Final Online subsmission

DEPARTMENT OF SPORT AND
EXERCISE SCIENCES
ASSESSMENT NUMBER: J10586
MODULE CODE: SS6050
DISSERTATION TITLE: An assessment of motion
demands and its effect on technical playing
performance in University football players.
WORD COUNT: 9,630

i
Acknowledgments
Firstly, I would like to thank my supervisor, Dr Edd Thomson. For the unflagging
support and guidance which made the writing of this dissertation manageable.
Next, I would like to thank the University of Chester men’s football team, for their
commitment and patience when participating in this research.
Most importantly, I would like to dedicate this dissertation to my parents. For their
loving, moral and financial support which they have provided for the past three years,
without that, the completion of this dissertation and my Undergraduate degree would
not have been possible.

ii
Abstract
PURPOSE: To analyse and appraise a relationship between the movement demands and
technical performance in central playing positions in University level football players.
METHODS: Twenty individual performances were extracted from five recorded University of
Chester football matches. The performances represented players in central playing positions
such as centre defender (n=6), centre midfielder (n=7) and centre forward (n=7). The
frequency of 12 different technical KPI’s were recorded on a specialised notational analysis
system whereas movement demands were recorded through the use of GPS units. Motion
variables included total distance travelled per half and per game, and percentage of playing
time spent in three different playing intensities (0-10 km/h; 10-14 km/h; 14< km/h).
RESULTS: Descriptive statistics (mean ± SD) were used for all results. Firstly, for technical
efficiency, significant differences were found between CD (68.72%) and CF (52.31%), and
CM (64.43%) and CF (52.31%). Significant differences were recorded between first half and
second half distances travelled. CM travelled greater distances in both first and second half
(5369.77 ± 554.42; 4232.5 ± 948.64), whereas CD travelled greater distances (4363.05 ±
346.49; 3435.78 ± 954.15) than CF (4755.08 ± 573.10; 2779.6 ± 1201.66). Effect sizes
indicated that despite there being no significant differences in positional distances travelled,
there was large (d = >0.8) and medium effect sizes (d = >0.5) between each playing position
in the first and second halves. CONCLUSION: Arelationship was not established between
the technical efficiency and the movement demands in each of the central playing positions.
This suggests that the roles and demands of sub-elite central players are not identical to
professional football players and therefore emphasizes the differences in technical, physical
demands between sub-elite and professional players.

iii
Declaration
This work is original and has not been previously submitted in
support of a Degree, qualification or other course.
Signed ................................................
Date: 08-03-2016

iv
Contents:
1. Introduction
1.1: Background
1.2: Aims
1
1
4
2. Literature Review
2.1: Introduction
2.2: Primary Focus Points
2.2.1: Performance Analysis
2.2.2: Motion Analysis
2.2.3: Technical Analysis
2.2.4: Key Performance Indicators
2.3: Main Themes
2.3.1: Player Positions
2.3.2: Work-Rate Data
2.3.3: GPS
2.3.4: Movement Demands
2.4: Summary
6
6
6
6
7
9
10
12
12
15
16
18
20
3. Methodology
3.1: Sample
3.2: Procedures
3.3: Statistical Analyses
3.4: Test for Reliability
21
21
21
22
22
23
23
4. Results
4.1: Results for the Hypotheses
25
25

v
4.1.1: Technical Efficiency and H1
4.1.2: Movement Profiles and H2
4.1.3: The Relationship between Technical
Efficiency and Movement Demands when
Answering H3
25
25
27
5. Discussion
5.1: Discussing Hypothesis 1
5.1.1: Technical Efficiency
5.1.2: Oppositional Variables
5.1.3: Match Location
5.2.1: The Centre Defender
5.2.2: The Centre Midfielder
5.2.3: The Centre Forward
5.3.1: Simulated Studies
5.3.2: Formation Variables
5.3.3: The Centre Midfielder in Research
31
31
31
32
32
33
33
34
35
36
36
37
37
5.4: Conclusions
5.4.1: Limitations
5.4.2: Practical Applications
5.4.3: Considerations for future research
38
39
39
40
6. Reference List 41
7. Appendix 51

vi
List of Figures:
1. Intra and inter-operator agreement (%) reliability test for 12
individual technical key performance indicators.
24
2. A bar chart identifying the mean ± SD technical
efficiencies of the central playing positions from all
fixtures.
25
3. Differences in distance (m) covered during the first and
second half of playing fixtures between all playing
positions.
26
4. Effect sizes between 1st and 2nd half distances covered
between positions represented by d values according
to Cohen (1992.)
26
5. Differences in inter-positional distances covered at
varying intensities. intensities during the full 90 minute
fixtures.
27
6. Effect sizes between inter-positional distances covered
at varying work rate intensities.
27
7. A scatter diagram identifying the first half relationship
between movement demands and technical playing
efficiency in all central defenders.
28
8. A scatter diagram identifying the second half
relationship between movement demands and technical
playing efficiency in all central defenders.
28
efficiency in all central midfielders.
29
playing efficiency in all central midfielders.
29

vii
efficiency in all central forwards
30
playing efficiency in all central forwards
30

1
1. Introduction
1.1: Background
The creation and maintenance of optimal performance has been the sole
interest of sport scientists for many years (Blazevich, 2013) and with the
inception and application of performance analysis (PA), team sports have
seen vast improvements in meeting the movement and technical demands of
modern sport (Bloomfield, Polman & O’Donoghue, 2004). The most effective
forms of PA are automated match analysis systems (Valter, Adam, Barry, &
Marco, 2006). These multi-camera systems in conjunction with analysts,
quantify sporting movements and technical performance in order to provide
essential feedback to coaching staff.
Technological advancements in match-analysis systems now mean they
are a relevant feature within professional soccer (Carling, et al., 2008), with
increasing detail and focus on the movement profiles of players (Randers, et
al., 2010). It was with these technological advancements, that the objectivity
of athletic performance was able to improve. This has allowed insightful
information about various movement profiles and the increasing technical
demands of modern soccer. (Mackenzie & Cushion, 2013; Randers, et al.,
2010). However, research has suggested that the concurrent application of
soccer match-analysis systems such as the use of key performance
indicators (KPS’s) to appraise technical demands and the use of independent
monitors such as GPS, has had very little presentation within the existing
body of literature. Therefore, it was important for the research to examine the
technical and motion demands of soccer performance with a simultaneous
use of a match-analysis system and GPS units.

2
Another element of PA concerns the use of global positioning systems
(GPS) which quantifies the motion variables associated with athletic
performance. Although used more commonly in applied practice in sports
such as rugby (Waldron, et al., 2011), GPS, more specifically in soccer has
had various limitations to its application within competitive environments. This
is due to issues such as restrictive rules prohibiting their use in professional
football (Molinos Domene, 2013) and the reliability of units being scrutinized
for their reduction in accuracy during high intensity movements (Castellano &
Casamichana, 2010; Harley, et al., 2010). As a result, the involvement of the
units when focusing on competitive soccer performance is scarce due to the
aforementioned issues, allowing future scope for research opportunities.
(Aughey, 2011). However, with recent rule changes, GPS units are having
applied uses in competitive football and training sessions for the first time,
more categorically when applying position specific training programmes to
individual players in all team sports (Carling, Bloomfield, Nelsen & Reilly,
2008). GPS is capable of quantifying the movement profiles of athletes,
however, performance in team sports is also influenced by the technical
performance of the players. Therefore, simultaneous use of GPS and match
analysis should appraise both features of competition. The importance of both
features being closely examined together is to emphasise and create
understanding of the inter-dependency between both the movement and
technical demands associated with playing in different positions (Dellal, et al.,
2010). Individual movement demands in previous literature have been broken
down into various motion variables, including the total distance travelled
throughout a fixture, times spent in certain movement intensities and number
of sprints made (Di Salvo, et al., 2007). The movement patterns and execution

3
of successful technical skills used within football have been identified as game
changing variables (Bloomfield, Polman & O’Donoghue, 2007; Reilly & Thomas,
1976). However, research has overlooked the possible relationship between the
movement variables and the outcome of technical skills. The purpose of the
research was to appraise a relationship between the technical and movement
demands of central playing positions.
The purpose of PA in creating this understanding of a movement and
technical inter-dependency is to highlight technical deficiencies in overall
team performance; by doing so, position-specific training programmes can be
implemented in order to reduce technical error and improve overall team
performance. The need for providing overall technical profiles for players has
come as a result of research, which has identified the variability of soccer
performance based on the playing position of the individual (Cook, 1982,
Weimeyer, 2003). The literature addressing the relationship between
movement demands of modern soccer and the technical ability of players is
reasonably scarce. Nevertheless, Dellal, et al (2010) highlight that due to the
need to maintain a high number of aerobic and anaerobic movements,
technical performance could prove to be less qualitative, resulting in a
progressive depletion in technical performance throughout a 90-minute
fixture.
There is a consensus in literature however, that has evidenced a
definitive drop in positional work-rate profiles in the second half of a soccer
match, more specifically within the initial stages of the second half (Edholm,
Krustrup & Randers, 2015; Mohr, Krustrup & Bangsbo, 2005; Reilly, 1997).
However, further research has assumed that as the movement profiles of
players decrease between the first and second half of a fixture, the

4
successful execution of technical skills will drop. However, the time a player
has in possession of the ball and the number of touches they typically use
has shown to increase due to a reduction in the tempo of play. For example,
Dellal, et al (2010) identify this drop in match speed as a result of the
opposition being less likely to maintain high intensity movements when
pressuring the player in possession of the ball (Mohr, Krustrup & Bangsbo,
2003). Furthermore, current literature surrounding the motion demands of outfield
playing positions have been employed purely from a homogenous pool of elite
(Bradley, et al., 2011; Carling, et al., 2008; Rienzi, et al., 2000) and youth soccer
players (Buchheit, et al., 2010; Lago-Peñas, et al., 2014), often failing to also
examine the inter-half differences in performance. It was therefore the purpose of
the research to examine whether the motion demands and technical efficiency of
elite athletes can be extrapolated into a sub-elite population of University soccer
players. Whilst examining closely, the differences between first and second half
performances.
1.2: Aims
The aim of this research dissertation is to apply performance analysis to
appraise the relationship between the movement demands and technical
performance of different central playing positions in a soccer team. By doing so, the
study will be able to highlight the importance of the movement and technical
demands of certain positional roles and how training implementations can be applied
in order to reduce the chance of decrements in both technical and physical
performance. Consequently, the null hypotheses of the study were;
H1: There will be no difference in the technical efficiency of playing
performance between all central playing positions.

