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The Relationship between Strength, Power and Speed Measures and Playing Ability
in Premier Level Competition Rugby Forwards.
by
Wesley J. Bramley
B App Sci (HMS)
Submitted in Fulfilment of the Requirements of the Degree of
Masters of Applied Science (Research)
School of Human Movement Studies
Faculty of Health
Queensland University of Technology
2006
ii
Principal Supervisor: Professor Tony Parker
Queensland University of Technology
Associate Supervisor: Dr. Peter LeRossignol
Queensland University of Technology
iii
TABLE OF CONTENTS
Abstract ix
Keywords xi
Statement of Original Authorship xii
Acknowledgements xiii
Definitions of Terms xv
CHAPTER 1 INTRODUCTION 1
Statement of the Problem .............................................................................. 6
Study Aims ...................................................................................................... 6
CHAPTER 2 REVIEW OF RELATED LITERATURE 8
Activity Profile of Forward Players in Competition Rugby Union 8
Introduction.......................................................................................... 8
Static High Intensity Activity .............................................................. 9
Competition Work Rates.................................................................... 10
Striding and Sprinting ........................................................................ 13
Low Intensity Activity ....................................................................... 15
Utility Movements ............................................................................. 16
Summary ............................................................................................ 16
Physiological Correlates of Success for Elite Rugby Union Forwards 18
Muscle Strength ................................................................................. 18
Summary ................................................................................... 25
Anaerobic Performance...................................................................... 27
Summary ................................................................................... 31
Assessment of Individual Performance in Team Sports 33
Introduction........................................................................................ 33
Notational Analysis of Field Games .................................................. 34
Time and Motion Analysis of Field Games ....................................... 37
Performance Evaluations Models ...................................................... 40
Subjective Evaluation of Player Performance.................................... 42
Summary ............................................................................................ 47
iv
Literature Review Summary and Conclusions 48
CHAPTER 3 METHODOLOGY 50
Research Design ........................................................................................... 50
Subjects ....................................................................................................... 51
General Procedures .................................................................................... 53
Testing Protocols ........................................................................................ 54
Dynamic Horizontal Force Test .................................................... 55
Procedures ............................................................................ 56
Equipment, Data Collection and Analysis ........................... 56
Static Horizontal Force Test ......................................................... 59
Procedure ............................................................................. 59
Data Collection and Analysis ............................................... 61
Rationale – Horizontal Force Tests .............................................. 61
Countermovement Jump Test Procedure ................................... 62
Data Collection and Analysis ............................................... 63
Rationale .............................................................................. 65
Acceleration and Sprint Running Test ......................................... 66
Procedure ............................................................................. 67
Rationale .............................................................................. 67
Coaches’ Evaluation of Football Playing Ability ........................ 68
Statistical Analysis ......................................................................... 72
CHAPTER 4 RESULTS 75
Anthropometric Characteristics of the Sample .............................................. 75
Force Ergometer Measures ............................................................................ 76
Sprint Running Times .................................................................................... 77
Countermovement Jump Measures ................................................................ 79
Coaches’ Weighted Physical Capacity and Performance Skill Scores .......... 80
Correlations Between Coaches’ Scores and Force, Sprint and
Countermovement Jump Variables ................................................................ 81
Relationship Between the Coaches’ Estimates of Physical Capacity
and Performance Skill .................................................................................... 83
v
Prediction of Coaches’ Physical Capacity and Performance Skill Scores From
Force, Sprint and Countermovement Jump Variables ................................... 83
CHAPTER 5 DISCUSSION 86
Physical Performance Characteristics and Forward Playing
Positions ........................................................................................................ 86
Sustained Horizontal Force ................................................................ 86
Horizontal Impact Force .................................................................... 89
Dynamic Horizontal Force................................................................. 92
Sprint Running ................................................................................... 94
Acceleration Phase ................................................................. 96
Maximum Running Velocity Phase ....................................... 97
Countermovement Jump Performance............................................... 99
The Relationship Between Physical Performance Characteristics
and Coaches’ Evaluations of Football Playing Ability .......................... 102
The Relationship between Physical Performance Characteristics
and Player Physical Capacity Scores ............................................. 103
The Relationship between Physical Performance Characteristics
and Player Performance Skill Scores .............................................. 106
CHAPTER 6 SUMMARY AND CONCLUSIONS 109
Summary of Findings................................................................................... 110
Conclusions and Implications for Training, Testing and Selection ............. 112
Recommendations for Further Research...................................................... 116
REFERENCE LIST 118
APPENDICES ........................................................................................................ 128
Appendix 1 – Subject Participation Forms .................................................. 128
Appendix 2 – Coaching Evaluation Information ......................................... 134
Appendix 3 – Subject Anthropometric, Physical Capacity & Score Data... 141
Appendix 4 – Study Part A Statistics........................................................... 144
Appendix 5 – Study Part B Statistics ........................................................... 154
vi
LIST OF TABLES
CHAPTER 3 METHODOLOGY
Table I: Anthropometric and physical performance test variables ................ 51
Table II: Nine-point performance skill rating scale ....................................... 69
Table III: Criteria for rating of football playing ability performance ............ 71
CHAPTER 4 RESULTS
Table 4: Anthropometric characteristics of premier rugby union
Forwards......................................................................................................... 75
Table 5: The relationship between anthropometric, performance
measures and coaches’ physical capacity and performance skill scores........ 82
Table 6: Multiple regression equations, adjusted R, variance, and
standard error of the estimate for the individual performance tests and
the outcome variables (WPCS and WPSS).................................................... 84
vii
LIST OF FIGURES
CHAPTER 1 INTRODUCTION
Figure 1: Model showing the interaction between physical capacities,
game skills, cognitive skills, environmental factors and playing
performance in rugby ....................................................................................... 5
CHAPTER 2 METHODOLOGY
Figure 2: Diagram showing the order of data collection for each player
during one testing session .............................................................................. 54
Figure 3: Grunt 3000 sports ergometer .......................................................... 55
Figure 4: Schematic showing the equipment set-up and data collection
process for the dynamic and static horizontal force test ............................... 58
Figure 5: Lab view program interface displaying force – time, and
velocity-time curves ....................................................................................... 58
Figure 6: A typical force – time curve from the dynamic horizontal
force test ......................................................................................................... 59
Figure 7: Diagram showing the standardised at engagement position
in which forces were measured during the static horizontal force test .......... 60
Figure 8: A typical force – time curve from the static horizontal force
test .................................................................................................................. 62
Figure 9: A representative vertical ground reaction force curve
(normalised for bodyweight) showing the different phases of the CMJ
and the peak concentric force......................................................................... 65
Figure 10: Model showing the stages involved in coaches’ evaluation of
football playing ability ................................................................................... 69
CHAPTER 4 RESULTS
Figure 11: Differences in sustained horizontal force (A), horizontal
impact force (B) and dynamic horizontal force (C) between forward
positional groups ............................................................................................ 78
viii
Figure 12: Differences in 0 – 10m (A), 0 – 20m (B), 20 – 40m (C),
0 – 40m (D) sprint performances between forward positional groups........... 79
Figure 13: Differences in countermovement jump vertical displacement
of COG (A), and relative power (B), between forward positional groups
........................................................................................................................ 80
Figure 14: Differences in weighted physical capacity scores (A) and
weighted performance skill scores (B) between forward positional
groups............................................................................................................. 81
Figure 15: The relationship between coaches' physical capacity and
performance skill scores in 22 premier rugby union forwards ...................... 83
ix
ABSTRACT
Physical tasks such as scrummaging, rucking and mauling are highly specific to rugby
and also place unique physiological demands on the different playing positions within
the forwards. Traditionally, the recruitment and development of talented rugby union
players has focused on the assessment of motor skills and game intelligence aspects of
performance, with less emphasis placed on the specific physiological requirements of
playing positions in rugby. The purpose of this investigation was to measure the
position-specific strength, speed and power characteristics of Premier rugby forwards in
order (1) to determine whether any differences existed in the physiological
characteristics of the different forward playing positions (prop, lock and loose forwards)
and (2) to investigate the relationship between these physiological characteristics and
coaches evaluations of football playing ability.
Twenty-two male Premier level competition rugby forwards, consisting of eight prop
forwards, five lock forwards and nine loose-forwards participated in the study. The
Grunt 3000, a rugby specific force testing device was utilised to measure the static and
dynamic horizontal strength during simulated scrummaging and rucking/mauling
movements. Sprint times relating to acceleration ability (0 –10m, 0-20m) and maximum
running speed (20 – 40m) were measured during a 40m sprint running test. In addition,
force, power and displacement characteristics of a countermovement vertical jump were
calculated from trials performed on a force plate. Also, player performance skill and
physical capacity scores were determined independently by experienced coaches who
assessed them based on their performances during the season. One-way analysis of
variance and effect size statistics evaluated differences in the measured variables
between forward playing positions and linear regression analysis evaluated the
relationship between the coaches’ scores of player performance skill and physical
capacity and game specific measures of strength speed and power.
Since there were no statistical significant differences between forward groups for
horizontal force and countermovement jump variables and these analyses lacked
statistical power, an effect size statistic was used to establish trends for differences in
force and CMJ variables between the groups. There were moderate effect size
x
differences between groups for horizontal impact force with prop and lock forwards
producing 17.7% and 12.8% more force than the loose forwards respectively. No clear
differences were apparent between forward positional groups for mean dynamic
horizontal force and countermovement jump displacement of the centre of gravity. A
significant difference (p =0.049) was shown between forward positional groups over the
0-40m sprint distance. Also, moderate effect size differences between pairs of groups
were evident in 0-10m, 0-20m, 20-40m sprint times with both loose forwards and lock
forwards on average, 6% faster than the prop forwards. A backward linear regression
analysis revealed that the single best predictor of coaches’ physical capacity and
performance skill scores was the 20 – 40m sprint performance, accounting for 28% of
the variance in player’s physical capacity scores and 29% of the variance in player’s
performance skill scores.
Whole-body horizontal static strength and impact strength in prop forwards and
dynamic horizontal strength (relative to body mass) and sprint acceleration ability in
loose forwards represent key factors for consideration when selecting forward
players to these positions in the Premier rugby competition. The vertical jumping
ability of all forward positional groups needs to be confirmed in a future study
utilising a line-out specific countermovement jump test (free use of arm swing and
line-out lifters in the jump) on a force plate. Monitoring of performance in rugby
forwards should include an acceleration sprint test (0-10m) as this is specific to the
sprinting patterns of forward players during a game, and maximum sprinting speed
test (20-40m) as this test has the ability to discriminate between skilled and less-
skilled rugby union forwards.
Key words: rugby, performance, forward players, playing position, horizontal
force, sprint times, power, countermovement jump, physical capacity, relationship,
coach, physical capacity score, performance skill score, dynamic, static, measures,
football playing ability, ruck, maul, scrum, line-out.
xi
STATEMENT OF ORIGINAL AUTHORSHIP
The work contained in this thesis has not been previously submitted for a degree or
diploma at any other higher education institution. To the best of my knowledge and
belief, the thesis contains no material previously published or written by other person
except where due reference is made.
Signed:_________________________
Date:___________________________
xii
ACKNOWLEDGEMENTS
The author would like to acknowledge and extend a sincere thanks to the following
people and businesses for their contributions towards the current project:
Professor Tony Parker for his valuable guidance and comments in the preparation of
the thesis. Professor Parker has continually monitored my progress on the research
study and I wish to express my sincere gratitude to him for his encouraging support
in motivating the author to reach his full potential.
Dr. Peter Lerossignol for his academic and personal support and guidance which
have been invaluable as well as his belief in the author. Dr. Lerossignol’s advice,
commitment and enthusiasm provided a positive environment for the development
and completion of this project and for this, I would like to express a heart-felt thanks
to him.
My parents for their continuous love, support, and encouragement, particularly
toward the end of the project.
Sports Tec International for the provision of the Grunt 3000 sports ergometer for the
strength testing.
Mr Peter Condie and Dr. Markus Deutsch for their technical assistance with
preparing the force ergometer for testing and in coaching the author on the use of the
force ergometer.
The rugby players and Coaches from the Reds Rugby College and Premier Club
rugby teams of University, Easts, Sunnybank, Wests, Souths, Norths, GPS who
without their participation, this thesis would not be possible.
xiii
DEFINITION OF TERMS
Super 12 Rugby Competition: An annual provincial 12 team competition between
three countries including South Africa, New Zealand, and Australia. Super 12 Rugby
consists of five teams from New Zealand, four from South Africa and three from
Australia (NSW, Queensland and the ACT), and each team plays all the other teams
once during the yearly tournament.
Brisbane Colts Rugby Competition: An annual under-19 competition played
between first division teams in the Brisbane metropolitan region.
Queensland Premier Rugby Competition: A state-wide club rugby competition
involving elite club rugby players from club rugby teams.
Scrum: A scrum is the method used to re-start the game after the play has been
stopped because a rule has been broken. The scrum is formed by at least five players
from each side (usually eight players) binding together with their arms, in rows, and
pushing against the other team with their shoulders. The ball is put into play by
rolling or tossing it into the tunnel between the two teams.
Ruck: A ruck is formed anywhere on the field when the ball is on the ground and
one or more players from each team, on their feet and in physical contact, close
around the ball between them.
Maul: A maul occurs when a player manages to stay on their feet when tackled, and
the ball is held away from the opposition and is transferred with a handling
movement to a support player.
Line-out: A line-out is the method to re-start play when the ball goes off the field
and into touch, by contacting or crossing over the Touch-Line. A line-out is usually
formed by seven players from each team, who line up in two parallel lines, and at
right angles to the Touch-Line. The ball is thrown in by the team which did not last
contact the ball.
Acceleration: Rate of change of velocity that allows the athlete to reach maximum
speed in a minimum amount of time. In this study, initial and continued acceleration
were assessed as the sprint times for the distance interval between 0-10m and 0-20m
of a 40m sprint run, respectively.
xiv
Maximum Running Velocity: The highest speed of which an athlete is capable. In
this study, the time for the distance interval between 20 – 40m of a 40m sprint run,
was considered to be a measure of the maximum running velocity phase in sprinting.
Physical Capacity Ability: The coach’s objective rating of a player’s level of
development in physical capacities required for all areas of forward match play
including speed, endurance, agility, mobility, static scrummaging strength, dynamic
upper body strength and strength in dynamic contact rucking and mauling.
Performance Skill Ability: The coach’s objective rating of a player’s level of
development in performance skills required for all areas of forward match play. The
performance criteria consist of a number of cognitive, tactical and motor skill criteria,
specific to principle areas of match play including attack, defence, continuity, scrum
restarts and one criteria on attitude toward physical training and penalties conceded.
Dynamic Horizontal Force: A measure of the maximal horizontal force applied by
a player against a single-person sports ergometer during an accelerated pushing task
that simulates rucking and mauling motion.
Sustained Horizontal Force: A measure of the maximal sustained horizontal force
applied by a player after impacting a rigid single-person sports ergometer during a
static pushing condition, as related to individual force production during
scrummaging.
Horizontal Impact Force: A measure of the maximal horizontal force applied by a
player on impacting a rigid single-person sports ergometer during an explosive
pushing task, as related to force production at scrum engagement.
Countermovement Jump: A jump technique which involves a quick flexion of the
knee joint during which the body’s centre of gravity drops somewhat before being
propelled upwards during extension of the hip, knee and ankle joints. The
countermovement uses the stretch-shortening cycle in which eccentric muscle
stretching stores elastic energy, which is in part released during immediate
subsequent concentric muscle contractions.
1
Chapter 1
INTRODUCTION
Rugby Union is a complex sport in which 2 teams of 15 players compete in a
physical contest for possession of the ball and try scoring opportunities. The sport
has evolved greatly since its inception over one hundred years ago. The ‘structure’ or
‘style of play’ has undergone rapid changes since the introduction of the game and
major influences such as the media and consequent injection of a professional ethic
into the game. The modern game of rugby union is played at a faster speed with
greater player involvement in physical contests during the different phases of the
game. The changing nature of the game both on and off the field and the advent of
professionalism have led to greater physiological and psychological demands on the
players.
Several studies have attempted to detail the optimal physiological requirements of
professional and amateur rugby players (Mayes & Nuttall, 1995; Quarrie & Wilson,
2000; Tong & Wood, 1995). The primary focus of these investigations has been to
develop effective training programs aimed at maximising the athletic ability of
talented players. The process of talent development is enhanced if players possess
the specific, physiological and biomechanical prerequisites that underlie successful
performance in the various playing positions. It follows then that emphasis must be
placed on a scientific approach to the recruitment and development of talented rugby
union players.
Rugby is an intermittent high-intensity sport, in which activities that require high
levels of strength and power, for example scrummaging and sprinting, are
interspersed with periods of lower-intensity aerobic activity and rest (Nicholas, 1997).
Research into the physical requirements of rugby players has indicated body size and
somatotype (Quarrie, Handcock, Toomey & Waller, 1996) are factors associated
with successful performance. These findings have important implications for team
selection in rugby because players are most often selected for positional roles based
on their anthropometric and physical characteristics (Quarrie et al., 1996; Rigg &
Reilly, 1988). Successful performance in rugby union is also related to the physical
2
capacities of players such as muscular strength (Mayes & Nuttall, 1995; Quarrie &
Wilson, 2000; Tong & Wood, 1995), speed (Duthie, 2003; Quarrie et al., 1995),
muscular power (Carlson et al., 1994; Deutsch, Kearney, & Rehrer, 2002) and
aerobic fitness (Deutsch, Maw, Jenkins, & Reaburn, 1998; McLean, 1992). However,
the importance of these physical capacities to rugby performance appears to be
position-related, with forwards and backs having unique physiological requirements
(Nicholas & Baker, 1995; Quarrie et al., 1995).
Rugby forward players must possess a combination of acceleration, explosive leg
strength, maximal upper body strength, and horizontal power to compete
successfully in specific phases of the game (Duthie, 2003). Match analysis of elite
rugby forwards shows that approximately 80-90% of high-intensity work performed
by the forwards comprises ruck, maul and scrum activity (Deutsch et al., 2002).
Players experience high inertial loads during such activities as they are required to
express maximal and explosive strength in a horizontal direction (Robinson & Mills,
2000). Development of absolute strength and power is to a large extent supplied by
the heavy body weights of the forwards (Cheetham, Hazeldine, Robinson, &
Williams, 1988). Therefore a large, lean body mass is beneficial to forwards with
respect to stability, inertia and momentum (Nicholas, 1997). In addition, acceleration
and high running speed are considered critical for performance in these positions.
Forwards are often required to reach breakdowns in open play as quickly as possible,
thus highlighting the need to develop good running speed over short distances
(Duthie, 2003). Also, a conditioned anaerobic glycolytic system is beneficial for
forwards to adapt to the fatigue incurred in periods of repeated high intensity effort
(Deutsch et al., 2002).
Given the diversity of playing skills required by rugby union forwards it appears that
there may be specific speed, strength and power requirements of positional roles in
rugby union forwards (Nicholas & Baker, 1995). This hypothesis is supported by
earlier research in rugby union which has reported positional differences in the broad
physical requirements and skills of forward players (Nicholas & Baker, 1995;
Quarrie et al., 1996). For example, the loose forwards require strength and power to
gain and retain possession of the ball at the breakdown. However, there is presently
limited information on the position specific strength, speed and power qualities of
3
elite rugby union forwards. This type of information is essential to make informed
decisions when selecting players for different positions, when developing specific
training programs and in monitoring the impact of strength-training interventions
(Duthie, Pyne, & Hooper, 2003).
The development of statistical models to predict performance in rugby from
laboratory or field tests may be of practical significance in the identification
potentially talented rugby union players. Performance prediction models have been
successfully developed in team sports such as basketball (Hoare, 2000) and soccer
(Franks, Williams, Reilly, & Nevill, 2002) using both anthropometric and physical
performance variables such as body mass, running speed and leg power measures.
In a study of American college football players, Sawyer et al., (2002) developed a
prediction model which included vertical jump explosive leg power as the prime
predictor of football playing ability, with upper body strength and body weight
contributing to a lesser extent. Tests of individual physical capacity accounted for a
significant proportion of the variance in playing ability for both offensive (55.1%)
and defensive (55.6%) playing positions. In the same football code, Barker et al.,
(1993) identified explosive movements such as the vertical jump and short sprint
runs as major predictors of athletic ability, accounting for more than 50% of the
variance in
the criterion measure. In rugby union, there is little scientific information to suggest
which specific components of strength, speed or power best predict football playing
ability. As such, the development of performance prediction models as a function of
specific tests of physical capacity may provide insight into those factors which relate
to football playing ability. This information may also assist in the development and
assessment of sport specific training programs.
Assessment of the neuromuscular functions of strength and power are of vital
importance in forward play in rugby (Duthie et al., 2003) particularly in the more
physical phases of play such as occurs in scrummaging, rucking and mauling. These
factors are highly specific to successful performance in rugby and as such require the
efficient utilisation of force and power, specific to the movement patterns of the task.
For instance, scrummaging necessitates the development of force and power in a
4
static horizontal direction with a relatively constant opposing load, while
rucking/mauling are dynamic muscular activities which require the development of
horizontal power under changing resistance and movement velocities.
As these specific sporting movements are difficult to simulate, the assessment of
strength and power in rugby has been conducted using non-rugby specific tasks
(Abernethy, Wilson, & Logan, 1995). For instance, vertical jump and cycle
ergometer protocols have been utilised to assess maximal anaerobic power (Quarrie
& Wilson, 2000; Rigg & Reilly, 1988; Ueno, Watai, & Ishii, 1988), while upper and
lower body strength have been assessed using bench press and lifting tasks as well as
isokinetic dynamometry (Mayes & Nuttall, 1995; Quarrie & Wilson, 2000; Tong &
Wood, 1995). These tests have been employed to assess the strength of specific
muscle groups during rotational or vertical orientated movements but are limited in
the rugby context as they do not provide a measure of whole body strength or
strength expressed in a horizontal direction. The horizontal force measured on scrum
machines is considered to provide a more valid means of measuring scrummaging
strength (Milburn, 1990; Milburn, 1993; Robinson & Mills, 2000). However, these
tests are only capable of measuring isometric strength performance and thus lack
specificity and application to the more dynamic activities such as rucking and
mauling. There is therefore a need to develop test protocols which assess the capacity
to develop force at appropriate velocities in simulated ruck/maul conditions. This
type of measurement would need to be sports specific which in turn may increase the
sensitivity and validity of these functional performance tests when used to predict
playing performance.
Evaluating individual playing ability or performance within a team sport
environment can present as a difficult task for team sport coaches. Such is the case in
rugby, in which player performance relies on the interplay of individuals in tactical
moves, the competence of players in the basic skills of catching, passing, kicking,
and tackling and in the more specific skills associated with particular playing
positions (Reilly, 1997). The complexity of the various interactions between playing
performance and both physical, skill and cognitive dimensions of the game are
identified in Figure 1. Rugby union is a sport in which successful performance
requires players to demonstrate a wide array of cognitive competencies to execute
5
skills under conditions of a temporarily or spatially changing environment and
changing physiological demands. When evaluating rugby performance and ability,
coaches need to measure quantitatively the key sets of sporting skills of an individual
in unstructured game situations (Bracewell, 2003). In this context measuring the skill
set of an individual, that is, the mental skills as well as the physical and physiological
factors that underlie skill execution, is recognised as an essential element in the
evaluation of individual game performance.
Performance analysis research in American football has utilised subjective evaluation
and ranking systems as tools for assessing player skill levels and individual football
playing ability. For example, ranking of playing ability by specialised coaches on the
basis of match performance was used to differentiate levels of ability of football
players (Barker et al., 1993; Sawyer et al., 2002). A major limitation of this ranking
system was the lack of objective evaluation of performance against a common set of
performance criteria, thereby reducing the playing ability scores to a simple
qualitative opinion.
Figure 1. Model showing the interaction between physical capacities, game skills,
cognitive skills, environmental factors and playing performance in rugby.
MUSCULAR POWER
GAME PERFORMANCE
Physical Capacities
RUNNING SPEED
Acceleration Max run speed
Speed Endurance
Energy Systems Alactic Lactic Aerobic
Muscular Endurance
Agility & Mobility
MUSCULAR STRENGTH
Dynamic Isometric Isokinetic
Environmental / playing conditions
Team interaction and tactics
Coach instruction
Player fatigue & Injury
PLAYING ABILITY
Anthropometry
Cognitive skills
Psychological factors
Game Skills
6
Traditionally, the recruitment and development of talented rugby union players has
focused on the assessment of motor skills and game intelligence aspects of
performance, with less emphasis placed on the physical performance characteristics.
Given the complexity of rugby union football, it is understandable that a degree of
scepticism exists as to the relevance of specific physiological measures and
performance evaluation systems to identify talent and stage of development in rugby.
However, the importance of specific physiological measures to game performance is
largely untested and the enhanced understanding of this relationship was the focus of
the present study. Specifically, the study sought to investigate the relationship
between measures of static strength, dynamic strength, acceleration, maximal
running velocity and vertical jump performance qualities with playing ability in
Premier rugby union forwards playing at the Premier level. For this purpose, playing
ability was defined as the coach’s objective rating of a player’s football skills and
their physical attributes. Football skills combined a range of cognitive, tactical and
motor skill abilities, specific to the principle areas of match play including attack,
continuity, defence, scrum, and restarts. Physical attributes combined a range of
individual capacities required for all areas of forward match play including speed,
endurance, agility, mobility, static scrummaging strength, dynamic upper body
strength and strength in dynamic contact rucking and mauling.
Statement of the Problem
Do coaches' evaluations of football playing ability relate to the physical performance
characteristics of rugby union forwards and do these qualities vary according to the
position they play?
The study aimed to:
1. Utilise a rugby specific testing device to measure the static and dynamic strength
qualities of players during simulated scrummaging, rucking and mauling movements
to determine the differences in sustained horizontal strength, horizontal impact
strength, and dynamic horizontal strength qualities between forward playing
positions;
7
2. Determine the differences in acceleration, maximum running velocity, body mass
and countermovement jump displacement and power between forward playing
positions;
3. Determine the relationship between sustained horizontal strength, horizontal
impact strength, and dynamic horizontal strength qualities of Premier rugby union
forwards and coaches' evaluation of their football skills and physical attributes; and
4. Relate acceleration, maximum running velocity, body mass and qualities of
countermovement jump performance of Premier rugby union forwards and coaches'
evaluations of their football skills and physical attributes.
8
Chapter 2
REVIEW OF RELATED LITERATURE
Activity Profile of Forward Players in Competition Rugby Union
Introduction
Rugby union is a physical contact sport which involves 2 teams of 15 players. Both
teams are made up of a group of ball winners (forwards) and ball carriers (backs).
Each of the players within the two groups has a specific role to play based on the
physical demands of their position and their physical and physiological
characteristics. The movement patterns and energy demands of players in their
positional groups has been investigated using time-motion analysis (Deutsch et al.,
2002; Deutsch et al., 1998; Docherty, Wenger, & Neary, 1988; McLean, 1992;
Rienzi, Reilly, & Malkin, 1999; Treadwell, 1988). Typically, time motion analysis
involves the calculation of the distances travelled, time spent in different activities
and the frequency of occurrence for each activity for players in a variety of positions.
This occurs after players' movement patterns are categorised according to intensity
and speed of locomotion.
Time-motion analysis in rugby union has typically examined the timing of various
movements and activities, to overall forward match play and to individual playing
positions in the forwards. During competition, positional groups within the forwards
complete different tasks during specific phases of the game. However, the
relationship between the particular skills and performance characteristics associated
with the different forward and positions and the physical demands is unclear.
Analysis of the timing of movements provides some insight into the physical
demands of positional groups during the different phases of play. However a more
detailed understanding of the positional demands, is necessary to inform the
development of more position specific screening protocols for the matching of
players to particular positions and the implementation of targeted training programs
specific to physiological and other positional demands.
9
Static High Intensity Activity
Static and potentially high intensity activities in rugby union forward play are
defined as those activities which involve static exertion including scrums, rucks and
mauls. Time motion analysis of rugby union reveals forward players regularly
compete in high-intensity activities such as rucking, mauling and scrummaging. A
recent time motion analysis involving New Zealand club and Super 12 rugby union
forwards (Deutsch et al., 2002) indicated that 12 % (10.2 minutes) of total match
time was spent in a state of high intensity work. Approximately 90% of this high
intensity work consisted of static and dynamic activities such as rucking/mauling,
scrummaging and tackling, with relatively small contributions from running and
sprinting. Rucking and mauling activities (>50%) accounted for more than 50% of
the high intensity work. Additionally, this study showed that rugby union forward
players at both club and Super 12 level had similar levels of involvement in high
intensity activity irrespective of their position.
In a movement analysis of Australian Super 12 players, similar results were obtained,
with forward players averaging 10.5 % (9 minutes) of total match time in static
exertion activities (Duthie, 2003). The total time spent in static exertion activities
was 7 times higher for forwards than backs who were involved for only 1 minute.
The high static involvement for forward players is the result of forward players (7
seconds) spending double the time in exertion efforts as compared to the back line
players (3.9 seconds). Players in the forward positions performed a total of 80 static
exertion efforts during a game in comparison to 20 movements recorded for the
backs. In addition, small differences in the total time of static activities were
observed between front row (8.41 minutes) and back row (9.33 minutes) forwards,
indicating a slightly higher level of exertion for back row forwards.
An earlier time motion study investigating the position-specific movement patterns
of Australian Colts players, indicated that at this level, front-row (13.7%) and back-
row forwards (14%) spend a similar percentage of total match time competing in
static high intensity activities including rucks, mauls and scrums (Deutsch et al.,
1998). Forward players averaged between 32 and 35 scrums throughout the duration
of a match, while front-row and back-row forwards averaged 72 and 78 instances of
10
rucking or mauling, respectively. On average, these instances of rucking, mauling
and scrummaging lasted 5.6s for front-row forwards and 5.3s for back row forwards
(Deutsch et al., 1998). This data and the earlier data from the more senior Super 12
players indicates differences in the frequency of static work performed at different
levels of performance, with the more junior colts players being involved in a higher
proportion of static work than their senior elite counterparts. This difference in
frequency however, does not necessarily imply a higher intensity of exertion when
playing at the Colts level.
Time- motion analysis of specific forward positions indicated that prop forwards at
the club and international level average 16% of total match time in intense activities
such as tackling, pushing in the scrum, ruck or maul, and actively competing for the
ball. Further breakdown of the static high intensity activities showed that these
players compete for an average of 6.1s in each rucks maul and scrummaging activity
(Docherty et al., 1988). Overall, no major differences in static activity involvement
were found between club and international level forward players.
Competition Work Rates
To gain and retain possession of the ball, forward players are required to complete
passages of play requiring multiple sprints and repeated efforts of static and dynamic
exertion. The pattern of work-to-rest ratios is an important indicator of how hard
forwards are working throughout a game. Work periods have been defined as those
when a player is involved in running, sprinting, rucking, mauling or scrummaging,
with other activities (inactive, walking, jogging, shuffling sideways or backwards)
classified as rest (McLean, 1992). Work-to-rest ratios in the forward players have
been previously reported in a number of rugby union studies and can be calculated
by comparing the mean duration of work periods against the mean duration of rest
periods (Deutsch et al., 2002; Deutsch et al., 1998; McLean, 1992).
Research investigating the work demands of Australian Super 12 rugby union players
during competition found that forward players spent twice as much time in work
activities than rest (11mins 6 seconds vs 4 mins 25 seconds) and obtained work
durations two-fold longer than the back-line players (5.35 seconds vs 2.65 seconds)
11
(Duthie, 2003). These results are supported by other rugby research (Deutsch et al.,
2002; Deutsch et al., 1998) which also demonstrated distinctly different patterns of
work between the forward and back-line players, highlighting the individual nature
of these positional roles. Overall, the greater amount of total work evidenced in
forward players relative to backs is a function of the high degree of physical contact
and static exertion undertaken by forwards during competition.
