the rule change (1st

half: decrease in zone 1 (p= 0.04), increase in zone 5 (p= 0.04) and 6 (p= 0.04) and 2nd

half: decrease in zone 1 (p= 0.04) and m/min (p= 0.04)).It may not be possible for this

elite team to experience increases in game action profiles However, the tactical position

of midfielders, with the opportunity to play offensively and defensive, may benefits from

the additional 2 minute rest in between quarters under the new rules.

Key Words: Global Positioning System, Team Sports, Substitutions, Game Formatting

106

Introduction

Global Positioning System (GPS) has have provided coaches, trainers and sports scientist

with an understanding of the action profiles of athletes during field hockey games and

training (Dwyer & Gabbett, 2012; Gabbett, 2010; Jennings, Cormack, Coutts, & Aughey,

2012a, 2012b; Lythe & Kilding, 2011; Macutkiewicz & Sunderland, 2011; Polglaze,

Dawson, Hiscock, & Peeling, 2014; Vescovi & Frayne, 2015; White & MacFarlane,

2014; Wyldea, Yonga, Azizb, Mukherjeec, & Chiac, 2014). However, the rules of sport

are always evolving. Every two years rule changes are implemented by field hockey

governing body, the FIH. With the new match duration and clock stoppage during

corners and after goals, implemented by the FIH in January 2015, it is important to

determine if elite athlete action profiles have changed. Comparisons need to be made in

order to assess the need for altered high performance training. Comparing the action

profiles pre and post the rule change will allow for the further development of training

recommendations when athletes are transitioning from college (two 35 minute halves) to

international play (four 15 minute quarters). It has been speculated that the rule change

provides teams with the ability to maintain and produce more high intensity actions.

However, this potential alteration in play has not been documented.

The examination of the evolution of the players’ action profiles in response to changes in

the game time constraints due to the 2015 FIH rule changes is novel. The availability of

activity profile changes for women’s field hockey in relationship to rule changes are

scarce. For example only one study exists examining the effect of the 2009, free-hit rule

on the game (Tromp & Holmes, 2011). Performance analysis pre and post rule change

107

can be used to determine the effect of the change on varying aspects of the game and

examine the evolution of the players in response to the change. The information divulged

from this study will allow for the continued development of attributes specific to the new

style of match formatting. Knowledge of the ongoing action profile transitions taking

place will be beneficial for strength and conditioning practitioners when designing

training programs for the development of athletes, specifically those transitioning from

college to the national team.

Therefore the purpose of this study was to determine the effect of the FIH game

formatting rule change on the team and positional action profiles produced by the US

women’s national team during international tournaments before and after the game

formatting rule change.

Methods

Athletes

Twenty players form the US Women’s’ National Field Hockey team (pre rule age 24.8 ±

2.4 years, post rule age 25.5 ± 2.1 years, pre rule body weight 61.1 ± 4.6 kg, post rule

body weight 60.6 +/-5.1 kg) participated in this study. The East Tennessee State

University Institutional Review Board approved this study.

Data Collection

Wearable GPS technology allowed for the measurement of relative variables (m/min, and

percent of the total distance covered in zones 1, 2, 3, 4, 5, and 6) from the US Women’s

108

National Field Hockey Team. The GPS devices used during pre-collection were the

Catapult MinimaxX (Catapult Innovations, Melbourne, Australia), which sample at 10

Hz. The post rule activity profile data were collected using Optimye S5GPS units

(Catapult Innovations, Team S4, Melbourne, Australia), also sampling at 10 Hz. Both

GPS units are valid for measuring distance and velocity during liner and sport specific

movements (Johnston, Watsford, Kelly, Pine, & Spurrs, 2014; Rampinini et al., 2015;

Varley, Fairweather, & Aughey, 2012). Pre GPS data were collected during the 2014

Champions Challenge in Glasgow, Scotland and the 2014 Rabobank Hockey World Cup

in The Hague, Netherlands (Table 1). Post GPS data were collected during the 2015

World League 3 Semi Finals in Valencia, Spain and the 2015 Pan American Games in

Toronto, Canada (Table 1). All matches were played on a water based, synthetic turf,

which were in compliance with FIH field of play rules (FIH, 2014). GPS units were

positioned between the scapulae in a custom built harness worn in compliance with

previous research recommendations (Barrett, Midgley, & Lovell, 2014). Players wore the

same MinimaxX units during pre-collection, and the same Optimye S5 units during post

collection. All players were acclimated to wearing the GPS units as a result of previous

practice, and game monitoring.