5
H2: There will be no decrements in movement profiles in all central playing
positions between the first and second half of full sided fixtures.
H3: There will be no decrease in the technical efficiency of central playing
positions as a result of total distance covered for the duration of full sided
fixtures.

6
2. Literature Review
2.1: Introduction
The purpose of this chapter is to create an understanding of existing theories on
which the investigation is centred. The primary focus points of ‘performance analysis’,
‘motion analysis’, ‘technical analysis’ and ‘key performance indicators’ will be
examined to reveal the role of the research within the wider framework of traditional
theories, contemporary studies and practical applications. The review will then follow
the main themes involved within the research including ‘player positions’ and ‘work-
rate data’, which will condense the use of GPS units and movement demand profiles
in order to analyse performance and add discussion as to where possible
decrements in performance are and why they occur. Through contrast, comparison
and analytical discussion, the absent areas of research create a clear understanding
of the role of the investigation hypotheses in answering the research question
highlighted in the previous chapter.
2.2: Primary Focus Points
2.2.1: Performance Analysis
For 30 years, research in performance analysis (PA) has been a field of growing
interest within sport and exercise sciences. With an established position within elite
sport, it is now a necessary requirement as part of the coaching process (Hughes,
Bartlett, 2002; Mackenzie & Cushion, 2013; Nevill, Atkinson & Hughes, 2008).
According to the notable study by Franks and Miller (1986) on notational analysis, a
coach could only recall up to 30% of successful and purposeful sporting
performance. Laird and Waters (2008) built upon their research by proving that the
probability of qualified, elite coaches recalling purposeful events accurately, post-
competition was almost doubled (59.2%), with the recall ability of novice coaches

7
also proving to be 17.2% greater than the initial 30% reported by Franks and Miller
(1986).
Technological advancements and contemporary research has led to PA being
accepted as being the essential process of providing feedback in order to improve
future sporting performance and provide insight into the information which cannot be
recalled by coaches (Drust, 2010; Thomson, Lamb & Nicholas, 2013). Nevertheless,
further research is required when addressing the increasing demands of modern
football and how these analysis softwares and feedback procedures are being
translated into professional coaching environments. Glazier (2010) addresses the
importance of this by commenting on the role of performance analysis in professional
sport and its lack of multidisciplinary purpose within sport sciences, such as the
physiological and psychological impacts upon sporting performance rather than just
the process and outcome of the performance.
Continuing development of computer based systems has led to widespread
access to specialised video-based analysis softwares which compute movements
and quantify sporting characteristics of both individual and team performances (Di
Salvo, Collins, McNeill & Cardinale, 2006; Randers, et al., 2010). Due to the
availability of these analysis softwares, research makes practical use of the collected
data in order to contribute to the existing body of literature (Mackenzie & Cushion,
2013). For example, match and video-analysis has been applied to produce rich data
about sporting performance in both individual and team sports such as football, rugby
and golf (Carling, et al., 2008; Gréhaigne, Mahout & Fernandez, 2001; Groom,
Cushion & Nelson, 2012; James, 2009; Thomson, et al., 2013).
Despite performance analysis growing in all sports, football has seen the largest

8
advancement in applied use (Carling, Williams & Reilly, 2005; James, 2006). This
has spanned from contemporary models of notational analysis as a method to
classify and quantify sporting characteristics as adopted by Bloomfield, et al (2007) to
more sophisticated computerised match analysis systems (such as ProZone &
SportsCode). Introduction of match-analysis in modern professional football has led
to a more thorough “systematic understanding of football performance” (Mackenzie &
Cushion, 2013, p. 640). Research into the role of match-analysis, including motion-
analysis, in football has escalated over the past five years, with the increasing
number of accomplished analysis systems becoming available to professional
football (Randers, et al., 2010; Redwood-Brown, Cranton & Sunderland, 2012).
With regard to the traditional study by Reilly and Thomas (1976), the inception
and implementation of motion-analysis in football was deemed complex and time
consuming for early analysts, this meant that the archetypal procedures employed in
early studies were limited to a single player. (Carling, et al., 2008; Reilly & Thomas,
1976). It is evident that match and motion-analysis softwares have advanced to a
point where simultaneous analyses of team performances can be executed during a
full length match, whilst providing a plethora of rich data which can be applied into
further research or used practically for elite coaches and athletes to improve football
performance (Carling, et al., 2008; Gréhaigne, et al., 2001; Hughes, 2004). Similarly,
Randers et al (2010) supports this by stating that the implementation of improved
video-based analysis systems allows improved objectivity about performance by
being able to produce greater image quality, which in turn permits a wider
examination of movement patterns in professional football.
There is also area to expand within the applied use of motion analysis in soccer
and the ability of the data to be extrapolated from competition into position specific
training programmes. Carling, et al (2008, p.858) support this by stating that “motion

9
analysis may be employed to determine the effects of a training intervention on
competition work rate”. This assumes that the implementation of motion analysis in
the enforcement of position specific training interventions can prove to be beneficial
in improving competitive performance. However, the authors acknowledge that the
physical demands of actual performance are not efficiently replicated within training
sessions (Carling, et al., 2008), therefore suggesting that in order to fulfil the
demands of the training intervention, they must replicate the efforts of actual match
performance.
Despite the obvious area of investigation, there remains room for further
research combining the simultaneous use of video-based analysis systems and
independent monitoring systems such as GPS and heart rate monitors during the
same fixture. There also remains scope for the applied use of motion analysis in
measuring the replicability of movement demands between actual match
performance and training interventions in order to successfully act as a medium in
benefitting soccer performance.
Due to the convoluted nature of football, analysis systems have adapted in
order to fully compute technical and tactical performances of individual performances
within the wider team framework (Appleby & Dawson, 2002; Dawson, et al., 2004; Di
Salvo, et al., 2006). Contemporary studies assessed the technical aspects of soccer
performance, such as, passing sequences, shooting and phases of attacking
movements (Hughes & Franks, 2005; Rampinini, Impellizzeri, Castagna, Coutts &
Wisløff, 2009). The importance of technical analysis in soccer has been overlooked
by research surrounding the physical efforts of modern day players (Dellal, et al.,
2012), especially in those studies which have applied automated analysis systems
(Rampinini, et al., 2009; Taylor, Mellalieu, James & Shearer, 2008). Technical ability,

10
according to Russell, Rees and Kingsley (2013, p.2869), is the fundamental ‘building
block’ to success in soccer, and states that “it is surprising that limited descriptive
data exists to characterise skill-related performances during competitive soccer
match-play”. Similarly, Lago-Peñas, et al (2009) and Harper, et al (2014) agree with
this by summarizing individual technical performance as being ‘essential’ for overall
team successes.
With technological advancements, technical analysis softwares have been
employed to quantify and better individual performance and tactical team
performance (Harper, et al., 2014). Practical application and research suggest that in
order for these technical analysis softwares to compute technical playing
performance successfully, key performance indicators (KPI’s) are required in order to
quantify key components of a player’s technical performance (Hughes & Bartlett,
2002; Hughes, et al., 2012; James, 2006). The collective works by Choi (2006; 2006;
2008) identify and employ various methods in the identification and selection of
KPI’s, such as coach opinions, regression analysis, which regulates process and
outcome indicators and inferential statistics, in order to establish performance
indicators prescribed to winning and losing outcomes (Choi, et al., 2006;
O’Donoghue, 2008). The following section will define and further discuss the role of
KPI’s in performance analysis and how they are used to improve player performance.
2.2.4: Key Performance Indicators
According to research, performance indicators are defined as being “a
selection, or combination, of action variables that aims to define some or all aspects
of performance” (Hughes & Bartlett, 2002, p.739). KPI data in performance analysis
of football and across all sports produce the most important statistics concerning
actual sporting performance and the prediction of future performances according to
the quantifying of sporting actions (De Rose, 2004; Hughes, Evans & Wells, 2001).

11
Similar studies and historical books across team and individual sports have
employed the use of operational definitions with practical application through talent
identification when scouting for particular players and academically when creating
reliable analysis templates (Lewis, 2004; Thomson, at al., 2013). Similarly, Hughes
and Bartlett (2002) confirm that as a form of notational analysis, the use of KPI’s
produce more meaningful information about purposeful characteristics of
performance, especially when the data is expressed as a ratio e.g. number of shots
during the fixture to number of successful shots scored. On the contrary, Glazier
(2010, p. 626-628) citicises the methodological use of KPI’s in elite sports when
looking at gross movement patterns by deeming them “a concept of limited
application”, assuming that the ‘unscientific’ role of KPI’s in football analysis involves
the “dumbing down of the theory and methods of biomechanists”. Furthermore,
James, Mellalieu and Jones (2005) express the need for further research
surrounding the reliability of KPI’s when assessing and defining key characteristics of
sports performance, as previous research has not been competent enough when
targeting the reliability of their procedures surrounding the use of fundamental KPI’s
(Atkinson & Neville, 1998; James, et al., 2005; O’Donoghue, 2007). Hughes and
Franks (2004) emphasise this gap in research by stating that within the first three
World Conferences on Science and Football (Reilly, Lees, Davids & Murphy, 1988;
Reilly, Clarys & Stibbe, 1993; Reilly, Bangsbo & Hughes, 1997) 70% of experimental
investigations failed to address reliability within notational analysis research.
The earliest performance analysis studies conducted by Reilly and Thomas
(1976), Franks and Miller (1986) and Mayhew and Wenger (1985) inspired a
continuous flux of theoretical practice and practical experimentation of PA in football
(Bloomfield, et al., 2004; Bloomfield, et al., 2007; O’Donoghue, et al., 2001; Reilly,
1997). However, it has been acknowledged within the context of the review, that

12
there remains scope for research within performance analysis regarding individual,
position specific, physiological efforts within the wider framework of team sports,
more specifically, football. Further research into this area would become integral in
tactical coaching decisions, talent identification in youth soccer and the
implementation of position-specific training programmes, in order to meet the
progressive demands of modern-day football.
Prior to the inception of the notable KPI model the ‘Bloomfield Movement
Classification’ (Bloomfield, et al, 2004), early analysis studies did not encapsulate the
entirety of athletic performance, including the growing physical demands of modern
football. These studies stemmed from the results obtained from Reilly and Thomas
(1976) which proved that there are between 1000 and 1200 (later employed and
changed to 1500 by Bloomfield, et al., 2007) discrete changes in movement during a
game of football, including changes in pace, direction, jumping and movements
involved whilst tackling, making it difficult to fully quantify football performance. As a
result, following studies such as Mayhew and Wenger (1985) and O’Donoghue et al
(2001) incorporated these movements into KPI classification systems in order to
produce comprehensive models which encompassed all physical performance
characteristics of footballers (Bloomfield, et al., 2004; Bloomfield, et al., 2007;
Mayhew & Wenger, 1985; O’Donoghue, et al., 2001).
2.3: Main Themes
2.3.1: Player Positions
Traditionally, football teams field squads of 11 players, this is made up of four
sub-sectioned positions: goalkeepers, defenders, midfield and attackers. It is a
standard requirement for professional players and coaches to have an understanding
of the technical and tactical role of the playable positions (Hughes, et al., 2012).

