i

Understanding the Physical Characteristics of Elite Youth Soccer Players in Ireland

Craig Dunphy

A dissertation submitted in part of the requirement for the Bachelor of Business (Hons) Degree in Recreation and Sports Management

May 2020

Department of Sport and Exercise Science

School of Health Sciences

Waterford Institute of Technology

Supervisor: Chris Thompson

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Statement of Originality and Ownership of Work

Department of Sport and Exercise Science

BB (Hons) Recreation and Sports Management

Name: Craig Dunphy I confirm that all the work submitted in this dissertation is my own work, not copied from any other person’s work (published or unpublished) and that it has not previously been submitted for assessment on any other course, in any other institution. Double click the following box and select ‘ticked’ to agree with above statement: Date: 04/05/2020 Student Number: 20075573 Address: 8 Fairfield Court, Belvedere Manor, Waterford Word processor word count: 7336

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Acknowledgements

Firstly, I would like to thank my Supervisor Chris Thompson for getting me through

the year. Our weekly 15 minute meetings to chat about the thesis that turned into

hours speaking about football, which was truly enjoyable . Thanks for getting me

through the year Chris and I hope having me in your first year of thesis supervision

in WIT wasn’t too much of a pain.

To Dr. Patrick Delaney, thanks so much for your advice throughout the year. Even

though you could wreck my head at some stages I knew you were putting me on the

right track and thanks for keeping me motivated throughout the SPSS and Covid 19,

your work doesn’t go unnoticed.

To Gary Hunt, Shane Nolan & Jamie Dalton and all Coaches and players from

Waterford FC for allowing me to conduct the research in this area, thanks so much

lads and best wishes for the season.

To my family, thank you so much for putting up with me for the year and supporting

me throughout my journey over the last 4 years.

To all the staff in the Department of Sport and Exercise Science thank you so much

for making it an enjoyable experience.

The Rec Man group of 2020, what a laugh this has been over the four years, I don’t

think there has been a day where I regret going to college or haven’t smiled, we lost

a few faces over the years but I have certainly made friends for life, Thanks Lads.

To the lads in the WIT Vikings Soccer club and Sports Office thank you so much for

getting me through the years with football and placement. I will never forget the

night in Home Farm when the CFAI cup came back to WIT.

To my 2nd, 3rd & 4th Readers, Derek, Barry and Brendan thanks for your eagle eye

and making sure it was perfect.

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Table of Contents Acknowledgements ii

Table of Contents iiiTable of Figures v

List of Tables vi

List of Abbreviations vii

Abstract viii

1.0 Introduction 1

2.0 Literature Review 3

2.1 Physical demands of Football 3

2.2 Demands of Youth Football: 4

2.3 How frequent are accelerations, change of direction and jumping in football match-play? 52.4 Differences in football-specific physical performance between academy age categories. 52.5 Physical Bias: 6

2.5.1 Physical Profile of Player 6

2.5.2 Relative Age Effect 7

3.0 Rationale 8

3.1 Research Question 1 8

3.2 Research Question 2 9

3.3 Research Question 3 9

4.0 Methods 94.1 Research Design 9

4.2 Participants 9

4.3 Procedures 10

4.3.1 Pre-Testing Procedures 10

4.3.2 Jumping Ability 10

4.3.3 Acceleration Abilities 11

4.3.4 Change of Direction 11

4.4 Data Analysis 125.0 Results 13

5.1 Introduction 13

5.2 Anthropometrics 13

5.3 Acceleration 14

iv

5.4 Jump 15

5.5 Agility 16

6.0 Discussion 18

6.1 Introduction 18

6.2 Differences in Anthropometric data between age groups 186.3 Difference in age categories and physical testing data 19

6.4 Difference in physical tests from other countries 21

6.5 Practical application, what does the data mean for practitioners? 22

6.6 Limitations 23

6.7 Conclusion 24

References 26

Appendix: 33

Appendix A: 33Appendix B: 34

v

Table of Figures Figure 1: Countermovement Jump………………………………………………….11

Figure 2: Modified agility test free (MATF) or (‘’T’’ Test) …………………….....12

vi

List of Tables Table 1: Weight……………………………………………………………………..13

Table 2: Height……………………………………………………………………...14

Table 3: 30m Acceleration …………………………………………………………15

Table 4: Countermovement Jump…………………………………………………..16

Table 5: Change of Direction ………………………………………………………17

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List of Abbreviations COD Change of Direction

CMJ Counter Movement Jump

AACMJ Arm Assisted Counter Movement Jump

SJ Squat Jump

UEFA Union of European Football Associations

FAI Football Association of Ireland

PDP Player Development Plan

CD Central Defender

WD Wide Defender

CM Central Midfielder

WA Wide Attacker

ST Striker

MSS Max Sprint Speed

MAS Max Aerobic Speed

KM Kilometres

FIFA Fédération Internationale de Football

Association

Q1, Q2, Q3, Q4 Quarter 1,2,3,4

ETP Emerging Talent Programme

FA Football Association

MATF Modified Agility Test Free

SPSS Statistical Package for Social Science

viii

Abstract

Overview: The aim of this research was to understand the physical characteristics of

elite youth football players in Ireland using Vertical Jump Height, 30m Acceleration

and Change of Direction. The results will be compared to other nations globally to

see how Ireland compares to bigger countries.