109

Table 1. Tournament Schedule Tournament Finish Location Date/Opponent/Score/WorldRanking

ChampionsChallengeI2014

1st Glasgow,Scotland

4/27/2014ESP

4/28/2014RSA

4/30/2014IRL

5/1/2014INDIA

5/3/2014ESP

5/4/2015IRL

3to1(14) 1to2(11) 3to0(15) 1to0(13) 3to1(14) 3to1(15) RabobankHockey

WorldCup2014

4th TheHague,Netherlands

6/1/2014ENG

6/3/2014ARG

6/6/2014CHN

6/8/2014GER

6/10/2014RSA

6/12/2014AUS

6/14/2014ARG

2to1(6) 2to2(2) 5to0(5) 4to1(8) 4to2(11) 2to2(1to3)(3) 1to2(2)

WorldLeague3SemiFinals

5th Valencia,Spain

6/11/2015URU

6/13/2015RSA

6/14/2015GER

6/16/2015IRL

6/18/2015ARG

6/20/2015IRL

6/21/2016ESP

2to0(25) 4to1(11) 2to2(8) 0to2(15) 0to3(2) 6to1(15) 3to1(14)

PanAmericanGames

1st Toronto,Canada

7/13/2015URU

7/15/2015CHIL

7/17/2015CUBA

7/20/2015DOMREP

7/22/2015CAN

7/24/2015ARG

5to0(25) 2to0(21) 12to0(NA)

15to0(37) 3to0(19) 2to1(2)

110

Data Analysis

Data analyzed included three hundred forty-three samples, collected over four

tournaments matches, which included twenty-six games. GPS data were downloaded

using the manufacturer-supplied software (Logan Plus, version 5.1.7). The GPS data

were edited to only include time spent on the field of play, during the regulation game

time. The time on the substitution bench, and the time during the 2 minute and 10 minute

rest periods were not included in any calculations. This is in accordance with

recommendations from White and MacFarlane (2013). The velocity zones used to

determine the relative percent distance covered were chosen from previously published

international field hockey research (stand 0-.16 m/s, walk .19-1.6 m/s, jog 1.69-3.05 m/s,

run 3.08-4.16 m/s, fast run 4.19-5.27 m/s, and sprint>5.27 m/s) (Lythe & Kilding, 2011;

Macutkiewicz & Sunderland, 2011). GPS Data were then exported to Microsoft Excel for

further analysis

Each individual athlete’s data files were averaged across games to obtain the relative

measures. This data processing was chosen to account for several missing files due to a

few corrupted data files. Players were divided into four positional groups: forwards,

midfielder, screens, and defenders. In order to provide an estimate of a player’s action

profile playing a certain position, data obtained from all players of a particular position

were averaged across those players (e.g. forward player). Players were substituted into

the same positions; therefore each player only contributes to one positional group. It is

important to note that for these analyses, not all players played an equal number of

minutes, and the new match formatting caused variation between quarter lengths. . Total

111

distance covered was considered relative to the total number of minutes played for each

athlete (m/min), and total distance covered in each velocity zone (1-6) was considered

relative to the total distance covered in percentage to account for the difference in time on

pitch. Analysis of the relative variables allows for comparisons for the teams pre to post

rule change. Team relative action profiles across the 1st and 2nd half and positional

relative action profiles were then determined for 1st and 2nd half. The ten minute half

time, that takes place in both the pre and post-game formatting, was chosen to divide the

of game into 1st and 2nd halves

Statistical Analysis

All statistical procedures were performed with SPSS (IBM, New York, NY). Paired

sample t tests were used to compare team full game data pre rule change to the

corresponding team full game data post rule change at the team level. Paired sample t

tests were also used to compare each half of the pre rule change to the corresponding half

of the post rule change at the team level. Differences within a position between the pre

and post rule change were examined for each of the halves using a Wilcoxon signed rank-

test due to limited sample sizes. Statistical significance was set at p�≤0.05, and all data

were reported as mean ± SD and 95% confidence interval. Cohen’s d effect sizes (d) were

determined as trivial <0. 2, small 0.2-0.6, moderate 0.61-1.20, large 1.21-2.00 and very

large 2.01-4.0 (Hopkins, Marshall, Batterham, & Hanin, 2009).