13
Despite this understanding, the research into these roles is exceptionally scarce due
to an insufficient coverage of whether the tactical and technical role of certain
positions change when in or out of possession of the ball. Further research is
therefore required in order to gain an objective understanding of whether certain
positions work harder within possession of the ball or without.
According to early research, players needed to possess certain characteristics
in order to fulfil the positional roles required for successful team performance (Cook,
1982). It can be argued however, that these outdated views were incepted solely out
of coaching ideals and that with improved research coverage, the technical
characteristics and tactical roles of players can be defined in order to produce
valuable information about individual technical and tactical performance (Weimeyer,
2003). The study conducted by Williams (2012) acknowledged the use of operational
definitions by stating that they are a necessary requirement within performance
analysis, prior to the creation of coding systems. The paper goes on to recognise the
complexity of applying operation definitions to contemporary research, as previous
studies have redefined sporting characteristics, in similar, but different ways
(Williams, 2012). Nevertheless, Franks and McGarry (1996), whilst highlighting the
complexity of applying definitions to performance, go on to argue that by defining
performance characteristics, individual performance will be benefitted, thus resulting
in an overall successful team performance. Similarly, Williams (2009) verified this
ideal of enhancing sport performance through profiling, by concluding that the role of
operational definitions would in fact, amplify the quality of individual football
performance related data.
Technical playing performance is something which is often overlooked in
research, the focus often analysing the work-rate efforts across all playing positions
(Lago-Peñas, Rey, Lago-Ballesteros, Casais & Domínguez, 2009). Taylor, et al

14
(2008) however, discuss the influences of external factors such as location, the
status of the event and the quality of the opposition on technical football
performance. Previous research suggests that technical features of positional roles
such as, more successful shots on-target (attackers), successful crosses
(midfielders) and tackles (defenders) were more apparent when playing in familiar
conditions, such as a home games and more unsuccessful positional technical
performances were witnessed when playing away from home (Sasaki, Nevill & Reilly,
1999; Taylor, et al., 2008; Tucker, Mellalieu, James & Taylor, 2005).
Although technical performance is considered one of the most important factors
about positional success, it is surprising that there is not much research surrounding
the importance of it. Reilly, et al (2000) considers that technical performance is a
significant factor in creating a professional football player, by stating that having an
exceptional technical ability, will automatically provide a vital contribution to team
success and that it does not solely depend on the physical ability of the player. This
is often seen within older players that have a greater technical ability and provide the
essential key characteristics of performance without having to fully exert their efforts
(Matthys, et al., 2009). Rampinini, et al (2009) similarly accept that further research
into the technical ability of football players is essential, more importantly, the
decrement of technical, skill related performance throughout a fixture.
Performance decrements between halves is something that has had limited
appraisal in performance analysis research. Mohr, Krustrup and Bangsbo (2005)
originally identified that 20% of elite footballers experienced technical and movement
decrements within the initial 15 minutes of the second half. Further research
attempted to justify these decrements in performance, associating it with
“accumulated fatigue, the length of half time and lack of physical preparation prior to
the second half” (Edholdm, et al., 2015, p. e40; Mohr, et al., 2004; Mugglestone, et

15
al., 2013). It is important to consider that performance decrements within the wider
team framework can have detrimental impacts upon the success of the team,
especially within the initial 15 minutes of the second half. Rampinini, et al (2009)
reported that despite technical and physical differences between players, there was a
significant drop in physical efforts and technical performance within the second half
of a fixture, this would therefore determine the success of a team performance. On
the contrary, Edholm, et al (2015) argued that this decrease in performance can be
avoided by applying a half-time rewarm-up. This rewarm-up can be used to maintain
optimal performance within the first 15 minutes of the second half where most athletic
performance will deteriorate the most and the vulnerability to injury is at its highest
(Edholm, et al., 2015).
2.3.2: Work-Rate Data
The multifaceted, intermittent nature of football requires athletes to perform
many different actions, including sprints, changes in direction, jumping and kicking
(Lago-Peñas, et al., 2009) and with these increasing demands, there is a
simultaneous requirement for players to enhance their ability to meet high movement
demands (Carling, et al., 2008). Research suggests that contemporary motion
analysis systems are capable of performing concurrent measurements of technical
performances as well as physical exertions, such as total distances covered and total
number of sprints made (Bloomfield, et al., 2007; Carling, et al., 2008; Di Salvo, et
al., 2006; Varley, Fairweather & Aughey, 2012).
According to Gregson, et al (2010) 8% of distance covered during match play
accounts for the high intensity activity profile of individual performance. Reporting
that centre midfielders (916 ± 253m) and attacking players (941 ± 250m) have similar
sprint profiles, whereas centre defenders have a lower sprint index (604 ± 164m).
This could suggest the high intensity demands of the positions and the sprint

16
requirements of central playing positions. It is therefore important to consider the
individual demands of central playing positions and the importance of them in
maintaining team success.
2.3.3: GPS
Although there is an evident use of motion-analysis in football research, the use
of global positioning systems (GPS) has been scarce in nature, especially when
surrounding the analysis of physiological demands within certain positions. Molinos
Domene (2013) identifies this issue by stating that it was, until very recently, against
FIFA rules to allow analysis devices to be worn during matches, this has resulted in a
deficiency of GPS research in elite soccer.
Similar studies have applied the use of GPS in beach football (Castellano &
Casamichana, 2010) and youth football (Harley, et al., 2010). Regardless of FIFA
rules, Cummins, Orr, O’Connor and West (2013), argue that the use of GPS in
soccer would provide scope into the physical, physiological and positional demands
of the sport, maximising individual and team performance and increasing the
development of practical training protocols. Research has shown that testing of
individual work-rate demands through the use of GPS has led to increased positional
training interventions, such as sprint protocols for players who are involved in
performing more high intensity movements in game situations (Buchheit, et al., 2010)
and aerobic training for box-to-box midfielders who require greater aerobic capacities
to fulfil the physical demands of the position (Mirkov, et al., 2008). Further research
favours the use GPS units within team sports, by stating that it has overtaken
outdated video based motion-analysis systems, especially when directing its focus on
the position specific, physiological demands of team sports (Aughey, 2011; Wisbey,
Montgomery, Pyne & Rattray, 2010).

17
More contemporary research has discussed the validity and reliability of 5Hz
and 10Hz GPS units in different situations during team sports, such as movement
demands and distances travelled (Andrews, et al., 2010; Coutts & Duffield, 2010;
Jennings, et al., 2010; Johnston, et al., 2012). According to Coutts and Duffield
(2010) and other studies surrounding football (Castellano & Casamichana, 2010;
Harley, et al., 2010), the reliability of GPS units decreases with higher intensity
movements. This limitation of GPS applies to the obvious high intensity nature of
soccer, however not the small sided variants such as beach football or futsal due to
the number of players and the terrain of play, which as a result restricts the number
of high intensity movements. This limitation in early literature explained why there is a
deficiency in research surrounding the use of GPS in full-sided football. However,
with technological advancements and recent research, the reliability and validity has
been tested to prove that both 5Hz and 10Hz GPS units are accurate when
“measuring distance covered during high-speed phases of intermittent running”
(Rampinini, et al., 2014, p. 52).
According to Aughey & Falloon (2010), GPS monitors provide greater
effectiveness with individual analysis of player performance than automated analysis
systems. Similarly, recent research supports that wearable GPS monitors can
provide specific detail on movement demands of specific positions (Chambers,
Gabbett, Cole & Beard, 2015), whereas contrasting results argue that the units
require further technological advancements to incorporate all aspects of positional
roles to fully quantify sport-specific movements, such as accelerometers and
gyroscopes (Chambers, et al., 2015). Consequently, further research is required
when combining these purposeful movement patterns and its direct relationship with
technical skill execution within youth and sub-elite team sports, as previous literature
encapsulates only the elite performers (Cummins, et al., 2013).