Methods: This research was carried out using quantitative methods by using testing

procedure the population size was 65. All participants came from the same League of

Ireland club and played across four age groups (U13, U15, U17 & U19)

Results: The findings from the research found that the athlete’s performance across

Vertical Jump Height, 30m Acceleration and Change of Direction all improved as

the age group increased. The U19 had the most cluster score and the U15 had the

largest range of scores across the 3 tests.

Conclusion: As the athlete gets older their anthropometric scores increase and their

performance on the physical tests also improves. The new pathway for player

development here in Ireland shows that there is a pathway in Ireland to play

professional football when comparing the data in Ireland to data from professional

academies globally.

1

1.0 Introduction Football is an intense multi-directional and intermittent field sport which requires

high technical ability, tactical awareness and a high level of physical conditioning

(Wilson et al. 1995). Indeed, differences in physical performance are evident

between playing standards and age categories (Reilly et al. 2000). There are four

locomotor categories that define physical match demands: 1) standing; 2) walking;

3) low intensity running (these included both jogging and backwards running); and

4) high intensity running and sprinting. (Mohr et al. 2003). Previous research has

investigated the quantity of physical actions (e.g. total distance covered, high-

intensity running) performed in elite match-play (Mohr et al. 2003; Bangsbo et al.

1991; & Bangsbo, 1994), yet more specific actions (vertical jump height, change of

direction, acceleration) currently lack empirical evidence.

Strength and power have been shown to have a strong correlation with speed (5m,

10m & 20m acceleration) and change of direction (COD) ability (Rusell et al. 2017).

However, Strength and Conditioning coaches often prescribe programmes to

improve muscular power and translate that power into improvements in sprint and

COD ability. Previous research has suggested that strength, speed and COD ability

are physical match demands of football (Loturco et al. 2019). The importance of

elite athletes being monitored in speed, vertical jump and COD. Harley et al. (2010)

highlighted that there was an increase in velocity performance over five age groups

(U12 - U16) in an English academy using the flying 10m as a point of reference

throughout the research Faude et al. (2012) explicitly stated that straight line running

was the most used action demonstrated during goal scoring opportunities in the

German Bundesliga.

Despite the aforementioned literature, there is no known research on the physical

characteristics of football players in Ireland. There has been previous research

conducted in larger footballing countries such as Brazil who have a full-time

professional league for academies. For Ireland to progress on as a football nation to

compete at the top level the correct procedure must be in place to do so. Football is

the largest participation sport in Ireland at grassroots level with 4.1% of the

population take part (Woods et al. 2018). The Football Association of Ireland (FAI)

2

have introduced the League of Ireland (LOI) underage system in order to develop

players by creating the National League. Future research which provides data on

physical performance of youth Irish football players would provide normative values

on the physical demands in Irish football (which currently do not exist). It may aid

practitioners in identifying high and low physical performance values in youth

soccer players in Ireland. Therefore, the aim of the dissertation is to measure the

differences in vertical jump, acceleration and COD performance between four

different age groups (U13, U15, U17, U19) competing in a top-level League of

Ireland football club’s academy.

3

Prior to examining how jumping, acceleration and change of direction capabilities

vary between different age group football players within the League of Ireland, the

following literature review will give in detail the physical demands of football which

include several movements and there will also be a comparison between the several

different playing cohorts on the pitch.

2.0 Literature Review

2.1 Physical demands of Football Research has shown that the demands of elite football match-play are dependent on

multiple factors. These factors consist of (but are not limited to) physical capacity,

technical qualities, playing position, match tactics and quality of the opposition. A

study investigating an elite team competing in the UEFA Champions League and

Serie A (Mohr et al. 2003) illustrates four locomotor categories that define physical

match demands: 1) standing; 2) walking; 3) low intensity running (these included

both jogging and backwards running); and 4) high intensity running and sprinting.

According to DiSalvo et al. (2007), the average professional football player covers

an average distance of 10.86 km per match. In past research that focused on total

distance covered it was midfielders that covered the most distance within a football

match (Dellal et al. 2012, Guadino et al. 2012). Match play data in two of Europe’s

biggest competitions UEFA Champions League and La Liga shows that midfielders

cover a significantly greater amount of distance in a game compared to the defenders

and attacking players, Dellal et al. (2011). Due to a central midfielder being an

integral part of a team it is vital for them to cover every area of the pitch during a

match. DiSalvo et al. (2007) showed that using time motion analysis the central

midfielder covers over 12km in a game compared to a central defender who only

covers 10km. The central midfielder also ran at the highest intensity for the longest

period of time (11.1 km/h and 19 km/h). In the second half of games midfielders also

cover more distance by walking or jogging around the pitch. Although midfielders

have the greatest distance covered by high speed running all players cover over

10km in a game. Attackers perform shorter bursts of high speed running in a game,

but they cover less distance than a midfielder. This research has predominantly

described total distance covered and high-speed running as the significant factors

4

related to match-play performance yet has not considered acceleration as well as

other physical characteristics (e.g. jumping and change of direction) as a demand of

modern-day football (Dellal et al. 2011, DiSalvo el al. 2010, Bradley et al. 2009).