Analysis of work-to-rest ratios in Australian under-19 Colts players indicates that
rugby forwards achieve ratios in the range of 1: 1.4 during competition (Deutsch et
al., 1998). Periods of work in the forward players lasted, on average, 3-6 seconds,
with rest periods lasting, 8 –12 seconds. Similarly, McLean (1992) found that most
work-to-rest ratios for international forwards during match-play ranged from 1: 1 to
1: 1.9, despite the high occurrence of ratios in the 1: > 4 range. These values are
considerably higher than the estimated mean work-to-rest ratios of 1:7.3 and 1: 8.3
reported for New Zealand (Deutsch et al., 2002) and Australian (Duthie, 2003) Super
12 forward players respectively. The prolonged rest periods and resultant lower work
rates evidenced at the senior elite level of rugby union reflects the increased number
of stoppages in play for injury and goal kicking and more stringent refereeing at this
level. This is in contrast to the Colts level, in which rest periods are shorter and work
rates higher. This trend suggests a less structured, continuous style of rugby in under
-19 Australian Colts competition and a more structured, stop-start style of play at the
more senior level of rugby union. However, at the higher level, it is likely forward
players incur longer periods of work and shorter recovery times during continuous
passages of play compared to other phases of play which is not reflected in the work
rate data collected on New Zealand Super 12 forwards (Duthie, 2003).
Recent research has quantified the length and distribution of work and rest periods
during Australian Super 12 Rugby providing further insight into the work efforts and
demands of ruck and maul phases (Duthie, 2003). It was shown that approximately
70% of work efforts in forward match play were less than 10 seconds and 57 % of
rest periods in forward match play were less than 20 seconds. This represents a work-
to-rest ratio of approximately 1: 4 for forwards at the Super 12 level which is
approximately half of their overall work to rest ratio of 1: 8.3 (Duthie, 2003). These
results give a clearer indication of the work rates experienced by elite Super 12
12
forward players during continuous passages of play. The increased demands during
continuous play require players to compete in more frequent and longer static
exertion efforts with small rest intervals of 10 -20 seconds between activities. Further
analysis of work demands at the Australian Super 12 level indicated that forwards
average 155 work efforts of 5.4 seconds duration per game. The frequency, duration
and intensity of work efforts suggests a large demand on the anaerobic energy
systems for elite rugby forwards given the high number of short-to medium duration
and high intensity activities achieved during competition (Duthie et al., 2003).
Furthermore, the nature of static exertion appears to emphasise the development of
short duration glycolytic power during repeated high intensity work efforts.
When examining the pattern of work-to-rest ratios over the course of a game in colts
players, almost one-third (back row forwards 29.7%, front-row forwards 27.4%) of
the work periods completed by forward players were followed by rest periods of an
equal or shorter duration (Deutsch et al., 1998). In contrast, at the International level,
37% of the work periods completed by international forward players were greater
than the duration of rest periods (McLean, (1992). This result is expected considering
the higher intensity of competition associated with international match-play. In
addition, the presence of short, incomplete recovery periods during work phases (less
than 20 seconds) may limit the complete replenishment of creatine phosphate stores
following intense work bouts of 10 seconds and increase the reliance on anaerobic
glycolysis in subsequent work efforts (Balsom, Seger, Sjodin, & Ekblom, 1992).
Analysis of the work-to-rest ratios between positional groups indicates that back row
forwards incur increased work demands and higher work -rates than front row
forwards during rugby union competition. This is evident at the under-19 Colts level
with small differences being reported in work-to-rest ratios between back row (mean
1: 1.2) and front row forwards (mean 1: 1.8) (Deutsch et al., 1998). Similar positional
differences in overall match work rates have been reported at a Super 12 level
between back row (mean 1: 8.1) and front row forwards (mean 1: 9) (Duthie, 2003).
These results reflect the greater amount of time spent in work (11.52 min) by back
row forwards than front row forwards (10.19 min) and less time in recovery (76.21
min) than front row forwards (79.17 min) (Duthie, 2003). This work rate data
13
suggests a slightly higher degree of exertion for back row forwards and confirms the
notion of longer, more frequent high intensity movements for back row forwards.
Striding & Sprinting
Movements classified as striding are described as running with an elongated stride
but without full effort, while sprinting movements are classified as running at
maximal speed or full effort (Docherty et al., 1988).
Recent analysis indicates that forwards at the Super 12 level average 2.25% of total
match time in striding and sprinting activities (Duthie, 2003). These results are in
agreement with the estimated 2.2% of total match time spent striding and sprinting
for Colts players (Deutsch et al., 1998), however they are 3.5% lower than the values
obtained by Docherty (1988) for club (5.6%) and international players (5.7%) with
respect to relative time spent striding and sprinting. At all levels of rugby union,
striding efforts comprise the majority of running activities within the forwards and
back playing positions. Furthermore, forward players perform fewer sprints and
cover less distance at a sprinting speed compared to the back players (Deutsch et al.,
1998; Duthie, 2003). Within elite under-19 colts rugby, forward players complete
approximately ten less sprints (forwards 5 ± 1 : backs 14 ± 2) within a game and
cover 150 meters less distance at a sprinting speed (forwards 94 ± 27m : backs 253 ±
45m) when compared to the back players (Deutsch et al., 1998). The shorter sprint
distances covered by the forward players is consistent with their need for close
proximity (typically <5 m) to the opposition players and highlights the importance of
good running speed over short distances for forwards, particularly during offensive
match-play.
The ability to accelerate appears to be a major component of sprint performance in
rugby. This is particularly true for the forward players given that the mean duration
and maximum duration of sprints is less than 3 and 5 seconds respectively (Duthie,
2003). In this short time frame, forward players cover approximately 30 – 40m in
each sprint from a standing, stationary start (Delecluse, 1997). This is insufficient
time to achieve maximal running velocity considering that track sprinters typically
reach maximal velocity after 40m (Benton, 2001). However, high speeds may be
14
achieved when sprints of short duration are commenced from a striding rather than a
stationary start. However this advantage is not always applied, as it has been shown
that for Super 12 forward players less than 10% of the total sprints performed
involved players starting from a striding effort. The low sprint commencement speed
of forward players indicates limited opportunity for these players to reach maximal
running velocities during a game (Duthie, 2003).
The sprinting patterns of forward players indicate that acceleration ability is a
dominant factor in the sprint performance of forwards given their high involvement
in short sprints of 5-15m. However, in longer sprints of 40m, a slow increase in
running velocity is evident with players achieving fast lower limb movements
between approximately 15 and 40m (Delecluse, 1997).
Studies have shown that back row forwards undertake more striding and sprinting
efforts than front row forwards, reflecting their greater involvement in high intensity
activity during competition (Deutsch et al., 1998; Duthie, 2003). This is particularly
evident at a Super 12 level with marked differences being reported in striding efforts
between back row forwards (mean = 47 strides) and front-row forwards (mean = 31
strides). Such results reflect the specific running demands incurred by back row
forwards in sprinting to retain the ball in attack and in the attempt to regain
possession of the ball in defence (Duthie, 2003).
There is incomplete information on the influence of level of performance on the
sprinting patterns of rugby forwards. For example, in a study commissioned by the
Rugby Football Union (1978-79) props playing at club level covered significantly
less distance sprinting over a match (204m) than props at an international level
(1600m). Conversely, Docherty et al., (1988) found international prop forwards
(0.6% of total time) spent a similar percentage of time in sprint activity as compared
to club prop forwards (0.8% of total time). The limited comparative data on club and
international players precludes the confirmation of these differences at the different
levels of performance.
Comparisons between the sprinting demands of colts and senior rugby forwards are
possible, however, due to the existence of comprehensive time-motion data. Analysis
15
of under-19 Colts forward players shows an average of 5 instances of sprinting
during match-play covering a total sprint distance of 83 meters during a 70-minute
match (Deutsch et al., 1998). At a Super 12 rugby level, sprint activity data on 31
forward players revealed that they perform twice as many sprints ( ~ 11 sprints per
game ) and cover more than two fold the distance in sprinting mode ( ~ 230m per
game) as compared to colts forwards (Duthie, 2003). This suggests that senior
players are required to sprint considerably more than players in colts competitions to
keep pace with the high intensity phases of play. Moreover, the increased sprinting
demands placed on senior forward players confirms the essential requirement of
close to top sprinting speed and acceleration in game performance for senior forward
players, especially for forwards playing at the international level.
Low Intensity Activity
Movement analysis of the elite international and club rugby players indicates that
forwards spend 80 -85% of total playing time in low intensity activities (standing,
walking, jogging) (Deutsch et al., 1998; Docherty et al., 1988). Analysis by position
shows props and locks (47.1%) and back row forwards (44.7%) spend the majority of
this time standing still, passively recovering from intense activity. The percentage of
time spent walking reported by Deutsch and colleagues (1998) for Colts players
(front row forwards 15%; back row forwards 16%), were lower than the 22% of total
match time spent walking reported for club prop forwards (Docherty et al., 1988) and
considerably lower than those observed by (Duthie, 2003) at a Super 12 level
(forward players averaged 27% of total match time). Duthie (2003) suggests the
more stop-start, structured style of play of elite rugby forwards, with the longer
breaks in play, may be the result of more stringent refereeing at higher levels of
competition.
Furthermore, the total distances covered by front-row forwards (m = 3050m) and
back row forwards (m =2940m) at a jogging pace, indicate a large component of
match-play (for both groups) is of low exercise intensity (Deutsch et al., 1998).
Similarly, front-row and back row forwards spent a comparative amount of relative
playing time (20.8 ± 0.9% & 20.3 ± 0.9% of total playing time respectively) in a
jogging motion. These results are in agreement with the estimated 17% of total
16
playing time spent jogging for elite international and club prop forwards (Docherty et
al., 1988). The large distances covered by forwards at a low-intensity pace indicate
more continuous activity and generally greater involvement for these players given
the proximity to the contest.
Utility Movements
Forwards and back-line players require great mobility and agility as the passage of
play continually moves backwards and sideways during a match. Utility movements
are described as any lateral or backward movement performed by a player. Limited
studies in rugby have measured the utility movements of forwards during a match. A
study of Australian Colts players indicated that back row forwards (154m) cover
greater distances in backwards and sideways movements in comparison to front row
forwards (106m). Similarly, back row forwards achieved 25 instances of utility
movements during a game, while front row forwards acquired 19 instances of utility
movements during a game (Deutsch et al., 1998). The positional differences in utility
movements reflect different positional roles of front row and back row forwards, the
latter requiring a greater capacity for mobility and agility around the rucks and mauls.
Summary
Time-motion analysis of rugby union reveals that the game is indeed a multi-sprint,
multi-activity sport for forwards. The vast array of movements and activity changes
during a game reflects the highly intermittent nature of forward match-play.
Generally, forwards are required to repeatedly compete in high intensity activities
(rucking, mauling, sprinting) of short duration (3-6s) interspersed with longer periods
of low to moderate intensity activity including walking and jogging (Deutsch et al.,
2002).
There is high demand for muscular strength and power in forward players engaged in
intense physical work as players push and compete for the ball with the opposition
(Duthie, 2003). Forward players utilise energy supplied from the anaerobic energy
system for completion of static exertion and running efforts. In particular, there is
substantial demand on the anaerobic glycolysis pathway for energy supply during
17
periods of repeated effort with short incomplete recovery (Deutsch et al., 2002). A
large component of forward match-play involves walking and jogging and sound
aerobic fitness is necessary in forward players so that this may meet the energy
demands of these lower intensity elements of the game and speed up recovery of
alactic and lactic acid energy systems. Sound aerobic fitness will also aid the
replenishment of energy following high intensity effort. In addition, the short
duration of sprints for forward players during competition highlights the importance
of acceleration to reach the breakdown as quickly as possible.
In terms of position specificity within the forwards, back row forwards exhibit higher
work durations, shorter recovery times, increased frequency of sprinting and
increased utility movements. The resultant demand of the back row forwards
encompasses a greater need for dynamic horizontal strength, acceleration, anaerobic
conditioning and agility as compared with front row forwards (Deutsch et al., 2002).
Time-motion analysis reveals the demands of the game vary depending on the level
of competition. Minor differences in movement patterns have been found between
club and Super 12 level competitions (Deutsch et al., 2002), and club and
international levels (Docherty et al., 1988). Time-motion data indicate higher work
rates during intense phases of play for Super 12 forward players as compared to club
forward players. This reflects the high intensity nature of elite match play and the
increased requirement for anaerobic conditioning for forward players at an elite level.
The lack of comparative data on current elite club and Super 12 rugby players limits
our current understanding of the specific requirements of competition at various
levels of rugby.
There are clear differences in the patterns of play between under-19 and senior
competition. The time-motion data reflect a more continuous, moderate intensity
style of play for Colts forward players and a more structured, intermittent nature of
play for senior forward players. These structured phases of the game, at a senior level,
exceed any passage of play in Colts rugby with respect to speed of play and player
competitiveness. This intensity requires senior forwards to display higher levels of
physical exertion during contact with the opposition and also the necessity for
18
players to acquire superior levels of acceleration and close to top sprinting speed to
keep up with the pace of play in attack and defence (Duthie et al., 2003).
Physiological Correlates of Success for Elite Rugby Union Forwards
Muscle Strength
Abernethy et al., (1995) defines strength as the peak force (in newtons, N) or torque
(in newtons-metres, Nm) developed during a maximal voluntary muscle
contraction(s) under a given set of conditions (with conditions influenced by posture,
pattern and velocity of movement). In rugby union, the varied nature of tasks
performed by forwards, means that the velocity of movements and loads imposed on
players vary within and between tasks. For instance, forwards can experience static
muscle loads and slower movement velocities in a scrum situation and then in the
next phase of play encounter dynamic muscle loading patterns and rapid movement
velocities during a sprint or while making and breaking tackles (Reilly, 1997).
Because of the varied nature of force application and strength demands imposed on
rugby forwards, it is imperative that forwards possess high levels of both static and
dynamic strength. Importantly, the degree of strength development in rugby forwards
will influence their ability to generate power in skilled tasks, as well as influence
their level of injury risk during a game situation.
In view of the many ways forces are exerted in a game it is not surprising that muscle
strength of rugby union forwards has been measured using a variety of testing
protocols. These have included various forms of isotonic, isometric and isokinetic
dynamometry, as well as sports-specific instruments to measure force application
during simulated tasks such as scrummaging.
Static and dynamic strength are essential physical capacities for performance and
injury protection in the rugby scrum. Substantial stresses are experienced by both
forward packs, particularly in the front row, during engagement of the scrum and
during the second push to retain or win possession of the ball. Forward – directed
forces at engagement ranging from 6540N for a university front-row to 7982N for an
international front-row (representing forces of 650 ~ 800kg), were recorded from
19
teams packing down against an instrumented scrum machine. Up to 3778N were
carried by the hooker alone. In this case, the prop forwards transmitted lower forces
than the hooker in the simulated scrum engagement, ranging from 1580N for
University level players to 2097N for International level players (Milburn, 1990a;
Milburn, 1990b). In these studies, the measured force was an impact force of very
short duration necessary to stop the motion of the scrum. These forces could be
larger in an actual scrum as they represent speed of engagement as much as the
capacity of the scrum to exert force.
Rodano & Pedotti (1988) used 2 floor-mounted force plates to examine the ground
reaction forces produced by each of the 8 junior forwards during continuous and
impulsive thrusts against a scrum machine. In this study, it was assumed the total
forward thrust recorded (left leg plus right leg) was equal to the forward-directed
force at the shoulder. It was found that each prop was capable of producing a forward
impulsive (“impact”) force (defined as the peak force on engagement of the scrum
machine) of between 1569 and 1942N, locks of between 1599 and 1844N, and the
loose forwards in the range of 1873 and 1981N (Rodano & Pedotti, 1988). No
attempt was made to determine statistically significant differences in impulsive force
between forward positional groups given the small sample size of the study.
Isometric strength or sustained force is essential for rugby forwards to withstand the
efforts of their opponents in ‘holding’ their position against all efforts. Compared to
the previously reported 800 kg of force at the scrum engagement, the static nature of
the sustained second push produces considerably less forward force than the impact
force. Milburn, (1990a; 1990b) recorded forces in the range of 4610N - 5761N for
University and International front-row forwards who sustained exertion against an
instrumented scrum machine for 2 seconds after engagement. The mean individual
sustained force generated by University level forwards ranged from 1270N for the
prop forwards to 2070N for the hooker while forces ranged from 1505N to 2751N
for elite level hookers and props, respectively. More recently, Parker & Milburn
(1995) reported the individual sustained forces produced by 19 year-old forwards
pushing against an instrumented single-person scrum machine. Mean forward forces
exerted by the front-row forwards were 1401N.
20
Previously reported scrummaging forces in rugby forwards (at various levels of
competition) indicate a trend for higher sustained and impact forces in elite level
forwards relative to the more novice performers. This may be in part due to the
higher body mass of elite players but is also likely to reflect a muscle adaptation
which occurs as a function of the increased strength requirements during
scrummaging for elite level prop and hooker playing positions.
Quarrie & Wilson, (2000) utilised an instrumented scrum machine to assess the
scrummaging strength of 56 New Zealand, rugby forwards. Scrum force data was
obtained by measuring the mean forward force applied by individual players to an
instrumented scrum machine during the active phase of scrummaging. To determine
player contributions to scrum performance, comparisons of individual scrummaging
force were made between four positional groups: hookers, props, locks and loose-
forwards. The results indicate more force was produced by the props (1420 N; effect
size =0.53) and the locks (1450N; effect size =0.63) than the loose-forwards (1270N),
although these differences in mean sustained force were not statistically significant.
This pattern of results were similar to those reported in a study by Milburn, (1990a)
who examined the scrummaging contributions of the various positional groups
within a group of international rugby union forwards. In this study, estimates of sub-
unit contributions were made by subtracting the total forward force exerted by 3 front
row players from the total force produced by the scrum formations of props and lock
forwards and prop, lock and loose-forwards. The mean sustained force values
indicated that the 3 members of the front-row produced 38% of the total 5761 N
generated by the entire pack, while the locks produced 42% and the loose-forwards
20% (Milburn, 1990a). Similar force contributions for positional groups were
observed in a group of University level forward players involved in a study on the
kinetics of rugby scrummaging . From the measurement of the sustained force
applied by different scrum formations to a stationary scrum machine, Milburn
(1990b) estimated the contribution of the complete front-row of a scrum to be 34%
of the total 4610N produced while the locks produced 46% and the loose-forwards
produced 20%. Milburn, (1990b) suggested that the relatively low force contribution
of the loose-forwards was related to the body alignment of the players when
scrummaging, with the props and locks transmitting force directly forward, in
21
contrast to the flankers who pushed into the scrum at an angle. However, the greater
force contributions of the prop and lock forward playing positions relative to the
loose forwards may also reflect a muscle adaptation which occurs as a function of the
increased strength requirements during scrummaging for these positional groups. The
trend toward differences in sustained scrummaging force between positional groups
along with the contributing mechanisms require further investigation in a study
which utilises statistical tests of significance in the analysis of the force data.
Quarrie & Wilson, (2000) examined the relationship between various strength
measures and the players’ ability to apply force when scrummaging. The strength of
various body segments (under isometric and isokinetic loading patterns) where tested
and related to scrum force data in 40 club rugby forwards. Measures included knee
extension strength, grip strength and isometric 'leg and back' strength. Maximal
isokinetic knee extension torque was assessed using a isokinetic dynamometer device
with torque measurements recorded at angular velocities of 1.05 and 3.14 rad . s-1.
Analysis by positional groups showed a similar pattern of results for knee extension
at both angular velocities with the locks producing significantly more torque than the
props and hookers and moderately more torque than the loose-forwards. Pearson
correlation analysis indicated a moderate correlation between isokinetic knee
extension and individual scrummaging force at both 1.05 (r =0.39; p=<0.05) and 3.14
rad . s-1 (r=0.41; p=<0.01). Neither maximal force data from isometric grip strength
(r=0.28) or isometric strength during a leg and back lift (r=0.25) correlated
significantly with individual scrummaging force. The positive correlation between
individual scrum force and isokinetic knee strength (at the level of the knee),
indicates that the level of strength in the knee extensor muscles may be an important
indicator of scrummaging strength for rugby forwards.
The regression model used to predict individual scrum force was not associated with
knee extension strength. However, the large variance in force accounted for by body
mass in conjunction with the significant correlation between body mass and knee
extension at both 1.05 (r =0.40) and 3.14 rad . s-1 (r=0.49) indicates heavier players
are more likely to have greater knee extension strength capacities which may
increase the force-producing capabilities of forwards during a scrum.
22
Isokinetic dynamometry has been used to determine upper body strength for several
sports (Alderink & Kuck, 1986). However, few studies have employed isokinetic
dynamometry to assess upper body strength in rugby players. This data is surprising
considering the importance of upper body strength to performance at higher levels of
competition.
Kearney & Colleagues (1998) developed an isokinetic testing protocol to assess the
shoulder strength of 19 senior, 2nd division rugby players. Isokinetic Dynamometry
was used to measure the mean peak torque during shoulder adduction and abduction
at two angular velocities of 1.05 and 2.09 rad . s-1. The results indicated that the
forwards were significantly stronger than the backs in concentric abduction at
angular velocities of 1.05 rad . s-1 (F = 97 ± 17.6 N m and B =73 ± 10.3 N m) and, in
eccentric adduction at velocities of 2.09 rad . s-1 (F = 127 ± 20.1 N m and B = 101
±23.4 N m) (Kearney et al., 1998).
This difference in muscular strength can be partially explained by the larger body
mass of the forwards, but is also may reflect the muscular adaptation which occurs as
a function of the strength requirements of forward playing positions. While this study
provided normative strength data on groups of rugby players it has been suggested
(Kearney et al., 1998) that future research should concentrate on using similar
isokinetic assessment protocols to identify the specific strength requirements of each
playing position.
The upper body strength of rugby union players has also been assessed using
isoinertial dynamometry, a procedure which commonly includes weight lifting tasks
that are performed with a constant gravitational load (Abernethy et al., 1995). For
example, maximal isoinertial strength tests such as the one repetition or three-
repetition maximum bench press and bench pull tests have been used to profile the
strength characteristics in various rugby populations. Descriptive studies in rugby
union have employed these measures based on the premise that when used together
they provide a reasonable index of upper body strength (Jenkins & Reaburn, 2000).
To provide normative data on elite rugby union forwards, Jenkins & Reaburn (2000)
used the three-repetition maximum bench press and chin-ups tests to evaluate the
upper-body strength of a group of elite senior, Australian rugby union players. The
23
results showed that the props and second row players combined had higher bench
press scores (118.9 ± 16.8kg) than the back row and hookers combined (112.7 ±
11.1kg). However, this difference was reversed when the players performed the 3RM
chin-up test in which the back row and hookers lifted their individual body weight
plus 10.0 ± 10.2kgs, while the props and second row forwards lifted their individual
body weight plus 3.5 ±3.7kgs (Jenkins & Reaburn, 2000). These findings indicate
that at this level the props and second row forwards have higher absolute dynamic
strength capacities while the back row and hookers have greater strength relative to
individual body weight.
Tong and Wood (1995) measured the upper body strength of 30 collegiate rugby
forwards using 11 strength-related tests including the three-repetition maximum
bench press and bench pull tests. The results showed differences in the level of upper
body strength between forward playing positions with the front-row players
outperforming the second-row and back-row forwards in 9 of the 11 tests, although
these differences were not significant. For example, the front-row forwards (101.5 ±
1.2kg) outperformed the back row (93.5 ± 9.7kg) and second row players (89.5 ±
11.7kg) in the three repetition maximum bench press. A similar pattern of results
were evident in the three repetition bench pull test with front row forwards (82 ±
8.6kg) exhibiting higher levels of dynamic upper body strength than the second row
(78 ± 5.4kg) and the back row forward players (74.5 ± 9kg).
It would appear, from the observation that second row forwards with a greater body
mass were outperformed in strength measures by the smaller, front row forwards,
that strength values share little relationship with body mass. However, it is unclear
whether the differences in strength were related to the fat-free mass of the players in
this study, as no descriptive information on these characteristics were presented.
Mayes and Nuttall (1995) utilised a battery of physiological tests including the three-
repetition bench press to compare the strength characteristics of elite senior and
under 21 Welsh rugby union players. Significant differences between seniors and
under 21 players were found in body mass (95.4 ± 12.8kg vs 88.7 ±12.0kg), fat-free
mass (79.7 ±14.5kg vs 74.5 ±7.9kg) and the three-repetition maximum bench press
(98.7kg ±13.7kg vs 83.1 ±14.4kg). The bench press values recorded for these elite
24
senior Welsh players were higher than those recorded in a group of Welsh collegiate
rugby forwards who achieved a mean bench press of 94.8kgs (Tong & Wood, 1995).
This strength differential suggests a marked increase in upper body strength
requirements at an elite senior level of football. It is postulated that the superior
upper body strength of senior players may be a function of their greater fat-free mass
and the muscular adaptation which occurs as functions of the increased strength
requirements associated with elite forward match play.
Maud (1983) used one repetition maximum bench press and leg press tests to assess
the dynamic muscular strength of fifteen USA amateur rugby union players.
Although not statistically significant, the mean data indicated that on the bench press
test the forwards recorded higher values (mean bench press = 90.4 ± 9.8kg)
compared to the backs (79.9 ± 8.6kg). This differential was reversed in the one-
repetition maximum leg press, with the backs (mean leg press = 288.1 ± 38.1kg)
outperforming the group of forwards (mean leg press = 269.3 ± 25.2kg). The study
was limited by the small sample size, which prevented greater discrimination
between players in different positions.
A similar pattern of results in strength measures was found between positions among
11 Welsh international rugby union players (Bell, Cobner, Phillips, & Cooper, 1990).
On average, forwards had superior upper body strength while the backs showed
greater lower body strength than the forwards, although these differences between
sub-groups were not significant. In this study, upper and lower body strength was
assessed using the one-repetition maximum bench press and half-squat. In addition,
morphological and compositional assessments such as body mass and fat-free mass
were gathered to enable investigation of the relationship between size, fat-free mass
and strength in rugby union players. Strength results in the half squat reveal the
rugby union backs (211 ± 27kg) outperformed the forwards (205 ± 28kg) by a small
margin while results in the maximal bench press show the forwards (111 ±9kg)
produced greater scores than the rugby union backs (100 ± 11kg). Correlation
analysis revealed upper body strength, as measured by the one-repetition bench press,
showed a significant high correlation with body mass (r= +0.82) and fat-free mass of
the players (r= +0.81). Based on the results from this study it seems that upper body
strength are required particularly in the forward positions as evidenced by the
25
superior bench press results. Furthermore, the high correlation between upper body
strength, fat-free mass and body mass indicates that is possible to use these
dimensions to predict the upper body strength (dynamic) of rugby union players.
Summary
Many activities in rugby are forceful and explosive such as tackling, jumping, and
competing for the ball in rucks and mauls. As the forwards spend a greater amount of
time in contact situations in rugby, it is not surprising these players develop greater
absolute strength levels than the back-line players. For forwards, dynamic muscular
strength (leg, shoulder and arm regions) is essential for the production of power in
activities that involve larger external resistances such as in rucking or during ‘snap
shoves’ in the scrum. Earlier research indicates a trend for differences in strength
levels across certain positional groups in rugby union forwards. For instance, the
prop forwards have been shown to have the greatest strength capacity among forward
players during simulated scrummaging testing which suggests static strength plays a
role in superior scrummaging performance and in providing appropriate injury
protection safety margins for this playing position. However, currently there is
limited research that details and relates the strength capacities of different positional
groups within the forward players, and as a consequence relatively little is known
about the specific strength requirements of these positions and the importance of
these to skill performance. Furthermore, the majority of profile studies have utilised
measurement protocols that have prevented discrimination between playing positions
because of the inclusion of small sample sizes and non-specific measures of strength
performance.
Measurements of lower and upper body strength have involved free weight
protocols, sports-specific tests such as instrumented scrum machines, as well as
laboratory-based measures such as isokinetic dynamometry. Isokinetic dynamometry
used in rugby studies show a moderate correlation with scrummaging performance
and an ability to discriminate between sub-groups of players such as forwards and
backs. Isokinetic protocols resemble some tasks performed by forwards in terms of
the muscle actions and movement speeds however, because they lack the ability to
assess whole body strength or strength expressed in a horizontal direction they do not
26
simulate most conditions of forward play. Future research should utilise isokinetic
assessment protocols only for identifying and relating the strength of specific
muscles groups to specific skills such as running and jumping.
Maximal isoinertial strength protocols such as 1RM and 3RM bench press tests are
dynamic in nature and thus have a greater external validity than isometric
assessments. While the use of this type of testing has identified differences between
positions with respect to strength characteristics it may not be predictive of
performance in match situations as the protocols lack measurement of whole-body
strength and are unlikely to mimic the posture, pattern or speed of movements
experienced in forward match-play. It appears, dynamic muscular strength plays a
vital role in football performance, and as such future research should concentrate on
developing isoinertial test protocols which assess dynamic strength specific to the
mechanics of functional movements. Testing of isometric strength in rugby has
involved individual scrummaging machines which increase the validity of strength
assessment with respect to the player’s scrum performance. In addition, these sport
specific tests have proven ability to distinguish between players that perform well
during scrummaging (using all performance factors), from those that have a good
level of leg strength but limited scrummaging technical skills (Robinson & Mills,
2000).
The review of the studies in this section show that the levels of strength required for
superior performance (relative to each playing position) are difficult to determine due
to the different design of rugby studies and the varied nature of the testing protocols.
Furthermore, a limited number of studies have utilised tests of maximum strength to
discriminate between athletes of different performance levels, and playing positions
within rugby union. Future research should therefore focus on individual playing
positions at the elite level. A greater number of individuals within each positional
category and the application of more discrete analyses of strength capabilities would
also enable differences between positional roles to be more clearly identified.
Anaerobic Performance
27
When considering anaerobic performance, a distinction has to be made between
anaerobic power and anaerobic capacity. Anaerobic power represents the highest rate
of anaerobic energy release, whereas anaerobic capacity reflects the maximal
anaerobic energy production an individual can obtain in any exercise bout performed
to exhaustion (Reilly, Bangsbo, & Franks, 2000). Of particular importance to
forward match-play, is the players' ability to produce great amounts of power when
accelerating off the mark, making and breaking tackles and competing with
opposition players in rucks, mauls and scrums (Nicholas, 1997). The degree of power
produced by the player is dependant on the player’s ability to exert their dynamic
strength at great movement speeds (Schmidtbleicher, 1992).
The importance of anaerobic power to game activities of scrummaging and mauling
is highlighted in a recent study by (Quarrie & Wilson, 2000) who investigated the
relationship between maximal anaerobic power of forwards and ability to apply force
when scrummaging. Scrum force data was obtained by measuring the mean forward
force applied by individual players to an instrumented scrum machine during the
active phase of scrummaging. Maximal anaerobic power, in fifty-six forwards, was
assessed using a cycle ergometer with power outputs recorded over ten second bouts
of maximal effort pedalling. Results showed that maximal anaerobic power attained
on the cycle ergometer correlated most with individual scrummaging force, with
26% of the variance in force explained in the prediction model. Analysis by position
revealed lock forwards produced the greatest mean power in the cycle test (1360 ±
220 W), applied the greatest forces to a scrum machine (1450 ± 270 N) and recorded
the highest body mass (102.4 ± 5.6kg) in relation to other positional categories of
the forwards. These findings indicate that heavier, more powerful players, who are
highly mesomorphic - are capable of producing greater individual scrum forces
(Quarrie & Wilson, 2000).
Several methods have been employed to measure the power characteristics of rugby
players at various performance levels. Typically, measurements of power in rugby
union players have been obtained using maximal cycle ergometer, treadmill efforts
(using a variety of testing protocols), vertical jump tests, sprint and repeated high
intensity effort protocols. A number of these studies have been concerned with
28
identifying differences in the anaerobic performance of players between positional
categories (forwards and backs) and between various performance levels (first and
second class players).
Rigg & Reilly (1988), used the Wingate Anaerobic Test for measurement of peak
power and mean power over 30 seconds in forty-eight first class rugby union players.
Results showed that absolute power output was higher in the forwards than in the
backs but this superiority was reversed when the data were expressed relative to body
mass. Significant differences were noted between first and second class back row
forwards in absolute power outputs (peak 1071 ± 108 W v 878 ± 121W), mean
power outputs (903 ± 39W v 735 ± 118W) and peak power relative to body weight
(10.6 ± 1.2 W kg -1 v 8.6 ± 0.9 W kg -1 ). This data suggests that a high standard of
anaerobic fitness is required for back row forwards at the elite performance level.