18
2.3.4: Movement Demands
Individual, position specific work-rate in soccer has been a consistent area of
research in sport science (Bloomfield, et al., 2007; Edholm, et al., 2015; Lago-Peñas,
et al, 2009). However, with the increasing physical demands of soccer, research
needs to be updated and built upon in order to fully understand how these physical
stressors have an effect on performance. Reilly (1997) acknowledged simple
measures of work-rate, such as total distance travelled, heart rate (HR) and speed of
the player when running with, or without the ball. Further research has adopted the
use of these measures, often adapting them to include speed zones, time spent in
certain work-rate intensities and other time-motion variables (Di Salvo, et al., 2007).
A number of recent studies have employed the use of speed zones to
differentiate phases of high intensity movements throughout fixtures (Di Salvo, et al.,
2007; Hill-Haas Roswell, Dawson & Coutts, 2009). The practical use of speed zones
compliments the intermittent nature of football, Kaplan et al (2009) support this by
explaining the benefits of identifying speed zones and the inter-transferability of them
into training and competitive match situations. Other motion-variables such as time
spent in work-rate intensities have been appraised in previous literature but still
remains very limited with room for further scope, especially with soccer-specific
research. A recent study conducted by Folgado, Duarte, Marquez and Sampaio
(2015) employed the use of speed-categories as a variable which could potentially
dictate the decrease in individual performance throughout a fixture. Although
reporting that there were no significant differences in the distances covered in
specific intensities during congested fixtures periods. The authors accept the use of
speed-zones and movement intensities as motion variables as they prove to be a
true reflection of actual soccer-specific performance.
According to Carling et al (2008) the practical benefits of motion variables such

19
as distance covered and speed-zone categorization allow training interventions to be
implemented, thus resulting in improved individual and team match performances. In
contrast, Davey, Thorpe and Williams (2002), argue that Reilly’s (1997) simple
measures of movement demands such as distance covered, devalues a player’s
energy expenditure, concluding that decrements in energy can possibly result in a
decline in skill related performance.
Research has compared the work-rate profiles of outfield players, dividing them
into positional and sectional categories, allowing comparisons to be made between
players. For example, Di Salvo, et al (2007), created a schema highlighting the
‘techno-tactical’ roles of various positions, outlining clear parameters in which those
players would perform the most purposeful movements.
The wide array of research papers surrounding the individual work-rate of
different player positions has concluded that central midfielders travel the furthest
during a full sided match and spend more time in low intensity running than any other
position. For example, Di Salvo, et al (2007) reported that centre midfielders cover
significantly greater distances (12027 ± 625m) than all other outfield positions (centre
defenders-10627 ± 8 93m; centre forwards-11254 ± 894m) except external
midfielders (wingers). Lago-Peñas, et al (2009) similarly reported that centre
midfielders travelled significantly greater distances (11541 ± 594m) during a game
than centre defenders (10070 ± 534m) and centre forwards (10626 ± 1242m). These
results, although not always statistically significant (defenders & attackers), similarly
agree with the research conducted by Deprez, et al (2015) where midfielders across
six youth categories (U9; U11; U13; U15; U17; U19), out-performed all other outfield
positions across five youth teams.

20
2.4: Summary
This review of literature has evaluated and critically analysed the works of
well-established theories and practical implementations to examine the role of
performance analysis within soccer and how work-rate data differentiates between
positional roles. Through detailed appraisal of the relevant literature, a logical
background has been established to provide insight into the possible variables
surrounding decrements in both movement demands and technical ability of soccer
players. The review has brought to light the role this study has in bridging together
two extensive areas of research, work-rate analysis and technical analysis of soccer
performance. These areas have been discussed in order to highlight the position of
the research hypotheses in answering the dissertation’s research question which
addresses whether there is a direct impact upon technical playing ability as a result
of increased movement demands.

21
3. Methodology
3.1: Sample
Twenty individual male performances from the University of Chester football
players were monitored throughout three BUCS league fixtures and two friendlies
during the 2015/2016 season. All fixtures were recorded on the University of Chester
3G football pitch. Opponents were pre-selected by the BUCS league fixture lists
which included other competing Universities. Whereas friendlies were decided upon
by the Captains of the individual University of Chester football teams. All home
fixtures kicked off over four successive weeks, where two fixtures were played on the
same afternoon. Participant selection and recruitment was based upon central
playing positions, which were, centre defender (n = 6), centre midfield (n = 7) and
centre attack (n = 7). Voluntary consent forms and health screening questionnaires
were obtained from each participant before engaging with the study, ethical approval
was sought and approved of through the University of Chester Sports and Exercise
Science Ethics Committee.
3.2: Procedures
The deductive research conducted involved a repeated measures design with
independent groups. 20 individual performances from central playing positions (CB;
CM; CF) took part in the study. All participants were required to wear a 5Hz GPS unit
(GPSPORT, Australia), whilst playing full sided 90 minute fixtures. They were also
asked to play their ‘natural game’ in their allocated position. All the games were video
recorded using a HD camera (HC-V770, Panasonic), which was tripod mounted and
placed on a raised platform (0.5m) at pitch side (3.0m). Camera actions included
panning and zooming in order to accurately capture all performances.

22
Motion analysis was conducted immediately following all fixtures, where
motion variables selected and included, total distance covered (which was broken
down into 1st and 2nd half total distances km/h), number of sprints, average speed
(km/h) and maximum speed (km/h). Existing research also supports the use of speed
zones, these break down an individual’s percentage of game time spent travelling in
different intensities (Dawson & Coutts, 2009; Di Salvo, et al., 2007; Hill-Haas
Roswell, et al., 2009; Kaplan, et al., 2009). This meant that the speed zones
employed within the study included the percentage of match performance spent
travelling within the individual zones. Finalized speed zones which were to be
applied to the data collection were: low intensity (0-10 km/h), moderate intensity (10-
14 km/h) and high intensity (>14 km/h).
Prior to technical analysis, a list of KPI’s were devised, highlighting key
components of technical performance. Operational definitions were created and
adapted from previous research in order to maintain reliability and reduce operator
subjectivity during reliability testing (Williams, 2012). The frequency of 12 technical
KPIs was analysed post-fixture at normal speed on QuickTime 10.4 (Apple Inc.,
California, USA) and Dartfish Easy Tag (Fribourg, Switzerland), quantifying the
technical performance of all participants. Efficiency of technical performance was
also calculated (number of successful KPIs/ total number of analysed KPIs * 100) in
order to highlight the increase or decrease between 1st half and 2nd half performance.

23
3.3: Statistical Analyses
Means and standard deviations were calculated for all variables. A Shapiro-
Wilk normality test presented non-significant values which proved a normal
distribution with the data collected from the three central playing positions. A one-way
ANOVA was used to examine the significant differences in technical efficiency
between all three playing positions. A post-hoc Bonferroni adjusted independent t-
test was employed to reduce the chances of type 1 error and to answer H1.
In order to answer H2, a two-way ANOVA with repeated measures was
employed to examine the positional differences in inter-half decrements in movement
profiles. A one-way ANOVA was also used to measure the differences the positions
had in work-rate intensities. Effect sizes were applied to further strengthen the results
and quantify the size of the differences between the positional movement profiles.
Interpretation of the effect sizes followed the guidelines created by Cohen (1992),
reporting >0.2 as a small effect, >0.5 as a medium effect and >0.8 as a large effect.
Finally, a bivariate Pearson’s product moment correlation was applied to
answer H3 of the study. It appraised the strength and direction of the relationship
between the movement profiles and the technical efficiencies of the outfield playing
positions. All statistical analyses were conducted on SPSS (v.22; SPSS, Inc.
Chicago, IL). The coefficient of determination (R2) was used to prove the strength of
compatibility between the two variables, technical efficiency and movement
demands. The level of significance was set at P<0.05.
3.4: Test for Reliability
Reliability was assessed using the intra- and inter-observer method
constructed by Hughes, Cooper and Nevill (2002), the equation used was: Sum of all
analyst 1 scores / Sum of all analyst 2 scores * 100. Reliability was only assessed for

24
the technical performances of the participants. Previous research has
comprehensively appraised the reliability of GPS units in soccer therefore reliability
testing for this aspect of performance was not needed. Two additional novice
analysts were employed to analyse all fixtures and were given a list of operational
definitions of all KPI’s which were to be analysed and were familiarised with Dartfish
Easy Tag prior to examination of the fixture footages. Familiarization involved an
explanation of the tagging board with reference on how to begin, end and extract the
data which was collected. Following the recording of the performances, analysis was
conducted the following day. Whereas inter-operator reliability analyses were
returned four days following performance.
Figure 1. Intra and inter-operator agreement (%) reliability test for 12 individual
technical key performance indicators.
Observer Overall Agreement within
5%
Overall Agreement within
10%
Intra 50% (6/12) 50% (6/12)
Inter 1. 75% (9/12) 25% (3/12)
Inter 2. 75% (9/12) 25% (3/12)
Figures in brackets represent the number of matched KPI analysis during reliability
testing.
The intra and inter-observer reliability results presented in Table 1, proved the
analysis of all KPI’s were within 90-95% agreement. Therefore, the reliability of the
results was deemed acceptable according to the methods developed by Hughes, et
al (2002).

25
4. Results
4.1: Results for the hypotheses
4.1.1: Technical Efficiency and H1
Close examination of the KPIs allowed a calculation of the technical
efficiency of all central playing positions. A one-way ANOVA with a post-hoc
Bonferroni adjusted independent t-test, was used to present differences in
technical efficiency across the three central playing positions.
Figure 2. A bar chart identifying the mean ± SD technical efficiencies of the
central playing positions from all fixtures. * Significant greater values than CF; #
Significantly lower values than other playing positions (p<0.05).
4.1.2: Movement Profiles and H2
To answer H2, a two-way ANOVA with repeated measures was used to
examine the positional differences in the distances covered between the first and
second playing halves. Descriptive statistics were applied to all first half and
second half distances covered (means ± standard deviations). The average total
68.72% 64.43% 52.31%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
CB CM CF
TECHNICALEFFICIENCY(%)
PLAYING POSITION
Technical Efficiency of Central Playing Positions
* * #

26
distances over the whole match when independent of positions was 7264.92 ±
1595.85 which ranged between 6176.3 to 10218.9.
Figure 3. Differences in distance (m) covered during the first and second half of
playing fixtures between all playing positions means ± SD
Centre Defence Centre Midfield Centre Forward
1st Half 4363.05 ± 346.49* 5369.77 ± 554.42* 4755.08 ± 573.10*
2nd Half 3435.78 ± 954.15 4232.5 ± 948.64 2779.6 ± 1201.66
* Significantly greater distances covered in first half than second half when
independent of groups (p < 0.05). No Significance found in between group first
half and second half total distances.
Reported data presented significant inter-half decrements in all work-rate
profiles when positions were looked at as a whole. Therefore, effect sizes, using
Cohen’s d (Cohen, 1992), were applied to measure the magnitude of the
noticeable differences in mean first and second half distances between the
central playing positions.
Figure 4. Effect sizes between 1st
and 2nd
half distances covered between
positions represented by d values according to Cohen (1992).
1st half 2nd Half
CB vs CM -1.4* -0.77#
CM vs CF 0.95* 1.11*
CB vs CF -0.75# 0.57#
* Large effect sizes (d = >0.8); # Medium effect sizes (d = >0.5).