112

Results

Team Full Game Pre vs. Post Rule Change

The paired samples t-test used to evaluate the relative actions profiles from pre to post

rule change, indicated no statistical difference in zones 2, 3, 4, 5, 6 and m/min. However,

statistical significance was produced for zone 1 with a very large effect size (d = 2.55) for

the decrease in percent distance covered pre to post rule change in zone 1 (Table 2).

Team pre 1st vs. post 1st and pre 2nd vs. post 2nd

The paired sample t-test indicated statistical significance in zones 1, 5 and 6 during the 1st

half of the game. Statistical differences in zone 1 represented a decrease in the percent

total distance covered from pre to post rule change. Statistical differences in zones 5 and

6 represented an increase in the total percent of distance-covered pre to post rule change,

with small effects sizes (Table 3a). During the 2nd half of the game statistically significant

decreases occurred in zone 1 and m/min (Table 3b).

Position pre 1st vs. post 1st and pre 2nd vs. post 2nd

Mean differences in relative variables during a positional breakdown for per 1st to post 1st

and pre 2nd to post 2nd were assessed with a Wilcoxon Signed Rank Test. No statistical

significance was found for all relative variables for forwards, screens and defenders

(Table 4a, 4c, 4d). Midfielders were found to have statistical changes in both halves of

the game pre to post. They had a statistical decrease in zone 1, and a statistical increase in

zones 5 and 6 pre to post during the 1st half of the game. During the 2nd half of the game,

zones 1 and 3 and m/min had statistical decreases (Table 4b). The Wilcoxon Signed Rank

113

Test showed that all 5 players in the midfielders positional group were involved in the

significant changes during the 1st and 2nd half of the game.

114

Table 2. Paired t-test for Team Full Game Action Profiles Pre vs. Post

95% Confidence

Interval of the Difference

Pre Full Game

Mean ± SD Post Full Game

Mean ± SD Mean

Difference Qualitative

Descriptor Lower Upper t df p-

value ES Zone 1 0 -.16m/s 1.06 ± 0.32 0.42 ± 0.16 0.65 0.48 0.81 8.35 15 0.00 2.55 Very Large

Zone 2 .17 - 1.68 m/s 24.12 ± 4.83 23.87 ± 5.19 0.25 -1.51 2.01 0.30 15 0.76 0.05 Trivial

Zone 3 1.69 - 3.07 m/s 33.11 ± 3.62 32.07 ± 4.41 1.04 -0.31 2.39 1.64 15 0.12 0.26 Small

Zone 4 3.08 - 4.18 m/s 24.89 ± 3.23 24.46 ± 2.84 0.43 -0.23 1.09 1.39 15 0.19 0.14 Trivial

Zone 5 4.19 -5.27 m/s 12.04 ± 3.04 13.19 ± 3.66 -1.15 -2.51 0.21 -1.81 15 0.09 -0.34 Small

Zone 6 > 5.27 m/s 4.75 ± 2.04 5.52 ± 2.35 -0.77 -1.58 0.05 -2.01 15 0.06 -0.35 Small

m/min 120.58 ± 10.25 118.16 ± 9.38 2.42 -1.75 6.58 1.23 15 0.24 0.25 Small Note: ES is effect size, p – value ≤0.05

115

Table 3a. Paired t-test for Team Full Game Action Profiles pre 1st vs. post 1st

95%

Confidence Interval of the

Difference

Pre 1st Mean SD Post 1st Mean SD Mean

Qualitative Descriptor Difference Lower Upper p-value t df ES

Zone 1 0 -.16m/s 0.97 ± 0.25 0.36 ± 0.12 0.61 0.49 0.73 0.00 10.63 15 3.11 Very Large

Zone 2 .17 - 1.68 m/s 23.50 ± 4.72 22.85 ± 5.14 0.65 -0.59 1.88 0.28 1.12 15 0.13 Trivial

Zone 3 1.69 - 3.07 m/s 33.37 ± 3.59 32.24 ± 4.47 1.12 -0.42 2.66 0.14 1.56 15 0.28 Small

Zone 4 3.08 - 4.18 m/s 25.30 ± 3.15 25.03 ± 2.85 0.27 -0.33 0.87 0.35 0.96 15 0.09 Trivial