Ueno et al., (1988) used a cycle ergometer test to measure the mechanical power
outputs of Japanese university rugby union players. The measurement protocol
involved imposing a load relative to the body weight (0.1 kp kg –1) of each subject
for seven seconds. Significant differences in peak power were discovered between
the forwards (1047.4 ± 119.2W) and half-back players (907.7 ± 99.7W) and three-
quarter players (948.3 ± 79.7W). As for these values relative to body weight, Ueno
(1988) reported the peak power of forwards (13.02 W kg -1) were greater than that of
age matched middle and long-distance runners (10.63 W kg –1 and 10.51 W kg -1) but
less than that of sprinters (14.16 W kg -1). These results are expected, as the power
required to win the ball is, to large extent, supplied by the heavy bodyweights and
large muscle mass of the forwards.
Bell et al. (1993) used a cycle ergometer test for measurement of peak power and
mean power over 30 seconds, in international rugby union players. Absolute power
outputs were higher in hookers and back row players (peak = 1388 ± 315 W, mean
over thirty secs = 1144 ± 279 W) and lower in props and second row (peak = 1342 ±
261W, mean = 992 ± 179 W). When standardised in relation to body weight, the
backs obtained the highest performance (12.1 W kg -1) followed by the hookers and
back row players (11.3 W kg -1), and the props and locks (9.7 W kg -1).
29
The power outputs of rugby union players have also been studied using treadmill-
running protocols. Cheetham (1988) and colleagues utilised a 30 second test on a
non-motorised treadmill to measure the peak power outputs of English student rugby
union forwards. Results show mean peak power outputs (830 ± 149W) were reached
between 2 and 8 seconds into the test for the 10 individuals. When power outputs
were expressed in relation to body weight the peak power outputs were 8.96 W kg -1.
Comparing these results to those previously observed for a group of student rugby
backs (10.12 W kg -1 ) these forwards achieved a markedly lower peak power output
(Cheetham et al., 1988).
Anaerobic performance in forward match -play also includes the assessment of leg
power. The vertical jump test has frequently been used to assess explosive leg power
in various rugby populations. Descriptive studies reporting data on jumping ability of
rugby players show that the backs generally score higher than the forwards (Carlson
et al., 1994; Maud, 1983; Quarrie et al., 1996). The data collected for the USA
national rugby team players shows the vertical jump test allowed discrimination
between backs and forwards (Carlson et al., 1994). Jumping height data of 65 elite
players shows the backs (mean = 62cm) achieved higher jump displacements than
the forwards (mean = 58.8cm). The results of the discriminant function analysis
indicate that vertical jump height along with the repeated jump in place, and push up
test best discriminated between backs and forwards, with 76% correct classification
using these variables.
These findings are consistent with those of Quarrie et al., (1996) who reported first
grade rugby forwards obtained lower vertical jump heights (mean = 59.35cm)
compared to the backs (mean = 63cm) in the Sargent vertical jump test. The highest
jump scores values were obtained in the outside backs (mean = 65.3cm), while in the
forwards the back row obtained the highest vertical jump scores (mean = 62.3cm).
When the results were analysed between positional groups in New Zealand forwards,
the hookers (mean = 55.9cm) and props (mean = 58.1cm) had the lowest vertical
jump heights, while the locks (mean = 61.1cm) and back row forwards (mean =
62.3cm) obtained the greatest vertical jump heights. However, these differences were
not significant due to the lack of statistical power in the study (Quarrie et al., 1996).
30
More recently, Quarrie & Wilson, (2000) demonstrated significant differences in
vertical jumping ability between prop (mean = 45cm) and back row forwards (mean
= 54.7cm) from a sample of 38 New Zealand Premier rugby players. In this study,
the vertical jump and reach test was used to indirectly measure the explosive leg
power of rugby forwards. The results also indicate a trend toward higher mean jump
displacements in the lock forwards relative to the prop forwards as indicated by the
moderate effect size differences between these groups (ES = 0.59). The higher jump
displacements of the lock and loose forwards as compared to the props may be a
function of their roles during a game, with the locks required to utilise their leg
power in line-out jumping and the loose forwards mainly required to utilise their leg
power during rucking, mauling and sprinting activities.
The vertical jump heights reported by (Maud, 1983; Maud & Shultz, 1984) on lower
grade rugby players show no such trends between forward positional groups, and
relatively lower values for lower grade USA players (mean = 50.6cm) compared to
more skilled elite US players (mean = 56.9cm). These findings are consistent with
those of Rigg & Reilly (1988) who reported British first class players (mean = 53.4cm)
performed better than their second class counterparts (mean = 49cm) over three trials
of standing vertical jump. These results support the contention that as the skill of the
side is increased, the difference in jumping power between positional roles become
more distinct. Furthermore, the relatively high values reported in these studies for first
class players emphasize that explosive power is an essential attribute to players who
participate in line-outs, scrums and those who require proficiency in leg speed.
Development of running speed over short distances from either a stationary or a
moving start is an important component of anaerobic performance in rugby union.
Measurement of sprinting ability has involved use of infra-red timing lights (two
beam) with speed and acceleration recorded over various distances with players
starting from a standing or rolling start. Rigg & Reilly (1988) timed first and second
class over a 40m distance using a standing start and two time trials. Results show
backs (mean = 5.8 ± 0.4s) and half-backs (mean = 5.82 ± 0.2s) were the fastest, and
the front-row (mean = 6.38 ± 0.5s) and second-row forwards (mean = 6.28 ± 0.22s)
the slowest at both playing levels.
31
Sprinting speed over a 30m distance with both standing and a 5m rolling starts has
also measured in a group of New Zealand club rugby players. Comparisons between
Senior A and Senior B players shows Senior A players performed significantly better
than the Senior B players on the 30m sprint times. Quarrie et al., (1996) noted the
greatest differences in sprint times occurred in the forwards, with Senior A forwards
(mean = 4.5s) outperforming their Senior B counterparts (mean = 4.8s) in the 30m
sprint from a standing start. Analysis by position in the forwards reveals backrow
forwards (mean = 3.9s) were faster than the props (mean = 4.1s) and locks (mean =
4.0s) over a 30m sprint with a 5m running start, although these differences were not
statistically significant.
Recent analysis of rugby union match play has shown that the average distance that
players sprint ranges from 10m to 20m (Deutsch et al., 1998). Speed over 10 m
provides a good index of a player's sprint ability specific to the distances typical of a
game. However, currently there is a paucity of information on the acceleration
abilities of rugby forwards over short sprint distances of 10 - 20m. Acceleration over
10 m with a standing start has been recorded in a group of elite Australian rugby
players. Analysis by positional categories revealed the tight four (front-row and
second row forwards) scored a mean 10m sprint time of 1.70 ± .04s, while the back
row and hookers scored a mean time 10m sprint time of 1.65 ± 0.03 s (Jenkins &
Reaburn, 2000). Comparatively, Mednis (2001) tested the sprinting capacity of a
group of amateur rugby forwards reporting mean 10m sprint times of 1.78 ± 0.04s
for the tight five players (front row, second row, and hooker) and 1.75 ± .07s for the
back row players. These sprint times show lower grade players are considerably
slower than elite players over short sprint distances of 10 meters. In addition, the
previous finding of no differences in acceleration ability between tight five and back
row forwards requires further analysis across all forward playing positions.
Summary
This review of research suggests that anaerobic variables play a dominant role in
game-related performance at the elite level of rugby union. A high anaerobic power
in rugby union forwards is a key predictor of successful participation in high
intensity passages of play. The production of anaerobic power is vital for force
32
production in the rugby union scrum and is required for explosive acceleration,
making and breaking tackles and forceful ripping in the rucks and mauls. Specifically,
the development of leg power is vital for forwards in the lineout and scrum. In
addition, forwards require a conditioned anaerobic energy system to resist fatigue
and aid recovery from repeated bursts of high intensity exercise.
The high requirement for anaerobic power in elite rugby forwards is reflected in the
higher values for anaerobic power among first class rugby forwards as compared to
second class rugby forwards. In terms of the specific anaerobic requirements for
different positions within the forwards, these are difficult to ascertain due to the
limited information on the performance characteristics of back row, second row,
props and hookers as distinct playing units.
The physiological testing of anaerobic power of rugby union forwards has involved
field-testing and laboratory based measures each with their own advantages and
limitations. Assessment of maximal anaerobic power via cycle ergometry and
treadmill protocols proves to be reliable method of measurement. Furthermore, there
is evidence that these test devices are able to detect differences across positional
groups for maximal anaerobic power in the forwards. However, these laboratory
based assessment methods may have limited specificity to player movements during
a game and it is unlikely these measures will be able to detect small differences in
performance in forwards of similar physical capability.
Previous research demonstrates the utility of the vertical jump for monitoring the leg
power of rugby players. This assessment has increased specificity to the lock forward
playing position in the lineout situation where these players must utilise their leg
power to outjump the opposition and win possession of the ball. The major limitation
associated with the use of the vertec jumping apparatus is that they only allow
measurement of jump displacement to the nearest cm. In addition, the vertical jump
performance scores from the jump and reach assessments do not provide information
about the players’ ability to develop force during the different phases of the jump.
33
Assessment of Individual Performance in Team Sports
Introduction
Developments in the field of sport science have created the opportunity for elite
athletes to move closer to their full potential and sports scientists continue to develop
new strategies to optimise the performance capabilities of elite athletes. The process
of effectively improving individual and team performance is dependent upon quality
performance analysis.
The issues relating to the analysis of performance have been reviewed in the sports
science literature (Atkinson & Nevill, 2001). A major discussion point in sports
performance research concerns the issues relating to the selection of measurement
instruments and in particular the reliability, objectivity and validity of the measuring
tool. These principles are particularly important considering the impact that small
increases in performance capabilities can have on performance within competition.
The measurement of athletic performance within the context of a team sport
environment presents further challenge. Such is the case in field games in which
player performance is dependent upon a complex combination of factors, which are
difficult to objectively measure. In sports such as soccer (Luhtanen, Vanttinen,
Hayrinen, & Brown, 2002; Reilly, Williams, Nevill, & Franks, 2000) and rugby
union (Duthie et al., 2003; Reilly, 1997), players are required to combine individual
skills and physical abilities with intricate teamwork to achieve a desired outcome. In
such contexts, the assessment of player performance must consider the physical
attributes, as well as the tactical and technical aspects of performance.
Previously, the assessment of individual player performance in a team context has
occurred through the utilisation of a variety of methods. Such methodologies include
notation and motion analysis (Luhtanen et al., 2002; Olsen & Larsen, 1995)
mathematical models of performance evaluation (Swalgin, 1998) and player ranking
systems in team sports (D. G. Hoare & Warr, 2000; Secunda, Blau, McGuire, &
Burroughs, 1986). These methods have been utilised to obtain objective data on a
player's performance, enabling tactical, technical or physiological interpretation.
34
The primary methodologies for assessing individual player performance in a team
context (as mentioned above) will be considered separately for review in this section.
Notational Analysis of Field Games
Notational analysis provides a means of recording observations in an objective
manner in order to compile statistical details of performance parameters. The main
uses of notational analysis include the evaluation of technical and tactical aspects of
play, investigation of movements during play and compilation of statistical data.
Early notational analysis systems used in soccer consisted of recording behavioural
events by means of short-hand code. Reilly and Thomas (1976) combined the use of
hand notation and audio tape recorder to analyse in detail the movements of English
First Division soccer players. Subsequently, Withers (1982) applied a similar
technique in the analysis of movement patterns of Australian soccer players. The
means by which notational analysis was conducted was revolutionised by Franks in
1983 when he developed a computerised notational system to analyse the movement
patterns of soccer players during competition using a concept keyboard. The design
involved configuring a keyboard on a mini-computer to resemble the layout of a
soccer field with the keys specifically labelled to represent different players and their
on-field actions. The keyboard was programmed to accept input into the computer,
with the computer program designed to yield frequency tallies of various features of
play. Since the introduction of the concept keyboard much of the sports performance
research has concentrated on using specifically designed keyboards and hardware
systems to analyse soccer matches at the elite level of soccer performance (Partridge,
Mosher, & Franks, 1993; Yamanaka, Hughes, & Lott, 1993). The main function of
these computer systems involved collecting, storing and analysing large amounts of
performance data relating to the team as a whole, or individual team members, as
well as particular aspects of performance such as attacking or defensive play.
Detailed aspects of team performance including the types of attack which create
scoring opportunities and player performance including loss and gain of possession
could be entered into the computer so that with each movement the position on the
field, the players involved, and the action and its outcome could be analysed. This
allowed for comprehensive evaluation of team playing patterns and each player's ball
35
involvement during the match. Also, when combined with video recordings,
computerised notational systems allowed for evaluation of player movements in
either real-time or at a self-directed pace using slow-motion re-play (Reilly, 2001).
The use of computerised notational systems in game analysis has extended beyond
soccer into other field games such as lacrosse (Reilly, Bangsbo et al., 2000), field
hockey (Reilly & Borrie, 1992) and the other football codes. Treadwell (1988)
developed a computer based analytical system for the analysis of rugby union similar
to those previously used in soccer. The system hardware included a micro-computer
with information input via a concept keyboard. The specially designed computer
software allowed for processing of frequency and time-based data relating to four
types of activity, (scrummaging, rucks and mauls, non-purposeful rest and purposive
running), identified as important movement activities in rugby union. The computer
program included a number of internal 'clocks' each linked to the movement types
and were activated and stopped via touching the appropriate cell on the concept
keyboard. The time-motion analysis was structured so that players were studied as
‘groups’, in that players often performed in the match situation as part of a unit, that
is, back row/half backs. This method of analysis allowed insight into the movement
patterns and demands of specific playing positions in a rugby union team. However,
the performance evaluation system lacks a set of detailed criteria relating to other
types of movements performed in rugby such as utility movements and striding and
sprinting activities. Therefore it may be difficult to determine the true physiological
demands of different activities in rugby when using this evaluation method.
The CABER sports analysis system was designed to capture and analyse behavioural
events in real-time during Australian Rules Football games (Patrick & McKenna,
1988). The system allowed for recording of quantitative data, including the type and
frequency of all actions during an AFL game, such as ball possessions and disposals,
team events, "pressure" actions and forced errors actions. The CABER system may
be used in Australian football to describe one player’s game performance, summarise
team and opponents match statistics and analyse any one specific game activity.
More recently, Olsen and Larsen (1997) developed a computerised notational
analysis system to examine the attacking styles of play of the Norwegian national
36
soccer team and measure the effectiveness of attacking play in relation to the plays
leading to scoring goals. This system permitted soccer games to be represented
digitally, with data collected directly onto the computer so the play could be
evaluated from start to finish. Data collection involved recording the frequency of
player and team actions (involved in the attacking movement) on a computer screen
using the list of parameters and their categories. The collection of data for the match
and player analysis included a detailed list of variables describing features of
attacking play, such as the type of passing and the type of space the pass penetrating
during the attacking movement. This method of analysis allows for evaluation of
discrete aspects of attacking play from a team and player perspective and as a result,
a more reliable measurement of the team’s effectiveness during attacking play than
the outcome of the game (Olsen & Larsen, 1997).
Other performance analysis research in soccer has focused solely on the
identification and analysis of offensive movement patterns in professional soccer
teams in order to define the patterns of attacking play associated with a team's
success (Abt, Dickson, & Mummery, 2002; Garganta, Maia, & Basto, 1997; Jinshan,
Xiaoke, Yamanaka, & Matsumoto, 1993). Collectively, this type of analysis have
relied on video-recording based notation analysis systems to identify key features of
scoring movements, including the sector of the field where the team gained
possession of the ball, the attacking reaction time and the number of passes involved
in the play (Garganta et al., 1997). Other specific uses of computerised notational
analysis systems in soccer have included the characteristics of the successful patterns
of play and the changes in patterns of play by international soccer teams during
World Cup football matches (Reilly, 2001).
Notational analysis systems have been widely accepted by coaches and sports
scientists as an essential part of sports science support programs. Olsen and Larsen
(1997) described how notational analysis had benefited the national football team of
Norway in competing with the best teams in the world. Currently, its main use is in
analysing team performance post-event, however when used with video-analysis it
has the ability to provide interim feedback to players and coaches at half time
intervals. Whilst largely a descriptive tool, notational analysis could be employed by
sport scientists in research to investigate the relation between technical and tactical
37
elements of performance and individual physical performance characteristics (Reilly,
2001).
Time and Motion Analysis of Field Games
Investigations of the physical and physiological demands of team sports can be
conducted by making relevant observations during match-play or by monitoring
physiological responses of players during simulated football games. Time-motion
analysis provides a means to quantify the type, intensity, and duration or distance of
various activities during competition. In addition, work-rate profiles of team sport
players can be established according to the frequency, intensity and duration of
categorised activities (e.g., walking, moving sideways or backwards, jogging,
cruising, and sprinting).
The overall distance covered in a game gives a general indication of the
physiological load imposed upon players in real match play and various methods
have been employed to estimate the distance covered during football matches. The
early approaches focused on the use of hand notation systems for the determination
of activity patterns. These methods of analysis utilised a system of visual cues and a
scaled plan of the football pitch to measure distances and track player movements
(McLean, 1992; Reilly & Thomas, 1976). Also, distances have been measured using
stride characteristics extracted from video recordings to evaluate the total distance
covered over an entire match play (Withers et al., 1982).
Alternate methods include the triangular surveying method, which has been used to
calculate the movement speeds and distances of soccer players during match play
(Ohashi, Togari, Isokawa, & Suzuki, 1988). This method uses a pair of synchronised
cameras with potentiometers (positioned to overlook each half of the pitch) to survey
player movements. The computer system uses two angle data and the distance
between two cameras to calculate players’ movements in x - y coordinates every 0.5
seconds. The distance between the consecutive two x - y coordinates is then
calculated for every time interval followed by the calculation of speed of movement
using the time and distance data. This system shows a high degree of precision in its
ability to measure the distance of player movements and the time covered at various
38
speeds (Ohashi et al., 1988). At present, the most technologically advanced system
involves six cameras, three placed high on a stand on each side, allowing recordings
to be made on all 22 players on the pitch. Currently, this system is used by a number
of European professional soccer clubs, however this technique requires scientific
validation (Reilly, 2001).
Time-motion analysis in soccer and rugby union have utilised computerised
notational systems to determine total distances covered over the entire match or in
different activities (Deutsch et al., 1998; Mayhew & Wenger, 1985; Reilly &
Thomas, 1976). Player movement patterns around the pitch in each activity category
can be plotted, with estimations of the total distance covered being based on
predetermined pitch dimensions. Alternatively, velocity and time-based data from
computer analysis have been used to predict the distances covered considering the
relationship between time, distance and speed (time x speed = distance) (Bangsbo,
Norregaard, & Thorso, 1991; Deutsch et al., 1998). A high degree of reliability and
validity has been reported (when estimating total and mean distances for each
running speed) for this method (Deutsch et al., 1998). Additionally, a number of
studies in soccer and rugby union have used distances, along with the total time for
each activity, to calculate the mean velocity of player movements (McLean, 1992;
Reilly & Thomas, 1976; Withers et al., 1982).
Compared to measurement of distance covered, the assessment of time spent in each
activity provides a more objective measure of the activity patterns during a game and
a clearer indication of the metabolic demand of various match play activities (Duthie,
2003). For example, the measurement of distance assumes constant movement
velocities, while no such assumptions are needed for the calculation of time spent in
each activity. Further, the calculation of time allows for varying velocities
throughout a movement and differing velocities amongst players.
Time-motion studies across various codes of football have assessed player activity
patterns using computerised analysis of the time spent in different match activities.
For example, Treadwell (1988) and Deutsch et al., (2002) completed time-motion
analysis of elite rugby union players using a microcomputer to record each player’s
time in view from the playback of video recordings. Such computer analytical
39
systems relied upon in- built clocks to determine the relative time and average time
spent in four types of activity. These were scrummaging, rucks and mauls, non-
purposeful rest and purposive running which were identified as important movement
activities in rugby union. This method of time based match analysis has been adapted
for use in other sports such as AFL (McKenna, Patrick, Sandstrom, & Chennells,
1988) and soccer (Mayhew & Wenger, 1985; Yamanaka et al., 1993).
The physiological demands of intermittent activity depend not only on the duration
and distance covered in various activity modes, but also on the density of physical
work. The pattern of work: rest ratios throughout a game have been used to
determine the metabolic demands stressed by match play in various codes of football
such as, soccer (Drust, Reilly, & Rienzi, 1998), rugby union football (Deutsch et al.,
2002; Deutsch et al., 1998; McLean, 1992), and touch football (O'Connor, 2002).
Generally, work-to-rest ratios are calculated using time-based data related to
activities classified as periods of work (cruising, sprinting, rucking, mauling or
scrummaging) or rest (walking, jogging or utility movements). Similarly, work-rate
profiles have been presented in distances covered at different intensities, which is
useful when monitoring individual variations from game to game and in identifying
the onset of fatigue (Drust et al., 1998). Furthermore, the recording of other
physiological responses during match play such as heart rate and blood lactate levels,
has proven to be beneficial in data interpretation (Deutsch et al., 1998).
Work-rates in football are influenced by factors such as player position,
environmental factors and level of competition (Reilly, 1997). In rugby union, work-
rate profiles are also influenced by physiological factors such as endurance capacity
(Deutsch et al., 2002), and anaerobic capacity (Nicholas, 1997). Work-rate profiles
can also be related to anthropometric characteristics, although anthropometric
characteristics are relatively heterogeneous among elite rugby union teams.
Rienzi et al., (1999) investigated anthropometric and work-rate profiles of rugby
sevens players. Their results suggested that the anthropometric characteristics of
players in the Rugby-Sevens international tournament were significantly correlated
with work-rate components, mesomorphy and muscle mass being negatively
correlated to the total and average time spent in high intensity running during the
40
game. This may reflect a selection strategy for rugby union whereby the more
muscular players are chosen for their capability to contest and win possession of the
ball rather than for their speed of locomotion or their endurance. Despite this
significant finding, neither work-rate nor anthropometric measures necessarily
determine whether a rugby sevens match is won or lost (Rienzi et al., 1999).
Similarly, research investigating the anthropometry and work rates characteristics of
international soccer players have found similar results to those obtained on rugby
sevens players (Rienzi et al., 1999).
Time-motion analysis remains as one of the most effective measurement tools for
extracting information regarding the activity patterns and energy demands of players
in team sport competitions. Quantifying the duration of time spent in different
activities has resulted in knowledge of the energy demands of specific components of
the match-play e.g., continued ruck and maul activity. More detailed analysis of the
activity patterns of principle movements in team sports is needed to gain knowledge
on the specific demands of movements such as sprint running, so that game specific
training programs can be designed. In rugby, there is a need for greater data
collection on the current game to accurately establish match demands on
contemporary elite level players.
Performance Evaluation Models
Evaluating individual performance within a team sport environment is an essential
aspect of coaching as it leads to improved performance for the individual and
eventually the team. The process of effectively improving individual and team
performance often centres upon the coach’s ability to observe, measure and analyse
performance. This can present as a difficult task for team sport coaches as many
player evaluation systems lack fundamental elements in their evaluation processes,
such as a common set of objective performance criteria and a measurement system
which can accurately measure performance in relation to the structure of the sport
(Swalgin, 1992). To overcome this problem, performance evaluation models have
been designed which incorporate three common team sport concepts (criteria, context
and measurement system) used to measure individual performance.
41
Swalgin (1992) designed a quantitative model to evaluate individual performance in
the team sport of basketball referred to as the “Basketball Evaluation System,
(BES)” .This system is a computerised performance evaluation model which grades
player performance in relation to “position of play” and “time played” under game
conditions. The BES utilises a mathematical model to grade eight game related skill
factors on a scale ranging from zero to four. The model produces Scaled
Performance Scores (SPS) for each skill factor and an overall grade for each player
called the Graded Performance Score (GPS).
The validity of the scores produced by the BES has been established for individual as
well as overall performance scores. Swalgin (1993) calculated a correlation matrix to
test the variability between BES scores and a set of criterion scores established from
16 USA division one college coaches. From the correlation matrix, the average
correlation was determined between the BES overall performance rating and the
coaches’ overall ratings. The results showed that the correlation (r = .695) for BES
was higher than the correlation (r = .591) among coaches. These findings indicate
that the Basketball Evaluation System (BES) shows less variance than the coaches’
ratings when combining performance scores to produce an overall performance
rating (Swalgin, 1993). Since the design of the original BES model, modifications
have been made to the structure of the system to strengthen the validity of the scores
produced for overall performance.
Swalgin (1998) extended his original work on the BES by developing a factor
weighted BES performance evaluation model which considered the importance of
individual performance factors to the different playing positions in basketball. To test
the validity of the factor weighted model, overall performance scores of 45 division
one college players were correlated with a set of criterion scores established from a
group of 15 USA division one college coaches. The results indicate that the weighted
(r = .798) and unweighted (r = .757) models both correlated highly with the coaches’
criterion scores, with no significant differences between the correlations. The
findings indicated that the factor weighting did not increase the validity of overall
performance scores produced by the BES weighted model, however, the addition of
factor weighting did add to the face validity of the model.
42
Central to the development of the BES model is three structural concepts that form
the framework of a performance evaluation model. Swalgin (1992) suggests these
constructs can be applied to most team sport structures to design a performance
evaluation model for team sport players. These concepts include: (a) a common set
of performance criteria specific to the sport (factors that can be objectively
measured), (b) a norm based context to measure the criteria, and (c) an accurate,
functional measurement system inherent to the structure of the sport (Swalgin, 1992).
A model to evaluate individual performance for most sports can be designed by
incorporating these elements into the current performance evaluation system. Despite
the proven abilities of the BES to measure individual performance in a team sport
environment, limited attempts have been made to adapt the model for use in other
team sports such as soccer and rugby union.
The BES model provides coaches with an effective tool to measure the performance
elements that lead to successful play in various playing positions in basketball.
Generating quantitative information on player performance enables coaches to
provide feedback and training interventions designed to improve athletic
performance (Swalgin, 1998). The BES model could be employed by coaches to
select talented basketball players as part of the talent identification process. This
quantitative scoring system could be applied to other team sports such as rugby
where coaches find it difficult to measure the level of skill and physical ability
development in rugby players.
Subjective Evaluation of Player Performance
Subjective evaluation of player performance involves a group of sports experts
recording observations of players in a game situation. The main aim of subjective
evaluations is to evaluate the technical and tactical aspects of player performance and
arrive at a score, rating or ranking of performance for team sport players. Evaluations
are usually based on key competency areas, or a set of simplified performance
factors related to successful performance in a particular sport. Previous research
examining the link between individual characteristics and performance capability in
team sports have used subjective evaluations of player performance as the criterion
measure of match playing ability (Reilly, 2001).
43
Hoare, (2000) investigated the relationship between anthropometric and
physiological characteristics with playing performance in elite, junior basketball
players. Assessment of player performance included coaches rating the performance
of players in basketball championship games only. The rating process employed by
coaches consisted of two parts. Firstly, four experienced basketball coaches ranked
the players in order of 'playing ability' during the championships from 1 through to
130 (highest to lowest performing player in the championship). In addition, each
player in the championship received a performance score (which was then converted
to a player rank) calculated from rating players in four key competency areas of
basketball match-play (offensive skills, defensive skills, catch/pass skills, overall
ability). Each skill category received equal weighting in the rating process with a
highest possible score of 7 for each of the four categories (Hoare, 2000).
The validity of the coaches’ rankings of player performance was examined by
making comparisons to a test rank for each player. The test rank for each player was
established by summing the player’s score on each of the performance tests (height,
20m sprint run test, vertical jump test, suicide run and multistage fitness test) relative
to their playing position. The results indicate a good alignment was achieved
between the top ranked player on the tests and the top ranked coach player on 60% of
occasions (athlete with best physical performance qualities ranked either 1 or 2 by
coach). In addition, the use of coaches’ ratings as the criterion measure of playing
performance allowed for the identification of physical capacities which closely relate
to game performance in basketball. The results of the regression analysis indicated
that vertical jump displacement and 20m sprint time accounted for a significant
proportion of variance in playing performance for females (~35%). In junior male
basketballers, vertical jump displacement and explosive basketball throw explained
19% of the variance in coaches’ ratings of playing performance (Hoare, 2000).
It is evident from these results that expert coaches have demonstrated ability to
correctly determine the physical performance ability of basketball players. This gives
support to the claim that they can be used in combination with other measures of
playing ability as criterion measures of player performance in performance
prediction research. An opportunity exists to validate this method of performance
44
analysis against other scientific measures of playing performance in basketball, such
as the Basketball Evaluation System. Also the validity of coaches’ ratings may be
enhanced by considering the weighting of each individual performance factor and
their relationship to the different playing positions.
An enhanced rating system of player performance was designed by Secunda et al.,
(1986) in their study of performance factors and playing ability in collegiate
American football players. The main aim of this research was to determine the
influence of selected biological, psychological, and motor performance capabilities
on individual football-playing ability. Evaluating the playing ability of 19 college
football players involved expert coaches' ratings on 15 sub-variables deemed to be
important to the offensive backfield positions. Firstly, a thorough task analysis of the
offensive backfield football position was performed to identify the 15 constructs
including the skills, abilities and personal attributes important to this position.
Secondly, the three coaches rated each dimension (on a five-point scale) to determine
the relative importance of the skill/attribute to overall playing ability (Secunda et al.,
1986).
To determine the football-playing ability of each player, three coaches independently
completed the football skills check sheet at the end of the football season on each of
the 19 tryouts for the offensive backfield position. A six-point scale was used for
these criterion with athletes assessed as excellent, good, average, below average,
poor or not able to rate. Each coach considered the performance of players over the
duration of the season in their performance evaluations.
In the same study, Secunda (1986) examined the reliability of coaches’ ratings and
reported significant variability between the coaches' ratings on the criterion scores of
playing ability. In light of these findings, future studies using subjective assessment
of playing abilities should aim to limit the number of personnel involved in the rating
process.
More recently, Sawyer & colleagues (2002) related strength, speed and power
measures to coaches’ ratings of football performance in 40 Division 1-A American
football players. In this study, evaluation of football performance involved 2
45
specialised coaches providing a simple ranking of football players on their overall
playing ability from the highest to lowest rank. However, no indication was given as
to whether the coaches’ rankings were based on a core set of performance skill
criteria chosen to reflect the key physiological requirements of match play in
American football. Vertical jump height was determined from the performance of a
skilled countermovement jump using a vertec apparatus, while strength was
determined by a 1RM bench press and squat and speed from a 9.1 and 18.2m straight
sprint run. Despite the uncertainty surrounding the validity of coaches’ evaluations, 2
significant regression models indicated that leg power, assessed indirectly via
vertical jump displacement, was the key factor in coaches’ rankings of playing ability.
Both upper body strength and body weight were also included into the models,
however their contribution was much smaller relative to the vertical jump. The 2
regression models developed for the offensive and defensive playing positions each
shared 50% of the variance in coaches’ rankings of playing ability (Sawyer et al.,
2002).
In the same football code, Barker & colleagues (1993) measured a range of physical
capacities and linked these to measures of athletic ability in 42 Division 1-AA
football players. Athletic ability was determined by the average of the 3 coaches’
rankings based on each player’s functional performance in fixtures over the last half
of the season. The significant correlation results, expressed as the coefficient of
determination (r2), showed that explosive movements such as agility sprints (r2 = 38 -
45%) and countermovement jump height and estimated power (r2 = 29 - 35%), were
key factors in coaches’ rankings of athletic ability in American football players
(Barker et al., 1993). However, it was difficult to determine the common element
between these variables, as there was no indication given of the type of performance
factors which coaches considered in their rankings.
Previous research in American football has found a strong link between playing
ability as rated by team sport coaches and measures of physical capacity. However,
no equivalent study has been performed in rugby union which determines those
measures of physical capacity which closely relate to football playing ability as rated
by experienced coaches.
46
In previous studies, coaches’ evaluation of playing ability has included an
assessment of athletic ability only, or a combined measure of skills and physical
capacities. In future research, investigating performance in team sports, there is a
need for a more systematic approach to evaluation of playing ability. This requires
coaches to assess the level of performance skill and physical capacity as separate
components of playing ability. In addition, it is fundamental that physical capacities
which reflect the specific performance requirements of match play are investigated.