27
Figure 5. Differences in inter-positional distances covered at varying intensities.
intensities during the full 90 minute fixtures (mean ± SD).
% of distance
0-10 km/h
% of distance
10-14 km/h
% of distance
14< km/h
CB 65.41 ± 4.2 20.91± 2.3 13.66 ± 2.13
CM 64.14 ± 2.06 20.31 ± 4.04 15.42 ± 4.37
CF 62.65 ± 6.83 19.2 ± 3.74 18.14 ± 3.55
No significant differences in inter-positional distance work-rate intensities.
Figure 6. Effect sizes between inter-positional distances covered at varying work
rate intensities.
% of distance 0-
10 km/h
% of distance 10-
14 km/h
% of distance
14< km/h
CB vs CM 0.3s 0.1s -0.5#
CM vs CF 0.2s 0.2s -0.6*
CB vs CF 0.4s 0.5# -1.2*
*Large effect sizes (d = >0.8); #Medium effect sizes (d = >0.5); s Small effect
sizes (d = >0.2).
4.1.3: The Relationship between Technical Efficiency and Movement
Demands when Answering H3.
A bivariate Pearson’s product moment correlation was run to determine the
relationship between the technical efficiency and movement demands of central
playing positions. A statistical significance and a medium correlation was found
(rpb = 0.464; p< 0.01) between the two variables. The coefficient of determination
was recorded in order to appraise the interdependent relationship between the
movement and technical demands of central playing positions (O’Donoghue,
2012).

28
Figure 7. A scatter diagram identifying the first half relationship between
movement demands and technical playing efficiency in all central defenders
(R2
=0.00799).
Figure 8. A scatter diagram identifying the second half relationship between
movement demands and technical playing efficiency in all central defenders
(R2
=0.27469).
R² = 0.008
66.00%
68.00%
70.00%
72.00%
74.00%
76.00%
78.00%
3500 4500
TechnicalEfficiency(%)
FirstHalf Distance(m)
The relationship between firsthalf total distance
and technical efficiency in Centre Defenders
R² = 0.2747
50.00%
55.00%
60.00%
65.00%
70.00%
75.00%
80.00%
2000 2500 3000 3500 4000 4500
Second half distance(m)
The relationship between second half total
distanceand technical efficiency in centre
defenders

29
movement demands and technical playing efficiency in all central midfielders
(R2
=0.10597).
movement demands and technical playing efficiency in all central midfielders
(R2
=0.08649).
R² = 0.106
40.00%
45.00%
50.00%
55.00%
60.00%
65.00%
70.00%
75.00%
80.00%
4700 4800 4900 5000 5100 5200 5300 5400
Firsthalf total distance(m)
The relationship between firsthalf total distance
and technical efficiency in centre midfielders
R² = 0.0865
55.00%
56.00%
57.00%
58.00%
59.00%
60.00%
61.00%
62.00%
2000 2500 3000 3500 4000 4500 5000 5500
The relationship between second half total
distanceand technical efficiency in centre
midfielders

30
movement demands and technical playing efficiency in all central forwards
(R2
=0.40414).
movement demands and technical playing efficiency in all central forwards
(R2
=0.1035).
R² = 0.4041
40.00%
45.00%
50.00%
55.00%
60.00%
65.00%
70.00%
3700 4200 4700 5200 5700
Firsthalf distance(m)
The relationship between firsthalf distance and
technical efficiency in centre forwards
R² = 0.1035
35.00%
40.00%
45.00%
50.00%
55.00%
850 1350 1850 2350 2850 3350 3850 4350
The relationship between second half distance
and technical efficiency in centre forwards.

31
5. Discussion
Previous research has focused primarily on the movement demands on all
outfield players, or it has focused on the impact of simulated protocols on the technical
performance of players (Russell, Benton & Kingsley, 2011; Stone & Oliver, 2009).
However, research has failed to establish a relationship between the two variables
during actual competition. Therefore, the aim of the present study was to examine the
movement demands and technical playing performance of central playing positions in
isolation, in order to appraise a direct relationship between both performance variables
in a sample of University football players.
5.1.1: Technical Efficiency
Technical performance is extensively considered as one of the most important
elements in successful soccer performance (Rampinini, et al., 2007). Therefore, H1 was
applied to question the differences in technical efficiency between all central playing
positions. The results from this study failed to support the findings of previous research
in emphasising the technical effectiveness of central playing positions (Dellal, et al.,
2010). The consensus amongst established research suggests the midfielder, in
professional football, maintains the highest technical ability (Dellal, et al., 2010; Dellal,
et al., 2012; Rampinini, et al., 2007). However, the results found in this study
established central defenders as having a significantly greater technical efficiency
between all central playing positions (68.72%). In contrast, the results from Dellal, et al
(2010), which used a sample of professional players from the French first league, saw a
63% technical average in all recorded KPI’s. This 5% rise in sub-elite, University level
football suggests the importance of the technical performance in central defenders is
much greater at this level than at the professional level. These results demonstrate that
the demand for a higher technical performance in centre defenders at the professional

32
level is lesser than that of a centre midfielder. Di Salvo, et al (2007) confirms this by
emphasising the demanding technical and tactical role of the centre midfielders in
linking up defensive and attacking play.
5.1.2: Oppositional Variables
Oppositional variables in research, such as formation, status and location have
been widely considered to be contributing factors towards match outcome (Bradley, et
al., 2011; Carling, 2011; Taylor, et al., 2008). Formation, was seen to be prevalent in
this study, as the players faced different opponents every week, who adopted different
playing formations. Individual performance has been seen in wider research to differ
depending on the oppositional formations (Carling, 2011). This can therefore be used to
reason the inconsistencies in individual technical performances which were observed
during the five recorded fixtures. Similarly, Carling (2011) emphasised the impact of
oppositional formation on skill related performance by highlighting technical changes in
passing length and frequency from central midfielders when faced with 4-5-1 and 4-3-2-
1 formations.
5.1.3: Match Location
All fixtures from this research were conducted in familiar conditions,
emphasising the ‘home ground advantage’ which has been extensively researched in
sport science (Lago, 2009; Taylor, et al., 2008; Tucker, et al., 2009). When looking at
the attacking KPI’s in isolation (attacking entries, shots), research has suggested that
there is usually a higher recording of these performance indicators when playing at
home. Lago-Peñas and Lago-Ballesteros (2011) identified this using a sample of
professional players in the Spanish league, recording on average 14.41 ± 5.16 shots in a
game with 5.60 ± 2.80 of those being on target. Attacking entries in their study were
comprised of attacking moves and box moves (122.65 ± 15.46). The results found by Lago-
Peñas and Lago-Ballesteros (2011) were directly comparable to those obtained within this
study, as attacking technical performance, regardless of the oppositional formation variables,
was maintained throughout the recorded fixtures. The number of shots recorded per game

33
(16.2 ± 1.76), unsurprisingly led to a higher number of shots landing on target (9 ± 2.06). This
supports the claim that 61.5 – 61.95% of worldwide professional games when played at
home will result in a successful team victory (Lago-Peñas & Lago-Ballesteros, 2011; Pollard,
2006a; Pollard, 2006b), as more shots being taken are more likely to lead to goal scoring
opportunities. The results obtained from this research and the wider body of established
literature suggests that the home advantage has a significant impact upon technical
performance. Covering all levels of soccer performance (professional; sub-elite; youth), the
home advantage highlights improved technical performances, leading to the revised statistic
of 61.95% of home games having successful outcomes as a result of increased individual
technical performances.
The movement profiles and physiological efforts of soccer players have been the
core areas of focus in performance analysis research (Di Salvo, et al., 2008), it was
therefore the aim of H2 to compare the central playing positions in appraising this
aspect of performance.
5.2.1: The Centre Defender
The central defenders in this study managed to cover significantly greater
distances in the first half than in the second half of their performances (4363.05 ±
346.49m; 3435.78 ± 954.15m). However, although travelling greater distances than
centre forwards, previous research has reported contrasting results, highlighting the
centre defenders to cover less distances than any other position on the pitch. Lago-
Peñas, et al (2009) disparately identified centre defenders to travel the shortest
distances during full sided fixtures (10070 ± 534m). Wider research has clarified this
contrasting result, evidencing the need for greater central defensive performance in sub-elite
soccer players (Bloomfield, et al., 2007; Di Salvo, et al., 2006; Mohr, et al., 2003; Reilly, et
al., 2000).

34
Furthermore, Lago-Peñas, et al (2009), as well as Di Salvo, et al (2006) reported
similar results identifying centre defenders to cover greater distances in the lowest work-rate
intensities (7080 ± 420m; 6977±213m). These directly replicate the results found from this
study, identifying that sub-elite central defenders, although not travelling the shortest
distances, perform more purposeful movements than the other central playing positions at
low intensity, spending 65.41 ± 4.2% of game time in the low intensity speed zone (0-10
km/h) and 20.91 ± 2.3% in the moderate intensity speed zone (10-14 km/h).
5.2.2: The Centre Midfielder
The centre midfielder in this research travelled significantly greater distances
in the first half (5369.77 ± 554.42m) than second half (4232.5 ± 948.64m). This
contributes to the consensus within wider literature which states that midfielders are
more capable of travelling greater distances throughout a full sided fixture than other
positions (Bloomfield, et al., 2007; Di Salvo, et al., 2007; Gregson, et al., 2010).
According to Sutton, Scott, Wallace and Reilly (2009), the anthropometric
measurements of central midfielders are generally lower than that of the other central
playing positons (1.78 ± 0.05m; 78.0 ± 5.8kg). The study justifies the ability
midfielders have in surpassing performance thresholds due to their anthropometric
measurements, which allows them to have higher levels of aerobic fitness. This
supports the results in explaining why the centre midfielders are more capable of
performing consistently for longer periods of time resulting in greater distances
travelled.
The significantly shorter distances travelled in the second half of fixtures, build
upon the results recorded by Di Salvo, et al (2007), who justified the decrement in
second half distances as being a preservation of energy in order to increase skill
execution. Players were reported to increase their time spent in low moderate
intensity movements such as running and jogging. This was seen to be directly