Zone 5 4.19 -5.27 m/s 12.19 ± 3.13 13.83 ± 4.08 -1.64 -2.98 -0.30 0.02 -2.61 15 -0.45 Small

Zone 6 > 5.27 m/s 4.67 ± 2.17 5.70 ± 2.54 -1.03 -1.90 -0.16 0.02 -2.52 15 -0.44 Small

m/min 122.24 ± 10.12 124.21 ± 11.02 -1.98 -5.96 2.00 0.31 -1.06 15 -0.19 Trivial Note: ES is effect size, p – value ≤0.05

116

Table 3b. Paired t-test for Team Full Game Action Profiles pre 2nd vs. post 2nd

95%

Confidence Interval of the

Difference

Pre 2nd Mean SD Post 2nd Mean SD Mean

Qualitative Descriptor Difference Lower Upper p-value t df ES

Zone 1 0 -.16m/s 1.14 ± 0.39 0.49 ± 0.22 0.65 0.44 0.86 0.00 6.53 15 2.05 Very Large Zone 2 .17 - 1.68 m/s 24.79 ± 4.94 25.06 ± 5.02 -0.27 -1.75 1.20 0.70 -0.40 15 -0.06 Trivial Zone 3 1.69 - 3.07 m/s 32.91 ± 3.71 32.39 ± 4.02 0.53 -0.93 1.98 0.45 0.77 15 0.14 Trivial Zone 4 3.08 - 4.18 m/s 24.43 ± 3.38 24.04 ± 3.09 0.39 -0.67 1.45 0.45 0.78 15 0.12 Trivial Zone 5 4.19 -5.27 m/s 11.88 ± 2.96 12.75 ± 3.58 -0.88 -2.12 0.37 0.15 -1.50 15 -0.27 Small Zone 6 > 5.27 m/s 4.80 ± 1.97 5.27 ± 2.16 -0.47 -1.19 0.25 0.18 -1.39 15 -0.23 Small m/min 120.34 ± 10.49 115.45 ± 10.39 4.89 1.90 7.89 0.00 3.48 15 0.47 Small Note: ES is effect size, p – value ≤0.05

117

Table 4a. Forwards Pre 1st vs. Post 1st & 2 Pre 2nd vs. Post 2nd Wilcoxon Signed Rank Tests

Pre Post

Mean SD Mean SD p-

value Qualitative

Descriptor Z ES Quarter 1 Zone 1 0 -.16m/s 0.90 ± 0.09 0.34 ± 0.05 -1.83 0.07 7.26 Very Large

Zone 2 .17 - 1.68 m/s 21.95 ± 2.09 20.83 ± 1.92 -1.46 0.14 0.56 Small

Zone 3 1.69 - 3.07 m/s 30.14 ± 1.54 29.20 ± 2.35 -1.10 0.27 0.48 Small

Zone 4 3.08 - 4.18 m/s 25.89 ± 1.78 25.19 ± 2.27 -1.46 0.14 0.34 Small

Zone 5 4.19 -5.27 m/s 14.21 ± 0.57 16.20 ± 1.08 -1.46 0.14 -2.30 Very Large

Zone 6 > 5.27 m/s 6.91 ± 1.24 8.25 ± 1.20 -1.46 0.14 -1.09 Moderate

m/min 126.83 ± 3.45 128.90 ± 7.48 -0.73 0.47 -0.36 Small Quarter 2 Zone 1 0 -.16m/s 0.93 ± 0.17 0.34 ± 0.09 -1.83 0.07 4.33 Very Large

Zone 2 .17 - 1.68 m/s 23.49 ± 1.80 23.01 ± 1.46 -0.73 0.47 0.29 Small

Zone 3 1.69 - 3.07 m/s 29.84 ± 1.88 29.34 ± 1.61 -1.46 0.14 0.28 Small

Zone 4 3.08 - 4.18 m/s 25.13 ± 2.24 24.56 ± 0.89 -0.73 0.47 0.33 Small

Zone 5 4.19 -5.27 m/s 13.70 ± 0.25 15.30 ± 1.23 -1.46 0.14 -1.80 Large

Zone 6 > 5.27 m/s 6.91 ± 0.31 7.47 ± 1.30 -1.10 0.27 -0.59 Small

m/min 126.00 ± 4.34 120.67 ± 7.20 -1.46 0.14 0.90 Moderate

Note: ES is effect size, p – value ≤0.05

118

Table 4b. Midfielders Pre 1st vs. Post 1st & 2 Pre 2nd vs. Post 2nd Wilcoxon Signed Rank Tests