In rugby forward play these include such factors as acceleration and horizontal
strength and power.
Research concerned with talent identification programs in team sports has
highlighted the importance of performance measures in the selection and
development of sporting talent. Assessment tools designed to evaluate the physical
abilities, technical skills and game understanding of players are an important element
of talent identification processes in most team sports (Franks et al., 2002). For
example, talent identification programs in women’s soccer have utilised skill tests
and match play situations to evaluate the playing ability of individual team sport
players (D. G. Hoare & Warr, 2000).
The approach used by Hoare and Warr (2000) involved high level coaches
(accredited level 2 and 3 soccer coaches) independently rating player performance
during skill assessment tasks such as passing and ball control and small sided game
scenarios such as 3 versus 3 and 6 versus 6 scenarios. These ratings were based on a
simple rating scale with athletes assessed as excellent, good, average or poor (D. G.
Hoare & Warr, 2000). The inclusion of match play evaluations enabled coaches to
assess the performance of all the players in three areas considered important for
player success; foot eye coordination, match play ability and game awareness. Each
competency area received equal weighting in the rating process. Similar to previous
approaches, this performance evaluation system provides a systematic way of
analysing player performance. However, no scientific information exists on the
importance of each of the performance criteria to a player’s overall success.
Subjective methods of player evaluation provide a practical approach for coaches to
evaluate the performance of athletes in game situations. Rating and ranking systems
are relatively simple to administer and provide coaches with a time efficient method
47
of evaluating the general performance capabilities of team sport players. The
relatively simple structure of rating systems, however, limits their ability to detect
small differences in the playing performance of athletes competing at the same
performance level.
Summary
Objective data on performance of game players provides a useful database for
monitoring the contributions of individuals towards the team’s collective efforts.
Notation analysis and motion analysis are different methods of recording patterns of
play and work-rates of players. These types of observations yield data from which
sports specific tests may be designed. Performance evaluation models provide a valid
and objective means of measuring the game-related performance of individual
players. Developed correctly within the structure of team sports, performance
evaluation systems have the potential to become an integral part of the selection
process in team sports. Subjective assessment of athletic performance provide a
practical approach to performance evaluation, however, accurate measurement of
player performance is dependent on the structural validity of the rating system and
the reliability of the raters’ observations.
48
Literature Review Summary and Conclusions
Rugby union is a multi-sprint, multi-activity sport for forward players, as they are
required to repeatedly compete in high intensity activities of short duration
interspersed with longer periods of low to moderate intensity activity. For forward
players at all performance levels, approximately 90% of the high intensity work
performed during a game consists of static and dynamic exertion such as rucking
/mauling, scrummaging and tackling. Players experience high inertial loads during
such activities as they are required to express maximal strength and power in a
horizontal direction. While sprinting activities occurs relatively infrequently during
forward play, quickness and leg speed are required around crucial match actions such
as making a break away from the opposition or reaching the breakdown in open play.
High levels of muscular strength and power and speed are essential physical
capacities for all forward players, however previous research has indicated the
importance of these qualities may vary across positional groups to reflect the specific
demands of positional groups in forward players. For example, static strength plays a
more important role in scrummaging for locks and prop forwards than for loose-
forwards, while for loose-forwards the development of leg power is of critical
importance for participation in ruck and maul activity. However, there is presently
limited information on the relevance of specific strength, speed and power qualities
to positional groups in elite rugby union forwards and to the performance skill of
forward players operating as one playing unit.
Physical tasks such as scrummaging, rucking and mauling are highly specific to
rugby and as such require the utilisation of force and power specific to the movement
patterns of the task. As these specific sporting movements are hard to imitate,
assessment of strength and power in rugby has been conducted using non-specific
tasks. For instance, tests have been employed to measure the strength of single body
segments during rotational or vertical orientated movements and as such do not
provide a measure of whole body strength or strength expressed in a horizontal
direction. Consequently, the first aim of the current study was to utilise horizontal
strength, vertical power and sprinting speed tests which closely reflect the movement
patterns of forward players during a game (in particular the dynamic rucking and
49
mauling activities), to determine differences in the strength, speed and power
qualities between individual forward playing positions.
Evaluating individual playing ability or performance within a team sport
environment can present as a difficult task for team sport coaches. Such is the case in
rugby, in which the forward player’s performance is dependent on the interplay of
individuals in tactical moves, the competence of players in basic skills of catching,
passing, kicking, tackling and skills specific to the playing position. In rugby,
objective performance data on a forward player’s performance has only been
obtained via motion analysis of movement patterns of play and work-rate profiles
during a match. This method of performance analysis has provided insight into the
physiological demands incurred by forwards during a match, however it does not
permit evaluation of the quality of skill execution and physical performance
attributes of players in relevant activities. Subjective evaluations and ranking systems
provide a means by which specialised coaches can assess the skill levels and physical
abilities of team sport players. The development of a performance rating system for
football that incorporates a core set of performance criteria and a measurement tool
than can accurately quantify performance will provide rugby coaches with a more
systematic and discerning method of measuring individual playing ability.
Several performance prediction studies in team sport have shown that strength, speed
and power variables strongly relate to the performance skill and athletic performance
of individual players during a game situation. Rugby forwards require strength, speed
and power to successfully compete in high intensity activity, however no equivalent
study has been performed in rugby union which determines the measures of physical
capacity which closely relate to the playing ability of rugby forwards during a match.
Consequently, the second aim of the current study was to relate the static and
dynamic strength, vertical power and sprinting speed qualities of Premier rugby
union forwards to coaches' scores of their football skills and physical attributes.
50
Chapter 3
METHODOLOGY
Research Design
This study comprised 2 interrelated phases. The first phase aimed to determine
whether body mass, strength, speed and power qualities differed between playing
positions in Premier rugby union forwards. On the basis of the results of a small
number of earlier studies which have investigated differences in various
physiological factors between forward playing positions (Nicholas & Baker, 1995;
Quarrie et al., 1996), it was anticipated that 15 subjects in each of 3 positional groups
would be sufficient to achieve the statistical power required to demonstrate
differences between the groups. However, despite persistent efforts to recruit and
encourage the attendance of subjects, the final number of players representing each
of the different positional groups fell below this number and comprised 5 locks, 8
props and 9 loose forwards. This primarily reflected the difficulty experienced by
players at this level to fulfil the time commitments of their training and the additional
time required for participation in this study. In view of these limitations and the
reduced statistical power, when statistically significant differences where not
achieved using a one-way analysis of variance an effect size statistic was used to
provide some indication of the magnitude of these differences across the 3 forward
positional groups. The independent variable involved in this design was:
Playing position, whereby the subjects were assigned to one of 3 positional groups
(prop forwards, lock forwards, loose forwards) according to the position that they
most regularly occupied during the season. The dependant variables involved in the
study are listed in Table I. These variables consisting of different measures of
strength, speed and power and were selected to reflect the specific requirements of
forward play in rugby union.
51
Table I. Anthropometric and physical performance test variables.
TEST CATEGORY DEPENDANT VARIABLES
Anthropometry Body mass (kg)
Height (cm)
Static horizontal force test Impact force (N)
Sustained horizontal force (N)
Dynamic horizontal force test Peak dynamic force (N)
Acceleration and sprint running test 0 -10m sprint performance (secs)
0 - 20m, sprint performance (secs)
20 - 40m sprint performance (secs)
0 - 40m sprint performance (secs)
Countermovement jump test (CMJ) CMJ displacement of centre of gravity (cm)
CMJ relative power (W.kg-1)
The second phase of the study aimed to determine the relationship between the
strength, speed and power qualities of Premier rugby union forwards and coaches'
evaluation of their performance skill and physical capacity ability. Performance skill
ability was defined as the coach’s rating of a player’s level of development in a
number of cognitive, tactical and motor skills specific to principle areas of match
play including attack, defence, continuity, scrum and restarts. In contrast, physical
capacity ability was defined as the coach’s rating of a player’s level of development
in a number of physical capacities required for all areas of forward match play
including speed, agility, and dynamic and isometric strength (refer to Appendix 2 for
full list of criteria). The research design utilised linear regression analysis to establish
relationships between the outcome variables (coaches' performance skill and physical
capacity scores) and all the dependant variables listed in Table I excluding height, 0-
10m, 0-40m sprint performances and CMJ relative power and including CMJ relative
dynamic force and force impulse.
Subjects
Twenty- two male rugby forwards volunteered to participate in the study. Nineteen
forwards were regular starters in first division club rugby teams participating in the
52
2003, Brisbane Premier rugby competition. Each rugby team played 16 fixtures
during the Premier rugby season. The Premier rugby competition represents the
highest level of club rugby in the State of Queensland. The remaining 3 subjects
were regular starters in first division club rugby teams participating in the Brisbane
Metropolitan under-19 competition. These 3 subjects were part of a group of 8
players who held development scholarships with the Reds Rugby College (RRC) and
who had been selected for entry into the College on a range of performance criteria
and the perception of experienced coaches of their potential to play Premier rugby or
Super 12 rugby for the Queensland Reds. All participants in the study were involved
in skills and periodised conditioning training throughout the course of the year.
Each player was assigned to one of 3 positional groups, according to the position
most regularly occupied during the 16 match season. The groups were props (tight-
head and loose-head props), locks (right and left) and loose-forwards (flankers,
hookers and number eights). These positional groups were chosen because the
players occupying them are considered to have similar roles in the game (Duthie et
al., 2003). The final number of players representing each of the different positional
groups was 5 locks, 8 props and 9 loose forwards. Five of the 9 loose-forwards
occupied the open-side flanker position with another 2 forward players identified as
blindside flankers. The two players occupying the hooker playing position were
assigned to the loose forwards group due to the observation that these two positions
perform a similar roving role around the line-out and in broken play, and do not push
during scrummaging to the same degree as the other front-row (prop) forwards
(Deutsch, Kearney & Rehrer, 2006). Similar to the loose-forwards, the hooker
playing positions are often positioned loosely around the ruck and line-out area
during play. As a result, both the hookers and loose-forwards are required to use their
mobility, agility and strength capabilities to perform repeated covering tackles in
defence and sprints into attacking positions, as well as to reposition themselves
around the ball in play.
An information package was given to participants detailing the procedures and
expectations for their participation in the study. Prior to the study, all players
provided informed consent for participation. The study was conducted in accordance
53
with the NH&MRC guidelines for Human Experimentation and with the approval of
the Queensland University of Technology Ethics Committee.
General Procedures
On the day of testing, potential participants were first administered a self-evaluating
questionnaire to identify illness, injury, motivation, past 48-hour training history and
fatigue levels. Players were excluded from participating in the study if their self-
evaluation indicated any pre-existing illness, injury or fatigue conditions which may
have influenced the results of the testing. Information gathered on the player’s
motivation and their past 48 hour training history was not analysed or referred to in
this study. No players were excluded from participating on the basis of the pre-test
screening data.
Anthropometric, strength, power and speed measures were then obtained from each
participant during a single testing session which lasted approximately 1 hr and 30
minutes (Figure 2). Prior to the commencement of testing, participants performed a
self-selected warm-up, which included light to moderate jogging followed by
stretching of the major muscle groups. The testing session included collection of
sprint data on the Queensland University of Technology (QUT) Sports Oval,
followed by collection of countermovement jump data in the QUT Biomechanics
Laboratory. Collection of static and dynamic horizontal force data also occurred in
the QUT Biomechanics Laboratory using a sports ergometer (Grunt 3000 Sports
Ergometer, Sportstec International, Sydney).
Collection of performance data from the 22 rugby forwards occurred over a 4 -
month period (June - September, 2003). This time period coincided with the entire
Premier rugby season. Players were tested after one day of rest from training to allow
time to recover from the previous training session. During the 4 - month testing
period all players were involved in regular club training sessions which involved 2 to
3 organised sessions per week. As indicated in figure 2 the order of testing was kept
consistent for each player across each testing session.
54
Figure 2. Diagram showing the order of data collection for each player during one
testing session.
Testing Protocols
Prior to administration of the performance tests, the basic anthropometric measures
of body weight and height were taken. Weight measurements were recorded using a
calibrated portable scale located on a hard, flat surface. Each player wore only shorts
and a training shirt and weight was recorded to the nearest 0.05kg. Standing height
was measured with a stadiometer with the subject standing on a flat, hard surface.
Height was measured as the distance from the vertex of the head to the ground and
was measured to the nearest 0.5cms. The subjects were bare footed and the
measurement was taken while holding an inspired breath and with eyes focussed at a
point on the horizontal.
Body weight and Height Measurement
General Warm - up (15mins)
Acceleration and Maximum Running Speed Test
10 mins recovery
Countermovement Jump Test
5 mins recovery
Static Horizontal Force Test
Dynamic Horizontal Force Test
5 mins recovery
55
Performance tests were determined on the basis of the following protocols:
Dynamic Horizontal Force Test
Peak dynamic force was measured during a test of peak horizontal force. This
involved pushing an instrumented single-person sports ergometer which was
designed to simulate a rucking/mauling action (Figure 3). Peak dynamic force was
obtained by measuring the force applied to the ergometer during a moving condition.
The moving condition was created by attaching an elastic cord to the ergometer at
one end and to a solid anchor point (steel beam) at the other end, thus creating a
dynamic resistance to the applied force. To ensure the system was effective in
operation, the elastic cord, situated between the anchor point and the sports
ergometer, was applied taut before the commencement of each performance trial.
The average pretension in the elastic cord for the 22 performance trials equalled
10.03 ± 5.7 N (mean ± 1sd). All force ergometer tests were performed in the QUT
Biomechanics Laboratory on a synthetic matting surface.
Figure 3. Grunt 3000 Sports Ergometer.
56
Procedure
Prior to the commencement of each trial, participants were allowed 2 - 3 practice
trials to become familiar with the test set-up. During the practice trials each
participant was instructed to push forward on the ergometer, with the emphasis
placed on leg drive to maximise power production.
When participants felt that they had had sufficient practice and were confident in
their use of the sports ergometer testing was begun following a rest period of 3
minutes. At the commencement of each trial, participants were instructed to stand
with their heels against the back of a yoke system. On the command to “engage”,
participants surged forward to make contact with the grunt machine. Each trial lasted
for 5 seconds, during which time participants were instructed to push as hard and fast
as possible against the marked hitting zone located on the two central pads of the
sports ergometer. Participants were permitted 3 trials with 3 minutes rest between
each trial. During each trial, verbal encouragement was given and participants were
allowed to repeat trials if the acquisition of the data was not precisely coordinated
with the commencement of the test performance.
Equipment, Data Collection and Analysis
The sports ergometer was used to measure the applied force and distance travelled.
Force was measured using a load cell (Sensortronics Company, S-Beam Load Cell
Model 60001, Covina, CA, USA) connected between an anchor point and an elastic
cord that is attached to a yoke system located behind the subject (Figure 4). The load
cell provided a force reading accurate to 0.001 of a Newton. The distance travelled
by the sports ergometer was measured using an optical encoder (Hewlett Packard.
HP HEDS5701-A00, Palo Alto, CA, USA) mounted on the left front wheel. The
optical encoder outputs a single electronic pulse for each millimetre of movement in
the forward direction, thereby allowing for the calculation of distance travelled by
the sports ergometer during each trial. During data collection, the pulses emitted by
the optical encoder and the force signal from the load cell were simultaneously
acquired at 1000 samples per second by a laptop PC using a National Instruments
DAQ 16-XE-50 data acquisition card. However, the sampling rate of 1000 samples
57
per second did not provide an accurate instantaneous velocity due to the sampling
rate being too slow to capture the pulses reliably. Consequently, the velocity values
were not included in the final analysis as accurate figures on all the subjects were not
available. Before each testing session, the load cell was calibrated by comparing
known loads, from 0 to 5000 N to the force signal recorded.
Prior to this study, the commercially available software which enabled
communication between the PC and the sports ergometer hardware had not been
sufficiently developed to interface with the hardware reliably. As such it was
difficult to calibrate the ergometer prior to each test. To overcome this problem, a
Lab View data acquisition system was developed using National Instruments
hardware and software as an alternative (National Instruments, Austin Texas, 2001).
The hardware consisted of a data acquisition card that was inserted into the PC MC
IA data socket of a laptop computer, as well as connecting cables and junction boxes
that transmitted signals from the load cell and optical encoder to PC MC IA data
acquisition card (Figure 4). The software consisted of a program developed in Lab
View (version 6i) graphical programming language that programmed the laptop
computer and data acquisition card with respect to the speed of data collection. The
program also allowed the data to be presented through a user interface (Figure 5), as
well as performing calculations to determine the required force-time characteristics
such as maximal dynamic force.
Force recordings were initiated prior to the test performance using a manual trigger.
This represented a starting point for acquisition of the data. The force measurements
were then scaled using the data acquisition computer program developed on Lab
View, to calculate the force produced during each test. The force-time curves (Figure
6) generated in this program were then analysed to calculate the peak horizontal
force for each test performance. Peak horizontal force was determined as the
maximal force produced after engagement with the sports ergometer. This usually
occurred at the end of the movement where movement velocity was approximately
one-quarter of their maximal test velocity. The trial resulting in the best performance
was used in the statistical analysis. All force values were expressed in absolute terms
in units of Newtons.
58
Figure 4. Schematic showing the equipment set-up and data collection process for
the dynamic and static horizontal force test.
Figure 5. Lab view program interface displaying force –time and velocity-time
curves.
Laptop PC
Junction Box
Data Acquisition
Card
Yolk Load Cell
Elastic Cord (Dynamic) or Chain (Static)
Sports Ergometer
Steel Beams
(Anchor Point)
Force, Velocity Outputs
Test Starting Position
Optical Encoder
Impact Pads
Direction of Push to Impact
59
Figure 6. A typical force-time curve from the dynamic horizontal force test.
Static Horizontal Force Test
The static horizontal force test was a measure of both the impulsive impact force and
sustained pushing force a player could exert against the instrumented single-person
sports ergometer. This was achieved by measuring the force applied to the rear of the
ergometer during a static pushing condition. This static pushing condition was
created by attaching a solid chain to the ergometer and to an anchor point which
comprised a post which provided static resistance to the applied force.
Procedure
Sustained pushing force was measured with each participant adopting a crouched
scrummaging position before engaging the grunt machine (Figure 7). To control for
engagement technique, each participant adopted a trunk, leg and foot position that
closely resembled the technique used in a scrum. On engagement, the participant’s
position was visually checked to ensure the feet were set in the pushing block. The
pushing blocks were used to prevent the subject’s feet from slipping during the
performance of the sustained push. The knee was set at an angle of approximately
120o and the vertebral column was positioned horizontally so that the participant's
shoulders were in line with their hips. The 120° knee angle was chosen as it was
within the range typically encountered by forwards in a scrum. Furthermore, the
0
200
400
600
800
1000
1200
1400
0 1 2 3 4 5
Time (ms)
Dyn
amic
Hor
izon
tal F
orce
(N)
60
development of maximum knee extension torque in the scrum has been shown to
occur at an angle of 120° (Robinson & Mills, 2000).
Before testing, participants were allowed practice trials to become familiar with the
test set-up. During the practice trials each participant was instructed to push directly
forward on the ergometer, rather than to push upward or sideward, to limit the
production of shearing forces (upward and sideward forces) in the movement.
Participants were permitted 2 - 3 practice trials to become comfortable and confident
with the engagement technique utilised in this test.
At the commencement of each trial, participants were given the command "…and
push" at which they then attempted to maintain a maximum sustained shove for 4s.
Each participant was permitted three, 5s trials with a 3 minute rest period between
each trial. During each trial, verbal encouragement was given and participants were
allowed to repeat trials if either the participant or the subject was dissatisfied with the
performance.
Figure 7. Diagram showing the standardised at engagement position in which forces
were measured during the Static Horizontal Force Test.
Horizontally Aligned
Pushing Blocks
Sports Ergometer
Knee Angle ~ 120°
61
Data Collection and Analysis
Static horizontal force data were collected and analysed using the same method and
instrumentation used in the dynamic horizontal force test (Figure 3 and 4). Using the
data analysis programs on Lab View version 6I and the force-time data, static force
curves were generated and analysed to calculate the impulsive impact force and the
sustained pushing force (Figure 8). Impulsive impact force was determined as the
peak force occurring on engagement of the sports ergometer (Milburn, 1990a;
Milburn, 1993). It is usually characterised by a sharp peak in the horizontal force on
the static scrum force-time curve. Sustained pushing force was determined as the
maximal component of force averaged over the period of 1 - 4 seconds after the
subject had engaged the sports ergometer. The trial resulting in the best sustained
pushing force, and corresponding impact force value, was used in the statistical
analysis. All force values were expressed in absolute units of Newtons.
Rationale – Horizontal Force Tests
The measurement of muscular strength and power is an important assessment of
physical performance in rugby union, particularly for rugby forwards. Approximately
90% of high intensity work performed by forwards in a match is spent in intense
pushing activities such as scrummaging, rucking, mauling and tackling. These
activities involve the production of muscular force, power and endurance in a
horizontal direction (Deutsch et al., 2002).
The force ergometer provides a highly specific means of assessing rucking/mauling
and scrummaging while simulating the techniques during a game situation. Peak
force testing, requires players to maintain a low body position and utilise a horizontal
drive technique similar to that used in ruck and maul activities experienced during a
game. Similarly, measurement of sustained horizontal force requires the players to
exhibit an optimal body posture for scrummaging, which ensures that forces are
transmitted efficiently through the shoulders at an angle maintained as close to the
horizontal as possible.
62
The force ergometer testing also accurately reflects the energy demands associated
with high intensity efforts performed in rugby union. In rugby union, the mean
duration of work and recovery periods associated with the scrum, ruck and maul for
forwards is approximately 5 and 30 s respectively (Deutsch et al., 2002). This means
that the creatine phosphate and anaerobic glycolytic systems are the main sources
from which energy is utilised in order to perform muscular work (McCartney et al.,
1986). Testing the force application over 5s periods during force ergometer testing
ensures that most of the energy for forwards to perform these tests comes from
alactic energy and anaerobic glycolysis. Therefore, each force test remains energy
specific with respect to ruck, maul or scrum activity.
Figure 8. A typical force – time curve from the static horizontal force test.
Counter Movement Jump Test Procedure
Three trials of the counter movement jump test were performed in each testing
session using a triaxial force platform (Kistler, Type 9287, Switzerland) mounted in
the floor of the Biomechanics Laboratory. A vertec jumping apparatus (Swift
Yardstick, Swift Performance Equipment, Australia) was also used as a motivational
tool for jump performance. Prior to testing, participants performed 3 practice jumps
to familiarise themselves with the sequence of actions involved in a counter
movement jump. Following the practice trials, each participant was instructed to
0
1000
2000
3000
4000
5000
6000
0 1 2 3 4 5Time (s)
Forc
e (N
)
Sustained Force
Impact Force
63
stand behind the force platform in an upright posture with their reaching hand held in
a vertical overhead position and their opposite hand positioned on their hip.
Participants then stepped onto the plate and from a stationary position performed a
countermovement which involved quick flexion of the knees to a depth of
approximately 90 degrees. They then immediately and vigorously extended their
knees and hips, jumping as high as possible to displace the markers of the vertec
apparatus before landing back on the force plate. Participants kept their hands in the
standardised position for the duration of the jump. All trials were visually checked to
ensure that the appropriate depth was achieved during the counter movement.
Participants were permitted a recovery time of 3 minutes between trails. The counter
movement jump was performed with body weight alone.
Data Collection and Analysis
Force recording was initiated just prior to test performance using a manual trigger.
Prior to each test, the force platform was reset to 0 N to normalise for the subject's
weight. The vertical component of the ground reaction force (VGRF) of all subjects
was sampled by a Kistler force plate at a rate of 1000 Hz and recorded by an IBM PC
with a Windows 95 operating system. The force platform was calibrated before and
after each testing session by comparing known loads from 0 to 5000 N to the voltage
recorded.
Data acquisition and analysis of the jumps was performed using a computer program
that was written using Lab View Version 6i virtual instrument software. The
computer program allowed for the integration of the force-time record from the force
platform and the production of force-time curves (Figure 9). To calculate the peak
concentric force for each of the jumps analysed, bodyweight was subtracted from the
force-time curve. Peak dynamic force was defined as the highest vertical force
reading on the force-time curve during the concentric phase of the jump.
The force-time data for each jump were then used to calculate the vertical
displacement of the subject's centre of gravity using the flight time procedures
outlined by Linthorne, (2001). In this method, the accuracy of the calculation of
displacement depends on the height of the jumper’s centre of mass at the instant of
64
landing being the same as at the instant of take-off. However, the jumper’s centre of
mass is usually lower at landing than at take-off during a vertical jump performance
which can lead to an overestimation of true flight height by 0.5-2cm (Linthorne,
2001). In this study, every effort was made to reduce the inaccuracies associated with
displacement estimation by closely monitoring the jumper’s landing position to
ensure the knee and ankle joints were extended as much as possible when landing on
the force platform.
Calculation of flight-time involved moving the two cursors on the force-time curve
to select the times at the instant of take-off and instant of landing. Flight time was
estimated by counting the time in ms from the moment that the force–time curve
descended below the zero value (the moment of take–off) to the moment that the
curve exceeded the zero value again (the moment of landing). The trial resulting in
the longest flight time for each player was then used to calculate vertical take-off
velocity, vertical displacement of the subject's centre of gravity and impulse using
the formulas:
v = g • flight t / 2 h = v2 / 2 • g I = v • m
where v = vertical take-off velocity (m.s-1), g = gravitational acceleration of -9.81
m.s-1, flight t = time of flight from the instant of take-off to the instant of landing, h =
vertical displacement of the subject's centre of gravity, m = body mass (kg). Vertical
displacement of the subject's centre of gravity was defined as the difference between
the height of the centre of gravity at the peak of the jump and the height of the centre
of gravity at the instant of take-off (Linthorne, 2001). CMJ force impulse
represented the change in body momentum occurring over the eccentric and
concentric phases of the jump (Siff, 2000). Displacement and body mass data were
used to derive CMJ peak power for each of the jumps analysed using the peak power
regression equation published by Sayers et al., (1999). Peak power was calculated as
follows:
Peak power (w) = (60.7 • Jump displacement) + (45.3 • m) -2055
Validation of this power prediction equation against direct power measures on a
force platform indicates a high degree of accuracy in the equation with only a 2.7%
65
overestimation of peak power using countermovement jump scores (Sayers et al.,
(1999). All force values were normalised to units of N / per kg body weight, and all
power values normalised to units of Watts/per kg body weight.
Figure 9. A representative vertical ground reaction force curve (normalised for
bodyweight) showing the different phases of the CMJ and the peak concentric force.
Rationale
The Countermovement jump test is a test of explosive leg strength – an attribute
considered important for rugby forwards for successful performance in scrum, ruck,
maul and line out situations (Carlson et al., 1994; Nicholas, 1997; Rigg & Reilly,
1988). Line-out jumping requires players to generate explosive leg strength to
achieve maximal jumping height. A line-out jump is characterised by a stretch-
shorten cycle (SSC) movement, in which a rapid concentric contraction of the muscle
is preceded by a rapid stretching of the muscle, otherwise known as a
countermovement. The production of leg power in a line-out jump relates to the
capacity of the knee, hip and ankle extensor muscles to rapidly develop force during
the SSC movements. The countermovement jump assesses the speed-strength
qualities of the lower limb musculature during eccentric and concentric hip, knee and
ankle extension. This reflects the lower-body movements of players during a line-out
jump, and can be considered as a valid measure of leg power (Wilson & Murphy,
1995; Zamparo et al., 1997).
-1500-1000
-500
0500
10001500200025003000
0 0.5 1 1.5 2 2.5 3
Time (s)
Ver
tical
Gro
und
Rea
ctio
n Fo
rce
(N) Eccentric phase
Concentric phase
Flight phase
Peak concentric force
66
Obtaining direct force measurements of an explosive jump action enabled analysis of
the force production capabilities of players such as the countermovement jump force
impulse. Direct measurement of force involved the use of a ground reaction force
plate to measure the player’s capacity to develop force over time (Cordova &
Armstrong, 1996; Dowling & Vamos, 1993; Wilson & Murphy, 1995). This included
analysis of force-time curves produced from a countermovement jump to determine
ground reaction force parameters such as the maximum force generated, force
impulse and peak power output.
In sporting activities such as weightlifting and high jumping, both the rate of force
development and the maximum force produced strongly relate to performance
(Hakkinen, Kauhanen, & Komi, 1986; Viitasalo, 1985; Wilson & Murphy, 1995).
For explosive movements such as SSC jumps, in which the force contact times are in
the order of 330 to 370ms (Hakkinen et al., 1986; Harman, Rosenstein, Frykman, &
Rosenstein, 1990), the rate at which force is developed has been suggested to be the
most important physical capacity (Schmidtbleicher, 1992). The maximal force
generated during a SSC jump has also been shown to significantly influence vertical
jump performance (Baker, 1996). Furthermore, these force-time parameters were
selected as they represent factors that differentiate specific aspects of the player’s
capacity for explosive strength (Cordova & Armstrong, 1996; Wilson & Murphy,
1995) (Dowling & Vamos, 1993),
Acceleration and Sprint Running Test
The sprint test session consisted of 3 trials of running a distance of 40 meters. All
sprint performances were conducted on an outdoor grass surface that was checked to
ensure surface compliance (dry, level and short grass length) before each testing
session. In addition, an attempt was made to limit sprint testing on days in which
wind or high temperatures may have affected sprint performance. An electronic
timing system accurate to 0.01 s was used for all acceleration runs (Speedlight Sports
Timing System, Swift Performance Equipment, Australia). This incorporated 4 sets
of retro-reflective timing gates, with dual beam modulated visible red lights passing
between them. For the sprints, timing gates were set to upper-torso height for each
67
participant to give the most reliable recording of data. Four sets of timing lights were
set up at 5m intervals between the start lines, providing timing data over 10, 20, and
40m distances. A start line was marked on the ground approximately 30 cm behind
the first timing lights.
Procedure
Participants performed a self-selected warm-up, which included light to moderate
jogging for 5 – 10m followed by stretching. Subsequently, each participant
performed 3, 20m practice trials at near maximal effort. Upon commencement of the
sprints, participants assumed a standing start position behind the start line. They were
instructed to begin from a crouched position with the head positioned over the toes
and were not permitted any shoulder movement. They were then instructed to start
when ready, thus eliminating the influence of reaction time on sprint performance.
Participants then sprinted 40m, with split times being electronically recorded at 10,
20 and 40m. They were instructed to run as quickly as possible over the 40m
distance making sure not to slow down before the finish line.
At the conclusion of each sprint, participants recovered by walking back to the
starting position. This allowed them a recovery time of approximately 3 to 5min.
between trials. The test provided a measure of running velocity over 0 –10m, 0 –
20m, 20 – 40m and 0 – 40m distances for each 40m sprint. Running velocity was
measured to the nearest 0.01s, with the fastest time from the 3 trials being recorded.
Rationale
The measurement of acceleration and speed is a vital component in the assessment of
physical capability. Speed and acceleration are important qualities in rugby, with
good running speed over short distances fundamental to successful performance. The
ability to accelerate appears to be a critical factor in performance for forwards given
that the mean duration of sprints is less than 3s and the maximal sprint duration is
less than 5s (Deutsch et al., 1998; Duthie, 2003). From a standing start, this would
allow a player to cover approximately 30 to 40m, with the short time course
precluding the attainment of maximal velocity: track sprinters typically reach
68
maximal velocity after 40m (Benton, 2001). However, according to Delecluse (1997)
the ability of a player to achieve high running velocities is dependent on the
performance of the player over the acceleration and transition phases of a sprint.
During the acceleration phase, players acquire high initial acceleration over the first
10m of the sprint, whereas the transition phase involves the attainment of high
velocities over the next 30m of the sprint. Therefore, a sprint running test employing
distances of 10, 20, and 40m should measure the acceleration of the player and their
ability to develop high running velocities over match-specific sprint distances.
Assessment of sprint times at these intervals allowed for analysis of the player’s
capacity to develop velocity over short time periods.