35
replicable with this study as this can explain the increased percentage of game time
midfielders spent in low (64.14 ± 2.06%) and moderate intensities (20.31 ± 4.04%).
5.2.3: The Centre Forward
Once again, significant differences were seen between first half distances
(4755.08 ± 573.10m) and second half distances (2779.6 ± 1201.66m). When
compared to previous results, research has identified centre forwards to be less
inclined to travel greater distances, however, spend a greater proportion of their
movement patterns in high intensity movements (Bloomfield, et al., 2007;
Dawson & Coutts, 2009; Di Salvo, et al., 2007). This was seen to be directly
replicable with this study where centre forwards we recorded to spend 18.14 ±
3.55% of game time in the high intensity speed zone (14< km/h). Bloomfield, et
al (2007) justified this to be the case, as centre forwards are more likely to
engage in a greater number of high intensity movements during fixtures due to
the attacking demands associated with the position. This could therefore lead to
explaining the 41.54% decline in performance related decrements in movement
profiles in the second half. Di Salvo, et al (2007) similarly reported greater levels
of centre forward movement profiles (621 ± 161m; 404 ± 40m) in the higher
intensity zones (19.1-23 km/h; >23 km/h). These defining decrements in
movement profiles have been described as being the fault of the increasing
demands of modern soccer, identifying the modern game to have a greater
tempo in attacking phases of play (Bloomfield, et al., 2007; Di Salvo, et al.,
2007), including more dribbles, passes and crosses, therefore, requiring centre
forwards to possess higher levels of anaerobic fitness in order to carry out
repeated sprints and fulfil the attacking demands of the position.

36
Despite previous research focusing on the physical attributes of outfield
playing positions (Di Salvo, et al., 2008; Lago-Peñas, et al., 2009; Rampinini, et
al., 2007), it is important to consider the direct impact this has on the technical
profiles of the central playing positions, amalgamating in the process two broad
areas of research (Mackenzie & Cushion, 2012). H3, was set out to appraise a
relationship between technical efficiency and the movement profiles of all the
central playing positions.
5.3.1: Simulated Studies
Application of different methods of performance analysis has enabled an
acceptance of an existing relationship between the technical and movement
profiles of the outfield players. The data obtained in this dissertation, which
suggests that there is a drop in technical efficiency dependent on first and
second half total distance travelled, corroborates with the simulated results
collected by Russell, Benton and Kingsley (2011). The study highlighted that
soccer-specific match simulations (SMS), which replicated individual match
motion and technical demands, had a detrimental impact on passing and
shooting efficiency. Despite there being contrasting methodological procedures,
Russell, et al (2011), justify their research by highlighting the reliability of their
methods in replicating actual soccer-specific performance. This study has
therefore supported, and evidenced the temporal inter-related decline in
movement profiles and technical performance which has proven to be evident
within the wider framework of established research (Bradley, et al., 2011;
Rostgaard, Marcelo Iaia, Simonsen & Bangsbo, 2008).

37
5.3.2: Formation Variables
Previous research has often attempted to justify a reason behind why this
drop in both movement profiles and technical performance simultaneously
occurs. In order to explain this, the results obtained in this study, were closely
compared to those collected by Bradley, et al (2011). The research attempted to
evidence a decline in both the movement profiles and the technical aspects of
performance, dependant on the formation they played in. It was evident that all
formations, such as 4-4-2, 4-3-3 and 4-5-1 all experienced similar drops in inter-
half movement profiles. Highlighting greater distances in the first half than in the
second, which was similar to the data obtained from this study, where all central
playing positions encountered performance decrements in their respective work-
rate profiles. Furthermore, although mentioning there was no difference in
technical performance depending on the playing formation, the paper failed to
address the decline in playing performance a result of a greater movement
profile, highlighting the need for the inter-dependant relationship between
technical and movement demands to be appraised.
5.3.3: The Centre Midfielder in Research
Data highlighting the positional technical efficiencies, when compared to
the distances covered, evidenced obvious tactical differences between each
playing position. This proved that midfielders were able to maintain high levels of
athleticism in their movement profiles (5388.15 ± 554.42m; 4088.92 ± 948.64m),
whilst being able to preserve a high level of technical efficiency (64.43%).
Despite centre defenders having greater technical efficiency within this study,
contrasting research has produced results highlighting the centre midfielder as
being dominant in movement and technical profiles during competitive situations.
Clemente, Mendes & Martins (2013), comparably constructed the idea that the

38
central midfielder was tactically crucial to all attacking and defensive phases of
play, suggesting the high demand in work rate and technical performance found
in this research. In addition, Clemente, et al (2015), recently supported, central
midfielders, when independent of tactical roles, to be pivotal in the the control
and tempo of the game, be that with defensive duties, distribution, or attacking
phases of play. The underpinning research surrounding the dominant role of the
centre midfield supports the results found from this study, highlighting the
movement and technical demands required to successfully fulfil the attacking
and defensive duties of the position.
5.4: Conclusions
The purpose of this study was to build upon the existing literature and add
scope to the position of sub-elite soccer players in a homogenous pool of elite
and youth soccer research. The focus of the research was to establish and
appraise a relationship between technical and movement demands in the central
playing positions. Although the results failed to appraise a relationship in all
central playing positions, these results can be seen to be the norm for sub-elite
performers in highlighting the technical and movement demands of sub-elite
soccer players.
The results obtained in this study have replicated those found in simulated
protocol studies. This suggests that the differences in competitive and simulated
environments are minimal and therefore differences between sub-elite and elite
soccer players can be directly related to the optimal training methods and
conditions as well as the selection processes implemented at both levels of the
game. The differences in actual total distances covered by all players have been

39
identified to be critical in the development and potential transition of players from
sub-elite to the elite level (Rebelo, et al., 2012).
5.4.1: Limitations
The research conducted showed methodological limitations in attempting
to establish a relationship between the technical and movement demands of
central playing positions. Upon reflection, the sample size used was too small,
this hindered the establishment and appraisal of the relationship in technical and
movement profiles. This limitation was uncontrollable as time and deadline
constraints meant the research conducted had to be time efficient and as
effective as possible.
Furthermore, the reliability measurements used in the analysis phase
showed inter-operator reliability to be lower than the intra-operator reliability. In
order to overcome this limitation, analyst reflection and familiarisation processes,
such as clarification on operational definitions must prove to be concise and
effective when properly recording KPIs.
5.4.2: Practical Applications
The results obtained from this research have identified the distinct gap
between elite and sub-elite soccer players. The results suggest that there is a
greater need on defensive efficiency, therefore position-specific training
interventions are required to maximise defensive performance in order to
promote greater numbers of successful team outcomes. The research has also
identified centre midfielders are able to maintain higher levels of work-rate
demands as well as high levels of consistent technical performance. Therefore,
position-specific technical and movement demand protocols, similar to those
implemented in previous research, should be used to reconstruct the increasing

40
demands of centre midfielders in training environments in order for them to
benefit competitively.
5.4.3: Considerations for Future Research
Further research should consider applying multi-camera match and
motion-analysis systems in order to reliably collect KPI data, this way, data can
be recorded without inter- and intra-observer reliability issues which were found
to be problematic with this research. These will also be able to measure
movement patterns more effectively without the reliability issues associated with
high intensity movements in 5Hz GPS units. Furthermore, larger samples of sub-
elite soccer players should be used in order to get a clearer distinction in inter-
positional differences between technical and movement demands. Using both of
these factors, a relationship should be able to established and appraised.
Finally, further research should consider using a sample of elite soccer
players in order to draw direct comparisons between elite and sub-elite athletes.
This way previous research can be built upon and continue to bridge together
the movement profiles and technical ability within the wider framework of
performance analysis of soccer performance.

41
7. Reference List
Andrews, M. H., Gray, A. J., Jenkins, D., Taaffe, D. R., & Glover, M. L. (2010).
Validity and reliability of GPS for measuring distance travelled in field-based
team sports. Journal of Sports Sciences, 28(12), 1319-1325.
Appleby, B., & Dawson, B. (2002). Video analysis of selected game activities in
Australian rules football. Journal of Science and Medicine in Sport, 5(2), 129-
142. doi:10.1016/S1440-2440(02)80034-2
Atkinson, G., & Nevill, A.M. (1998). Statistical methods for assessing measurement
error (reliability) in variables relevant to sports medicine. AUCKLAND: Adis
International. doi:10.2165/00007256-199826040-00002
Aughey, R. J. (2011). Applications of GPS technologies to field sports. Int J Sports
Physiol Perform, 6(3), 295-310
Aughey, R. J., & Falloon, C. (2010). Real-time versus post-game GPS data in team
sports. Journal of Science and Medicine in Sport, 13(3), 348-349.
Bangsbo, J., Hughes, M., & Reilly, T. (1997). Science and football III: Proceedings of
the third world congress of science and football. London: E and FN Spon.
de, J., Polman, R., & O'Donoghue, P. (2004). The 'Bloomfield movement
classification': Motion analysis of individual players in dynamic movement
sports. International Journal of Performance Analysis in Sport, 4(2), 20-31
Bloomfield, J., Polman, R., & O'Donoghue, P. (2007). Physical demands of different
positions in FA premier league soccer. Journal of Sports Science & Medicine,
6(1), 63-70.
Blazevich, A. J. (2013). Sports biomechanics: the basics: optimising human
performance. A&C Black

42
Bradley, P. S., Carling, C., Archer, D., Roberts, J., Dodds, A., Di Mascio, M.. .
Krustrup, P. (2011). The effect of playing formation on high-intensity running
and technical profiles in English FA premier league soccer matches. Journal of
Sports Sciences, 29(8), 821-830. doi:10.1080/02640414.2011.561868
Buchheit, M., Mendez-Villanueva, A., Delhomel, G., Brughelli, M., & Ahmaidi, S.
(2010). Improving repeated sprint ability in young elite soccer players:
repeated shuttle sprints vs. explosive strength training. The Journal of
Strength & Conditioning Research, 24(10), 2715-2722.
Buchheit, M., Mendez-Villanueva, A., Simpson, B. M., & Bourdon, P. C. (2010).
Match running performance and fitness in youth soccer. International Journal
of Sports Medicine, 31(11), 818-825. doi:10.1055/s-0030-1262838
Carling, C., Bloomfield, J., Nelsen, L., & Reilly, T. (2008). The role of motion analysis
in elite soccer: Contemporary performance measurement techniques and work
rate data. Sports Medicine, 38(10), 839-862. doi:10.2165/00007256-
200838100-00004
Carling, C., Williams, A. M., & Reilly, T. (2005). Handbook of soccer match analysis:
A systematic approach to improving performance. London: Routledge.
Carling, C. (2011). Influence of opposition team formation on physical and skill-
related performance in a professional soccer team. European Journal of Sport
Science, 11(3), 155-164.
Casamichana, D., & Castellano, J. (2010). Time-motion, heart rate, perceptual and
motor behaviour demands in small-sides soccer games: Effects of pitch size.
Journal of Sports Sciences, 28(14), 1615-1623.
doi:10.1080/02640414.2010.521168