2.2 Demands of Youth Football: Soccer matches across the Republic of Ireland, under the guidance of the Football

Association of Ireland (FAI), are being performed in different formats depending on

age and development stage of the players. Until recently, youth soccer players

competed on a regular full-size pitch (110m x 65m) regardless of age or physical

development. In 2015 the FAI introduced the Player Development Plan (PDP) to

develop youth soccer in Ireland. The FAI introduced smaller pitches for younger

ages. For example, U10 players compete on a 90/70m pitch, with a transition to full-

size pitches (110/80m) in the U13 age category. The first time that the demand of

adult football correlates with the demand of youth football in Ireland is at under

fifteen level. A plausible relationship between player and relative pitch area per

player was noted by Catellano et al. (2015), with findings showing that higher match

demands were related to greater pitch size rather than a decrease of the number of

players per team. The FAI have also introduced different playing equipment between

different age groups. For example, all age groups use a size five football but there is

a weight difference, (270g, 320g, 370g and 450g). Future work should consider

measuring the match-running profiles of young players in different game formats

(i.e. 5v5, 7v7, 9v9, 11v11) across different age groups (i.e. 8-14 years).

This is a complex issue, but worthy of note is the evidence which is cited by Harley

et al. (2010) stated that as the athlete aged there was an increase in the performance

of their velocity threshold. This was based on 112 players across five different age

groups (U12-U16) from two professional soccer clubs in England. When examining

the age groups, a flying 10m velocity test was used. The U16 player scored the best

with a mean score of 1.31±0.06. As the players decreased in age the results of the

test decreased. The findings of this study would suggest that work rate profiles are

similar between age groups but as the athlete gets older a maturation kicks in the

performance becomes stronger. There isn’t enough academic research available on

jumping and change of direction.

5

2.3 How frequent are accelerations, change of direction and jumping in

football match-play? Worthy of note is the fact that there is match-play evidence which demonstrates the

frequency of those physical characteristic’s acceleration, change of direction and

jumping patterns. Empirical evidence from the German Bundesliga has demonstrated

that high-intensity straight line running is the most dominant action during goal

scoring opportunities (Faude et al. 2012). In this study, 83% of goals (from 298

analysed matches) were preceded by a power action (e.g. sprinting and jumping).

Moreover, the volume of high-speed running has been shown to be a distinguishing

factor between top-class players and those at lower level (Bangsbo, 2014). Change of

direction is also a common characteristic of a football match. It has been reported

that in the English Premier League, the average player makes approximately 700

turns during match-play, with around 600 being greater than 90 degrees (Bloomfield

et al. 2007). Similarly, Chaouachi et al. (2010) revealed there are 1300 changes of

activity in a football game (e.g. jumping, sprinting, shooting). Furthermore, a study

conducted within a talent program by the Greek Amateur Soccer Association

concluded that players average 16 jumps in a match with 9 of those contesting

headers in a 90-minute game (Gerodimos et al. 2006). This can vary with position,

with defenders more likely to complete headers. To summarise, match-play data has

reported that jumping, acceleration and change of direction are three vital

characteristics that are performed during match play. It is therefore important to train

these components in youth players in order to maximise the potential in their

physical development.

2.4 Differences in football-specific physical performance between

academy age categories. What follows is an outline of evidence which addresses the question that the stability

of the relationships between acceleration, COD and vertical jumps can vary between

players across professional playing standards (Los Arcos et al. 2017). Forty-two

senior players from the second and third Spanish Leagues performed numerous tests

in relation to jumping, acceleration and change of direction abilities. To test vertical

6

jump height, three tests were used; squat jump (SJ), countermovement jump (CMJ)

and arm assisted CMJ (AACMJ). The results of the CMJ showed that there was a

range of 33.00-51.80cm. Within the study it also shows that there is a correlation

between acceleration and COD ability. When testing the acceleration, it was broken

down into three parts for the results 0-5m, 5-10m & 10-15m. There was a range of

2.15-2.53 in the 15m acceleration. The COD test is 5m bout and they can measure

the acceleration and deceleration of the athlete. The 15m acceleration test and the

505 COD test results are similar. The quickest 15m acceleration time was 2.15

meters per second (m/s) and the quickest 505 COD result was 2.32 m/s. When

broken down the same distance was covered between the two tests and less than 0.2

m/s was the difference between the two results even though during the COD the

athlete must stop and change direction.

Abbott et al. (2018) reported the position-specific variation in the physical demands

of match-play in an English Premier League Academy team. Thirty-seven U23

Premier League players (central defender (CD), wide defender (WD), central

midfielder (CM), wide attacker (WA) & striker (ST)). The data was collected using

GPS measurements over a two-season period with at least one match per week. The

results of the data showed that the CD produced the lowest total of both MSS and

MAS (7.4 ± 0.3). CM produced the highest total distance (11570 ± 420). This is

likely due to the fact that CM have a role of combining all positions on a pitch

together in a game situation. The WA produced the highest running speed (8.6 ±

0.4). The observational evidence demonstrates an advantage for older/more

physically developed players in favour of the concept that age has an effect of athlete

performance in a football specific setting.

2.5 Physical Bias:

2.5.1 Physical Profile of Player

Physical profiles of players can vary with age and position on the pitch. A study

conducted by Sporis et al. (2009) examined the physical profiles of 270 professional

soccer players from the Croatian Premier National League. The participants were

split into 4 groups in order of position (Goalkeeper, Defender, Midfielder &

Attacker). Initial body weight, height and body fat percentage were measured at the

7

beginning of the study. Furthermore, participants completed 5m, 10, 20m sprints,

CMJ, SJ and a VO² max test. The results showed that the goalkeepers were the tallest

and heaviest players in the team. They were also the slowest over 10m and 20m in

the team. Moreover, there was a difference between defender and attacker in vertical

jump height which the midfielders edged out by insignificant differences (Defender-

44.2±1.9, Midfielder- 44.26±2.1) by squat jump. The goalkeepers had the best

performance in the explosive power test (CMJ & SJ). Midfielders had significantly

greater VO² max test values. Goalkeepers had a physical advantage due to their size

in the physical test but when it came to the aerobic VO² max test the goalkeeper was

reduced due to the goalkeeper having a smaller aerobic capacity. It is important for

coaches to calculate the physical profile of a player to design specific training

programs for each position in order to maximise the overall fitness ability of each

player in order by position.