Coaches' Evaluation of Football Playing Ability
The coaching staff of the Reds Rugby College designed position analysis proformas
to assess the level of development in game skills for forward players on scholarship.
These proformas comprised the skills and tactical abilities determined by the Red's
Rugby College to be important in each of these positions. These skills/ abilities were
categorised under 6 key competency areas of rugby match play including attack,
defence, continuity, scrum, line-out, restarts and other (attitude toward physical
training and penalties conceded). The current investigation utilised the skill/ability
criteria in the proforma (Appendix 2) to determine coaches’ ratings of Football
Playing Ability (FPA) in all subjects who had previously been measured for physical
capacities. In this study, the original performance criteria were modified to include a
common set of performance skill required by all forward players. Modifications
included the exclusion of line-out criteria, which is not a performance skill required
by all forward players and the addition of seven core physical capacity criteria (speed,
endurance, agility, mobility, static scrummaging strength, dynamic upper body
strength, and dynamic strength in rucking and mauling). These criteria were deemed
essential for rugby performance by Reds Rugby College coaches with the strength
criteria relating specifically to different contact phases of forward play in rugby
union. The modified criteria where then used to develop a performance evaluation
tool which enabled coaches to subjectively evaluate forward players which
subsequently could be converted into final continuous scores for the analysis (Figure
10).
69
Figure 10. Model showing the stages involved in Coaches’ Evaluation of Football
Playing Ability.
In order to establish the relative importance of these identified skill and capacity
criteria with respect to FPA, 3 expert coaches were then requested to rate the relative
importance of each skill and physical capacity measure using a 9-point scale
developed by Secunda, (1986), and identified in the table below.
Table II. Nine-point performance skill rating scale.
5 - Must have this skill highly developed 4.5
4 - Should have this skill well developed 3.5
3 - This skill is important, but need not be highly developed 2.5
2 - This skill is unimportant, minimal development necessary 1.5
1 - This skill is unimportant and not needed
Following the ranking procedure weights were assigned to each skill and physical
capacity relative to its importance to the overall forward position as determined by an
average of the 3 ratings. The skills and physical capacities that were deemed to be
important to football playing ability were more influential in the rating process than
Determine performance skill
Weighting of criteria
Measurement scale - 7 point
Performance over 3 month comp.
One coach from each team
A. Development of Coaching Evaluation
Tool
C. Calculation of Final
Scores
B. Coaches
Evaluations
Weighted scores for each
Sum of weighted scores for each skill
PHYSICAL CAPACITIES
SCORE
PERFORMANCESKILLS SCORE
70
those skills that were considered to be less important. The three coaches were current
or former state or national level coaches and all were highly experienced in selecting
and matching individuals to the various forward positional roles. A 7 point
measurement scale was added to the performance evaluation tool which allowed
coaches to grade skills and capacities on a scale of 1 (poor) to 4 (excellent)
(Appendix 2).
One highly experienced coach / selector from each of the 8 rugby teams was then
given the tool with the scoring criteria at the beginning of the rugby season and asked
to evaluate the FPA of their respective players who were subjects in the study. The
instructions to the team coaches were to observe the performance of the players over
the 16 fixtures and then rate the players at the end of the season on all the criteria in
the performance evaluation tool using the measurement scale.
The final phase of coaches’ evaluations involved calculation of performance skill and
physical capacity scores. Initially, a weighted score for each of the core skills and
physical capacities was determined by multiplying the coaches’ original scores by
the weighting factor for each skill or capacity. The final performance skill score was
calculated by summing the weighted scores in each skill/ability (physical and
cognitive) criteria occurring in the 6 key principle areas of forward match play
(Table III). The final physical capacity score represented the sum of the weighted
scores in each of the 7 physical capacity criteria (Table III).
71
Table III. Criteria for rating of football playing ability performance.
PRINCIPLES OF PLAY
CORE SKILL/COGNITIVE
ABILITIES/PHYSICAL CAPACITY
SPECIFIC SKILLS
ATTACKING Ball Handling Catching Passing Ball on Ground Ball Carry Running Lines Identification of Space Support Running Lines Communication Offloads
CONTINUITY Winning the Tackle Situation Attacking Shoulders Leg Drive Ruck Clean Out Pick and Drive Effectiveness Decision Making Maul Effectiveness Body Height Leg Speed
DEFENCE Tackle Technique Impact Low Tackle Alignment Positioning Communication Pressure Denying Time and Space Tracking Attitude
SCRUM Body Position Shape Height
RESTARTS Movement to Ball Catch Catch Handling in Contact
OTHER Penalties Conceded
Attitude towards physical training
Speed Agility
Mobility Endurance
Dynamic Upper Body Strength Static Scrummaging Strength
Dynamic Strength in Rucking and Mauling
72
Statistical Analysis
Statistical procedures were conducted using SPSS 11.5 for Windows. Descriptive
statistics for each of the anthropometric, force, sprint, CMJ and coaches’ evaluations
of performance skills and physical capacities were calculated for each of the forward
positional groups, that is, prop, lock and loose forwards. These included means and
standard error of the mean. Within-subject coefficients of variation were determined
for each subject in all of the strength and speed variables listed above, as well as
CMJ displacement of centre of gravity. Calculations were performed by comparing
the average mean and standard deviation of a player’s performance over 3 trials
(Appendix 4).
One-way Analysis of variance (ANOVA) was used to examine whether there were
statistically significant differences between positional groups in the strength, speed,
CMJ variables and physical capacity and performance skill scores listed above.
Differences among groups were considered statistically significant at the level of
p<0.05. Where the results of the ANOVA indicated significant F-ratios between
groups, a Scheffé test was applied post hoc to determine in which groups the
differences occurred. However, as indicated earlier the relatively small numbers
within each positional category limited the statistical power of the data and the
ability to identify statistically significant differences. To compensate for the lack of
power and to provide an indication of the magnitude of the differences across the 3
positional groups, the effect size (ES) was calculated using the method outlined by
(Thomas, Salazar, & Landers, 1991). The effect size for each level of comparison
was calculated by taking the difference between the group means, and dividing by
the pooled standard deviation of the groups. The magnitude of the effects were
interpreted according to the criteria of Hopkins, (2002), from which an effect size of
0.2 is considered to represent small differences between groups; an effect size of 0.6
shows a moderate difference between groups and an effect size of 1.2 signifies a
large difference between groups. Where statistical significance was not established,
retrospective power calculations were also performed to identify the power
associated with the comparisons in question. Power calculations were performed on
those strength, speed and CMJ test variables which showed a moderate effect size
difference between two positional groups. The power for each level of comparison
73
was calculated using a SPSS Syntax command (see Appendix 4) for a given
difference between two mean values and the group sample sizes, and the standard
deviation for each group mean. Estimated sample sizes for each positional group
were calculated to provide an indication of the sample sizes required to show a
statistically significant difference between positional groups in variables of 0 - 10, 0 -
20, 20 - 40m sprint times, sustained horizontal force, horizontal impact force, and
vertical jump displacement and force impulse variables. Sample size estimations
were calculated using a formula outlined by Hopkins (2001), from which the total
number of subjects (N) is given by: N = 32/ES2, where ES is the smallest effect size
worth detecting.
Pearson product – moment correlation coefficients were calculated to determine the
strength of the relationship between the weighted physical capacity and performance
skill scores and strength, speed and power variables. Backward multiple linear
regression analysis was performed to identify separate prediction models for the
outcome variables (weighted physical capacity and performance skill scores) from 3
performance test categories including force ergometer, sprint running, and
countermovement jump tests. The backward regression technique enters all of the
predictor variables into the analysis in a single step and then removes them one at a
time based on the removal criteria, which in this case was set as inclusion p < 0.05
and exclusion p > 0.1. The technique was used to determine 1) significant
relationships between the coaches’ physical capacity and performance skill scores
and the physical performance variables, and/or 2) the relationship trends between the
coaches’ physical capacity and performance skill scores and the physical
performance variables. For statistical strength, the data from a minimum of 5 to 10
subjects is required for each predictor measure in a linear equation. Therefore, a
maximum of 4 predictor variables was used in these prediction equations. The
significance level for selecting variables that contributed to explaining the variation
in physical performance scores and performance skill scores was set at p ≤ 0.05.
Regression analysis indicates the linearity of the relationship between the predictor
measures and the outcome variable (Thomas & Nelson, 2001). A regression
coefficient (R) of 1.00 describes a perfectly linear relationship, whereas a regression
coefficient (R) of 0.00 describes no relationship. The multiple R indicates the
74
proportion of variance in the outcome variable (e.g., physical capacity score) that can
be explained by the predictor measures. A multiple R of 0.75 indicates, for example,
that 75% of the outcome measure data variability for a group can be adequately
calculated from the predictor measure using the linear equations. The adjusted
multiple R, was chosen over the sample multiple R to assess the proportion of
variance explained by the predictor variables. The adjusted multiple R provides a
more accurate estimate of the goodness-of-fit of the prediction model as it considers
both the number of predictor variables and the sample size, resulting in a shrinkage
multiple correlation coefficient which is approximately corrected for the upward bias
of the sample multiple R.
75
Chapter 4
RESULTS
Data obtained from the cross-sectional sample of Premier rugby union forwards were
used to determine the differences in speed, horizontal forces and vertical force and
power characteristics between forward playing positions (props, locks and loose-
forwards). These factors were also related to the coaches’ evaluations of performance,
skill ability and physical capacity ability required for successful performance in these
positions.
Anthropometric Characteristics of the Sample
A summary of the age, height, and body mass characteristics of the forward players
and the effect size differences between the three forward positional groups are
presented in Table IV. In terms of body mass, although the effect size results
indicated the prop forwards and lock forwards had a greater mean body mass than
the loose-forwards a one- way analysis of variance test showed that these differences
were not statistically significant (p =0.56). There was a statistically significant
difference between the positional groups for height (p =0.000), with the post-hoc
analysis revealing the lock forwards were significantly taller than the prop forwards
(p =0.000) and loose-forwards (p =0.000).
Table IV. Anthropometric Characteristics of Premier Rugby Union Forwards (Means ± SEM) Positional Group Prop Forwards Lock Forwards Loose-forwards Characteristic (n = 8) (n = 5) (n = 9) Age (years) 21.6 ± 1.0 19.4 ± 0.7 21.6 ± 0.7 Height (cm) 180.6 ± 0.8* 194.6 ± 2.1* ◊ 180.6 ± 1.4 ◊ Body mass (kg) 109.1 ± 5.4 # 102.9 ± 2.1 ◊ 94.6 ± 3.4 # ◊ Height * Effect size = 4.21; ◊ Effect size = 3.17, Body mass # Effect size = 1.13; ◊ Effect size = 0.96
76
Force Ergometer Measures
The results of the static and dynamic horizontal force assessments of the players in
positional groups are presented in Figure 11. Application of the one-way analysis of
variance procedure showed that there were no statistically significant differences
between positional groups in sustained horizontal force (p = 0.139), horizontal
impact force (p = 0.139), dynamic horizontal force (p =0.460) and relative dynamic
horizontal force (p = 0.208). The effect size statistics indicated a moderate mean
difference between groups for sustained horizontal force with the prop forwards (µ =
2555.8 ± 120.2N) and loose forwards (µ = 2466.3 ± 98.9N) generating greater mean
force than the lock forwards (µ = 2146.4 ± 203.4N). The group standard error of the
means (SEM) indicate a higher degree of variation in sustained horizontal force for
lock forwards relative to the prop and loose forward playing positions. As seen in
Figure 11, the greater variation in the lock forwards may be due to 2 out of the 5
subjects achieving considerably lower force values than the others.
The horizontal impact force data shows the prop forwards (µ = 5357.0 ± 308.4N) and
the lock forwards (µ = 5056.9 ±537.5N) produced moderately more mean horizontal
impact forces than the loose forwards (µ = 4409.0 ± 290.7N). Across all playing
positions, the average within-subject coefficient of variation for the horizontal
impact force measure (8.2%) was higher than the coefficients for the sustained
horizontal force (3.2%) and dynamic horizontal force measures (3.4%). The high
within-subject variability may be due to 1 subject in the lock and prop forwards and
2 subjects in the loose forwards achieving impact force values in the range of 3.6 -
6.8 SEM outside the group means (Figure 11).
There was a moderate effect size difference between groups for dynamic horizontal
force with the prop forwards (µ = 1456.1 ± 25.1N) producing a higher average force
than the loose forwards (µ = 1392.2 ± 38.1N). Expression of the dynamic horizontal
force values relative to body mass revealed moderate mean differences between
positional groups with the loose-forwards (µ = 14.78 ± 0.35N.kg-1) producing higher
average dynamic horizontal force than the lock forwards (µ = 13.73 ± 0.55N.kg-1)
and prop forwards (µ = 13.57 ± 0.67N.kg-1). The power calculations in group
77
comparisons of horizontal sustained and impact force variables (Appendix 4)
indicated power in the range of 21.4 – 60.9%.
Sprint Running Times
Figure 12 illustrates the results of the sprint performances at distances of 0 -10, 0 -
20, 20 - 40, 0 - 40 m for the forward players. The results showed a common pattern
over all sprint distances with the prop forwards recording slower mean times than the
lock and loose forwards. The latter two groups recorded similar sprint times. No
statistically significant differences were found between the positional groups for
sprint times over 0-10m (p =0.111), 0-20m (p =0.054) and 20-40m (p =0.053) sprint
distances. A significant difference (p =0.049) was shown between forward positional
groups over the 0-40m sprint distance, however the post hoc analysis did not confirm
the precise location of this difference among groups. Figure 6 (D) shows that the
greatest apparent effect size difference associated with the 0-40m mean sprint time
occurred between prop (µ = 5.89 s) and loose forwards (µ = 5.55 s) with the latter
group recording the lower sprint time. Moderate effect size differences between
positional groups were evident in the 0-40m sprint performances with the loose
forwards recording lower sprint times than the prop forwards over 0-10 m (LF µ =
1.82 s: P µ = 1.91 s), 0- 20m (LF µ = 3.11 s: P µ = 3.29 s), and 20-40 m (LF µ = 2.43
s: P µ = 2.60 s). A similar trend in mean sprint times was apparent for lock forwards
and prop forwards with the former group demonstrating moderately lower sprint
times over sprint distances of 0 -10m (L µ = 1.82 s: P µ = 1.91 s), 0 - 20 m (L µ =
3.13 s: P µ = 3.29 s), 20 - 40m (L µ = 2.39 s: P µ = 2.60 s), and 0 – 40m (L µ = 5.51
s: P µ = 5.89 s). The power calculations in group comparisons of all sprint
performance variables (Appendix 4) indicated power in the range of 30.1 - 65.1%.
78
Positional Group
Lock Forwards Prop Forwards Loose-forwards
Sust
aine
d H
oriz
onta
l For
ce (N
)
0
1500
2000
2500
3000
(2)(2)
Positonal Group
Lock Forwards Prop Forwards Loose-forwards
Hor
izon
tal I
mpa
ct F
orce
(N)
02000
3000
4000
5000
6000
7000
(5)
Positional Group
Lock Forwards Prop Forwards Loose-forwards
Dyn
amic
Hor
izon
tal F
orce
(N)
01200
1300
1400
1500
1600
Figure 11. Differences in sustained horizontal force (A), horizontal impact force (B)
and dynamic horizontal force (C) between forward positional groups: Individual
values and means expressed as ●
Effect Size: * = 1.06 # = 0.90 *
#
*#
Effect Size: * = 1.09 # = 0.65
* # *
#
Effect Size: # = 0.66
##
A
B
C
79
Positional GroupLock Forwards Prop Forwards Loose-forwards
Sprin
t Tim
e (s
)
0.001.70
1.75
1.80
1.85
1.90
1.95
2.00
Positional Group
Lock Forwards Prop Forwards Loose-forwards
Sprin
t Tim
e (s
)
0.003.00
3.05
3.10
3.15
3.20
3.25
3.30
3.35
3.40
Positional Group
Lock Forwards Prop Forwards Loose-forwards
Sprin
t Tim
e (s
)
0.002.25
2.30
2.35
2.40
2.45
2.50
2.55
2.60
2.65
2.70
Positional Group
Lock Forwards Prop Forwards Loose-forwards
Sprin
t Tim
e (s
)
0.005.20
5.40
5.60
5.80
6.00
6.20
Figure 12. Differences in 0 – 10m (A), 0 – 20m (B), 20 – 40m (C), 0 – 40m (D)
sprint performances between forward positional groups: values expressed as Means ±
SEM.
Countermovement Jump Measures
Countermovement jump (CMJ) displacement of the centre of gravity, relative
concentric force, relative power and force impulse values for each of the forward
positional group are presented in Figure 13. A one-way analysis of variance test
showed no significant difference between forward positional groups in vertical
displacement of the centre of gravity (p =0.236), relative power (p =0.241), relative
concentric force (p=0.783) and force impulse (p=0.060) during a countermovement
jump. The effect size statistics revealed a moderate mean difference between groups
for CMJ vertical displacement of the centre of gravity (COG) with the lock forwards
(µ = 41.8 ± 5.4cm) producing greater mean vertical displacement than the loose-
◊ #
# ◊
Effect size: # = 0.83 ◊ = 0.91
#
#
◊
Effect size: # = 1.12 ◊ = 0.99
# ◊
# ◊
A B
C D
◊
# ◊
Effect size: # = 0.91 ◊ = 1.13
Effect size: # = 1.03 ◊ = 1.08
# ◊
80
forwards (µ = 34.3 ± 1.3cm) and the prop forwards (µ = 35.0 ±3.1cm) with these last
two groups recording similar mean displacement values. As seen in Figure 13, the
higher variability in countermovement jump displacement values between subjects in
the lock forwards relative to prop and loose forwards may be due to the displacement
score of 1 lock forward which was considerably higher than the other subjects in this
group. The relative power data indicate the lock forwards (µ = 50.0 W.kg-1)
generated more mean power per kilogram of body mass during the countermovement
jump than the loose-forwards (µ = 45.61 W.kg-1) and the prop forwards (µ = 46.17
W.kg-1), as indicated by the moderate effect size differences between these pairs of
groups. The power calculations in group comparisons of CMJ displacement of COG,
relative power and force impulse (Appendix 4) indicated power in the range of 21.2
– 72.8%.
Positional Group
Lock Forwards Prop Forwards Loose-forwards
CM
J D
ispl
acem
ent (
cm)
0
10
20
30
40
50
60
70
Positional Group
Lock Forwards Prop Forwards Loose-forwards
CM
J Rel
ativ
e Po
wer
(w/k
g)
042
44
46
48
50
52
54
56
Figure 13. Differences in countermovement jump vertical displacement of COG (A),
and CMJ relative power (B) between forward positional groups: (A) individual values
and mean expressed as ●; (B) values expressed as Means ± SEM.
Coaches Weighted Physical Capacity (WPCS) & Performance Skill Scores (WPSS)
The mean physical capacity score and performance skill scores for the 3 forward
positional groups are demonstrated in Figure 14. A one-way analysis of variance test
B
Effect size: # = 0.67 ◊ = 0.95
Effect size: # = 0.68 ◊ = 0.99
A
# ◊
# ◊
#◊
# ◊
81
showed no significant difference between forward positional groups in the mean
physical capacity score (p =0.227) and mean performance skill score (p =0.477).
There were moderate effect size differences between pairs of groups for the mean
physical capacity scores with the loose-forwards (µ = 86.6 / 111.3 points) obtaining a
higher score than the prop forwards (µ = 76.6 / 111.3 points) and the lock forwards
(µ = 79.0 / 111.3 points). The latter two groups recorded similar mean physical
capacity scores. In terms of the performance skill scores, the effect size statistics
indicate a moderate difference in the mean scores between the lock forwards (µ =
393.6 / 504.3 points) and the prop forwards (µ = 366.7 / 504.3 points), with the
former group obtaining the higher mean performance skill score. The loose forwards
(µ = 387.5 / 504.3 points) recorded similar mean performance skill scores to the lock
and prop forwards.
Positional Group
Lock Forwards Prop Forwards Loose-forwards
Phys
ical
Cap
acity
Sco
re (/
111.
3 po
ints
)
70
75
80
85
90
95
Positional Group
Lock Forwards Prop Forwards Loose-forwards
Perf
orm
ance
Ski
ll Sc
ore
(/504
.3 p
oint
s)
340
350
360
370
380
390
400
410
Figure 14. Differences in weighted physical capacity scores (A) and weighted
performance skill Scores (B) between forward positional groups: values expressed as
Means ± SEM.
Correlations between Coaches’ Scores and Force, Sprint and Countermovement
Jump Variables
The correlation coefficient between the coaches’ physical capacity and performance
skill scores and the anthropometric and physical performance measures are provided
# ◊
◊
Effect size: # = 0.76 ◊ = 0.69
#
A B Effect size: # = 0.66 #
#
82
in Table V. A correlation matrix was also computed showing all the possible
correlations between pairs of predictor variables (Appendix 5). The predictor
measures significantly related to the physical capacity scores estimated by the
coaches included 20m sprint performance (r = -0.47, p < 0.05), 40m sprint
performance (r = -0.53, p < 0.05) and 20 - 40m sprint performance (r = -0.56, p <
0.01). With respect to the performance skill scores estimated by the coaches, both the
40m sprint performance (r = -0.51, p < 0.05) and the 20 - 40m sprint performance (r
= -0.57, p < 0.01) were significantly negatively correlated. No significant correlations
were found between the force ergometer and countermovement jump variables and
the physical capacity and performance skill scores.
Table V. The relationship between anthropometric, performance measures and
coaches’ physical capacity and performance skill scores: values expressed as
correlations (r) (n = 22 for all variables).
Measurement Correlations with
WPCS Correlations with
WPSS Height (cm) 0.12 0.38
Body mass (kg) -0.20 -0.16
Sustained scrum force (N)# 0.21 0.01
Impact force (N)# -0.33 -0.16
Peak dynamic force (N)# # 0.22 0.21
10 m sprint (s) -0.42 -0.39
20 m sprint (s) -0.47* -0.42
40 m sprint (s) -0.53* -0.51*
20 – 40 m sprint (s) -0.56** -0.57**
CMJ displacement (cm) 0.35 0.40
CMJ peak force (N.kg-1) 0.13 0.10
CMJ peak power (W.kg-1) 0.32 0.38 CMJ impulse (N.s) 0.17 0.25 # Force ergometer measure obtained with the subject pushing against a static resistance. # # Force ergometer measure obtained with the subject pushing against a dynamic resistance. CMJ = countermovement jump; WPCS = weighted physical capacities score; WPSS = weighted performance skill score. * p < 0.05, ** p < 0.01.
83
The Relationship between the Coaches’ Estimate of Physical Capacity and
Performance Skill
Figure 15 demonstrates a high correlation between the physical capacity scores and
performance skill scores in Premier rugby union forwards (r = 0.76, p < 0.01), as
rated by Premier rugby coaches. The coefficient of determination of 0.58 indicates
58% shared variance between physical capacity and performance skill scores.
Figure 15. The relationship between coaches' physical capacity and performance
skill scores in 22 Premier rugby union forwards.
Prediction of Coaches’ Physical Capacity and Performance Skill Scores from
Force, Sprint and Countermovement Variables
Backward multiple linear regression analysis was performed to identify separate
prediction equations for the outcome variables (coaches’ physical capacities score
or performance skills score) from 3 performance test categories including the sports
force ergometer, sprint performance, and countermovement jump tests. The
independent variables entered into each prediction model included specific
performance test measures as well as one anthropometric measure - body mass
(Table VI). Body mass was selected for inclusion in each prediction model, as it
was likely to be a factor in force and speed development.
r = 0 .7 6( p < 0 .0 1 )
0
2 0
4 0
6 0
8 0
1 0 0
1 2 0
2 0 0 2 5 0 3 0 0 3 5 0 4 0 0 4 5 0 5 0 0P e r fo rm a n c e S k il l S c o re ( /5 0 4 .3 p o in ts )
Phys
ical
Cap
acity
Sco
re (/
111.
3 po
ints
)
84
Table VI. Multiple regression equations, adjusted R, variance, and standard error of
the
estimate for the individual performance tests and the outcome variables (WPCS and
WPSS).
R Performance test category Variance (adj. R2) (variables in model) Multiple-regression equations SEE Force Ergometer Sustained scrum force (N)* WPCS = 0.050 x (peak dynamic force) 0.42 Impact force (N)* - 0.006 x (impact force) + 39.80 18 % Peak dynamic force (N)** (p = 0.059) 11.03 points Body mass (kg) Sprint Performance 20 m sprint (s) WPCS = 0.330 x (body mass) - 55.143 0.56 20 – 40 m sprint (s) x (20 - 40 m sprint) + 184.558 31 % Body mass (kg) (p = 0.007) 10.11 points
20 m sprint (s) WPSS = 1.426 x (body mass) - 207.030 0.60 20 - 40 m sprint (s) x (20 - 40m sprint) + 750.125 36 % Body mass (kg) (p = 0.006) 33.89 points Countermovement Jump CMJ displacement (cm) WPCS = 2.064 x (CMJ displacement) 0.35 CMJ force impulse (N.s) + 306.420 12 % CMJ relative force (N.kg-1) (p = 0.062) 39.64 points * Force ergometer measure obtained with the subject pushing against a static resistance. **Force ergometer measure obtained with the subject pushing against a dynamic resistance. CMJ = countermovement jump. CMJ = countermovement jump; WPCS = weighted physical capacities score; WPSS = weighted performance skills score.
The multiple regression equations utilising force ergometer, sprint and
countermovement jump variables to predict coaches’ scores are shown in Table VI.
The equation from the model targeted for force ergometer test variables indicate that
a higher dynamic force and increased production of force on impact may have a
minor contribution in predicting player physical capacity scores estimated by coaches.
A shared variance of 18% between force ergometer measures and WPCS factors
85
combined with a model p value of 0.059, demonstrate a trend for a relationship
between these factors. Similarly, the regression model targeted for the CMJ test
demonstrates a trend for a relationship between player physical capacity scores and
jump displacement with the model indicating a shared variance of 12% between these
factors.
While these two findings represent trends in the data, a regression model aimed at
sprint performance indicating the 20 – 40m sprint time was significantly related to
WPCS (Table VI). The sprint performance model resulted in an adjusted R value of
0.56 indicating approximately one-third of WPCS (31 %) can be explained by the 20
- 40m sprint time along with body mass, with the largest relative contribution
assigned to the 20 – 40m sprint time (28%). This result indicates that a lower body
mass and quicker running speeds over 20 - 40m are important for estimating player
physical capacity scores.
The only prediction model to produce an equation related to player performance skill
scores estimated by coaches involved a sprint performance measure. The regression
model to predict player performance skill showed sprint time over 20 – 40m and body
mass contributed significantly to the regression equation, resulting in a an adjusted R
value of 0.60. The model was highly significant (p = 0.006) and accounted for 36% of
the variance in performance skill. The largest contributing variable was sprint time
over 20 – 40 m (29%), while body mass accounted for 7% of the common variance.
The model indicates players with lower body mass and quicker running speeds over
20 – 40 m were scored as having greater performance skills.
86
Chapter 5
DISCUSSION
This chapter discusses how the physical performance characteristics of rugby union
forwards, measured in a field-testing environment, relate to coaches’ evaluations of
their level of development in football skills and physical attributes and to the position
they play during competition. Firstly, the problem is analysed by examining the
differences in physical performance characteristics between forward playing
positions in rugby union with data derived from forwards playing in the Premier
competition in Brisbane, Queensland. The physical performance variables measured
included different types of strength, speed and power parameters, which were
selected to reflect the requirements of forward play in rugby union. Secondly, the
relationships between the physical performance variables and coaches' evaluations of
performance skill and physical capacity were investigated.
The fulfilment of the original aims in the initial phase of this study was limited by
the inability to achieve the anticipated participation of rugby players at the level of
competition required. This reduced the numbers representing the different positional
groups within the forwards, resulting in a lack of statistical power in the data.
Consequently, where statistical significance was not obtained any difference in the
data with respect to mean differences between positional groups has been interpreted
with caution and effect size statistics used to demonstrate trends in the data. In
addition, retrospective power calculations were performed to identify the power in
comparisons which showed moderate effect size differences.
Physical Performance Characteristics and Forward Playing Positions
Sustained Horizontal Force
Isometric strength or sustained force, is essential for rugby forwards to effectively
perform activities such as scrummaging, which are predominantly static in nature
(Hazeldine & McNab, 1991). The sustained push following scrum engagement
requires players to maintain force application against the opposition pack in an
87
attempt to hold their position and push over the advantage line. To simulate this
activity in the current study, sustained horizontal force was determined as a measure
of the maximal sustained force applied by a player after impacting a rigid force
machine or ergometer, and while in a simulated scrummaging position.
Although the lock forwards generated 16% and 13% less mean sustained horizontal
force than the prop forwards, and loose forwards respectively, these differences were
not statistically significant due mainly to the small sample sizes and insufficient
statistical power in the study. In future studies, a sample size of 20 in each positional
group would increase the power to 80% and allow increased possibility of
demonstrating statistically significant differences in sustained horizontal force
between forward positional groups (refer to power calculations in Appendix 4). The
main findings of the current study are similar to those reported in an earlier study
(Quarrie & Wilson, 2000) which assessed the sustained horizontal force of individual
forward players exerted against an instrumented scrum machine. Their results
indicated that the sustained horizontal force produced by the prop, lock, and loose-
forwards did not differ significantly between groups. However, as in previous
research the mean sustained horizontal force outputs showed a different pattern
across positional groups, with the loose forwards producing 11% and 13% less
sustained horizontal force than the prop forwards and the lock forwards respectively.
The similar force measurements for lock forwards and prop forwards in this earlier
research may have reflected the similar body mass of the two positional groups
(props forwards mean =101.8 kg; lock forwards mean = 102.4kg). This is in contrast
to the findings in the present study which found a non-significant correlation
between these two variables suggesting that body mass did not have the same
influence on the sustained horizontal force measurements as compared to the
findings of earlier research. Although lighter, the loose forwards (mean =94.6kg)
showed higher mean force outputs than the heavier lock forwards (102.9kg).
A likely explanation for the lock forwards scoring lower mean force outputs than the
other 2 groups may be found in the wide within group variation in scores when
performing the test (Figure 11). The test results identified two subjects with
scrummaging force values that were consistently lower (coefficient of variations of
0.50% and 0.95%) than the other lock forwards. Qualitative observational analysis of
88
the performance of these 2 players indicated their inability to attain a low horizontal
body position, which may have contributed to their more limited effectiveness in the
application of forward force by comparison with other players in the group. More
detailed analyses of the biomechanics of scrummaging performance is necessary to
confirm this observed relationship with respect to the lock forwards, together with an
increased sample size to limit the effect of potential outliers within a group.
It has been suggested (Hopkins, 2000) that examination of the typical variation in a
player’s performance in repeated trials may in part explain the differences in force
estimates between subjects. For example, Quarrie and Wilson (2000) reported effect
size differences between players in different forward positions, however no
reliability data was provided to support the precision of these differences. In the
present study, the within-subject variation for each positional group represented by
an average coefficient of variation was 2.6% for lock forwards, 4.4% for prop
forwards and 2.7% for the loose-forwards group. This relatively low within-subject
variation for horizontal force estimates across positional groups indicates a high
degree of reliability in performance between trials for the static horizontal force test.
Importantly, the percentage differences in force estimates observed between lock
forwards and the other positional groups were not masked by the within subject
variation, therefore giving extra confidence that the effect sizes represent real
differences between the positional groups. Furthermore, the high reliability of the test
results indicated that subjects were well familiarised with the test conditions, thus
reducing the likelihood of learning effects influencing the performance test results.
The current finding of a substantially lower sustained horizontal strength in the lock
forwards is surprising given that the major role of lock forwards in the scrum is one
of transmitting large forward forces and maintaining pressure against team members
in the front row. This theory is supported by previous scrummaging research
(Milburn, 1990b) in which force data from a comparison of scrum sub-units
contributions showed lock forwards added 46% and flankers added only 20 to 27%
of forward force to that produced by the front-row during scrummaging against a
scrum machine. In this case, estimates of the sub-units contributions were made by
subtracting the total forward force on all 3 front row players from the total for the
complete scrum.