Pre Post

Mean SD Mean SD p -

value Qualitative


Zone 2 .17 - 1.68 m/s 20.94 ± 3.96 20.47 ± 5.41 -0.14 0.89 0.10 Trivial

Zone 3 1.69 - 3.07 m/s 32.75 ± 1.64 29.94 ± 2.44 -2.02 0.04 2.44 Very Large

Zone 4 3.08 - 4.18 m/s 26.59 ± 2.48 26.56 ± 2.24 -0.41 0.69 0.01 Trivial

Zone 5 4.19 -5.27 m/s 13.70 ± 1.61 16.14 ± 3.16 -2.02 0.04 -0.97 Moderate

Zone 6 > 5.27 m/s 5.18 ± 1.71 6.53 ± 1.27 -2.02 0.04 -0.90 Moderate


Zone 2 .17 - 1.68 m/s 21.81 ± 4.40 22.62 ± 5.58 -0.41 0.69 -0.16 Trivial

Zone 3 1.69 - 3.07 m/s 32.10 ± 2.06 30.41 ± 1.98 -2.02 0.04 0.84 Moderate

Zone 4 3.08 - 4.18 m/s 25.59 ± 2.63 25.49 ± 3.01 -0.14 0.89 0.03 Trivial

Zone 5 4.19 -5.27 m/s 13.63 ± 1.11 14.93 ± 1.94 -1.48 0.14 -0.82 Moderate

Zone 6 > 5.27 m/s 5.77 ± 0.63 6.10 ± 0.64 -0.94 0.35 -0.52 Small

m/min 126.79 ± 9.89 119.46 ± 13.18 -2.02 0.04 0.60 Small


119

Table 4c. Screens Pre 1st vs. Post 1st & 2 Pre 2nd vs. Post 2nd Wilcoxon Signed Rank Tests

Pre Post

Mean SD Mean SD p-

value Qualitative


Zone 2 .17 - 1.68 m/s 21.79 ± 0.88 21.95 ± 4.15 0.00 1.00 -0.05 Trivial

Zone 3 1.69 - 3.07 m/s 32.61 ± 4.58 31.26 ± 2.00 0.00 1.00 0.38 Small

Zone 4 3.08 - 4.18 m/s 27.35 ± 1.82 26.87 ± 0.63 -0.54 0.59 0.35 Small

Zone 5 4.19 -5.27 m/s 13.11 ± 2.41 14.18 ± 3.14 0.00 1.00 -0.38 Small

Zone 6 > 5.27 m/s 4.21 ± 1.68 5.31 ± 2.19 -0.54 0.59 -0.56 Small

m/min 127.96 ± 2.83 124.55 ± 9.78 -0.54 0.59 0.47 Small Quarter 2 Zone 1 0 -.16m/s 1.14 ± 0.17 0.80 ± 0.26 -1.60 0.11 1.58 Large

Zone 2 .17 - 1.68 m/s 23.74 ± 2.04 24.71 ± 4.14 0.00 1.00 -0.30 Small

Zone 3 1.69 - 3.07 m/s 32.84 ± 5.45 31.62 ± 1.41 0.00 1.00 0.31 Small

Zone 4 3.08 - 4.18 m/s 26.21 ± 3.13 26.41 ± 0.82 0.00 1.00 -0.09 Trivial

Zone 5 4.19 -5.27 m/s 12.25 ± 2.29 11.97 ± 2.82 0.00 1.00 0.11 Trivial

Zone 6 > 5.27 m/s 3.74 ± 1.27 4.50 ± 1.91 0.00 1.00 -0.47 Small

m/min 119.53 ± 1.07 114.79 ± 7.75 -0.54 0.59 0.86 Moderate


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Table 4d. Defenders Pre 1st vs. Post 1st & 2 Pre 2nd vs. Post 2nd Wilcoxon Signed Rank Tests