2.5.2 Relative Age Effect

The Relative Age Effect (RAE) is a phenomenon in which an athlete born close to a

cut off period in a year may have a physical and maturation advantage. The RAE

refers to the overall difference in age between individuals within each group, which

may result in significant performances (Barnsley, Thompson & Legault, 1992). Due

to their maturity state, players that are born in the first quarter of the year (January,

February & March) have a greater representation youth football teams compared to

those born in the final quarter (October, November & December). During a

longitudinal study over 21 seasons (Mukija et al. 2009), 189 players (aged 11-18)

were divided into four subgroups (Senior, Elite Youth, Regional Youth & Schoolboy

youth) and birthdate related to the four quarters of the year (Q1, Q2, Q3, Q4). Q1

made up most of the sample size with 46.6% born in Q1 compared to Q4, where

only 10% of the athlete’s part of the elite group were born. Similar research

(Finnegan et al. 2016) elaborated further in illustrating that in the first quarter of the

year RAE amongst players in Ireland is up around 38.2%. The participants of this

study were part of the youth elite section in Ireland who took part in the Emerging

Talent Program (ETP). The study took place across 12 different ETP centres in

Ireland across a six-year period. While it is evident that RAE does exist in the Irish

8

youth system, it is difficult to gauge an athlete's development throughout their

experiences as a youth.

3.0 Rationale As margins of increasing match day performance in elite youth athletes are lessening

due to advancements in training methods (in both pitch and gym-based sections), it

is vital for players to maintain the highest performance levels. The demands of the

game show the importance of movements such as running, jumping and change of

direction. Bloomfield et al. (2007) stated that players who play in the Premier

League change direction 700 times in a game. The average player jumps sixteen

times in a game with nine of those being contesting a header Gerodimos et al.

(2006). The LOI underage structure has only been in existence through four age

groups the last year (introduced in the 2019 season). However, there has been

research done in other countries (Los Arcos et al. 2017). A study like this is

completely novel in Irish football. In Ireland there is no professional underage set up

compared to the underage systems across England so effectively the participants in

this study would be classed as part time athletes and it is important to note that and

compare that to the results of professional youth athletes. Goal scoring can be

decided by short and explosive actions (Faude, O., Koch, T., & Meyer, T. (2012) and

it is therefore essential to train this in elite youth soccer players. Previous research

has quantified physical characteristics in elite youth soccer players in Brazil

examined age-specific development of vertical jump, straight line acceleration and

change of direction speed (Locturo et al. 2019). As the LOI is such a new and

emerging league, it is important to understand the physical characteristics of its

players. The information that is collected during this research will be useful to

compare youth Irish players to their counterparts from around the world.

3.1 Research Question 1

• What are the physical (Acceleration, Jumping and Change of Direction)

characteristics of Irish youth level football players?

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3.2 Research Question 2

• Do the results in Acceleration, Jump and Change of Direction performance

vary between the different age groups?

3.3 Research Question 3

• Understanding how the average values in Ireland compare to the values of

other countries?

4.0 Methods

4.1 Research Design The present quasi-experimental study will compare jumping, acceleration and

change of direction capabilities between four different age groups (U13, U15, U17 &

U19) from a LOI Academy team and will also compare the data collected in Ireland

to data that has been collected from countries across the world (Spain, Slovakia &

Brazil). A convenient sample will be used during this testing. The study population

was chosen due to an elite level of the academy players. This type of sampling is

beneficial because a profile can be created around the results and future research

with a larger sample size can be conducted if appropriate. Prior permission has been

granted by the Head of the Academy to proceed with the testing. Each

participant/parent will be provided with a participant information sheet and informed

of data protection policies. The study protocol will take place over two days. The

first day will involve the U17 and U19 groups and the second day will involve the

U13 and U15. This will reduce participant numbers in the testing location and

minimise external distractions under testing conditions.

4.2 Participants The studies participants will consist of 60-80 underage elite youth soccer players in

Ireland who compete in the SSE Airtricity League (U13, U15, U17 & U19). The

players were recruited on convenient sampling by contacting the Head of the

academy of the local LOI club. The players from U15, U17 & U19 players would

have competed at the LOI level previously; however, it is the first step on the League

10

of Ireland pathway for the U13. They were previously exposed to grassroots level.

The U19, U17 & U15 player would have experienced competing at LOI level

previously, with some U19 level players being in the League of Ireland system since

the U15 age categories.

4.3 Procedures

4.3.1 Pre-Testing Procedures

The testing will be explained verbally, and each group will be given an example of

each test (CMJ, 20m Sprint and ‘’T’’ Test). Each participant that has taken part in

the testing was given a participation information sheet which gives them a written

explanation of each test which was performed so they are fully aware of the

procedures which has taking place. Before the testing will began, every athlete took

part in a controlled warm up to avoid injuries and to give each participant a level

playing field. Athletes performed a standardised warm-up which includes running at

a moderate intensity in order to increase heart rate followed by dynamic stretching

and movement that is related to the testing (i.e. Squat jump).