43
Chambers, R., Gabbett, T. J., Cole, M. H., & Beard, A. (2015). The use of wearable
microsensors to quantify sport-specific movements. Sports Medicine, 45(7),
1065-1081. doi:10.1007/s40279-015-0332-9
Choi, H. (2008). Definitions of performance indicators in real-time and lapsed time
analysis in performance analysis of sports (Doctoral dissertation, Cardiff
Metropolitan University).
Choi, H., O’Donoghue, P., & Hughes, M. D. (2006). A study of team performance
indicators by separated time scale real-time analysis techniques within English
national league basketball. In Dancs, H.; Hughes, MD and O’Donoghue, P.:
World Congress of Performance Analysis of Sport VII–Proceedings (pp. 138-
141).
Choi, H., Reed, D., O’Donoghue, P. G., & Hughes, M. (2006). The valid numbers of
performance indicators for real-time analysis using prediction models within
men singles in 2005 Wimbledon Tennis Championship. Performance Analysis
of Sport, 7(23), 220-226.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.
doi:10.1037/0033-2909.112.1.155
Clemente, F. M., Mendes, R., & Martins, F. M. (2013). Applying Centrality Metrics to
Identify the Prominent Football Players. VIII International Congress of the
Spanish Association of Sports Science.
Clemente, F. M., Martins, F. M. L., Wong, D. P., Kalamaras, D., & Mendes, R. S.
(2015). Midfielder as the prominent participant in the building attack: A
network analysis of national teams in FIFA World Cup 2014. International
Journal of Performance Analysis in Sport, 15(2), 704-722.

44
Cook, M. (1982). Soccer coaching and team management. London: EP Publishing
Coutts, A. J., & Duffield, R. (2010). Validity and reliability of GPS devices for
measuring movement demands of team sports. Journal of science and
Medicine in Sport, 13(1), 133-135.
Cooper, S., Hughes, M., O'Donoghue, P., & Nevill, A. M. (2007). A simple statistical
method for assessing the reliability of data entered into sport performance
analysis systems. International Journal of Performance Analysis in Sport, 7(1),
87-109.
Cummins, C., Orr, R., O’Connor, H., & West, C. (2013). Global positioning systems
(GPS) and microtechnology sensors in team sports: a systematic review.
Sports medicine, 43(10), 1025-1042.
Davey, P. R., Thorpe, R. D., & Williams, C. (2002). Fatigue decreases skilled tennis
performance. Journal of sports sciences, 20(4), 311-318.
Dawson, B., Hopkinson, R., Appleby, B., Stewart, G., & Roberts, C. (2004). Player
movement patterns and game activities in the Australian football league.
Journal of Science and Medicine in Sport, 7(3), 278-291. doi:10.1016/S1440-
2440(04)80023-9
De Rose, D. (2004). Statistical analysis of basketball performance indicators
according to home/away games and winning and losing teams. Journal of
Human Movement Studies, 47(4), 327-336.
Dellal, A., Wong, D. P., Moalla, W., & Chamari, K. (2010). Physical and technical
activity of soccer players in the French First League-with special reference to
their playing position: original research article. International SportMed Journal,
11(2), 278-290.

45
Dellal, A., Owen, A., Wong, D. P., Krustrup, P., van Exsel, M., & Mallo, J. (2012).
Technical and physical demands of small vs. large sided games in relation to
playing position in elite soccer. Human Movement Science, 31(4), 957-969.
doi:10.1016/j.humov.2011.08.013
Di Salvo, V., Baron, R., Tschan, H., Calderon Montero, F. J., Bachl, N., & Pigozzi, F.
(2007). Performance characteristics according to playing position in elite
soccer. International journal of sports medicine, 28(3), 222-227.
Di Salvo, V., Collins, A., McNeill, B., & Cardinale, M. (2006). Validation of prozone: A
new video-based performance analysis system. International Journal of
Performance Analysis in Sport, 6(1), 108-11.
Drust, B. (2010). Performance analysis research: Meeting the challenge. Journal of
Sports Sciences, 28(9), 921-922. doi:10.1080/02640411003740769
Edholm, P., Krustrup, P., & Randers, M. B. (2015). Half‐time re‐warm up increases
performance capacity in male elite soccer players. Scandinavian Journal of
Medicine & Science in Sports, 25(1), e40-e49. doi:10.1111/sms.12236
Folgado, H., Duarte, R., Marques, P., & Sampaio, J. (2015). The effects of congested
fixtures period on tactical and physical performance in elite football. Journal of
Sports Sciences, 33(12), 1238-1247. doi:10.1080/02640414.2015.1022576
Franks, I., & McGarry, T. (1996). The science of match analysis. Science and soccer,
363-375.
Franks, I.M. and Miller, G. (1986). Eyewitness Testimony in Sport. Journal of Sport
Behavior, 9(1), 38-45.
Glazier, P. S. (2010). Game, set and match? substantive issues and future
directions in performance analysis. Sports Medicine, 40(8), 625-634.

46
doi:10.2165/11534970-000000000-00000
Gratton, C., & Jones, I. (2010). Research methods for sports studies (2nd ed.).
London: Routledge.
Gregson, W., Drust, B., Atkinson, G., & Salvo, V. D. (2010). Match-to-match
variability of high-speed activities in premier league soccer. International
Journal of Sports Medicine, 31(4), 237-242. doi:10.1055/s-0030-1247546
Gréhaigne, J.F., Mahout, B., & Fernandes, A. (2001). Qualitative observation tools to
analyse soccer. International Journal of Performance Analysis in Sport, 1(1),
52-61.
Groom, R., Cushion, C., & Nelson, L. (2012). Analysing coach-athlete 'talk in
interaction' within the delivery of video-based performance feedback in elite
youth soccer. Qualitative Research in Sport, Exercise and Health, 4(3), 439.
doi:10.1080/2159676X.2012.693525
Harley, J. A., Barnes, C. A., Portas, M., Lovell, R., Barrett, S., Paul, D., & Weston, M.
(2010). Motion analysis of match-play in elite U12 to U16 age-group soccer
players. Journal of Sports Sciences, 28(13), 1391-1397.
Harper, L. D., West, D. J., Stevenson, E., & Russell, M. (2014). Technical
performance reduces during the extra-time period of professional soccer
match-play. PloS One, 9(10), e110995. doi:10.1371/journal.pone.0110995
Hill-Haas, S. V., Rowsell, G. J., Dawson, B. T., & Coutts, A. J. (2009). Acute
physiological responses and time-motion characteristics of two small-sided
training regimes in youth soccer players. The Journal of Strength &
Conditioning Research, 23(1), 111-115.
Hughes, M. (2004). Performance analysis - a 2004 perspective. International Journal

47
of Performance Analysis in Sport, 4(1), 103-109.
Hughes, M. D., & Bartlett, R. M. (2002). The use of performance indicators in
performance analysis. Journal of Sports Sciences, 20(10), 739-754.
doi:10.1080/026404102320675602
Hughes, M., & Franks, I. (2005). Analysis of passing sequences, shots and goals in
soccer. Journal of Sports Sciences, 23(5), 509-514.
doi:10.1080/02640410410001716779
Hughes, M., & Franks, I. M. (2004). Notational analysis of sport: Systems for better
coaching and performance in sport. New York;London: Routledge.
Hughes, M., Atkinson, G., & Nevill, A. (2008). Twenty-five years of sport performance
research in the journal of sports sciences. Journal of Sports Sciences, 26(4),
413. doi:10.1080/02640410701714589
Hughes, M., Caudrelier, T., James, N., Redwood-Brown, A., Donnelly, I., Kirkbride,
A., & Duschesne, C. (2012). Moneyball and soccer - an analysis of the key
performance indicators of elite male soccer players by position. Journal of
Human Sport and Exercise, 7, 402-412.
Hughes, M., Evans, S., & Wells, J. (2001). Establishing normative profiles in
performance analysis. International Journal of Performance Analysis in Sport,
1(1), 1-26.
Hughes, M., Cooper, S., & Nevill, S. (2002). Analysis procedures for non-parametric
data from performance analysis. International Journal of Performance Analysis
in Sport, 2(1), 6-20
James, N. (2006). Notational analysis in soccer: Past, present and future.
International Journal of Performance Analysis in Sport, 6(2), 67-81

48
James, N. (2009). Performance analysis of golf: Reflections on the past and a vision
of the future. International Journal of Performance Analysis in Sport, 9(2), 188-
209.
James, N., Mellalieu, S., & Jones, N. (2005). The development of position-specific
performance indicators in professional rugby union. Journal of Sports
Sciences, 23(1), 63-72. doi:10.1080/02640410410001730106
Jennings, D., Cormack, S., Coutts, A. J., Boyd, L., & Aughey, R. J. (2010). The
validity and reliability of GPS units for measuring distance in team sport
specific running patterns. International Journal of Sports Physiology and
Performance, 5(3), 328-341.
Johnston, R. J., Watsford, M. L., Pine, M. J., Spurrs, R. W., Murphy, A. J., & Pruyn,
E. C. (2012). The validity and reliability of 5-Hz global positioning system units
to measure team sport movement demands. The Journal of Strength &
Kaplan, T., Erkmen, N., & Taskin, H. (2009). The evaluation of the running speed and
agility performance in professional and amateur soccer players. The Journal of
Strength & Conditioning Research, 23(3), 774-778.
Lago, C. (2009). The influence of match location, quality of opposition, and match
status on possession strategies in professional association football. Journal of
sports sciences, 27(13), 1463-1469.
Lago-Peñas, C., & Lago-Ballesteros, J. (2011). Game location and team quality
effects on performance profiles in professional soccer. Journal of Sports
Science and Medicine, 10(3), 465-471.
Lago-Penas, C., Rey, E., Lago-Ballesteros, J., Casais, L., & Dominguez, E. (2009).