Pre Post

Mean SD Mean SD p-

value Qualitative


Zone 2 .17 - 1.68 m/s 29.52 ± 4.47 28.52 ± 4.41 -1.83 0.07 0.23 Small

Zone 3 1.69 - 3.07 m/s 37.92 ± 1.38 38.90 ± 1.61 -0.73 0.47 -0.65 Moderate

Zone 4 3.08 - 4.18 m/s 21.58 ± 3.22 21.58 ± 2.34 0.00 1.00 0.00 Trivial

Zone 5 4.19 -5.27 m/s 7.57 ± 1.70 8.29 ± 2.18 -1.10 0.27 -0.37 Small

Zone 6 > 5.27 m/s 2.14 ± 0.62 2.40 ± 0.94 -0.37 0.72 -0.32 Small


Zone 2 .17 - 1.68 m/s 30.60 ± 5.23 30.44 ± 4.09 -0.37 0.72 0.03 Trivial

Zone 3 1.69 - 3.07 m/s 37.05 ± 1.79 38.47 ± 1.66 -0.37 0.72 -0.82 Moderate

Zone 4 3.08 - 4.18 m/s 20.95 ± 3.75 19.93 ± 1.53 -0.73 0.47 0.35 Small

Zone 5 4.19 -5.27 m/s 7.58 ± 2.07 8.08 ± 2.45 -0.73 0.47 -0.22 Small

Zone 6 > 5.27 m/s 2.29 ± 0.84 2.63 ± 1.11 -0.73 0.47 -0.34 Small

m/min 107.23 ± 7.63 105.70 ± 5.12 -1.10 0.27 0.24 Small

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Discussion

The official game time has changed from 70 minute to 60 minute. However, under the

newly implemented clock stoppage for corners, and goals, the actual game time will

depend on the number of corners, and goals awarded during the game.

Results indicate that this elite team did not show statistical changes in m/min and percent

distance covered in zones 2 – 6. The percent of distance covered in zone 1 (standing) had

a statistically significant increase. This is possibly due to the refined accuracy of the

Optimye S5 units used during post collection. The team experience decreases across the

1st and 2nd half of the game in zone 1.

During positional examination midfielders showed statically significance differences in

the 1st and 2nd half of the game pre to post. It is possible that the 2 minute rest provided

players with the ability to passively recover from the repeated sprints associated with

field hockey. This would be particularly important for midfielders, whose tactical

demands result in the playing both offense and defense. Their greater involvement gives

players less recovery time on pitch, compared to forwards and defender. The high

intensity intermittent activities of field hockey occur above most players’ critical speed

(Spencer, Bishop, Dawson, & Goodman, 2005; Spencer et al., 2004), resulting in the

need to return below critical speed to recover. The recovery time from the 2 minute rest

periods in between quarter might allow the athlete to subsequently work above critical

speed again.

122

Recovery is dictated by a multitude of factors. The more anaerobic mechanisms used

during an action the more likely it takes a longer time to recover below critical speed.

The farther below critical speed an athlete is allowed to rest, the more likely they will

recover faster. Passive recovery may facilitate quicker recovery than active recovery

(Buchheit et al., 2009). Upon numerical observation of the relative percent of total

distance covered by position, midfielders cover the least percent distance in zones 1, 2,

and 3 compared to screens and defenders. All three zones are indicative of active

recovery on pitch. Previous research has noted that forwards are substituted more often

than midfields (MacLeod, Bussell, & Sunderland, 2007; Polglaze et al., 2014) allowing

for passive recovery off the pitch.

Strategic techniques likely allow the US Women’s National Team to produce high m/min

compared other field hockey team, and other sports. The team produced 120.58 ± 10.25

m/min before the rule change and 118.16 ± 9.38 m/min after the rule change. Lythe and

Kilding (2011) reported an average of 116.6 m/min during men’s field hockey. This is

less than the average m/min produced by the Women’s National Team (Pre 1 - 124.88

±11.90 Post 1- 129.82 ±11.52 and Post 1 126.79 ± 9.89 to Post 2 119.46 ± 13.18).

However it is important to note that m/min are affect by the number of substitutions and

analysis methods used (Varley, Gabbett, & Aughey, 2014).