The order of assessments was as follows: 1) Counter Movement Jump; 2) Linear

speed acceleration (20m test with speed gates at 5m, 10m and 20m); 3) Change of

direction ability (‘’T’’ Test). Demonstrations of each test (CMJ, 20m Acceleration &

‘’T’’ test) will be provided by the researcher. On the day of testing each player

completed an online wellness questionnaire to measure the athlete’s mood, readiness

to train, tiredness and level of injury. All U15, U17 and U19 players took part in

previous testing and are familiar with the procedures involved in the tests. The U13

age group will be fully familiarised with the study protocol tests in order to achieve

an accurate result for the age group.

4.3.2 Jumping Ability

Vertical jumping ability was measured using the CMJ. A CMJ is where the jumper

starts from an upright standing position, makes a preliminary downward movement

by flexing at the knees and hips, then immediately extends the knees and hips again

to jump vertically up off the ground. Each athlete had three attempts at the jump

11

with fifteen seconds recovery between each jump for recovery. Jump height of the

CMJ will be determined by flight time using a contact mat (Chronojump,

Boscosystems, Spain).

Figure.1. Countermovement Jump (Cheraghi et al. 2017).

4.3.3 Acceleration Abilities

Speed gates (Witty Timing Systems, Microgate, Italy) were positioned at distances

0m, 5m, 10m and 30m along the course with 1.5m between the pair of speed gates as

a track. To avoid any environmental influences and provide reliable time

measurements, the acceleration test took place in an indoor court. The acceleration

was measured in three different distances along the track (0-5m, 5-10m and 10-30m).

In order to provide a mean time, the player repeated the test twice.

4.3.4 Change of Direction

The COD test was performed on a hard-indoor surface and consists of the “T” test.

To perform this test, one speed gate and three cones are required. The is placed 5m

perpendicular to the speed gates. The cones are placed in a horizontal line 2.5m

between each cone. The test begins with the athlete starting at the speed gate. The

participant then accelerates in a straight line and when reaching the cone, decelerates

and turns to the left, then accelerates and decelerates before they reach the cone in

order to turn and accelerate once again to the furthest cone 5m away. The athlete will

then accelerate 2.5m towards the middle cone and decelerate and turn left once and

accelerate through the speed gates.

12

.

Figure 2. Modified agility test free (MATF) or (‘’T’’ Test). (Los Arcos et al. 2017)

4.4 Data Analysis Statistical Package for Social Science (SPSS) software was used to analyse all data

collected throughout this research. SPSS was used to determine the results between

the age groups. Then Microsoft excel was used to input the data in to receive a chart

output.

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5.0 Results

5.1 Introduction This chapter provides a detailed and complete report of all data obtained during the

testing procedures in this research. The purpose of this chapter is to present a clear

explanation of the results.

5.2 Anthropometrics The anthropometric data is displayed Table 1. (Weight) and Table 2. (Height). The

data shows a steady increase in both weight and height between all age groups.

There is a strong correlation in both weight and height between U13 and U15 age

group players, there is also a strong relationship between the U17’s and U19’s in

both height and weight. The minimum weight for the U13’s and U15’s is similar

(U13 – 33.3kg & U15 – 34.0 kg).

Table 1. Weight data for all U13-U19

25

35

45

55

65

75

85

95

WeightKG

AgeGroup

Weight

U13 U15 U17 U19

14

Table 2. Height data for all U13-U19

5.3 Acceleration There is a linear association between age and acceleration. In the U13 30m

acceleration the scores were (4.64 ± 0.47 s). The median score for the U15 team

increases by 0.01 s but the standard deviation is lower (4.65 ± 0.36 s). The U13 and

U15 age groups are similar with the median result (U13 - 4.64±0.47 & U15 -

4.65±36). The U17 & U19 scores decrease over 0.25 s. As the players have not fully

developed at U17 the deviation is greater (4.35 ± 0.34 s) compared to the U19 who

scores are more clustered and the difference between the maximum and minimum is

considerably lower (4.21 ± 0.19 s).

135

145

155

165

175

185

195

Height(CM

)

AgeGroup

Height

U13 U15 U17 U19

15

Table 3. 30-meter acceleration data

5.4 Jump The CMJ scores increased with age. There is an association in age and CMJ

performance which gives the player a great jump height. The mean score is listed in

Table 4. The U13 have scored the lowest again (24.93 ± 5.13). The U15 are

marginally greater than the U13 (29.13 ± 8.05) but the deviation is greater at U15

compared to U13. The U17 (40.94 ± 5.2) also has a lower deviation compared to the

U15 but the deviation is still just greater than the U19 deviation. The U19 jumps

score is the largest and the jumps are clustered compared to the other teams and the

deviation is smaller (41.06 ± 5.06).

0

1

2

3

4

5

6

U13 U15 U17 U19

Time(Seconds)

AgeGroup

16

Table 4. Countermovement Jump data for all age groups

5.5 Agility As players get older the scores improve. This can be seen in Table 3, Table 4 and

Table 5. Table 3 shows there is a correlation between results between U13 & U15 as

well as U17 & U19. In the U13 ‘’T-Test’’ scores were (7.36 ± 0.56). The median

score for the U15 team decreases by almost 0.5 s but the standard deviation is greater

(6.78 ± 0.91). The U13 and U15 age groups are similar with the median age. The

U17 & U19 scores are similar with only 0.01 s the difference of the median score.

The U19 has a greater standard deviation (6.31 ± 0.6) compared to the standard

deviation of the U17 (6.32 ± 0.48).