49
Analysis of work-rate in soccer according to playing positions. International
Lago-Peñas, C., Rey, E., Lago-Ballesteros, J., Casais, L., & Domínguez, E. (2009).
Analysis of work-rate in soccer according to playing positions. International
Lago-Peñas, C., Rey, E., Casáis, L., & Gómez-López, M. (2014). Relationship
between performance characteristics and the selection process in youth
soccer players. Journal of Human Kinetics, 40(1), 189. doi:10.2478/hukin-
2014-0021
Laird, P., & Waters, L. (2008). Eyewitness recollection of sport coaches. International
Lewis, M. (2004). Moneyball: The art of winning an unfair game. New York; London:
W.W. Norton.
Mackenzie, R., & Cushion, C. (2013). Performance analysis in football: A critical
review and implications for future research. Journal of Sports Sciences, 31(6),
639-676. doi:10.1080/02640414.2012.746720
Macutkiewicz, D., & Sunderland, C. (2011). The use of GPS to evaluate activity
profiles of elite women hockey players during match-play. Journal of Sports
Sciences, 29(9), 967-973. doi:10.1080/02640414.2011.570774
Matthys, S., Mujika, I., Philippaerts, R., Goiriena, J., Santisteban, J., & Vaeyens, R.
(2009). The relative age effect in a professional football club setting. Journal of
Mayhew, S. R., & Wenger, H. A. (1985). Time-motion analysis of professional soccer.
Journal of Human Movement Studies, 11(1), 49-52.

50
Mirkov, D., Nedeljkovic, A., Kukolj, M., Ugarkovic, D., & Jaric, S. (2008). Evaluation of
the reliability of soccer-specific field tests. The Journal of Strength &
Mohr, M., Krustrup, P., & Bangsbo, J. (2003). Match performance of high-standard
soccer players with special reference to development of fatigue. Journal of
Mohr, M., Krustrup, P., & Bangsbo, J. (2005). Fatigue in soccer: a brief review.
Journal of sports sciences, 23(6), 593-599.
Mohr, M., Krustrup, P., Nybo, L., Nielsen, J. J., & Bangsbo, J. (2004). Muscle
temperature and sprint performance during soccer matches–beneficial effect
of re‐warm‐up at half‐time. Scandinavian journal of medicine & science in
sports, 14(3), 156-162.
Molinos Domene, Á. (2013). Evaluation of movement and physiological demands of
full-back and center-back soccer players using global positioning systems.
Journal of Human Sport and Exercise, 8(4), 1015-1028.
doi:10.4100/jhse.2013.84.12
Mugglestone, C., Morris, J. G., Saunders, B., & Sunderland, C. (2013). Half-time and
high-speed running in the second half of soccer. International journal of sports
medicine, 34(6), 514-519.
O'Donoghue, P. (2012). Statistics for sport and exercise studies: an introduction.
Routledge.
O'Donoghue, P. (2007). Reliability issues in performance analysis. International
O'Donoghue, P. (2008). Principal components analysis in the selection of key

51
performance indicators in sport. International Journal of Performance Analysis
in Sport, 8(3), 145-155.
O'Donoghue, P. (2006). The use of feedback videos in sport. International Journal of
O'Donoghue, P. G., Boyd, M., Lawlor, J., & Bleakley, E. W. (2001). Time-motion
analysis of elite, semi-professional and amateur soccer competition. Journal of
Human Movement Studies, 41(1), 1-12.
Pollard, R. (2006a). Home advantage in soccer: variations in its magnitude and a
literature review of the inter-related factors associated with its existence.
Journal of Sport Behavior, 29(2), 169-189.
Pollard, R. (2006b). Worldwide regional variations in home advantage in association
football. Journal of sports sciences, 24(3), 231-240.
Rampinini, E., Alberti, G., Fiorenza, M., Riggio, M., Sassi, R., Borges, T., & Coutts, A.
(2015;2014;). Accuracy of GPS devices for measuring high-intensity running in
field-based team sports. International Journal of Sports Medicine, 36(1), 49-
53. doi:10.1055/s-0034-1385866
Rampinini, E., Impellizzeri, F. M., Castagna, C., Coutts, A. J., & Wisløff, U. (2009).
Technical performance during soccer matches of the Italian serie A league:
Effect of fatigue and competitive level. Journal of Science and Medicine in
Sport, 12(1), 227-233. doi:10.1016/j.jsams.2007.10.002
Stone, K. J., & Oliver, J. L. (2009). The effect of 45 minutes of soccer-specific
exercise on the performance of soccer skills. International Journal of Sports
Physiology and Performance, 4(2), 163-175.
Randers, M., Bischoff, R., Krustrup, P., Solano, R., Mujika, I., Zubillaga, A., & Hewitt,

52
A. (2010). Application of four different football match analysis systems: A
comparative study. Journal of Sports Sciences, 28(2), 171-182.
doi:10.1080/02640410903428525
Rebelo, A., Brito, J., Seabra, A., Oliveira, J., Drust, B., & Krustrup, P. (2012). A new
tool to measure training load in soccer training and match play. International
journal of sports medicine, 33(4), 297-304.
Redwood-Brown, A., Cranton, W., & Sunderland, C. (2012). Validation of a real-time
video analysis system for soccer. International Journal of Sports Medicine,
33(8), 635-640. doi:10.1055/s-0032-1306326
Reilly, T. (1997). Energetics of high-intensity exercise (soccer) with particular
reference to fatigue. Journal of Sports Sciences, 15(3), 257-263.
doi:10.1080/026404197367263
Reilly, T., & Thomas, V. (1976). A motion analysis of work-rate in different positional
roles in professional football match-play. Journal of human movement studies,
2(2), 87-97.
Reilly, T., Bangsbo, J., & Franks, A. (2000). Anthropometric and physiological
predispositions for elite soccer. Journal of sports sciences, 18(9), 669-683.
Reilly, T., Clarys, J. P., & Stibbe, A. (1993). Science and football II: Proceedings of
the second world congress of science and football, Eindhoven, Netherlands,
22nd-25th may 1991. Spon:
Reilly, T., Lees, A., Davids, K., & Murphy, J. (1988). Science and football:
Proceedings of the first world congress of science and football, Liverpool, 13-
17th April 1987. Spon:

53
Rienzi, E., Drust, B., Reilly, T., Carter, J. E., & Martin, A. (2000). Investigation of
anthropometric and work-rate profiles of elite south American international
soccer players. The Journal of Sports Medicine and Physical Fitness,
28(3), 222-227
Russell, M., Benton, D., & Kingsley, M. (2011). The effects of fatigue on soccer skills
performed during a soccer match simulation. International journal of sports
physiology and performance, 6(2), 221-233.
Russell, M., Rees, G., & Kingsley, M. I. C. (2013). Technical demands of soccer
match play in the English championship. Journal of Strength and Conditioning
Research, 27(10), 2869-2873. doi:10.1519/JSC.0b013e318280cc13
Sasaki, Y., Nevill, A., & Reilly, T. (1999). Home advantage: A case study of Ipswich
Town football club during the 1996-1997 season. Journal of Sports Sciences,
17, 831.
Sutton, L., Scott, M., Wallace, J., & Reilly, T. (2009). Body composition of English
Premier League soccer players: Influence of playing position, international
status, and ethnicity. Journal of Sports sciences, 27(10), 1019-1026.
Taylor, J., James, N., Mellalieu, S., & Shearer, D. (2008). The influence of match
location, quality of opposition, and match status on technical performance in
professional association football. Journal of Sports Sciences, 26(9), 885-895.
doi:10.1080/02640410701836887
Thomson, E., Lamb, K., & Nicholas, C. (2013). The development of a reliable
amateur boxing performance analysis template. Journal of Sports Sciences,
31(5), 516-527.
Tucker, W., Mellalieu, S. D., James, N., & Taylor, J. B. (2005). Game location effects

54
in professional soccer: A case study. International Journal of Performance
Analysis in Sport, 5(2), 23-35.
Valter, D. S., Adam, C., Barry, M., & Marco, C. (2006). Validation of Prozone®: A
new video-based performance analysis system. International Journal of
Varley, M. C., Fairweather, I. H., & Aughey1, 2, R. J. (2012). Validity and reliability of
GPS for measuring instantaneous velocity during acceleration, deceleration,
and constant motion. Journal of sports sciences, 30(2), 121-127.
Waldron, M., Twist, C., Highton, J., Worsfold, P., & Daniels, M. (2011) Movement and
physiological match demands of elite rugby league using portable global
positioning systems. Journal of Sports Sciences, 29(11), 1223-1230, DOI:
10.1080/02640414.2011.587445
Weimeyer, J. (2003). Who should play in which position in soccer? Empirical
evidence and unconventional modelling. International Journal of Performance
Analysis in Sport, 3(1), 1-18.
Williams, J. (2012). Operational definitions in performance analysis and the need for
consensus. International Journal of Performance Analysis in Sport, 12(1), 52-
63.
Wisbey, B., Montgomery, P. G., Pyne, D. B., & Rattray, B. (2010). Quantifying
movement demands of AFL football using GPS tracking. Journal of science
and Medicine in Sport, 13(5), 531-536.

55
8. Appendix
The following appendices have been saved on the submitted CD entitled
‘J10586: Appendices’:
Appendix 1. Digital, signed consent forms.
Appendix 2. Image of list of operational definitions table.
Appendix 3. Image of Dartfish Tagging Board.
Appendix 4. Folder containing Excel output files.
Appendix 5. Folder containing SPSS output files.
Appendix 6. Folder containing raw GPS output data.
The following appendix has been saved on SES HD 5 (CDN102 – with Matt
Palmer):
Appendix 8. Recorded Match Footage.

Final Online subsmission

More Related Content

Viewers also liked (12)

Similar to Final Online subsmission (20)

Final Online subsmission