89
Comparisons of scrum force estimates across scrummaging research studies are
difficult due to different methodological definitions, procedures and measurement
devices. For example, previous research (Milburn, 1990a; Milburn, 1990b; Quarrie
& Wilson, 2000) reported individual sustained horizontal force values for prop
forwards ranging from 1420N – 1800N, which is approximately 800N less than the
sustained horizontal force estimates for players in similar positions in the current
research. This disparity in force data between the studies may be a consequence of
differences in the measurement tool and test set-up.
The earlier researchers used either force platforms incorporated into an extended
scrum machine (Milburn, 1990a; Milburn, 1990b) or strain gauge force transducers
fitted to the scrum machine (Quarrie & Wilson, 2000) to obtain force readings whilst
scrummaging. In these tests, force recordings were detected by two force platforms
which measured the pressure applied directly as the player pushed against the
shoulder pads of the scrum machine. In contrast, in the present study, one force
transducer linked by wires to the ergometer and operating under tension resistance
was used to measure the forces exerted by a player against the shoulder pads of the
sports ergometer. In these conditions, the force transducer is likely to detect a higher
magnitude of force as one maximal force is distributed over a small area as opposed
to measurement via force platforms, where forces are distributed over a larger area
and then averaged to detect the resultant force. In future research and in practical
measurement situations, a standardised approach to measure individual sustained
horizontal force would be more meaningful, including adoption of a standard
measurement tool. Such an approach could provide normative data, allow for direct
comparisons between performance levels and determine the level of isometric
strength required for superior performance across the different positions (Duthie et
al., 2003).
Horizontal Impact Force
In the game of rugby, the development of maximal impact forces from a stationary
position is critical for forwards in the scrum. Application of maximal force at
engagement is essential for forwards to maintain player position, thus providing
stability within the pack from which forward drive can occur during the scrum. The
90
magnitude of the impact forces at engagement depends on the ability of the players
to accelerate their own body mass at impact and a particular need to execute a rapid
and forceful extension of both legs prior to impact. In the current study, impact force
was determined as a measure of the maximal horizontal force applied by a player on
impacting a rigid force machine, which is similar to the situation found in relation to
force production at scrum engagement (Milburn, 1990a; Milburn, 1990b).
No significant differences across positional groups were observed in horizontal
impact force during a simulated scrummaging task. However, the effect size results
associated with this finding indicated mean differences between positional groups
with the prop forwards (difference = 17.7%) and lock forwards (difference = 12.8%)
generating moderately more mean impact force than the loose forwards. The trend
toward higher impact forces of the prop and lock forwards, relative to the loose
forwards may reflect a strength adaptation for these groups as a function of their
continued exposure to high impact forces in the scrum. The relatively high
coefficients of variation for prop, lock and loose forwards indicated a large variability
in horizontal impact force over the repeated trials which contributed to the lack of
statistical significance for this measure. In addition, the wide within group variation
in scores across playing positions when performing the impact force test (Figure 11)
may have also prevented greater discrimination between lock, prop and loose forward
playing positions for this measure. In future research, a sample size of 14 in prop and
loose forward playing positions may assist in reducing the within group variation in
horizontal impact force and in confirming the trend estimated by the effect size
between these two positions (refer to power calculations in Appendix 4). In addition,
more detailed biomechanical analysis of the engagement technique together with
increased sample sizes will assist in establishing the number of trials needed to reduce
the coefficient of variation.
In rugby union, the loose- forwards are required to undertake heavy physical contact
in scrums, rucks and mauls and are expected to use their explosive leg strength to
gain advantage in these situations. Consequently the trend toward lower impact force
values for loose-forwards was surprising given their superior ability to rapidly
develop maximal external force compared with other forward playing positions, as
91
indicated in their vertical jump performance results and previous research (Quarrie et
al., 1996; Quarrie & Wilson, 2000; Rigg & Reilly, 1988). However, data derived
from the vertical jump performance is specific to vertical based power movements
and as such may have little relationship to horizontally based movements as involved
in scrummaging. The mechanical, neural and structural differences between these
types of activities implies that muscle strength qualities, which underlie each of these
tasks, are specific to that particular task (Baker, Wilson, & Carlyon, 1994; Robinson
& Mills, 2000).
Two potential mechanisms exist to explain the lower mean impact force values
expressed for the loose forwards. Firstly, it is highly likely that the larger body mass
associated with the prop and lock forwards in this study assists in the development of
greater impact forces during scrummaging. The loose forwards were approximately
15 and 8 kg lighter than the prop and lock forwards respectively. As there is a strong
positive relationship between the absolute force a muscle can produce and its cross-
sectional area (Bosco & Komi, 1979; Semmler & Enoka, 2000), it is not surprising
that the heavier prop and lock forwards produced higher absolute impact force than
their lighter counterparts. The influence of body mass on the absolute force of a
muscle was reinforced by the finding of a significant moderate correlation (r = 0.44)
between body mass and impact force in this study.
Secondly, applying the mechanical concept of force production (Force = mass x
acceleration) to the analysis of the simulated scrum engagement enables the
determination of other potential performance limiting factors. In this case, the
magnitude of force applied by a single player to the rigid sports ergometer is
proportional to the mass of the subject, and the rate of change of velocity
(acceleration) at engagement (McClymont & Cron, 2002). On the assumption that
the subjects are of similar mass, the principle of conservation of momentum ensures
that the player who is moving faster at engagement will apply a greater force. Thus,
the ability of a player to initiate the movement rapidly from a stationary position is a
critical factor in the production of maximal force at impact (Siff, 2000). Hence the
finding of low impact force values in loose forwards as compared to lock and prop
forwards, may in part be attributed to differences and/or deficiencies in the
92
neuromuscular coordination of the leg and hip extensor musculature to accelerate the
body sufficiently to overcome their lower mass disadvantage.
Dynamic Horizontal Force
The development of horizontal power would appear to be an important factor for
successful participation in the contact phases of rugby which require a quick
application of force such as rucking, mauling and tackling. Absolute muscular
strength is generally accepted to be the basic quality affecting power output during
dynamic muscular activity (Baker & Nance, 1999; Cronin, McNair, & Marshall,
2000; Schmidtbleicher, 1992). During rucking and mauling activity, muscular power
must be produced against large external resistances, thereby increasing the
contribution of maximal strength to the overall power output (Moss, Refsnes,
Abildgaard, Nicolaysen, & Jensen, 1997). The dynamic horizontal force test used in
the present study was designed to assess a player's ability to exert maximal
horizontal strength during a dynamic pushing condition that simulates rucking and
mauling movements. As such, the data derived from the test is the first to provide
knowledge of the dynamic assessment of maximal horizontal force in rugby union
players during a dynamic rather than static situation.
No significant differences were found between the forward positional groups with
respect to mean dynamic force output. The effect size results associated with this
finding indicated mean differences between positional groups ranging from 1.4 –
4.4% with the prop forwards (difference = 4.4%) generating moderately more mean
dynamic force than the loose forwards. However, the within-subject variations (3.2 –
3.6% error range) which were of a similar magnitude to the percentage differences in
dynamic horizontal force output indicate that the effect size may not represent a real
difference between these two positional groups.
The lack of any clear differences in dynamic peak force between positional groups is
counter to the concept of positional role specificity with respect to this measure and
suggests that dynamic horizontal strength is a functional requirement of all forward
playing positions at the Premier rugby competition level. It is also noteworthy that
despite having a lighter body weight the loose-forwards were able to match other
93
forward players in absolute dynamic strength performance. However, any inferences
with respect to the interpretation of these results in relation to the game must be
considered with caution. It is difficult to determine whether the levels of strength
measured under laboratory conditions reflect the requirements for strength
performance under match conditions where the velocity of application of force may
be operating at a different level. More specific comparative data and specific analysis
of the influence of velocity of movement on force application under laboratory and
game conditions in rugby is required to substantiate these findings.
Qualitative analysis of the force-time and velocity-time characteristics from the
dynamic horizontal force test, indicated that as the subject exerted more force to
overcome an increasing external load, the velocity at which they were moving the
force ergometer slowly decreased over the 5-second test duration. Application of the
force-velocity relationship to this test condition indicates that as the movement speed
decreases, the force exerted by the subject begins to have a greater influence on the
resultant dynamic force production (Siff, 2000). During the test, the final dynamic
force was recorded at a time when the subjects were exhibiting very slow movements,
typically 0.5m.s-1. Therefore, given that maximal strength is a dominating factor in
the dynamic force readings and that prop forwards had greater dynamic strength, it
was anticipated that this group would produce higher mean dynamic force than the
loose forwards.
When the absolute dynamic force values were expressed relative to bodyweight, the
results indicated the loose-forwards generated on average 7.1% and 8.2% more
relative dynamic force than the lock and prop forwards respectively. These findings
are consistent with the positional demands which require loose forwards to propel or
accelerate their own bodyweight in an attempt to reposition themselves to stay
involved with the play (Deutsch & Sleivert, 2000). This result is also consistent with
the frequent involvement of loose forwards in ruck and maul play which requires
high levels of relative strength for activities involving acceleration, changing
direction quickly, and getting up off the ground.
The selection of the dynamic horizontal force test used in this study was justified in
that it provides an assessment of leg strength and upper back strength under dynamic
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conditions, in a manner which simulates the movement pattern experienced during
ruck and maul activity. The test mimics the horizontal body position and the driving
leg action utilised in ruck and maul play and in breaking through tackles. In addition,
although not evaluated in this study, the added specificity achieved in this test may
provide some measure of the neuromuscular performance and gross coordination
required in rucks and mauls (Sheppard, 2004). Further testing using the dynamic
force ergometer in the rugby context, should concentrate on integrating the force and
velocity characteristics of the performance to obtain data on the peak power outputs
of the forwards. In particular, the focus should be on measuring other factors which
may be more related to the development of muscle power in ruck and maul situations,
such as the duration of sustained peak power and the time needed to develop peak
power.
Sprint Running
The sprint running test measured the running performance of rugby forwards over a
40m sprint run in relation to their acceleration ability (0-10m sprint time), maximum
running speed (20 - 40m sprint time) and ability to combine acceleration and
maximum running speed (0 - 40m sprint time) over the 40m sprint distance. This is
the first study to consider the implications related to the different components of
sprint running in rugby union forwards.
The maximum running speed of rugby players is usually measured over sprint
distances of 30 - 40m, on the basis that these players develop close to maximum
running speeds over similar sprint distances during a game (Duthie, 2003). In the
current study, a 40m sprint running test was employed to measure the player’s ability
to develop acceleration and high running velocities. Examination of the current 0-
40m sprint results revealed that although a significant difference was found, no
significant differences were discovered by the Scheffé test when differences in 0-
40m sprint times were compared between forward positional groups. However, the
effect size results associated with these findings, indicate the greatest apparent
difference in 0-40m sprint time occurred between the prop and loose-forwards, the
latter group attaining an 8% lower mean sprint time than their prop counterparts. The
effect size results also indicate a trend for locks to be faster than prop forwards, with
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a 6.5% mean difference in 0-40m sprint times observed between these groups. These
findings indicate that upon further investigation, there is an increased possibility for
the data to represent real differences. This finding was apparent despite the
observation of increased within-subject variation for the prop forwards in
comparison to the other forwards over the 40m sprint distances. Increasing the
sample size to a minimum of 13 in each positional group would increase the power
of the analysis to 80% and allow a greater chance of identifying significant
differences between lock, loose forwards and props in 40m sprint times (refer to
power calculations in Appendix 4).
Both Rigg & Reilly (1988) and Quarrie et al (1996) assessed the sprint ability of
amateur rugby union forwards over 40m and 30m respectively, and measurements
were made with subjects adopting a standing, stationary start. Unlike the current
findings, their results indicated no significant differences between forward positional
groups in 40m sprint performance and much smaller group mean differences across
all sprint performances. In the earlier research, the 0-40m sprint performance showed
a similar pattern across positions to those observed in the current study, with the
loose and lock forwards evaluated as 1.6 – 2.2% and 4.1 – 4.3% quicker than prop
forwards respectively over 30 and 40m distances. The trend towards faster running
speeds for loose-forwards by comparison with other forward players suggest position
specific differences in sprint ability between these playing positions over 40 and 30m
sprint distances.
The trend toward faster sprint times over 40m sprint distances in the lock forwards as
compared to the prop forwards, is in contrast to previous research, which clearly
illustrates close matching of the sprint ability of lock and prop forwards (Quarrie et
al., 1996; Rigg & Reilly, 1988). The discrepancy in results may reflect differences
between players at different levels of competition as those players in the earlier
research were playing at a lower level of competition than in the present study. At
higher levels of competition such as in Premier rugby, there may be more specific
selection criteria for the performance requirements of forward positional groups. In
this study, the improved sprint ability of Premier lock forwards may relate to a
greater need for this group to apply explosive speed in the ruck and maul phase of
play at this level, as opposed to lower levels of competition. These claims could be
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more firmly supported with increased sample size to identify statistically significant
differences between forward positional groups.
Fast leg speed and acceleration can be considered as separate components of a
forward player’s game as they are often required to accelerate quickly over short
distances or develop a fast rate of cyclic leg movements during longer distances
(Duthie et al., 2003). It has been demonstrated that the ability to accelerate is the
dominant factor in the attainment of speed for short sprints between 5 and 15m in
length and in the initial 10-15m of longer sprints. Also in 40m or longer sprints, a
slow increase in running velocity with an emphasis on fast leg movement is evident
between approximately 15 and 40m (Delecluse, 1997). Therefore evaluation of
sprinting velocity for the interval between 20 – 40m can be considered to be a
measure of the maximum running velocity phase in sprinting. As the acceleration and
maximum running velocity phases of a sprint demonstrate prominent differences in
leg speed, EMG activity and force production (Mero, Komi, & Gregor, 1992; Van
Ingen Schenau, De Koning, & De Groot, 1994) it is important to discuss them
separately.
Acceleration Phase
Rugby forwards typically perform 10-15 short distance (10-20m) sprints during a
game, therefore the initial acceleration over the first 10m of a sprint may be a critical
factor in their performance. Consequently, in the present study, the initial
acceleration capabilities of forward players were assessed at sprint distances of 10m.
No significant differences between forward positional groups were observed in the 0-
10m sprint performance. However, the mean sprint times over 10m indicated that
both the lock and loose-forwards were 4.7% quicker than the prop forwards over the
10m acceleration phase of the running sprint. These results are in contrast to previous
research (Mednis, 2001) which assessed the 10m sprint performance of a group of
junior forwards who had been selected on their potential to play rugby at the senior,
elite level. In this earlier study, no trends were identified with respect to differences
in the 10m sprint times between loose forwards and a combined group of lock and
prop forwards. It is possible that had the lock and prop forwards been considered
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separately, differences in acceleration ability between forward groups may have been
shown.
For rugby forwards, short sprints most frequently occur over distances of 20m from a
standing, stationary start (Duthie, 2003). While it is expected that sprints over this
distance are characterised by a rapid change of velocity, previous research indicates
that the major acceleration phase occurs from 0 -10m (Delecluse, 1997). This earlier
finding was further investigated in this study by evaluation of sprinting performance
of the forward positional groups over 10 and 20m distances. The 0-20m sprint
performances showed a similar pattern across positions with the loose forwards
+5.5% and lock forwards +4.9% showing mean times that were lower than the prop
forwards. The lack of significance in positional differences for 0-20m sprint times is
largely due to the small sample sizes and insufficient power of the study. In future
research, a sample size of 23 in each positional group would increase the power to
80% and allow a greater chance of identifying statistically significant differences
between lock, loose forwards and props in 10 and 20m sprint times (refer to power
calculations in Appendix 4). The trend toward faster sprint times for the loose
forwards as compared to the prop forwards is consistent with their more frequent
involvement in running efforts during competition and suggests that the loose
forwards rely heavily on their acceleration abilities to continually reposition
themselves during ruck and maul phases of play (Deutsch et al., 1998; Duthie, 2003).
Maximum Running Velocity Phase
For rugby forwards, the ability to attain maximum speed quickly following a break
from the opposition is an important performance requirement for this group.
Maximum running velocity in rugby players is usually achieved in the latter part of
longer sprints of 30-50m and there is a lack of information on the ability of rugby
players to develop maximum running speed over these distances. Consequently, in
the current study sprinting times were obtained over the 20-40m sprint distance to
reflect the development of maximum running speed in typical sprint distances during
a match.
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No significant differences between forward positional groups were observed in the
20-40m sprint performance. However the effect size results associated with this
finding indicated moderate differences between positional groups with both the lock
and loose forwards showing +8% and +6.5% lower mean sprint times than the prop
forwards over the 20 - 40m sprint distance. The low statistical power (Appendix 4)
associated with these positional differences may have contributed to the lack of
statistical significance for this measure. Increasing the sample sizes to a minimum of
17 in each positional group would increase the power of the study to 80% and allow
increased possibility of demonstrating statistically significant differences in 20-40m
sprint times between forward positional groups. The inability of the prop forwards to
match the maximum running velocity of their lock and loose forward counterparts is
most likely associated with their larger body mass which was on average 15 and 8 kg
heavier than the loose and lock forwards respectively.
This increased body mass of the prop forwards is likely to be associated with a
reduced strength-to-weight ratio compared to the other forward players, given that a
greater body size is not associated with a proportional increase in strength (Wrigley
& Strauss, 2000). Since dynamic leg strength relative to body mass has been shown
to be an important factor in short sprints (Dowson, Nevill, Lakomy, Nevill, &
Hazeldine, 1998; Newman, Tarpenning, & Marino, 2004) and in maximum sprinting
speed (Young, McLean, & Ardagna, 1995), it is possible that the reduced strength to
weight ratio associated with prop forwards may be a contributing factor in the slower
sprint times achieved by this group. The influence of body mass on the sprint running
performance was reinforced by the finding in the present study of a significant
correlation (r = 0.68 – 0.80) between body mass and sprinting times.
In summary, acceleration and maximum running velocity sprint times measured over
distances of 0 -10 and 20 - 40m, appear to differentiate between forward positional
groups. These differences may reflect the specific performance requirements of these
positions and differences in anthropometric characteristics such as body mass. The
ability to accelerate is an important quality for all forwards players, but for those
playing as loose forwards, it represents a position specific characteristic or adaptation
associated with the need to perform an increased number of shorter sprints during a
match than the other forward positional groups. For prop forwards, acceleration
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ability may be less important, given their higher involvement in the physical contact
aspects of the game. Screening programs for the selection and monitoring of
performance in rugby forwards should include the evaluation of sprinting
performance over the shorter distances (10 – 15m) as the majority of sprint runs in
forward play involve the acceleration phase only.
Countermovement Jump Performance
The vertical jump test measured on a force plate was used to evaluate the forwards
ability to perform a skill involved in forward play. It also represented an indirect
measure of each player’s capability to utilise the Stretch-Shorten Cycle (SSC)
mechanism to optimise performance in countermovement jumps (CMJ). This type of
jump relates to the performance requirements of forward play such as line-out
jumping. However, jump performance in the line-out is not solely reliant on the
countermovement with the lifting technique of the props crucial in determining the
final position of the line-out jumper.
In the current study, the mean vertical displacement value achieved by the forwards
players during the CMJ force plate test was 36.3cm. This value is 15 – 20cm lower
than that reported for a group of 39 New Zealand premier rugby players (Quarrie &
Wilson, 2000) and a group of 35 talented US rugby players (Carlson et al., 1994).
Importantly, the measurement of vertical jump height in this earlier research,
involved execution of a jump which was more familiar to players as it allowed the
free use of arm swing and a self-selected depth of countermovement. This was in
contrast to the more restricted jumping technique used in the current study, in which
the arms were in a set position and the jump was performed without arm swing and
from a consistent and prescribed depth of jump. These restrictions were implemented
to ensure that the countermovement jump test provided a measure of jumping power
as a function of the activity of the lower limb extensor muscles, without the
contribution of coordinated arm movements.
Using this technique allowed determination of leg power which was lower than
values found earlier in international level rugby forwards using a similar jumping
technique (Warrington et al., 2001). In this earlier study, the leg power of 20
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international rugby forwards was evaluated by a countermovement jump movement
performed with the free use of arm swing and measured using a jump meter
apparatus. This device consisted of a measuring tape which was used to determine
the jumping height of the subjects as the difference between standing and jumping
heights according to a relationship equation for the duration and height of jump.
Similarly, in the current study, countermovement jump height was derived from an
equation using jump flight time and converted to jumping power estimates using the
same jump power equation cited in research by Sayers and colleagues (1999).
Comparisons of jump power estimates with those obtained in the present study
indicated that the current group of forwards achieved an average of 47.26 W.kg-1
which was 20% less than the relative peak power output (59.4 W.kg-1) of
international rugby forwards (Warrington et al., 2001). This difference suggests a
reduced ability to generate force rapidly through a vertical distance in amateur level
athletes as compared to their elite counterparts. This reflects the lower power
requirements during jumping, sprinting and ruck/maul activity required at the
amateur level as opposed to elite rugby players.
Lock forwards play a major role in contesting possession of the ball in the line–out
and they are expected to use their superior height and jumping ability to gain
advantage in this situation. Although there were no significant differences between
positional groups in the countermovement jump displacement results, the current
data indicated that the lock forwards obtained 18% and 16% higher mean vertical
jump displacement values than the loose-forwards and prop forwards respectively.
The test results for lock forwards indicated a relatively high degree of variability in
countermovement displacement between subjects with one lock forward achieving
considerably higher jump displacements than the remaining 4 lock forwards (Figure
13). It is highly likely the jump performance of this one lock forward contributed an
upward bias to the mean displacement data for the group, leading to the identification
of the effect size differences between positional groups. Consideration of the test
results without the displacement data of the outlier dramatically changes the nature
of the results with no mean differences in jump displacement apparent between prop
(mean = 35.0cm), lock (mean = 36.8cm) and loose forward (mean =34.3cm) playing
positions. This pattern of results differs from the findings of previous research
(Quarrie et al., 1996; Quarrie & Wilson, 2000) which assessed the leg power of
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rugby forwards using a standard countermovement jump and reach test. In these
earlier studies, the lock and loose forwards jumped the highest vertical distance while
the prop forwards jumped the shortest vertical distance. For example, in a group of
32 New Zealand Premier level rugby forwards, a significant difference between prop
(mean = 45.0cm) and loose forwards (mean = 54.5cm) was observed in mean
displacement achieved during the countermovement jump test. In addition, a
moderate effect size difference was also apparent between lock (mean = 51.0cm) and
prop forwards in the mean jump displacement (Quarrie & Wilson, 2000). The
discrepancies in the mean jump displacement results of props and lock and loose
forwards between the current study and earlier research are difficult to explain as
different jump protocols were utilised in each of the studies. In future research, a
comparison of vertical jump test protocols is warranted to determine any additional
effect the restricted jumping technique has on jump performance. In addition, further
research, with an increased sample of lock forwards, is required to limit the effect of
potential outliers within a group and determine potential differences in jump
performance between lock, prop and loose forward playing positions.
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The Relationship between Physical Performance Characteristics and Coaches'
Evaluations of Football Playing Ability
The performance prediction modelling procedure used in this study was applied to
improve understanding of the physiological characteristics which underlie individual
playing ability in rugby as perceived by experienced coaches. For example, it is
generally accepted by those involved in the sport, such as coaches and conditioners,
that rugby forward players rely heavily on their acceleration, muscular strength and
power to compete for the ball, tackle, and apply speed around the ruck and mauls.
The results of this investigation provided some insight into the validity of these
beliefs with respect to the more specific strength, speed and power qualities which
best predict football playing ability in rugby union forwards.
In the evaluation of playing ability of rugby forwards consideration was given to the
different factors which underlie playing ability with respect to motor skill, physical
capacity and the tactical ability components of forward match play. These areas of
performance were then used, together with a quantitative scoring system, to develop
a procedure used by experienced coaches to objectively measure the level of skill and
physical ability development in rugby forwards. Highly experienced club coaches
were selected to evaluate the playing ability of forwards as they possessed expert
knowledge of the level of development of physical capacities and positional skills in
their respective forward players.
To determine the relationship between the coaches’ prediction of physical capacity
and the performance skill scores from force, sprint performance and
countermovement jump test variables, backward linear regression analysis was
performed to identify separate performance test prediction models for the outcome
variables of weighted physical capacity and performance skill scores. The three
performance test categories included the force ergometer, sprint running, and
countermovement jump tests. The backward linear regression technique was used to
determine:
1. Significant relationships between the coaches’ physical capacity and
performance skill scores and the physical performance variables; and/or
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2. Trends in the relationship between the coaches’ physical capacity and
performance skill scores and the physical performance variables.
The Relationship between Physical Performance Characteristics and Player
Physical Capacity Scores
Application of the modelling techniques revealed a close relationship between the
sprint running times and the player’s physical capacity scores determined by
experienced coaches in the Premier rugby competition. Physical capacity scores were
calculated from ratings on a range of weighted physical capacity criteria deemed
essential for functional performance during competition. Players were assessed on
their level of development in speed, endurance, agility, mobility, static scrummaging
strength, dynamic upper body strength and strength in dynamic contact phases of the
game such as rucking and mauling.
Sprint times over 0 - 20m, 20 - 40m and 0 - 40m all demonstrated a significant
moderate correlation with the coaches estimates of physical capacity. A higher
physical capacity score was associated with a faster sprint time over 0-20m and 20 -
40m sprint distances. The importance of maximum running speed to the coaches
evaluations of physical capacity was reinforced by the inclusion of 20 - 40m sprint
performance times in a significant regression model which predicted 28% of the
coaches’ physical capacities scores of rugby forwards. Importantly, running
performance in the maximum speed phase of sprint running is characterised by a
high rate of cyclic leg movement or more commonly known as fast leg speed (Mero
et al., 1992). It is possible that in the evaluation of performance coaches are looking
for and identifying those players that have a faster leg speed than their counterparts,
not only in straight sprinting but possibly in multi-directional tasks which require fast
leg speeds such as ‘cutting’ whilst running or whilst repositioning to stay with the
play. Those rugby forwards who were rated by coaches as possessing highly
developed running speed may have also been rated highly on mobility and agility
criteria. This potential theoretical association between coaches scores of different
physical capacities is supported by earlier research (Sheppard, Warren, Doyle, &
Newton, 2004) which found a significant relationship between speed and change of
direction speed in 31 male Australian Rules football players.
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The regression model based on predicting coaches’ physical capacity scores from all
horizontal force measures, demonstrated a trend towards a positive relationship
which included the dynamic and impact force variables (model p value of 0.059).
The trend reflects a degree of commonality between the coaches’ assessment of static
scrummaging strength, dynamic strength in rucking and mauling and the impact and
dynamic force variables. It is possible that the common element is a driving force
from the lower limb extensor musculature against a large resistance which the
coaches visualise during rucking/mauling and scrummaging. The lack of significance
in the regression model between the physical capacity scores and the force variables
is possibly due to a poor degree of matching between the force descriptors in the
coaching assessment tool and the various measures of horizontal force.
In the scrum, the impact push may be more dependent on the player’s ability to
sustain high levels of force over a brief period of time rather than the impact force as
measured in the current study. Therefore, the impact push as measured by the force
impulse may provide a better measure of sustained scrummaging strength and more
closely reflect the coaches’ evaluation of static scrummaging strength. This should
strengthen the relationship between coaches’ physical capacity scores and the static
scrummaging test for future research.
The low level of association between the dynamic horizontal force measures and the
coaches’ physical capacity scores may be due to an insufficient level of game
specific movement velocity in the dynamic force test. For example, peak horizontal
forces were measured at slow movement velocities of 0.5m/s-1 which are likely to be
much slower than the movement velocity associated with performance in ruck and
maul activities. The earlier part of the test experienced measures of 3m/s-1 which are
considered by the author to more closely resemble the movement speeds during ruck
and mauls, although this needs to be verified by video analysis. At a higher velocity
of force application, explosive force or peak power may increase to become the
predominant factor in force production in ruck and mauls. Consequently, peak power
measured in the dynamic force test may more closely relate to force production in the
ruck and maul and to the coaches evaluations of dynamic upper body strength and
strength in dynamic contact rucking and mauling. Further modifications of the test
protocols to include measurement of peak power in the dynamic force test and
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impulse measurement in the impact force test may improve the ability of force
measures to more closely relate to coaches’ scores of physical capacity.
The prediction model involving component measures of the countermovement jump
test and the coaches’ physical capacity scores demonstrated a trend for higher jump
displacements having a minor contribution (12%) in predicting coaches’ physical
capacity scores (model p value of 0.062). This result is in contrast to earlier research
(Barker et al., 1993) which identified countermovement jump performance as a
significant predictor of athletic ability in American football players. Their findings
indicated that vertical jump displacement explained 35% of the common variance in
coaches’ ranking of athletic ability. The sport of rugby union requires forwards to
possess a variety of physical skills, including the ability to perform explosive plantar
flexion of the ankle, and knee and hip extension - common elements that facilitates
vertical jump performance. In the current study, the factor shared by the coaches’
assessment of speed, agility, dynamic strength in rucking/mauling and
countermovement jump displacement may be a forceful and rapid extension of the
lower limb. The low level of association between countermovement jump
displacement and the coaches’ physical capacity scores may be due to coaches
viewing this physical skill as an important performance requirement for lock
forwards but of little importance to the game performance of other forward players.
Overall, only 12 - 28% of the coaches’ physical capacity scores of rugby forwards
could be predicted by the strength, speed and power variables. The lack of any
significant correlations between strength, power variables and the coaches’ physical
capacity scores may be due to a poor degree of matching between some of the
physical capacity criteria and the physical requirements specific to forward play. The
physical performance criteria in the evaluation related to broad physical requirements
such as sprinting speed, mobility, agility, power in contact and dynamic upper body
strength. However, the evaluation tool lacked some specific physical performance
criteria relating to acceleration, maximum speed factors and the various strength -
related fitness qualities utilised by forward players during a game such as whole
body horizontal strength and horizontal power related to ruck and maul activity. It is
likely that the criterion measure of a coach’s overall physical capacity score may be
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too simple in its design with the physical capacity criteria lacking specificity to the
variety of force and power measures in rugby forwards.
The Relationship between Physical Performance Characteristics and Player
Performance Skill Scores
The correlation analysis revealed a significant moderate correlation between 20 -
40m and 0 - 40m sprint times and coaches’ performance skill scores. Importantly, the
linear regression model involving sprint performance test variables and the coaches’
performance skill scores showed that 20- 40m sprint time is a significant factor in the
coaches’ assessment of player performance skill – the model accounting for 29% of
the variance in coaches’ performance skill scores. Player performance skill scores
were determined independently from coaches’ objective ratings of game performance
according to a number of weighted motor skills, cognitive and tactical performance
criteria. The skill criteria related specifically to principle skills of match play
including ball handling, winning the tackle situation, clean out during a ruck,
positioning and tracking in defence and body position in the scrum.
It is possible that there is a relationship because in the performance evaluations,
Premier rugby coaches may be identifying those players who can develop fast limb
movements while they perform skilled tasks. Examination of the performance skill
evaluation tool revealed a number of skill criteria which were related to speed of
movement. For example, positioning in defence and in the clean out situation during
a ruck requires players to move into position quickly. Speed of movement in also
reflected in the scrum engagement criteria with the performance influenced by the
player’s ability to initiate a movement rapidly from stationary position. The Premier
coaches’ ability to identify fast limb movements in skilled tasks is consistent with a
study of American football players (Sawyer et al., 2002) which showed significant
moderate correlations (r = -0.58 - 0.63; p<0.03) between shorter sprints of 18.2 m
and football playing ability scores of the back-line players rated by coaches. The
current regression analysis, suggests that coaches’ assessment of playing ability in
rugby forwards is in part related to how quickly players respond to the changing
demands of forward match play.
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Given that rugby skills and sprint running appear to be significantly related, it is
possible that the variation shared by these 2 factors is due to motor timing and force
control factors. This statement is supported by the work of Ivry and Corcos, (1993)
and Keele et al. (1987) who theorised the existence of a central common timing
process for movement production. These researchers pointed out that the temporal
aspects of a skill are organised in the central nervous system and that there is an
underlying timing element within the brain which acts to support the coordination of
a number of disparate perceptual and motor tasks. It is possible that there is a
common timing component related to top speed sprint running and forward
positional skills. The rugby player’s performance on individual skills may be
partially dependent on their ability to properly time the sequential movements of the
skill and to optimise the summation of forces necessary to achieve successful skill
execution.