0

5

10

15

20

25

30

35

40

45

U13 U15 U17 U19

Height(CM

)

AgeGroup

17

Table 5. Change of Direction data for all age groups

5.6

5.8

6

6.2

6.4

6.6

6.8

7

7.2

7.4

7.6

U13 U15 U17 U19

TIme(Seconds)

AgeGroup

18

6.0 Discussion

6.1 Introduction This section of the dissertation is a descriptive analysis and discussion of the results

collected over the duration of this research. This section will analyse the results in

depth and will be supported by previous research which was done in the literature

review chapter earlier.

The primary purpose of this dissertation was to get an understanding of the purpose

of physical characteristics of elite youth soccer players in Ireland with specific

attention being drawn towards jumping, straight line acceleration and change of

direction ability. The study adopted purely quantitative methods through the forms

of physical testing. The testing consisted of three tests. Countermovement Jump was

used for the jump scores, a 30m acceleration was used to calculate the players max

speed and the Modified agility test free (MATF) (Los Arcos et al. 2017) was used to

measure the players change of direction ability. Obtaining large amounts of data by

examining three tests across four different age groups (U13, U15, U17 & U19) this

allowed the researcher to conduct extensive testing. With the results inputted into

IBM SPSS Statistics. The output was then transferred to Microsoft Excel to create

the presentation table and give the researcher the output of results. The participants

from this study came from one LOI premier division side academy structure

(Waterford FC). The participants came from different age groups, U13, U15, U17

and U19.

6.2 Differences in Anthropometric data between age groups Gil et al. (2007) states that the maturation state of young players has been shown as a

selection factor, which leads to greater weight and height of the selected players as

compared with the unselected ones, giving prominence to the discussion of the

relative age and the potential impact on the future of these athletes. The findings

from previous research provided and mentioned that height, weight and body mass

increased as the athlete got older between the ages of 9-16 (Emmonds et al. 2018).

This research showed that as the player will progress through the age groups they

will develop into bigger and stronger players. The players from this study reported at

U19 (Height: 179.25 ± 8.75 cm & Weight: 74.5 ± 14.7 kg). The U19 age group

19

showed the lowest deviation out of the 4 age groups as the players have already been

through the maturation phase between the ages of 9-16 as stated previously by

Emmonds et al. (2018). The data collected in this research shows that the largest

deviations are between the U13 and U15 age groups (U13: Height: 156 ± 15.5 cm &

Weight: 47.2 ± 10.6 kg, U15: Height: 159.5 ± 16 cm & Weight: 53 ± 19.85 kg) The

U17 age group are similar to the U19 group where the players have mostly gone

through the main maturation and physical development phase as noted by Emmonds

et al. (2018). As there are still a small number of players just reaching the end of the

maturation phase there is a slightly larger deviation in the anthropometrics compare

to the U19 (U17: Height: 175.5 ± 10 cm & Weight: 67.6 ± 15 kg) but a smaller

deviation when comparing to the U13 and U15. Differences in height and body mass

are associated with increased maturity with increasing chronological age, along with

the biological, morphological, hormonal and neurological changes that occur during

this period of development (Ford et al. 2011). Previous research conducted by

Loturco et al. (2019) states anthropometrics of 3 age groups (U15, U17 & U20) of

football players who were part of two professional football clubs in Brazil. Loturco

states that (U15: 61.8 ± 7.3 kg; 172.6 ± 6.3 cm, U17: 64.8 ± 7.3 kg; 176.3 ± 8.3 cm

& U20: 73.4 ± 9.3 kg; 178.2 ± 9.6 cm). When comparing the finding from this study

to the data collected in Ireland there isn’t a significant difference between the

anthropometrics data from Ireland and Brazil at U17 as well as comparing the U19

from Ireland and the U20 from Brazil. However, there was a notable difference

between the anthropometrics at U15 age group between Ireland and Brazil. The Irish

players are a lot less physically developed when comparing them to the Brazilian

athletes. As the development of every child is different the anthropometric results

will fluctuate more at the younger age groups and as the athlete gets older there will

be less of a difference between the data.

6.3 Difference in age categories and physical testing data The data from the physical testing shows that there is a difference between all four

age groups in each of the three tests. The whole data scores increased as the players

did with age. At U13 the CMJ was the lowest out of the four age groups and this

trend continued throughout each of the three tests. There was also a difference in the

standard deviation for each of the tests. In the 30m ACC the standard deviation

20

decreased between the age group but the same cannot be said for the CMJ and the

COD tests. In the CMJ the U13 (24.93 ± 5.13) and when it is compared to the U15

CMJ (29.13 ± 8.05), the standard deviation increases in the U15 while the jumps

increase. There is justification to show why the deviation is great at the U15 age

group compared to the U13. The Relative Age Effect (RAE) comes into play at the

U15 age group as a lot of the players are born in Q4 of year two in the U15 age

group which really makes them U14. They are closer to the U13 age group in

anthropometric whereas the more physical and older athletes involved in the U15 age

group could pass as an U17 athlete with the data produced.