Conclusion

A major focus of the US Women’s National Team was to develop the athletes’ physical

capacity to maintain and repeat high intensity actions. The combination of physical

123

preparation and tactical strategies, such as player substitutions, allow the team to express

high m/min and high intensity actions throughout a match. Due to these previously used

substitutions techniques, and the high level of play examined in this study, the addition of

2 minute rest periods allowed the team to produce statistically significant high relative

action profiles during the entire game. However midfielders experienced changes during

the new rule system, possibly due to the tactical requirements of the position. They had a

decrease in zone 1, and an increase in zones 5 and 6 pre to post during the 1st half of the

game, and a decrease in zones 1 and 3 and m/min during the 2nd half of the game.

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10(4), 476-481.

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127

CHAPTER 6

SUMMARY AND FUTURE INVESTIGATIONS

The following conclusions are draw from the presented literature review, and the

findings from the studies in this dissertation. The relative action profiles before the rule

change revealed that defenders work at a lower meter per minute (m/min) when

compared with all other positions, and that forwards, midfielders, and screens perform

similar m/min during a game, and percent of total distance covered in zone 1 through 6.

This data supports the need for position specific conditioning with defenders.

Examination of pre rule change difference from the 1st to the 2nd half play showed that

elite level field hockey players are able maintain high-intensity actions in zone 6

throughout the game by increasing actions in zones 1 and 2, and decreasing actions in

zones 4 and 5.

The relative action profiles after the rule changed revealed the team was unable to

match the percent of distance covered in zones 4 and 5 during the 1st quarter all in

subsequent quarters. The low intensity actions in zone 1 and 2 gradually increased, while

m/min gradually declined. When positional differences were examined forwards covered

the greatest percent of distance in zones 5 and 6, followed by midfielders, screens, and

defenders. This pattern varies for zone 4, within which the midfielders possesses the

greatest percent distance covered.

The comparison of relative action profile comparisons for the team, pre to post

rule change did not indicate a significant change in zones 2, 3, 4, 5, and 6. However zone

1 experience a statistically significant decrease. Positional analysis of relative action

profiles pre to post rule change showed statistically significant changes for midfielders

128

only. The changes were a decrease in zone 1, and increase in zone 5 and 6 during the first

half of the game, and decrease in zone 1 and m/min during the second half of the game.

The combination of physical preparation, tactical strategies, and elite level of the athlete’s

profiles resulted in high intensity actions throughout a match. Further research, specific to

the US Women’s national field hockey team is needed in the utilization of recovery

strategies during 2 minute rest periods and 10 minute half time. This could allow the team

to reduce the increases in in zones 1 and 2, and dampen the reduction in m/min and

percent distance covered in zones 4 and 5.

Further research done on the new FIH game formatting should consider removing the 40

second running clock during corners, and goals to allow for a better understand of the

quarter system. Standardizing the time in each quarter would allow for the examination of

m/min in relation to fatigue. In addition to examining the current definition of velocity

bands used in the literature.

Understanding the requirements of field hockey is important for developing

effective training programs. The action profiles of elite level athlete’s provide coaches

with the ability to assess athlete’s current level of fitness in relationship to those produced

at the elite level. The more accurately the published action profiles represent the actual

action produced in a game the more useful the profiles are for coaches. Previous research

using 1 and 5 Hz GPS units may have underestimated the actions performed by athletes.

Field Hockey athlete profiling needs to be examined with GPS units sampling at 10 or 15

Hz.

Athlete game action profiling is also needed on a large-scale data collection

including multiple elite women’s field hockey teams should be conducted to allow for

129

sport rather than team descriptive analysis. This process could be assisted with the

creation of standard velocity zone definitions. A standard definition of velocity zones is

needed to allow for between study comparisons. This will allow for comparisons of

varying levels, and ages of athletes. The ability to compare velocity zones between

studies will allow for the development of long-term athlete development specific to field

hockey training.

130

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APPENDIX

ETSU Institutional Review Board Approval Form

Office for the Protection of Human Research Subjects x Box 70565 x Johnson City, Tennessee 37614-1707 Phone: (423) 439-6053 Fax: (423) 439-6060

