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

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.

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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: A relationship 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.

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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

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Contents:

1. Introduction

1.1: Background

1.2: Aims

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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

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3. Methodology

3.1: Sample

3.2: Procedures

3.2.1: Motion Analysis

3.2.2: Technical Analysis

3.3: Statistical Analyses

3.4: Test for Reliability

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4. Results

4.1: Results for the Hypotheses

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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

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5. Discussion

5.1: Discussing Hypothesis 1

5.1.1: Technical Efficiency

5.1.2: Oppositional Variables

5.1.3: Match Location

5.2: Discussing Hypothesis 2

5.2.1: The Centre Defender

5.2.2: The Centre Midfielder

5.2.3: The Centre Forward

5.3: Discussing Hypothesis 3

5.3.1: Simulated Studies

5.3.2: Formation Variables

5.3.3: The Centre Midfielder in Research

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5.4: Conclusions

5.4.1: Limitations

5.4.2: Practical Applications

5.4.3: Considerations for future research

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6. Reference List 41

7. Appendix 51

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List of Figures:

1. Intra and inter-operator agreement (%) reliability test for 12 individual technical key performance indicators.

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2. A bar chart identifying the mean ± SD technical efficiencies of the central playing positions from all fixtures.

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3. Differences in distance (m) covered during the first and second half of playing fixtures between all playing positions.

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4. Effect sizes between 1st and 2nd half distances covered between positions represented by d values according to Cohen (1992.)

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5. Differences in inter-positional distances covered at varying intensities. intensities during the full 90 minute fixtures.

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6. Effect sizes between inter-positional distances covered at varying work rate intensities.

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7. A scatter diagram identifying the first half relationship between movement demands and technical playing efficiency in all central defenders.

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8. A scatter diagram identifying the second half relationship between movement demands and technical playing efficiency in all central defenders.

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9. A scatter diagram identifying the first half relationship between movement demands and technical playing efficiency in all central midfielders.

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10. A scatter diagram identifying the second half relationship between movement demands and technical playing efficiency in all central midfielders.

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11. A scatter diagram identifying the first half relationship between movement demands and technical playing

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efficiency in all central forwards

12. A scatter diagram identifying the second half relationship between movement demands and technical playing efficiency in all central forwards

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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.

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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

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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

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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.

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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.

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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

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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.

2.2.2: Motion Analysis

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

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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

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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.

2.2.3: Technical Analysis

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,

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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).

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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

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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).

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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

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(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

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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

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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).

16

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).

17

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

18

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.

19

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.

20

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.

21

3.2.1: Motion Analysis

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).

3.2.2: Technical Analysis

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.

22

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

23

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).

24

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.

CB CM CF0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

68.72% 64.43% 52.31%

Technical Efficiency of Central Playing Positions

Playing position

Tech

nica

l effi

cien

cy (%

)

* * #

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

25

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).

26

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).

27

3500 450062.00%

64.00%

66.00%

68.00%

70.00%

72.00%

74.00%

76.00%

78.00%

R² = 0.00798963660724295

The relationship between first half total distance and technical efficiency in Centre Defenders

First Half Distance (m)

Tech

nica

l Effi

cienc

y (%

)

Figure 7. A scatter diagram identifying the first half relationship between movement demands and technical playing efficiency in all central defenders (R2=0.00799).

2000 2500 3000 3500 4000 450050.00%

55.00%

60.00%

65.00%

70.00%

75.00%

80.00%

R² = 0.274690826613941

The relationship between second half total dis-tance and technical efficiency in centre defenders

Second half distance (m)

Tech

nica

l Effi

cienc

y (%

)

Figure 8. A scatter diagram identifying the second half relationship between movement demands and technical playing efficiency in all central defenders (R2=0.27469).

28

4700 4800 4900 5000 5100 5200 5300 540040.00%

45.00%

50.00%

55.00%

60.00%

65.00%

70.00%

75.00%

80.00%

R² = 0.105965377125114

The relationship between first half total distance and technical efficiency in centre midfielders

First half total distance (m)

Tech

nica

l Effi

cienc

y (%

)

Figure 9. A scatter diagram identifying the first half relationship between movement demands and technical playing efficiency in all central midfielders (R2=0.10597).

2000 2500 3000 3500 4000 4500 5000 550052.00%

54.00%

56.00%

58.00%

60.00%

62.00%

64.00%

R² = 0.0864935067153781

The relationship between second half total dis-tance and technical efficiency in centre mid-

fielders

Second half distance (m)

Tech

nica

l Effi

cienc

y (%

)

Figure 10. A scatter diagram identifying the second half relationship between movement demands and technical playing efficiency in all central midfielders (R2=0.08649).

29

3700 4200 4700 5200 570040.00%

45.00%

50.00%

55.00%

60.00%

65.00%

70.00%

R² = 0.404138646378683

The relationship between first half distance and technical efficiency in centre forwards

First half distance (m)

Tech

nica

l Effi

cienc

y (%

)

Figure 11. A scatter diagram identifying the first half relationship between movement demands and technical playing efficiency in all central forwards (R2=0.40414).

850 1350 1850 2350 2850 3350 3850 435035.00%

40.00%

45.00%

50.00%

55.00%

R² = 0.103501360382048

The relationship between second half distance and technical efficiency in centre forwards.

Second half distance (m)

Tech

nica

l Effi

cienc

y (%

)

Figure 12. A scatter diagram identifying the second half relationship between movement demands and technical playing efficiency in all central forwards (R2=0.1035).

30

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: Discussing Hypothesis 1

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

31

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 (16.2 ± 1.76), unsurprisingly

32

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.

5.2: Discussing Hypothesis 2

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).

33

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

34

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.

35

5.3: Discussing Hypothesis 3

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).

36

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

37

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

38

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

39

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.

40

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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.

54

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.

55