The U17 and U19 scores for the CMJ (U17 40.94 ± 5.20 & U19 41.06 ± 0.19)

showed that once the athlete develops and matures physically the data scores reduce

in the standard deviation, but it also increases the median score achieved by the age

group. In the COD the median score decreased as the age group increased. As

expected, the U13 were by far the slowest in this test with the median time over half

a second slower than the U15. The standard deviation for the U13 was lower than the

U15 but the slowest score was still at U13 level. (U13 7.36 ± 0.53 & U15 6.78 ±

0.91). The same can be seen when comparing the U17 & U19 (U17 6.32 ± 0.48 &

U19 6.31 ± 0.60). The median score decreased by 0.01 of a second between the U17

and U19 but the deviation increased by 0.12 of a second for the U19 compared to

U19. There is a bigger gap between the results of the U19 compared to the U17

where the results are more clustered. Examining the 30m ACC test the trend that is

seen in the CMJ and COD test is seen in the U17 and U19 but in the U13 and U15

the median score increases by 0.01 of a second for the U15 compared to the U13, the

deviation increases in the U13 scores by 0.11 of a second. (U13 4.64 ± 0.47, U15

4.65 ± 0.36, U17 4.35 ± 0.34 & U19 4.21 ± 0.19). When analysing the data in the

League of Ireland club there was a notable difference in the performance of results

between the U15 and U17. The U17 squad scored significantly better in each of the

three tests. This is where the most notable difference was between each of the four

age groups. This could be due to the U15 squad’s majority being born later in the

year compared to the U17 squad where many of them are born in Q1, Q2 & Q3 of

year one for the U17 age group. The age gap in some of the players is 3 years and

this causes the U17 anthropometrics to be greater, but they are also more mature

compared to their U15 counterparts.

21

6.4 Difference in physical tests from other countries As the LOI is not a fully professional set up with the majority of first teams being

professional the academy structure in Ireland is run on a very semi-professional

basis. The original idea for this research was to examine whether or not elite youth

soccer players could stay in Ireland for their youth development and then when they

receive their education, they can make the step into professional football around the

world. Three countries were examined across the world and they were all compared

with Ireland in the following three tests, 30m ACC, CMJ and COD. The three

countries two of which are in Europe (South Europe and Central Eastern Europe) as

well as a country from South America. Each country has conducted at least one of

the tests that were conducted in an Irish context. As noted in research question 3 it is

important to understand the average values in these other countries who have full

time professional leagues as well as professional academies and compare them to the

semi-professional academies in Ireland. 30m ACC times in Ireland at U19 level

ranged between 4.21 ± 0.19 seconds, this test was compared to research which was

conducted by the Slovakian FA with the U21 squad who were preparing for a major

European championship at the time and the majority of the athletes involved in the

squad were involved with full time professional academies in Slovakia. The

Slovakian national team averaged a score of 4.59 second for the 40m ACC

(Pivovanrnicek et al 2014). When comparing these two countries in the Acceleration

test Ireland was conducted over 30m where the Slovakian test was conducted over

40m. There was a difference in scores by 0.39 of a second, when comparing the

scores at different distances the result is what you could expect with the addition of

an extra 10m to the Slovakian test. As the players are at max speed at 30 m the extra

0.39 second onto the 30m would be the equivalent if the Slovakian test was done at

30m.

When comparing the averages on the CMJ data Ireland will be compared with a

South American country in Brazil. The testing in Brazil was done with an U20 squad

so the data will be compared with the U19 CMJ data in Ireland. The U19 data from

Ireland shows that they have an average jump height of 42.42cm and the Brazilian

data shows that they average 41.91cm. The data from Ireland shows that Ireland has

a greater performance in CMJ height. Jump height is an important movement in

22

football for the action of heading if an outfield player or leaping to catch the ball if

the athlete is a goalkeeper. The data from Ireland shows that there is a difference of

0.51cm compared to the Brazilians. The Brazilian data comes from a professional

club which has a full-time academy. As the Irish data comes from a semi-

professional academy set up it shows that Irish players are more than capable of

progression through the Irish system and then making the move to professional

football in Ireland or around the world.

In context of answering the question asked, specific attention will focus on the U19

results and they will be compared to the results of a professional academy set up in

Spain. There is over a one second difference when comparing the academy from

Ireland to the academy from Spain. The U19 data from Ireland are significantly

slower over the Modified agility test free (MATF) (Los Arcos et al. 2017). The

scores from Ireland had a median of (6.31 ± 0.60s) being the deviation between the

results. The Spanish academy came in with times of (4.91 ± 0.16s). The Spanish

deviation is a lot lower than the deviation from Ireland. Out of the three tests

conducted in the professional academy in Ireland the Change of Direction is the only

test that there is a significant gap in the test scores when comparing them to another

professional academy in Spain. What was noticeable is that none of the athletes in

Ireland get close to the range of the Spanish academy (4.47 – 5.29). The quickest

time from Ireland in the MATF test was 5.71s. The Irish academy is 0.42 seconds

behind the slowest from the Spanish academy. If an athlete from Ireland wants to

make the step form underage academy football to professional football across the

world the MATF times must improve significantly to give the athlete any chance of

making it in the professional game at the top level.

6.5 Practical application, what does the data mean for practitioners? How can the data collected during this research be of use to practitioners? Most

importantly the data can be used each season to monitor the athlete’s performance

throughout the season as well as the athlete’s progression through the age groups in

LOI. As the research conducted has never been conducted before in this particular

League of Ireland club it would be worthwhile keeping record of the data collected

from the U13 team and see how the results improve in their League of Ireland

journey and see how many of the athletes that enter the system at U13 progress

23

through the age groups to U19 level and the onto professional football whether that

is in Ireland or across the world. It can also show that having a national level

competition at underage level can show the development system in Ireland can be a

progression for a player looking to make it as a full time professional. It can show a

natural ladder for players to come into the system early and go the whole way while

getting a full-time education in Ireland if professional football doesn’t work out for

the athlete. Previously players from Ireland looking to make it as a professional had

to go to England because the professionalism of the LOI wasn’t there, the player

would leave school at 16 without a full education and move to a different country to

play football. If it didn’t work out for that player they would return home to Ireland

with no education looking for work but now the LOI underage system is in place to

show that there is a chance of making a professional in Ireland and there is a backup

plan there in case football doesn’t work for that player. The data can be used by

Coaches, Strength and Conditioning, Sport Science, Academy Directors and also

parents to show that the FAI are making huge steps in elite underage football and

show that there is a pathway to professional football for these academy athletes.