Approximately, 70% of the variance in player performance skill ability remains
unaccounted for by the physical performance variables in this study. This suggests
that skill ability may also be determined by other factors besides maximum velocity
of movement such as perceptual /cognitive abilities and decision-making /game
intelligence abilities. Rugby forward players frequently perform skills which require
a combination of physical performance attributes, reaction time, decision time, or
problem- solving capabilities. The physical performance tests in the current study did
not assess the players’ capacity to utilise their physical attributes in simulated
pressure or fatiguing situations which may have influenced the strength of the
association between the physical performance variables and coaches performance
skill scores.
In conclusion, the results of this investigation reveal a trend toward specific speed,
strength and power requirements of positional roles in rugby union forwards which
need to be more firmly established in a larger sample size of rugby union forwards.
The consistently high horizontal forces achieved by the current prop forwards during
a simulated scrummaging and ruck/maul task support earlier findings that indicated
an increased horizontal strength requirement for this position. The study provides
new evidence to suggest prop and lock forwards can apply a greater level of strength
than loose-forwards during impact situations with opposition players. However, the
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countermovement jump results require further investigation before firm conclusions
can be drawn in relation to the power requirements of positional roles in rugby
forwards. Furthermore, the sprint running results support the suggestion that both
lock and loose-forwards require the ability to accelerate and reach high sprinting
speeds during play. There is evidence to support the use of sprinting speed over 20-
40m distances in predicting coaches’ physical capacity and performance skill scores
in rugby union forwards. The results of the performance prediction models supports
the contention that those player’s who are able to generate powerful driving leg
actions may be more likely to be perceived by coaches’ as having enhanced
individual physical capacity and skill development.
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Chapter 6
SUMMARY AND CONCLUSIONS
The present study utilised a novel coaches’ performance evaluation tool, as well as
sports specific testing protocols to measure individual rugby skills and a range of
physical performance parameters consistent with the demands of rugby to determine:
1. Differences in static and dynamic strength qualities, sprint running times,
body mass and qualities of countermovement jump performance between the
forward playing positions of props, locks and loose forwards in Premier
rugby union forwards; and
2. Relationships between static and dynamic force variables, sprint running
times, body mass and qualities of countermovement jump performance of
Premier rugby union forwards and coaches’ performance skill and physical
capacity scores.
Twenty-two male rugby forwards from club rugby teams participating in either the
2003, Brisbane Premier rugby competition or Brisbane metropolitan under-19
competition participated in the study. The sample consisted of 8 prop, 5 lock, and 9
loose forwards. At the beginning of each testing session, each player was measured
for body mass and height. This was followed by an acceleration and sprint running
test with each participant performing 3 trials of sprint running over a distance of 40m
on an outdoor grass surface. An electronic timing system recorded sprint times at 10,
20, and 40m sprint distances providing a measure of acceleration ability (0 –10m, 0 –
20m) and maximum running speed (20 – 40m). In addition, force, power and
displacement characteristics of a countermovement vertical jump were calculated
from trials performed on a force plate. The jump technique involved performing a
countermovement to a depth of approximately 90 degrees, with hands on hips and
using body weight alone. Dynamic horizontal force was obtained by measuring the
force applied to the rear of the single – person ergometer during an accelerated push
simulating a rucking/mauling movement. The static horizontal force test involved
measurement of both the impact force and sustained force applied by a player against
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a stationary sports ergometer during a static push. This pushing condition closely
resembled the technique used in a scrum.
All 22 subjects who had previously been measured for physical capacities were also
evaluated by team coaches on their football playing ability in the overall forward
playing position. One coach from each of the 8 rugby teams observed the
performance of the players over 16 fixtures and then rated their respective players at
the end of the season using a set of performance skill/physical capacity criteria. The
statistical analysis involved the use of one-way analysis of variance and effect size
statistics to evaluate differences between positional groups in the strength, speed and
CMJ displacement. Backward multiple linear regression analysis was performed to
identify the relationships between the coaches’ scores of performance skill and
physical capacity and rugby specific measures of strength, speed and power.
Summary of Findings
In relation to the first study aim of determining differences in static and dynamic
force variables, sprint running times, body mass and qualities of countermovement
jump performance between forward playing positions, the findings will be discussed
in two parts. Firstly, the findings that relate to the statically significant and non-
significant differences between positional groups will be mentioned, followed by the
findings which relate to the effect size differences between forward positional groups.
On the basis of the significant and non-significant results of this investigation the
following preliminary conclusions can be drawn:
1. The lock forwards were significantly taller than prop forwards and the loose
forwards (p =0.000);
2. A significant difference was shown between forward positional groups over
the 0-40m sprint distance (p = 0.049), however the post hoc analysis did not
confirm the location of this difference among groups. The greatest apparent
difference in 0-40m mean sprint times occurred between prop and loose
forwards with the latter group faster over the 40m sprint distance; and
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3. Non-significant differences were identified between lock, prop and loose
forwards in sustained horizontal force, horizontal impact force, dynamic
horizontal force, 0-10, 0-20, 20-40m sprint times and countermovement jump
displacement of the COG.
The trends identified between forward positional groups in the strength, speed and
power variables included:
1. A higher body mass and greater generation of horizontal impact force in the
prop and lock forwards as compared to the loose forwards;
2. A lower production of sustained horizontal force and higher
countermovement jump displacement of COG in the lock forwards as
compared to the prop and loose forwards;
3. A higher generation of dynamic horizontal force in the prop forwards relative
to the loose forwards;
4. Faster sprint times over the acceleration (0-10m, 0-20m sprint time),
maximum running speed (20 - 40m sprint time) phases of a 40m sprint run in
the lock and loose forwards as compared to the prop forwards; and
5. Similar sprint performances over the acceleration (0-10m, 0-20m sprint time),
maximum running speed (20 - 40m sprint time) and combined acceleration
and maximum running speed (0 - 40m sprint time) phases of a 40m sprint run
for the loose forwards and lock forwards.
The second aim of the study was to determine the relationships between static and
dynamic force variables, sprint running times, body mass and qualities of
countermovement jump performance of Premier rugby union forwards and coaches’
performance skill and physical capacity scores. On the basis of the correlation and
linear regression results of this investigation, the major findings relating to the
relationships in the physical capacity data were:
1. A significant negative correlation existed between acceleration abilities
measured via 20m sprint performance (r = 0.47; p < 0.05), maximum running
speed measured via 20 - 40m sprint performance (r = 0.56; p < 0.01) and 40m
sprint performance (r = 0.53; p < 0.05) and player physical capacity scores;
112
2. A significant negative correlation existed between maximum running speed
measured via 20 - 40m sprint performance (r = 0.57; p < 0.01) and 40m sprint
performance (r = 0.51; p < 0.05) and player performance skill scores;
3. Non-significant correlations were apparent between dynamic horizontal force,
horizontal impact force, sustained horizontal force, 0 -10m sprint performance,
countermovement jump displacement and force impulse and coaches’ physical
capacity and performance skill scores;
4. A significant relationship existed between 20 – 40m sprint performance and
coaches’ physical capacity scores (p = 0.007), the prediction equation
indicating sprint performance over 20 - 40m predicts 28% of the variance in
player’s physical capacity scores;
5. A significant relationship existed between 20 – 40m sprint performance and
coaches’ performance skill scores (p = 0.006), the prediction equation
indicating sprint performance over 20 - 40m predicts 29% of the variance in a
player’s performance skill scores;
6. Dynamic horizontal force, horizontal impact force, sustained horizontal force,
0 -10m, 0 - 20m, 0-40m sprint performance, and countermovement jump
displacement and force impulse were not significant factors in the prediction
of coaches’ physical capacity and performance skill scores;
7. A trend was shown towards a positive relationship between dynamic
horizontal force, horizontal impact force and player physical capacity scores
(p = 0.059), the prediction model indicated a shared variance of 18% between
these factors; and
8. A trend was shown towards a positive relationship between countermovement
jump displacement and player physical capacity scores (p = 0.062), the
prediction model indicating a shared variance of 12% between these factors;
Conclusions and Implications for Training, Testing and Selection
The current study provides scientific evidence to support the use of sprint running
times over 20 - 40m distances in predicting coaches’ physical capacity and
performance skill scores in Premier rugby forwards. The significant relationships
observed between 20 - 40m running speed and coaches’ physical capacity and
performance skill scores provides evidence to support the view that fast lower limb
113
movements are important indicators of the level of development of football skills and
physical capacities in rugby forwards. In contrast, acceleration ability does not
appear to be a significant factor in coaches’ assessment of football skills and physical
capacities in rugby forwards. This is an unexpected result considering that acceleration runs are the major component of all sprinting efforts in forward match
play. It is hypothesised that the key underlying elements common to maximum
sprinting speed, football skills and physical capacities are the quickness of movement
responses and also the timing and force control of lower body skill patterns.
The non-significant relationships observed between impact force, dynamic horizontal
force, jump displacement and the coaches’ physical capacity scores indicate that
explosive leg extension during rucking, mauling and jumping are not key predictors
of the coaches assessment of physical capacities in rugby forwards. However, these
findings warrant further investigation using more task-specific force ergometer
measures matched with more specific sprinting speed and strength-related
performance criteria in the coaching evaluation tool. Further examination of these
measures may provide a clear indication of the relevance of jump displacement and
dynamic horizontal force assessments in the monitoring and screening of rugby
forward playing performance.
The current sprint performance data provide baseline information which may applied
to sprint training in the current group of Premier rugby forwards to assist in the
monitoring of improvements in acceleration and maximum running speed. In
particular, training programs should focus on improving the coordination and power
attributes of the lower limb musculature of rugby union forwards in an attempt to
improve the speed of lower limb movements. Additionally, it would appear pertinent
not only to train players to improve leg speed, but also to test them using sprint
distances which reflect the development of fast leg speeds. Sprint testing in talent
development should include assessment of the player’s maximum speed ability
between 20 and 40m as this test has the ability to discriminate between skilled and
less-skilled rugby union forwards. This may assist coaching staff to monitor the
development of talented rugby forwards.
114
Matching of individual strength to certain positional roles requires knowledge of the
forces and velocities generated by players during game movements. In the current
study the measurement of specific speed and horizontal force variables in simulated
activities provided a clearer indication of the differences in performance profiles
between forward playing positions. However, the interpretation of these test results,
in relation to their role in assisting coaching staff to make player position decisions,
should be viewed with caution given the lack of strong statistical evidence in the data.
The current study has provided some direction toward establishing the specific
strength and speed requirements of 2 forward positional groups. The consistently
high levels of force production exhibited by Premier level prop forwards across force
assessments suggest that whole-body maximal isometric strength and impact strength
represent key factors for consideration when selecting forward players to this
position in the Premier rugby competition. In addition, the superior acceleration
capabilities (matched with increased sprint demands during a game) and dynamic
horizontal strength (relative to bodyweight) of the current group of loose forwards, is
consistent with their more frequent involvement in running effort during a game and
indicate these attributes should receive particular emphasis when selecting Premier
rugby players to the loose forward position.
The study provides new evidence to suggest prop and lock forwards apply a greater
level of strength than loose-forwards during impact situations with opposition
players. The trend toward higher impact forces of the prop and lock forwards is
consistent with their continued exposure to high levels of force at scrum engagement
and reflects their superior ability to develop momentum on impact largely because of
their higher bodyweight. At scrum engagement, it appears the locks accelerate their
body at a faster rate than the prop forwards in an attempt to generate similar impact
forces as the prop forwards. The loose forwards however, produced the lowest
impact force estimates out of all the forwards players, their performance limited by
their lower body mass relative to other forward players.
Specific training programs designed to improve maximal horizontal impact force
should be developed differently for groups of props/locks and loose forwards.
Physical training programs for loose forwards in this case, should aim at improving
115
force production at speed. Adoption of specific power training methods involving
athletes training with scrum specific resistances, in horizontal scrum postures, and at
high contraction velocities may be of significant benefit to loose forwards. In
addition, the specificity of training will be enhanced if stretch-shorten cycle exercises
(explosive horizontal jumps) are included as part of the physical preparation. This
method may help the current group of loose forwards to utilise the stretch-shorten
cycle in the scrum to deliver greater maximal force and increased rate of force
development at scrum engagement (Stone, 1993). Since no clear differences were
evident in terms of dynamic horizontal strength for different playing positions, this
aspect of training may allow all forward players to work together when developing
maximal horizontal strength and power for rucking, mauling and scrummaging.
In terms of the sprint performance, the results provide support for a link between
body mass and sprinting times in rugby forwards - the heavier prop forwards
achieving slower sprint times than the lighter lock and loose forwards over the
acceleration and high running speed phases of a sprint. This supports the contention
that forward players with a lower body mass and a higher strength-to weight ratio in
the lower limb musculature are more suited to the loose-forward playing position in
which superior acceleration and mobility provide an advantage in movement around
the rucks. It is interesting to note, a finding of this study is the trend for locks to
achieve similar sprint times to the loose forwards over both phases of sprinting. This
may relate to a greater need for this group to apply explosive speed in ruck and maul
phase of play at this level, as opposed to lower levels of competition.
Given the different levels of speed development between positional groups, the
strength and conditioning coach should consider separating the props from the locks
and loose forwards when prescribing sprint technique drills and when devising
specific speed development training programs for forward players. It is important
that sprint training drills be performed over match-specific sprint distances while
incorporating the sprint patterns of forward players (Meir, Newton, Curtis, Fardell,
& Butler, 2001). It is recommended that sprints be performed from a stationary start
with simulated sprint distances of 10 – 20m to ensure optimal transfer of training to
sprint running in rugby.
116
Currently, there is uncertainty surrounding the ability of measures of
countermovement jump displacement on the force plate to differentiate between
forward positional groups. Further examination of jump performance in the current
group of forwards is warranted before firm conclusions can be drawn on the specific
power requirements of positional roles in Premier level rugby forwards. In future
studies, a force plate countermovement jump test utilising free use of arm swing and
involving the lifters as well as the jumper, would provide a more specific assessment
of vertical jump performance in rugby forwards. As vertical jumping is a
performance requirement of the lock forwards, the vertical jump ability of this group
should be continually monitored using the force plate countermovement jump test to
enable further development of these players with physical training.
Recommendations for Further Research
1. While the current study has developed new measures of functional capacity in
rugby players, future research should explore the measures of force
production and movement velocity on the force ergometer to include:
- Power at common movement velocities of rugby forward during the Dynamic
Horizontal Force Test;
- Impulse during the impact push in the Sustained Horizontal Force Test;
- Investigation of fatigue over repeated pushing efforts in the dynamic
horizontal force test, with the duration of the push and recovery mimicking
the work : rest ratio of ruck and maul phases of forward match play.
2. There is a need to determine the precision of the protocols used to assess
scrummaging and rucking/mauling strength with a force ergometer testing
device. The horizontal force tests used in the current study provide an
assessment of strength under conditions similar to the body position and
muscle actions utilised in scrummaging, rucking and mauling. However, the
force ergometer testing protocols have not been scientifically validated
against other proven measures and testing devices such as scrummaging
machines and as such can only be evaluated on face validity.
117
3. There is also a need to modify the physical capacity criteria of the coaching
evaluation tool, to include specific performance factors such as acceleration,
maximum running speed, and power in ruck/maul activity, and muscular
endurance. The addition of specific performance factors may help to develop
significant models based on predicting coaches’ physical capacity scores
from horizontal force, running speed variables.
4. Physical capacities of strength, speed and power are key qualities of the back-
line playing positions in rugby and there is need to determine the ability of
the specific force, speed and power tests of this study to discriminate between
Premier level rugby forwards and backs. This will provide a clear indication
of the similarities and differences in performance profiles between backs and
forwards at this performance level and assist in identifying screening tests for
matching players to the back-line playing position.
5. There is a need for aspects of the regression analysis to be replicated with the
inclusion of data on the playing experience, training age and psychological
aspects of the forward players. Previous research (Gabbett, 2002) has shown
that playing experience and age are important determinants of selection of
players into first grade rugby league teams. Examination of these variables
would assist in identifying other predictors of performance skill and physical
capacities in rugby union forwards.
118
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Appendix 1 – Subject Participation Forms
Informed Consent Form
QUEENSLAND UNIVERSITY OF TECHNOLOGY Faculty of Health, School of Human Movement Studies
The relationship between strength, speed and power and playing ability in elite junior rugby union forwards.
Chief Investigator Project Supervisor Wesley Bramley Prof Tony Parker
[email protected] [email protected] 3864 5835 38643512
By signing below, you are indicating that you, the participant: Have read and understood the subject information package regarding this project;
Understand that if you have any additional questions you can contact the research team;
Understand that you are free to withdraw at any time, without comment or penalty;
Understand that you can contact the research team if you have any questions about the project, or the Secretary of the University Research Ethics Committee, on (07) 3864 2902 if you have any concerns about the ethical conduct of the project; Consent to participate in a number of physical tests of performance for the purposes described in the information package; Agree to participate in the study. Name _______________________________________________________ Signature _______________________________________________________ Date _______________________________________________________
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Participant Information Package
QUEENSLAND UNIVERSITY OF TECHNOLOGY Faculty of Health, School of Human Movement Studies
PARTICIPANT INFORMATION PACKAGE
The relationship between strength, speed and power and football playing ability in elite rugby union forwards
Chief Investigator Project Supervisor Wesley Bramley Prof Tony Parker
[email protected] [email protected] 3864 5835 38643512
Project description This project is being conducted as part of my postgraduate studies at Queensland
University of Technology (QUT). The purpose of this research is to establish a
relationship between football playing ability and strength, speed and power
characteristics of rugby union forwards. A second aim is to determine the degree to
which individual playing positions within the forwards require the physical
capacities of strength, speed and power. This research is being conducted with the
full support and cooperation of your rugby club, however your decision to participate
in the study is entirely voluntary.
Participant involvement If you choose to participate in the study, you will be asked to involve yourself in one
testing session involving collection of physical performance data relating to your
individual strength, power and speed. In the testing session you will asked to perform
three trials of 40 meter sprints, vertical jump with a counter movement, and maximal
static and dynamic pushes against a individual sports ergometer as well as eight
130
repeated maximal efforts on the sports ergometer. The duration of the testing session
will be approximately 1 hour 30 minutes with three or four players scheduled in for
testing at one time.
Expected outcomes I expect to publish the findings of various aspects of this research in a number of
journals and publications. In addition, I will be completing a thesis for examination.
You will not be identifiable as an individual in any of the publications and outcomes
arising from the research.
Benefits to the Participants Your involvement in this project may benefit you directly by increasing your
awareness of your own physical capacities used in the performance of game
activities. You will have access to your own information relating to your strength and
power measurements during ruck/maul and scrummaging situations and your
capacity to develop speed across the field. Additionally, this project will contribute
to current understanding of physiological capacities and game performance amongst
rugby union forwards.
Risks and Discomfort The discomfort and risk associated with all testing procedures will reflect an
intensity that is no greater than you would normally experience during training and
game activities.
Confidentiality All information gathered during your participation in this project will be kept strictly
confidential. Only the research team will have access to the data collected, which
will be identified only by alphanumerical code. Your name will not be used and you
will in no way be identifiable in any publication that arises from this research.
Voluntary Participation Your decision to participate in this project is entirely voluntary, and you can
131
withdraw anytime without comment or penalty. Your decision to participate will in
no way impact on your relationship with your rugby club or influence your present
and/or future involvement with Queensland University of Technology.
Enquiries Any enquires or further information regarding this research project is welcome at any
time and should be directed to:
Chief Investigator:
Wes Bramley
School of Human Movement Studies
Queensland University of Technology
Ph (07) 3864 5835
Complaints or any concerns regarding the conduct of this investigation may be
directed to the Secretary of the University Human Research Ethics Committee, Mr.
Gary Allen on 3864 2902.
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Pre-test Questionnaire
Personal Name:____________________ Age:____________________ Current Competition Level:______________ Playing Position (s) :__________ Assigned Code:_____________
Illness Are you currently suffering from any type of illness? NO YES
If yes, provide details (type, severity):
Injury
Do you currently have any injuries which may restrict or limit your ability to sprint,
jump, lift weights, or push a scrum machine? NO YES
If yes, provide details (type, location, duration etc): _________________________________________________________________ _________________________________________________________________
Motivation Evaluate your motivation for training today. POOR OK GOOD EXCELLENT Evaluate your motivation for testing today. POOR OK GOOD EXCELLENT
Training Evaluate your last week of physical training. EASY MODERATE HARD VERY HARD How fatigued are you today? (0 = not at all; 5 = extremely)
0 1 2 3 4 5
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How many hours ago did you last exercise?________________ Describe your training sessions over the last 48 hours. Time Training session Difficulty (easy, moderate, hard) __________ __________________ ___________________________ __________ __________________ ___________________________ __________ __________________ ___________________________ __________ __________________ ___________________________
Miscellaneous Please provide any additional information that you believe may influence your fitness test results. This includes noting any alcohol consumption in the last 24 hours. ___________________________________________________________________ ___________________________________________________________________
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Appendix 2 – Coaching Evaluation Information
Letter to Coaches & Coaching Evaluation Tool
PLAYER PERFORMANCE RATINGS Dear Coaches, I have recently conducted physical testing with some forward players who are currently holding scholarships with the Reds Rugby College (RRC). In addition to this testing, I would like to know how RRC and QRU coaches rate these players in terms of individual playing ability during a game situation. This will assist me in quantifying the relationship between the player’s test results and their on-field performance. The following is a list of players from the QAS U19 and RRC squads that have been involved in the study. I would like to obtain coach's ratings for these player, however, I am aware that your limited dealings with some of these players on a game performance level may prevent passing judgement on those players who have played Premier Rugby in 2003. In which case, the coaching staff may be able to collaborate in developing some ratings for this group of player Further instructions may be found in the coach’s instructions section of the Player Ratings Package. Please find enclosed a Player Ratings Package for your own viewing. Thank you for your time and I look forward to working with you and the Energex Reds Rugby College in the future. Kind Regards, Wes Bramley Masters Student School of Human Movement Studies Queensland University of Technology
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ANALYSIS OF FORWARD PLAYING POSITION PLAYER NAME ………………………………….…….…. Please rate each player's current level of competency (on a scale poor, fair, good and excellent) in each of the following physical attribute and individual skill categories.
ATTACKING Poor Fair Good ExcellentBall Handling
Catching Passing Ball on Ground
Ball Carry
Running Lines Identification of Space
Support
Running Lines Communication
Offloads
CONTINUITY Poor Fair Good ExcellentWinning the Tackle Situation
Attacking Shoulders
Leg Drive
Ruck
Clean Out
Pick & Drive
Effectiveness Decision Making
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CONTINUITY (continues) Poor Fair Good ExcellentMaul
Effectiveness
Body Height
Leg Speed
DEFENCE Poor Fair Good ExcellentTackle Technique
Impact Low Tackle
Alignment
Positioning
Communication
Pressure
Denying Time & Space Tracking
Attitude
SCRUM Poor Fair Good ExcellentBody Position
Shape
Height
RESTARTS Poor Fair Good ExcellentSupport of Catcher
Movement to Ball
Catch
Handling in Contact
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PHYSICAL ATTRIBUTES Poor Fair Good Excellent Speed
Endurance
Agility
Mobility
Static Scrummaging Strength
Dynamic Upper Body Strength
Dynamic Strength in Rucking and Mauling
OTHER Poor Fair Good Excellent Penalties Conceded
Attitude towards physical training
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Weighting of Criteria by Expert Coaches
Dear Coach, I am aiming to establish how each of the skills and attributes that make up a player
profile contribute to overall football playing ability. I have had two coaches from
each club individually score each Premier Rugby player on a number of different
performance criteria (these criteria are similar to the reds college position analysis
criteria). However, I would like to take these scores and combine them to obtain an
overall rating of each player's football playing ability.
To do so, I would appreciate your advice on how each of the identified skills and
attributes should constitute football playing ability at the Premier Rugby level (i.e.,
are some skills more important and therefore should be more heavily weighted than
others?). I would like you to rate each of the skill and ability criteria in terms of their
importance to football playing ability and the level of development required for
successful on-field performance at this level of competition. Can you please use the
following five-point scale to rate the relative importance of each of these skills and
abilities. The scale is as follows:
5 - Must have this skill highly developed;
4 - Should have this skill well developed;
3 - This skill is important, but need not be highly developed;
2 - This skill is unimportant, minimal development necessary;
1 - This skill is unimportant and not needed.
A description of the individual skills/attributes that make up the performance criteria
for each position is included. Thank - you for your time and effort in completing this position analysis.
139
Analysis: Overall Forwards Performance Criteria Rating Scale
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ATTACK 1 1.5 2 2.5 3 3.5 4 4.5 5 Ball Handling Catching 1 1.5 2 2.5 3 3.5 4 4.5 5 Passing 1 1.5 2 2.5 3 3.5 4 4.5 5 Ball on Ground 1 1.5 2 2.5 3 3.5 4 4.5 5 Ball Carry Running Lines 1 1.5 2 2.5 3 3.5 4 4.5 5 Identification of Space 1 1.5 2 2.5 3 3.5 4 4.5 5 Support Running Lines 1 1.5 2 2.5 3 3.5 4 4.5 5 Communication 1 1.5 2 2.5 3 3.5 4 4.5 5 Offloads 1 1.5 2 2.5 3 3.5 4 4.5 5 Continuity Winning the Tackle Situation
Attacking Shoulders 1 1.5 2 2.5 3 3.5 4 4.5 5 Leg Drive 1 1.5 2 2.5 3 3.5 4 4.5 5 Ruck Clean Out 1 1.5 2 2.5 3 3.5 4 4.5 5 Pick and Drive 1 1.5 2 2.5 3 3.5 4 4.5 5 Effectiveness 1 1.5 2 2.5 3 3.5 4 4.5 5 Decision Making 1 1.5 2 2.5 3 3.5 4 4.5 5 Maul Effectiveness 1 1.5 2 2.5 3 3.5 4 4.5 5 Body Height 1 1.5 2 2.5 3 3.5 4 4.5 5 Leg Speed 1 1.5 2 2.5 3 3.5 4 4.5 5 Scrum Body Position Shape 1 1.5 2 2.5 3 3.5 4 4.5 5 Height 1 1.5 2 2.5 3 3.5 4 4.5 5 Restarts Movement to Ball 1 1.5 2 2.5 3 3.5 4 4.5 5 Catch 1 1.5 2 2.5 3 3.5 4 4.5 5 Catch Handling in Contact 1 1.5 2 2.5 3 3.5 4 4.5 5 Other Penalties Conceded 1 1.5 2 2.5 3 3.5 4 4.5 5 Attitude towards physical training
1 1.5 2 2.5 3 3.5 4 4.5 5
Performance Criteria Rating Scale
140
Skill
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Physical Attributes Speed 1 1.5 2 2.5 3 3.5 4 4.5 5 Endurance 1 1.5 2 2.5 3 3.5 4 4.5 5 Agility 1 1.5 2 2.5 3 3.5 4 4.5 5 Mobility 1 1.5 2 2.5 3 3.5 4 4.5 5 Static Scrummaging Strength
1 1.5 2 2.5 3 3.5 4 4.5 5
Dynamic Upper Body Strength
1 1.5 2 2.5 3 3.5 4 4.5 5
Power in Contact 1 1.5 2 2.5 3 3.5 4 4.5 5 Performance Criteria Rating Scale
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DEFENCE 1 1.5 2 2.5 3 3.5 4 4.5 5 Tackle Technique Impact 1 1.5 2 2.5 3 3.5 4 4.5 5 Low Tackle 1 1.5 2 2.5 3 3.5 4 4.5 5 Alignment Positioning 1 1.5 2 2.5 3 3.5 4 4.5 5 Communication 1 1.5 2 2.5 3 3.5 4 4.5 5 Pressure Denying Time and Space 1 1.5 2 2.5 3 3.5 4 4.5 5 Tracking 1 1.5 2 2.5 3 3.5 4 4.5 5 Attitude 1 1.5 2 2.5 3 3.5 4 4.5 5
141
Appendix 3 – Subject Anthropometric, Physical Capacity and Score Data
POSITION AGE WEIGHT HEIGHT SHF HIF DHF DHF/WT 0-10m 0-20m 20-40m 0-40m 1 Lock 21.0 103.5 191.0 2186.4 5640.3 1433.7 13.85 1.86 3.24 2.52 5.76 2 Lock 18.0 110.0 200.0 1616.9 5573.3 1565.0 14.23 1.86 3.21 2.45 5.66 3 Lock 19.0 102.8 198.0 2739.3 3148.7 1256.3 12.22 1.77 3.03 2.31 5.34 4 Lock 18.0 100.8 195.0 2402.0 6236.5 1300.5 12.90 1.74 2.98 2.23 5.21 5 Lock 21.0 97.3 189.0 1787.3 4685.5 1500.9 15.42 1.86 3.17 2.43 5.60 6 Prop 25.0 135.1 175.5 2297.2 5684.8 1352.3 10.01 2.18 3.74 3.05 6.79 7 Prop 20.0 114.7 182.5 2581.8 5672.3 1469.3 12.81 1.87 3.24 2.53 5.77 8 Prop 18.0 96.2 181.0 2428.4 5679.0 1450.7 15.08 1.86 3.20 2.50 5.70 9 Prop 21.0 102.3 183.0 3151.3 3341.3 1505.5 14.72 1.79 3.13 2.59 5.72 10 Prop 26.0 104.0 180.0 3011.5 6148.2 1488.7 14.31 1.87 3.21 2.49 5.70 11 Prop 20.0 87.1 181.5 2282.0 4992.5 1396.8 16.05 1.78 3.08 2.30 5.38 12 Prop 20.0 123.9 181.0 2359.0 5675.8 1578.6 12.74 1.94 3.31 2.61 5.92 13 Prop 23.0 109.7 180.0 2335.0 5662.4 1407.2 12.83 1.96 3.42 2.71 6.13 14 LF 21.0 89.1 184.0 2970.9 4911.3 1366.8 15.34 1.82 3.08 2.35 5.43 15 LF 25.0 95.1 185.0 2422.6 4539.1 1308.6 13.76 1.73 3.03 2.46 5.49 16 LF 20.0 83.0 172.5 1983.0 4164.8 1312.7 15.83 1.77 3.06 2.50 5.56 17 LF 20.0 85.1 183.5 2403.9 2431.4 1243.9 14.63 1.77 3.05 2.29 5.34 18 LF 20.0 115.5 176.5 2568.4 4203.6 1508.1 13.06 1.91 3.25 2.51 5.76 19 LF 19.0 97.0 185.0 2647.2 4802.6 1373.8 14.16 1.86 3.25 2.62 5.87 20 LF 23.0 91.0 178.0 2552.4 4359.1 1500.2 16.49 1.77 3.02 2.36 5.38 21 LF 21.0 91.2 181.0 2079.8 4591.3 1326.7 14.55 1.86 3.16 2.47 5.63 22 LF 25.0 104.3 180.0 2568.5 5677.9 1589.4 15.24 1.86 3.13 2.33 5.46
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Individual Data (continued)
POSITION CMJDISPL CMJPF/ WT
CMJPP/WT
CMJIMPULSE
WPCS WPSS TWS
1 Lock 29.8 14.30 42.92 250.3 71.2 363.9 435.12 Lock 40.4 13.11 48.91 309.7 83.5 419.1 502.63 Lock 62.0 12.86 61.92 358.7 83.2 374.0 457.14 Lock 39.8 11.27 48.88 281.8 76.0 411.7 487.75 Lock 37.2 9.57 47.39 262.8 81.3 399.3 480.76 Prop 21.6 9.57 39.79 278.1 52.0 259.8 311.87 Prop 41.3 13.06 49.24 326.3 95.2 420.3 515.48 Prop 34.7 9.79 45.83 251.0 77.7 399.2 476.89 Prop 30.1 8.42 43.07 248.4 83.3 375.0 458.310 Prop 31.8 18.62 44.10 259.6 63.8 346.0 409.811 Prop 45.6 17.21 53.49 260.5 79.4 367.9 447.312 Prop 46.5 15.75 51.50 374.4 84.0 386.5 470.513 Prop 28.5 13.61 42.34 259.4 77.6 378.6 456.214 LF 31.1 13.83 43.42 220.3 95.2 383.7 478.915 LF 32.5 17.33 44.44 240.2 87.7 370.7 458.416 LF 29.6 10.40 42.19 199.8 67.5 293.3 360.717 LF 41.3 12.73 50.61 242.1 99.2 445.0 544.118 LF 31.6 15.06 44.12 287.8 95.5 445.3 540.819 LF 33.3 9.46 44.95 247.8 75.7 384.0 459.720 LF 33.9 11.89 45.33 234.7 83.5 376.2 459.721 LF 38.4 10.23 48.33 250.3 71.3 390.8 462.222 LF 36.9 10.76 47.07 280.7 103.6 398.1 501.8
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Data for Positional Groups Physical performance characteristics of Premier rugby union forwards (means ± sem).