Accredited Since December 2005

IRB APPROVAL – Initial Expedited Review

April 11, 2016 Heather Abbott Re: Analysis of the US Women’s National Field Hockey team in relationship to 2015 FIH rule change IRB#:c0316.9sw ORSPA #: The following items were reviewed and approved by an expedited process:

x New protocol submission xForm, references, PI CV, variables list

On March 31, 2016, a final approval was granted for a period not to exceed 12 months and will expire on March 30, 2017. The expedited approval of the study will be reported to the convened board on the next agenda. A waiver or alteration of informed consent has been granted under category 45 CFR 46.116(d). The research involves no more than minimal risk because it involves a retrospective analysis of data only. The waiver or alteration will not adversely affect the rights and welfare of the subjects as the athletes whose data is involved have already given consent to have their data added to the research repository whereby data may be drawn for research purposes. The research could not practicably be carried out without the waiver or alteration as many of the athletes in the study population are no longer available to consent. Providing participants additional pertinent information after participation is NOT appropriate. The following enclosed stamped, approved Informed Consent Documents have been stamped with the approval and expiration date and these documents must be copied and provided to each participant prior to participant enrollment:

x n/a retrospective

Federal regulations require that the original copy of the participant’s consent be maintained in the principal investigator’s files and that a copy is given to the subject at the time of consent.

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Projects involving Mountain States Health Alliance must also be approved by MSHA following IRB approval prior to initiating the study.

Unanticipated Problems Involving Risks to Subjects or Others must be reported to the IRB (and VA

R&D if applicable) within 10 working days.

Proposed changes in approved research cannot be initiated without IRB review and approval. The

only exception to this rule is that a change can be made prior to IRB approval when necessary to

eliminate apparent immediate hazards to the research subjects [21 CFR 56.108 (a)(4)]. In such a

case, the IRB must be promptly informed of the change following its implementation (within 10

working days) on Form 109 (www.etsu.edu/irb). The IRB will review the change to determine that it

is consistent with ensuring the subject’s continued welfare.

Sincerely,

Stacey Williams, Chair

ETSU Campus IRB

cc: Kimitake Sato

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VITA

HEATHER ABBOTT Education:

Doctor of Philosophy in Sports Physiology East Tennessee State University (ETSU), TN Graduation: August 2016

Master of Education Frostburg State University (FSU), MD

Graduation: May 2013 Bachelor of Health Science

Drexel University (DU), PA Graduation: June 2011 Experience:

Olympic Training Site at East Tennessee State University Head Sport Scientist Ph.D., Weightlifting 8/2013 to Current

USA Field Hockey

Assistant Sport Scientist Ph.D., 12/2013 to Current East Tennessee State University Teaching Associate (Teacher of Record), 8/2013 to Current

Head Coach and Strength & Conditioning Coach for the ETSU Men's Rugby Club, 4/2014 to Current

Women’s Golf Strength and Condition Coach, 8/2013 to 4/2014

Frostburg State University

Graduate Assistant Coach Field Hockey DIII, 8/2011 to 5/2013

Publications:

Refereed Publications Bazyler, C. D., Abbott, H. A,Bellon, C. R., Taber, C. B., & Stone, M. H. (2015). Strength Training for Endurance Athletes: Theory to Practice. Strength & Conditioning Journal, 37(2), 1-12.

Taber, C. B., Bellon, C. R., Abbott, H.A., Bingham, G. (2016). The Roles of Maximal Strength and Rate of Force Development in Maximizing Muscular Power. Strength &Conditioning Journal 38(1), 71-78.

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Refereed Proceedings Abbott, H.A., Taber, C.B., Griggs, C., Rhudy, J., Keck, S. Power Output at Varying Loads during Squat Jumps. East Tennessee State University 10th Annual Coaches and Sports Science College, December 2015.

Abbott, H.A., Taber, C.B., Ricker. D., Weiss M. Monitoring the Effects of Volume Load on Static Jump Height in Weightlifters. East Tennessee State University 10th Annual Coaches and Sports Science College, December 2015.

Swisher, A.M., Abbott, H.A. A Call to Reform Coach Education in the United States. East Tennessee State University 9th Annual Coaches and Sports Science College, December 2014.

Swisher, A.M., Dotterweich, A.R., Clendenin, S., Palmero, M., Greene, A.E, Walker, J.T., Abbott, H. Using Benefits-Based Models To Manage Sport Performance Enhancement Groups. East Tennessee State University’s 8th Annual Coaches and Sports Science College, December 2013.

Smith, S.A., Berry, H., Lenzi, R., Abbott, H. Effect of Altering Stride Frequency on Constant Speed Running Economy and Perceived Exertion. 34th Annual Meeting, Mid-Atlantic Regional Chapter of the American College of Sports Medicine, November 2011.