6.6 Limitations There are always limitations in research or else there would be no need for research.

A limitation of this study was the sample size was not equal between the age

groups.The validity of a study would have improved in strength if there was an equal

number of players involved in the testing procedures from this club, but this was due

to player availability on testing days. When analysing the data another limitation

could be that the research was only done within one club, one club does not represent

the whole country or league. To get more reliable data testing could have been done

in four clubs across Ireland, North, East, South and West. If the research was

conducted in this manor the data would more accurately represent the whole country.

Each test was used using professional equipment like the Chronojump mat by

Boscosystems and the Whitty Timing system by Microgate. All tests were conducted

2/3 times and the best score was taken in case there was a poor performance by the

athlete who was taking part in the test. As the athletes were elite underage players

and were part of a semi-professional environment the data will differ to data that is

collected from a club that has a fully professional academy with proper

24

infrastructures in place to make the elite youth into professional footballers. As the

athletes involved in this research are not professional athletes their physical

performance will improve immensely over the years in the underage structure of

LOI. As the academy was run on a semi-professional basis all the testing was done in

the evening time. A review conducted by Cappaert (1999), shows how the time of

day can affect an athlete’s performance. As the players are also in full time education

from 9am – 4pm fatigue can come into play ideally the player would want to be fresh

when undertaking the test to achieve maximum performance in all tests. All

participants were given the same familiarisation of each test and each one was

demonstrated so the participant fully understands what they have to do. There was an

open-door policy so if the player had any issues or queries, the participant could ask

them, and answers would be provided to help the participant achieve maximum

performance. There is also a lack of academic research done on understanding the

physical characteristics in elite youth soccer, especially in Ireland. This is a pilot

study and it definitely requires future research to see how the FAI compares to other

big footballing nations across Europe and the world. The FAI have been focused on

player pathway and coach education and there are structures in place for these but

physical characteristics are also crucial to development for a young footballer so this

area should be researched further in a Irish context, to help improve the standard of

Irish players as well as create a more improved pathway for players in Ireland and so

that they don’t have to look abroad

6.7 Conclusion This research provides novel findings which demonstrate the physical characteristic

of elite youth football players that compete in the LOI. The results show that

physical characteristics of soccer players improve with age with the 3 specific

movements that were examined. These findings provide a reference point for future

studies that can be undertaken in a larger context in Ireland. Research in the future

could be undertaken that looks at the pathway of a player that enters the system at

U13 and makes the steps the whole way through the system to U19. Monitoring the

players arthrometric, physical characteristic movements and development of a player

throughout the LOI pathway from U13 to U19 will be the best gauge of to see how

successful the system is and then from there comparing the system to other set up in

25

different countries that use national leagues for youth development. As the national

league is regionalised into North and South future studies could compare data from

the north and south. Time of year the study has been done could also be as the study

here was conducted in February (Pre-season for LOI, Summer Football) if the study

was done in a traditional season in August (Winter Football) would the data and

results shown vary. For more clear-cut evidence on the importance of physical

characteristics and anthropometrics data should be collected throughout the season to

monitor the athlete’s performance throughout the season.

26

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Appendix: Appendix A:

13th January 2020

Re: Craig Dunphy

To whom it concerns,

Waterford FC are delighted to be able to be in a position to assist your student Craig Dunphy for this Thesis. Craig is looking to carry out research with our Academy players and as soon as we have them all signed up for 2020 season, we will be working closely with Craig to assist him with this.

We are always delighted to work in collaboration with WIT students as they complete their studies. Many thanks for your continued good work with the students whom have gone on to become coaches within Waterford FC, including myself.

As Head of Academy I will be monitoring each players progression throughout the season closely and I am aware with GDPR rules and regulations that once the season has finished that the data will be destroyed.

Kind Regards,

Gary Hunt

Head of Youth Development

Waterford FC

Waterford FC: Club Secretary: Email: Phone:

WATERFORD FOOTBALL CLUB

Regional Sports Centre, Cork Road, Waterford.

Ireland.

34

Appendix B:

Participant Consent Form:

I Craig Dunphy am in my final year of a Level 8 Honours Degree in Bachelor of Business in Recreation and Sports Management in Waterford Institute of Technology. As part of my final year I must undertake and complete a Thesis/ Dissertation.

The aim of this research is to Understand the Physical Characteristic of Elite Irish youth soccer players using three testing method. Acceleration, Jumping and Change of Direction. I will be comparing the results across four age groups, U13, U15, U17 & U19.

This is a quasi-experimental study, which will include a Countermovement Jump to measure Jumping ability, a 20m Acceleration will be used to measure Acceleration and the Modified agility test free (MATF) or (‘’T’’ Test) will be used Change of Direction.

1. I confirm that I fully understand the study and am willing to cooperate to the best of my own potential.

2. I Understand that my participation in voluntary and can leave the study at any stage.

3. I understand that neither that myself (Craig Dunphy) nor Waterford Institute of Technology are not responsible if any injury occurs during testing.

Signature of Participant:

Signature of Researcher:

X

X