Positional Group Prop Forwards Lock Forwards Loose-forwards Measurement (n = 8) (n = 5) (n = 9) Sustained scrum force (N)* 2555.8 ± 120.2 2146.4 ± 203.4 2466.3 ± 98.9 Impact force (N)* 5357.0 ± 308.4 5056.9 ± 537.5 4409.0 ± 290.7 Peak dynamic force (N)** 1456.1 ± 25.1 1411.3 ± 58.5 1392.2 ± 38.1 Peak dynamic force (N.kg-1) 13.57 ± 0.67 13.73 ± 0.55 14.78 ± 0.35 0-10 m sprint (s) 1.91 ± 0.04 1.82 ± 0.03 1.82 ± 0.02 0-20 m sprint (s) 3.29 ± 0.07 3.13 ± 0.05 3.11 ± 0.03 0-40 m sprint (s) 5.89 ± 0.15 5.51 ± 0.10 5.55 ± 0.06 20 – 40 m sprint (s) 2.60 ± 0.08 2.39 ± 0.05 2.43 ± 0.04 CMJ displacement (cm) 35.0 ± 3.1 41.8 ± 5.4 34.3 ± 1.3 CMJ peak force (N) 1434.2 ± 141.6 1261.9 ± 102.2 1179.9 ± 104.6 CMJ peak force (N.kg-1) 13.25 ± 1.34 12.22 ± 0.82 12.41 ± 0.86 CMJ peak power (W) 5013.3 ± 252.9 5145.2 ± 344.3 4310.7 ± 161.6 CMJ peak power (W.kg-1) 46.17 ± 1.69 50.00 ± 3.17 45.61 ± 0.88 CMJ take-off velocity (m.s-1) 2.60 ± 0.12 2.84 ± 0.18 2.59 ± 0.05 CMJ force impulse (N.s) 282.2 ± 15.9 292.7 ± 19.3 244.8 ± 9.1 * Force ergometer measure obtained with the subject pushing against a static resistance. ** Force ergometer measure obtained with the subject pushing against a dynamic resistance. CMJ = countermovement jump.
144
Appendix 4 – Study Part A Statistics
One-way Analysis of Variance Output
Sum of Squares df Mean Square F Sig.
WEIGHT Between Groups 903.739 2 451.870 3.358 .056 Within Groups 2556.768 19 134.567 Total 3460.507 21 HEIGHT Between Groups 758.556 2 379.278 26.610 .000 Within Groups 270.808 19 14.253 Total 1029.364 21 GRUNTSHF Between Groups 540244.065 2 270122.033 2.193 .139 Within Groups 2340849.80
9 19 123202.622
Total 2881093.875 21
GRUNTHIF Between Groups 3963772.901 2 1981886.450 2.191 .139
Within Groups 17187708.481 19 904616.236
Total 21151481.382 21
GRUNTDHF Between Groups 17760.816 2 8880.408 .810 .460 Within Groups 208389.548 19 10967.871 Total 226150.363 21 GRUMF_WT Between Groups 7.158 2 3.579 1.706 .208 Within Groups 39.856 19 2.098 Total 47.014 21 TENMTR Between Groups .040 2 .020 2.474 .111 Within Groups .155 19 .008 Total .196 21 TWNTYMTR Between Groups .152 2 .076 3.423 .054 Within Groups .422 19 .022 Total .575 21 TWNTFORT Between Groups .173 2 .087 3.452 .053 Within Groups .477 19 .025 Total .650 21 FORTYMTR Between Groups .641 2 .320 3.562 .049 Within Groups 1.708 19 .090 Total 2.349 21 VJDISP Between Groups 203.098 2 101.549 1.561 .236 Within Groups 1236.190 19 65.063 Total 1439.288 21 VJPFBW Between Groups 4.360 2 2.180 .248 .783 Within Groups 167.104 19 8.795 Total 171.463 21 VJPBW Between Groups 67.334 2 33.667 1.533 .241 Within Groups 417.298 19 21.963 Total 484.631 21 VJFIMP Between Groups 9445.408 2 4722.704 3.269 .060 Within Groups 27453.027 19 1444.896 Total 36898.435 21
145
TWS Between Groups 4669.257 2 2334.628 .844 .445 Within Groups 52526.098 19 2764.531 Total 57195.354 21 WPCS Between Groups 450.636 2 225.318 1.606 .227 Within Groups 2665.656 19 140.298 Total 3116.292 21 WPSS Between Groups 2817.138 2 1408.569 .770 .477 Within Groups 34749.607 19 1828.927 Total 37566.745 21
Post Hoc Tests
Multiple Comparisons Scheffe
Dependent Variable (I) TYPE (J) TYPE
Mean Difference
(I-J) Std. Error Sig. 95% Confidence Interval
Lower Bound
Upper Bound
WEIGHT lock props -6.239 6.6132 .647 -23.790 11.313 loosie 8.302 6.4703 .454 -8.870 25.475 props lock 6.239 6.6132 .647 -11.313 23.790 loosie 14.541 5.6367 .058 -.419 29.501 loosie lock -8.302 6.4703 .454 -25.475 8.870 props -14.541 5.6367 .058 -29.501 .419 HEIGHT lock props 14.037(*) 2.1523 .000 8.325 19.750 loosie 13.989(*) 2.1058 .000 8.400 19.578 props lock -14.037(*) 2.1523 .000 -19.750 -8.325 loosie -.049 1.8345 1.000 -4.917 4.820 loosie lock -13.989(*) 2.1058 .000 -19.578 -8.400 props .049 1.8345 1.000 -4.820 4.917 GRUNTSHF lock props -409.377 200.1021 .151 -940.451 121.697 loosie -319.914 195.7797 .287 -839.516 199.688 props lock 409.377 200.1021 .151 -121.697 940.451 loosie 89.463 170.5565 .872 -363.196 542.122 loosie lock 319.914 195.7797 .287 -199.688 839.516 props -89.463 170.5565 .872 -542.122 363.196 GRUNTHIF lock props -300.176 542.2179 .859 -1739.229 1138.878 loosie 647.859 530.5056 .488 -760.110 2055.828 props lock 300.176 542.2179 .859 -1138.878 1739.229 loosie 948.034 462.1579 .149 -278.539 2174.607 loosie lock -647.859 530.5056 .488 -2055.828 760.110 props -948.034 462.1579 .149 -2174.607 278.539 GRUNTDHF lock props -44.879 59.7039 .757 -203.334 113.575 loosie 19.011 58.4143 .949 -136.021 174.044 props lock 44.879 59.7039 .757 -113.575 203.334 loosie 63.891 50.8885 .469 -71.168 198.950 loosie lock -19.011 58.4143 .949 -174.044 136.021 props -63.891 50.8885 .469 -198.950 71.168
146
GRUMF_WT lock props .1572 .82568 .982 -2.0342 2.3485 loosie -1.0572 .80785 .440 -3.2013 1.0868 props lock -.1572 .82568 .982 -2.3485 2.0342 loosie -1.2144 .70377 .251 -3.0822 .6534 loosie lock 1.0572 .80785 .440 -1.0868 3.2013 props 1.2144 .70377 .251 -.6534 3.0822 TENMTR lock props -.0882 .05154 .256 -.2250 .0485 loosie .0013 .05042 1.000 -.1325 .1352 props lock .0882 .05154 .256 -.0485 .2250 loosie .0896 .04393 .153 -.0270 .2062 loosie lock -.0013 .05042 1.000 -.1352 .1325 props -.0896 .04393 .153 -.2062 .0270 TWNTYMTR lock props -.1652 .08500 .178 -.3909 .0604 loosie .0116 .08317 .990 -.2092 .2323 props lock .1652 .08500 .178 -.0604 .3909 loosie .1768 .07245 .075 -.0155 .3691 loosie lock -.0116 .08317 .990 -.2323 .2092 props -.1768 .07245 .075 -.3691 .0155 TWNTFORT lock props -.2095 .09029 .093 -.4491 .0301 loosie -.0442 .08834 .883 -.2787 .1902 props lock .2095 .09029 .093 -.0301 .4491 loosie .1653 .07696 .127 -.0390 .3695 loosie lock .0442 .08834 .883 -.1902 .2787 props -.1653 .07696 .127 -.3695 .0390 FORTYMTR lock props -.3747 .17093 .117 -.8284 .0789 loosie -.0327 .16723 .981 -.4765 .4112 props lock .3747 .17093 .117 -.0789 .8284 loosie .3421 .14569 .089 -.0446 .7287 loosie lock .0327 .16723 .981 -.4112 .4765 props -.3421 .14569 .089 -.7287 .0446 VJDISP lock props 6.827 4.5984 .352 -5.377 19.032 loosie 7.551 4.4991 .269 -4.390 19.492 props lock -6.827 4.5984 .352 -19.032 5.377 loosie .724 3.9194 .983 -9.679 11.126 loosie lock -7.551 4.4991 .269 -19.492 4.390 props -.724 3.9194 .983 -11.126 9.679 VJPFBW lock props -1.0352 1.69067 .831 -5.5223 3.4519 loosie -.1901 1.65415 .993 -4.5802 4.2000 props lock 1.0352 1.69067 .831 -3.4519 5.5223 loosie .8451 1.44103 .843 -2.9794 4.6696 loosie lock .1901 1.65415 .993 -4.2000 4.5802 props -.8451 1.44103 .843 -4.6696 2.9794 VJPBW lock props 3.8338 2.67170 .376 -3.2570 10.9245 loosie 4.3982 2.61399 .267 -2.5393 11.3358 props lock -3.8338 2.67170 .376 -10.9245 3.2570 loosie .5645 2.27722 .970 -5.4793 6.6082 loosie lock -4.3982 2.61399 .267 -11.3358 2.5393 props -.5645 2.27722 .970 -6.6082 5.4793 VJFIMP lock props 10.437 21.6701 .891 -47.075 67.950 loosie 47.812 21.2020 .105 -8.459 104.082 props lock -10.437 21.6701 .891 -67.950 47.075 loosie 37.375 18.4704 .157 -11.646 86.395 loosie lock -47.812 21.2020 .105 -104.082 8.459 props -37.375 18.4704 .157 -86.395 11.646
147
TWS lock props 29.376 29.9745 .626 -50.177 108.928 loosie -1.383 29.3271 .999 -79.218 76.451 props lock -29.376 29.9745 .626 -108.928 50.177 loosie -30.759 25.5487 .497 -98.566 37.048 loosie lock 1.383 29.3271 .999 -76.451 79.218 props 30.759 25.5487 .497 -37.048 98.566 WPCS lock props 2.409 6.7525 .939 -15.512 20.330 loosie -7.538 6.6067 .533 -25.073 9.996 props lock -2.409 6.7525 .939 -20.330 15.512 loosie -9.948 5.7555 .250 -25.223 5.328 loosie lock 7.538 6.6067 .533 -9.996 25.073 props 9.948 5.7555 .250 -5.328 25.223 WPSS lock props 26.966 24.3803 .553 -37.739 91.672 loosie 6.155 23.8537 .967 -57.153 69.463 props lock -26.966 24.3803 .553 -91.672 37.739 loosie -20.812 20.7805 .613 -75.963 34.340 loosie lock -6.155 23.8537 .967 -69.463 57.153 props 20.812 20.7805 .613 -34.340 75.963
* The mean difference is significant at the .05 level.
148
Effect Size Comparisons Differences across various forward positional groups (e.g., prop forwards v lock
forwards) for anthropometric, force, power and speed variables: values expressed as
effect size.
Group Comparisons
Locks/Props Locks/LF LF/Props Measurement (n = 13) (n = 14) (n = 17) Height (cm) 4.21 3.17 0.01 Body mass (kg) 0.50 0.96 1.13 Sustained scrum force (N)* 1.06 0.90 0.28 Impact force (N)* 0.30 0.65 1.09 Peak dynamic force (N) ** 0.46 0.16 0.66 Peak dynamic force (N.kg-1) 0.09 0.94 0.81 10 m sprint (s) 0.83 0.02 0.91 20 m sprint (s) 0.91 0.12 1.13 40 m sprint (s) 1.03 0.17 1.08 20 – 40 m sprint (s) 1.12 0.40 0.99 CMJ displacement (cm) 0.68 0.99 0.11 CMJ peak force (N.kg-1) 0.32 0.08 0.26 CMJ peak power (W.kg-1) 0.67 0.95 0.15 CMJ impulse (N.s) 0.24 1.43 1.61 WPCS 0.22 0.69 0.76 WPSS 0.66 0.16 0.45 Mean effect size 0.80 0.83 0.65 * Force ergometer measure obtained with the subject pushing against a static resistance. ** Force ergometer measure obtained with the subject pushing against a dynamic resistance. CMJ = countermovement jump; WPCS = weighted physical capacities score; WPSS = weighted performance skills score.
149
Retrospective Power Calculations and Syntax Command 0-20m sprint (loose forwards vs props) MEANDIFF SD1 SD2 N1 N2 POWER .18 .09 .21 9.00 8.00 65.082 Number of cases read: 1 Number of cases listed: 1 0-20m sprint (locks vs props) MEANDIFF SD1 SD2 N1 N2 POWER .16 .11 .21 5.00 8.00 34.373 Number of cases read: 1 Number of cases listed: 1 20 - 40m sprint (loose forwards vs props) MEANDIFF SD1 SD2 N1 N2 POWER .17 .11 .22 9.00 8.00 53.705 Number of cases read: 1 Number of cases listed: 1 20-40m sprint (locks vs props) MEANDIFF SD1 SD2 N1 N2 POWER .21 .11 .22 5.00 8.00 50.135 Number of cases read: 1 Number of cases listed: 1 0 - 40m sprint (loose forwards vs props) MEANDIFF SD1 SD2 N1 N2 POWER .34 .18 .42 9.00 8.00 60.146 Number of cases read: 1 Number of cases listed: 1 0 - 40m sprint (locks vs props) MEANDIFF SD1 SD2 N1 N2 POWER .38 .23 .42 5.00 8.00 45.153 Number of cases read: 1 Number of cases listed: 1
150
Vertical jump force impulse (locks vs loose forwards) MEANDIFF SD1 SD2 N1 N2 POWER 47.82 27.23 43.16 9.00 5.00 72.816 Number of cases read: 1 Number of cases listed: 1 Vertical jump force impulse (loose forwards vs prop forwards) MEANDIFF SD1 SD2 N1 N2 POWER 37.38 27.23 44.83 9.00 8.00 55.834 Number of cases read: 1 Number of cases listed: 1 0-10m sprint (loose forwards vs props) MEANDIFF SD1 SD2 N1 N2 POWER .09 .06 .13 9.00 8.00 46.428 Number of cases read: 1 Number of cases listed: 1 0-10m sprint (locks vs prop forwards) MEANDIFF SD1 SD2 N1 N2 POWER .09 .06 .13 5.00 8.00 30.061 Number of cases read: 1 Number of cases listed: 1 Horizontal impact force (loose forwards vs locks) MEANDIFF SD1 SD2 N1 N2 POWER 647.86 872.00 1201.83 9.00 5.00 21.427 Number of cases read: 1 Number of cases listed: 1 Relative dynamic force (props vs loose forwards) MEANDIFF SD1 SD2 N1 N2 POWER 1.21 1.90 1.10 8.00 9.00 37.121 Number of cases read: 1 Number of cases listed: 1
151
Sustained Horizontal Force (locks vs prop forwards) MEANDIFF SD1 SD2 N1 N2 POWER 409.40 454.90 339.90 5.00 8.00 46.092 Number of cases read: 1 Number of cases listed: 1 Sustained Horizontal Force (locks vs loose forwards) MEANDIFF SD1 SD2 N1 N2 POWER 319.90 454.90 296.70 5.00 9.00 36.136 Number of cases read: 1 Number of cases listed: 1 Horizontal Impact Force (loose forwards vs prop forwards) MEANDIFF SD1 SD2 N1 N2 POWER 949.00 872.00 872.40 9.00 8.00 60.997 Number of cases read: 1 Number of cases listed: 1 CMJ Displacement of COG (loose forwards vs lock forwards) MEANDIFF SD1 SD2 N1 N2 POWER 7.50 3.80 12.00 9.00 5.00 42.516 Number of cases read: 1 Number of cases listed: 1 CMJ Displacement of COG (prop vs lock forwards) MEANDIFF SD1 SD2 N1 N2 POWER 6.80 8.80 12.00 8.00 5.00 21.861 Number of cases read: 1 Number of cases listed: 1 CMJ Relative Power (loose forwards vs lock forwards) MEANDIFF SD1 SD2 N1 N2 POWER 4.40 2.60 7.10 9.00 5.00 40.085 Number of cases read: 1 Number of cases listed: 1
152
CMJ Relative Power (prop vs lock forwards) MEANDIFF SD1 SD2 N1 N2 POWER 3.80 4.80 7.10 8.00 5.00 21.199 Number of cases read: 1 Number of cases listed: 1 Syntax Command * calculating power for a given difference between two means *** FOR TWO MEANS. * mean1 is mean in group 1. * mean2 is mean in group 2. * n1 is number in group 1. * n2 is number in group 2. * s1 is the sd of group 1. * sd2 is the sd of group 2. * if you only have access to one sd, use that for both sd1 and sd2 (assumes equal sds across groups). * if you only have access to the mean difference, use any two means as long as their difference * is the mean difference you want (eg 12 and 15 OR 32 and 35 would be equally valid if the mean difference * you are interested in is 3, as in the example below). * example compares observed mean of 12 to observed mean of 15, where sample size is 33 for group 1 and 33 for group 2. * for this example, power should be about 86%. * for the next example with 33 in one group and 46 in the other, the power is 90%. data list free / mean1 mean2 meandiff sd1 sd2 n1 n2. begin data 12 15 3 4 4 33 33 12 15 3 4 4 33 46 end data. compute diff = mean1-mean2. compute poolsd = (n1-1)*sd1*sd1 + (n2-1)*sd2*sd2. compute poolsd = poolsd/(n1+n2-2). compute poolsd=sqrt(poolsd). compute se = poolsd*sqrt(1/n1 + 1/n2). compute i = meandiff/se. compute z_beta = 1.96- i. * power is expressed as a percentage. compute power = 100 - cdfnorm(z_beta)*100. formats z_beta power (f8.3). list meandiff sd1 sd2 n1 n2 power.
153
Within- Subject Coefficient of Variations
Positional Group Prop Forwards Lock Forwards Loose-forwards Measurement (n = 8) (n = 5) (n = 9) Sustained scrum force (N)* 4.4% 2.6% 2.7% Impact force (N)* 6.3% 7.3% 11.1% Peak dynamic force (N)** 3.2% 3.6% 3.5% 0-10 m sprint (s) 6.1% 3.0% 3.1% 0-20 m sprint (s) 6.3% 3.1% 3.8% 0-40 m sprint (s) 7.3% 3.8% 3.2% 20 – 40 m sprint (s) 1.1% 1.0% 0.8% CMJ displacement (cm) 7.1% 5.5% 6.3% CMJ CMJ peak power (W.kg-1) 2.9% 2.7% 2.9% * Force ergometer measure obtained with the subject pushing against a static resistance. ** Force ergometer measure obtained with the subject pushing against a dynamic resistance. CMJ = countermovement jump.
154
Appendix 5 – Study Part B Statistics
Correlation Matrix for all Test Variables (n = 22)
CMJ = countermovement jump; WPCS = weighted physical capacities score; WPSS = weighted performance skill score. * p < 0.05, ** p < 0.01.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1. Height (cm) 1.00
2. Body mass (kg) -0.02 1.00
3. Sustained scrum force (N) -0.20 0.01 1.00
4. Impact force (N) 0.01 0.44* -0.16 1.00
5. Peak dynamic force (N) -0.10 0.41 -0.02 0.41 1.00
6. 10 m sprint (s) -0.33 0.80** -0.13 0.42 0.26 1.00
7. 20 m sprint (s) -0.31 0.79** -0.13 0.41 0.22 0.98** 1.00
8. 40 m sprint (s) -0.36 0.75** -0.09 0.34 0.17 0.93** 0.98** 1.00
9. 20 – 40 m sprint (s) -0.40 0.68** -0.04 0.26 0.13 0.85** 0.92** 0.98** 1.00
10. CMJ displacement (cm) 0.54** -0.12 -0.04 -0.26 -0.12 -0.41 -0.46* -0.53* -0.57** 1.00
11. CMJ peak force (N.kg-1) 0.02 0.04 0.13 0.19 0.06 -0.13 -0.12 -0.17 -0.20 0.19 1.00
12. CMJ peak power (W.kg-1) 0.53* -0.12 -0.05 -0.29 -0.17 -0.38 -0.42* -0.50* -0.55** 0.99** 0.18 1.00
13. CMJ impulse (N.s) 0.39 0.66** -0.02 0.18 0.29 0.26 0.22 0.14 0.06 0.66** 0.19 0.63** 1.00
14. WPCS 0.12 -0.20 0.21 -0.33 0.22 -0.42 -0.47* -0.53* -0.56** 0.35 0.13 0.32 0.17 1.00
15. WPSC 0.38 -0.16 0.01 -0.16 0.21 -0.39 -0.42 -0.51* -0.57** 0.40 0.10 0.38 0.25 0.76** 1.00
155
Backward Linear Regression Output
Force Ergometer Model with WPCS Variables Entered/Removed (b)
Model Variables Entered Variables Removed Method
1 GRUNTMXF, GRUNTSPF,
WEIGHT, GRUNTIMF(a)
. Enter
2 . GRUNTSPF Backward (criterion: Probability of F-to-remove >= .100).
3 . WEIGHT Backward (criterion: Probability of F-to-remove >= .100).
a All requested variables entered. b Dependent Variable: WAS Model Summary
Model R R Square Adjusted R
Square Std. Error of the Estimate
1 .557(a) .311 .148 11.2413
2 .536(b) .287 .168 11.1094
3 .508(c) .258 .180 11.0340
a Predictors: (Constant), GRUNTMXF, GRUNTSPF, WEIGHT, GRUNTIMF b Predictors: (Constant), GRUNTMXF, WEIGHT, GRUNTIMF c Predictors: (Constant), GRUNTMXF, GRUNTIMF ANOVA (d)
Model Sum of Squares df Mean Square F Sig.
Regression 968.070 4 242.018 1.915 .154(a)
Residual 2148.222 17 126.366
1
Total 3116.292 21
Regression 894.772 3 298.257 2.417 .100(b)
Residual 2221.520 18 123.418
2
Total 3116.292 21
Regression 803.040 2 401.520 3.298 .059(c)
Residual 2313.252 19 121.750
3
Total 3116.292 21 a Predictors: (Constant), GRUNTMXF, GRUNTSPF, WEIGHT, GRUNTIMF b Predictors: (Constant), GRUNTMXF, WEIGHT, GRUNTIMF c Predictors: (Constant), GRUNTMXF, GRUNTIMF d Dependent Variable: WAS
156
Coefficients (a)
Unstandardized Coefficients
Standardized Coefficients
Model B Std. Error Beta t Sig.
(Constant) 33.934 38.104 .891 .386
WEIGHT -.202 .222 -.213 -.910 .376
GRUNTSPF .005 .007 .156 .762 .457
GRUNTIMF -.005 .003 -.403 -1.689 .109
1
GRUNTMXF .056 .027 .476 2.071 .054
(Constant) 46.194 34.132 1.353 .193
WEIGHT -.189 .219 -.199 -.862 .400
GRUNTIMF -.005 .003 -.436 -1.884 .076
2
GRUNTMXF .056 .027 .481 2.119 .048
3 (Constant) 39.796 33.089 1.203 .244
GRUNTIMF -.006 .003 -.502 -2.313 .032
GRUNTMXF .050 .025 .428 1.971 .063
a Dependent Variable: WAS Excluded Variables (c)
Collinearity Statistics
Model Beta In t Sig. Partial
Correlation Tolerance
2 GRUNTSPF .156(a) .762 .457 .182 .965
3 GRUNTSPF .141(b) .695 .496 .162 .971
WEIGHT -.199(b) -.862 .400 -.199 .745
a Predictors in the Model: (Constant), GRUNTMXF, WEIGHT, GRUNTIMF b Predictors in the Model: (Constant), GRUNTMXF, GRUNTIMF c Dependent Variable: WAS
157
Sprint Performance Model & WPCS Variables Entered/Removed (b)
Model Variables Entered Variables Removed Method
1 TWNTYMTR, WEIGHT,
TENTWNT(a) . Enter
2 . TWNTYMTR Backward (criterion: Probability of
F-to-remove >= .100).
3 . WEIGHT Backward (criterion: Probability of
F-to-remove >= .100). a All requested variables entered. b Dependent Variable: WAS Model Summary
Model R R Square Adjusted R
Square Std. Error of the Estimate
1 .615(a) .379 .275 10.3702
2 .614(b) .376 .311 10.1134
3 .559(c) .312 .278 10.3531
a Predictors: (Constant), TWNTYMTR, WEIGHT, TENTWNT b Predictors: (Constant), WEIGHT, TENTWNT c Predictors: (Constant), TENTWNT ANOVA (d)
Model Sum of Squares df Mean Square F Sig.
Regression 1180.572 3 393.524 3.659 .032(a)
Residual 1935.720 18 107.540
1
Total 3116.292 21
Regression 1172.974 2 586.487 5.734 .011(b)
Residual 1943.318 19 102.280
2
Total 3116.292 21
Regression 972.546 1 972.546 9.073 .007(c)
Residual 2143.746 20 107.187
3
Total 3116.292 21
a Predictors: (Constant), TWNTYMTR, WEIGHT, TENTWNT b Predictors: (Constant), WEIGHT, TENTWNT c Predictors: (Constant), TENTWNT d Dependent Variable: WAS
158
Coefficients (a)
Unstandardized Coefficients
Standardized Coefficients
Model B Std. Error Beta t Sig.
(Constant) 197.079 57.203 3.445 .003
WEIGHT .373 .292 .393 1.280 .217
TENTWNT -47.638 33.288 -.688 -1.431 .170
1
TWNTYMTR -11.179 42.057 -.152 -.266 .793
(Constant) 184.558 31.649 5.831 .000
WEIGHT .330 .236 .348 1.400 .178
2
TENTWNT -55.143 17.193 -.796 -3.207 .005
3 (Constant) 177.268 31.958 5.547 .000
TENTWNT -38.687 12.844 -.559 -3.012 .007
a Dependent Variable: WAS Excluded Variables (c)
Collinearity Statistics
Model Beta In t Sig. Partial
Correlation Tolerance
2 TWNTYMTR -.152(a) -.266 .793 -.063 .106
3 TWNTYMTR .257(b) .535 .599 .122 .154
WEIGHT .348(b) 1.400 .178 .306 .533
a Predictors in the Model: (Constant), WEIGHT, TENTWNT b Predictors in the Model: (Constant), TENTWNT c Dependent Variable: WAS
Sprint Performance Model with WPSS Variables Entered/Removed (b)
Model Variables Entered
Variables Removed Method
1 TWNTYMTR, WEIGHT,
TENTWNT(a) . Enter
2 . TWNTY
MTR
Backward (criterion: Probability of F-to-remove
>= .100).
a All requested variables entered. b Dependent Variable: WSS
159
Model Summary
Model R R Square Adjusted R
Square Std. Error of the Estimate
1 .654(a) .428 .332 34.5562
2 .647(b) .419 .358 33.8898
a Predictors: (Constant), TWNTYMTR, WEIGHT, TENTWNT b Predictors: (Constant), WEIGHT, TENTWNT ANOVA (c)
Model Sum of Squares df Mean Square F Sig.
Regression 16072.415 3 5357.472 4.487 .016(a)
Residual 21494.330 18 1194.129
1
Total 37566.745 21
Regression 15744.943 2 7872.472 6.854 .006(b)
Residual 21821.802 19 1148.516
2
Total 37566.745 21
a Predictors: (Constant), TWNTYMTR, WEIGHT, TENTWNT b Predictors: (Constant), WEIGHT, TENTWNT c Dependent Variable: WSS Coefficients (a)
Unstandardized Coefficients
Standardized Coefficients
Model B Std. Error Beta t Sig.
(Constant) 667.922 190.617 3.504 .003
WEIGHT 1.141 .971 .346 1.175 .255
TENTWNT -256.303 110.925 -1.066 -2.311 .033
1
TWNTYMTR 73.391 140.145 .287 .524 .607
(Constant) 750.125 106.055 7.073 .000
WEIGHT 1.426 .789 .433 1.806 .087
2
TENTWNT -207.030 57.612 -.861 -3.593 .002
a Dependent Variable: WSS
160
Excluded Variables (b)
Collinearity Statistics
Model Beta In t Sig. Partial
Correlation Tolerance
2 TWNTYMTR .287(a) .524 .607 .123 .106
a Predictors in the Model: (Constant), WEIGHT, TENTWNT b Dependent Variable: WSS
Countermovement Jump Model with WPCS Variables Entered/Removed (b)
Model Variables Entered
Variables Removed Method
1 VJTOIMP, VJPFBW,
VJDISPCM(a) . Enter
2 . VJTOIMP Backward (criterion: Probability of F-to-remove >= .100).
3 . VJPFBW Backward (criterion: Probability of F-to-remove >= .100).
a All requested variables entered. b Dependent Variable: WSS Model Summary
Model R R Square Adjusted R
Square Std. Error of the Estimate
1 .405(a) .164 .025 41.7647
2 .405(b) .164 .076 40.6607
3 .404(c) .163 .121 39.6437
a Predictors: (Constant), VJTOIMP, VJPFBW, VJDISPCM b Predictors: (Constant), VJPFBW, VJDISPCM c Predictors: (Constant), VJDISPCM
161
ANOVA (d)
Model Sum of Squares df Mean Square F Sig.
Regression 6169.581 3 2056.527 1.179 .345(a)
Residual 31397.164 18 1744.287
1
Total 37566.745 21
Regression 6154.183 2 3077.091 1.861 .183(b)
Residual 31412.562 19 1653.293
2
Total 37566.745 21
Regression 6134.363 1 6134.363 3.903 .062(c)
Residual 31432.382 20 1571.619
3
Total 37566.745 21
a Predictors: (Constant), VJTOIMP, VJPFBW, VJDISPCM b Predictors: (Constant), VJPFBW, VJDISPCM c Predictors: (Constant), VJDISPCM d Dependent Variable: WSS Coefficients (a)
Unstandardized Coefficients
Standardized Coefficients
Model B Std. Error Beta t Sig.
(Constant) 306.575 66.217 4.630 .000
VJDISPCM 2.130 1.461 .417 1.458 .162
VJPFBW .376 3.261 .025 .115 .909
1
VJTOIMP -.027 .289 -.027 -.094 .926
(Constant) 302.838 51.538 5.876 .000
VJDISPCM 2.042 1.091 .400 1.873 .077
2
VJPFBW .346 3.160 .023 .109 .914
3 (Constant) 306.420 38.830 7.891 .000
VJDISPCM 2.064 1.045 .404 1.976 .062
a Dependent Variable: WSS Excluded Variables (c)
Collinearity Statistics
Model Beta In t Sig. Partial
Correlation Tolerance
2 VJTOIMP -.027(a) -.094 .926 -.022 .566
3 VJTOIMP -.024(b) -.085 .933 -.020 .571
VJPFBW .023(b) .109 .914 .025 .966
a Predictors in the Model: (Constant), VJPFBW, VJDISPCM b Predictors in the Model: (Constant), VJDISPCM c Dependent Variable: WSS
