Research paper identifying future football talent

David Da Silva

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Talent Identification for youth footballers

Researcher: David Da Silva, s4095652, The University of Queensland

Supervisors: Dr Cliff Mallett, PhD (QLD), Senior Lecturer in Pedagogy. The University of Queensland

Dr Dennis Taaffe, PhD, Senior Lecture, School of Human Movement Studies.

The University of Queensland

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Abstract

Identifying future football talent through testing must take into account the specific demands of the sport in question suggest Regnier (n.d.). The purpose of this study is to identify similarities and differences in football players’ attributes at the national, state, regional and club level.

Attributes such as speed, agility, power, ball skill and motivation will be tested as they have been identified by previous research (Hoare & Warr, 2000), (Reilly et al., 2000) and football coaches in a study conducted by Vrljic & Mallett (2008) as key indicators of football success. 97 participants took part in the survey from three schools in the Brisbane region three players, three were 14 years of age and 11 additional surveys were not legibly completed. The physical test consisted of ball juggling, vertical jump, 20m sprint and Illinois agility test. A motivation inventory SMS-6 was utilised. Results indicated that juggling (p>0.002) and agility (p>0.005) were predictors of performance level. There was no relationship between motivation scores and performance levels.

This may be due to the sample being homogenous, sharing similar interests and motivations.

These results imply that technical ball skills were better predictors of performance than a specific physical test such as a 20 meter sprint. Gender differences arose in relation to training age (p>0.002) with males (median training age 10 (8.5-11) having more years of experience in football than females (median training age of 6.5 (5-9.8). This result identified a possible lack of opportunities for young girls to gain experience in football or possibly females lack interest in playing football. This finding was unforeseen and warrants further research.

Introduction

Multivariate analysis has previously been used with success in discriminating first-team professional players from those in the reserve teams. The test battery included anthropometric, physiological and psychological measures (Reilly & Thomass, 1977). Reilly, Williams, Nevill, & Franks (2000), identified that elite football players have an advantage over their sub-elite counterparts in relation to body composition, speed, speed endurance, vertical jumping, agility, motivation orientation, control and perception of anxiety, anticipation and technical skill. The purpose of this investigation is to compare the level of competition a youth football player is currently playing at and the scores achieved in the administered tests. It is suggested that players at a higher performance level compared to lower performance level players will exhibit unique qualities that make them suited to the game of football (eg speed, agility and ball control).

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Literature Review General Talent Detection and Talent Identification Balyi (1997) suggest, that the long term development of the athlete (LTAD) should consider the sport in question, as the type of sport may need earlier specialisation than other sports i.e. gymnastics where peak performance occurs around late teens to early twenties (Balyi, 1996). Early specialisation would require the athlete to focus on training to train, training to compete and training to win (Balyi, 1997). While sports that do not require early specialisation should initially attempt to develop fundamental skills in the child from six to ten years of age before specialising into specific sports. The introduction of fundamentals (skill development) before specialisation is characterised by over-all preparation and should be primarily concerned with the development of general movement skills including running, jumping, throwing, balance, agility and co-ordination (Balyi, 1997).

Within the context of talent detection the primary aim of the process is the discovery of potential performers/individuals that can best carry out the task within a specific situational context (William & Reiley, 2000), then directing the individual towards sports to which they are most suited (Woodman, 1985).

Sport talent detection has to be viewed as a process within the larger context of sport talent development and relies on long term predictors of an individual’s performance. Most systems employed to detect potential talent establish an ideal model for the specific sport in question and develop the model further by considering data from numerous world-class athletes (Jarver, 1981).

Since sport is multifaceted, successful sport talent detection must rely on a multidisciplinary approach suggest Regnier (n.d.). Factors which have previously been identified as having an impact on the performance capacity of the athlete generally include their physical characteristics including anthropometry, physiology and motor skills, psychological characteristics/abilities, previous sporting experience/s, player trainability and current lifestyle/environment (Jarver, 1981).

Physical characteristics have continuously been identified by researchers of talent detection as key indicators of performance. Cited key performance indicators include height, weight, speed, motor skills, work capacity or endurance, strength, agility and power (Jarver, 1981) and (Regnier, n.d.).

Parents, schools and coaches all have an impact on the younger athletes’ performance and these variables are quite hard to accurately measure as they have differing levels of input to each individual player.

Previous research by Blooms (1985) recognises that athletic talent must be nurtured by positive influences from parents, mentors and peers, yet it is very hard to accurately measure how significant these positive

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influences can be on the athlete. Research on expert athletes has repeatedly shown that talent development is dependent upon quality coaching (Bloom, 1985; Cote, Baker, & Abernethy, 2003). Although the coachs’ influence will vary across cultures, sports and stages of talent development (Bloom, 1985; Samela & Moraes, 2003), guidance from a competent coach is essential to becoming an expert performer (Mallett, 2005).

Individual

Performer

Mentor

Parents’

Career Phase Initiation

Development

Perfection

Joyful, playful, excited, “special”

“Hooked” Committed

Obsessed responsible

Kind, Cheerful Caring Process-centred

Strong, respecting Skilled demanding

Stressful, Respected/feared emotionally bonded

Shared excitement Supportive Sought mentors Positive

Made sacrifices, Restricted activity

Table 1. Characteristics of talented performers (and their Mentors and Parents) at various stages of their careers Bloom (1985) Psychological characteristics and social variables can impact on the athletes’ attitude towards the sport in school, participation in extra-curricular sporting activities, and can impact the athletes’ success (Regnier, n.d.). Seminal work by Bloom (1985), examines the development of talent in individuals from a variety of endeavours (eg, sport, music, and science). Blooms’ (1985) findings identified high levels of motivation characterised these individuals and this characteristic is one that is required to develop talent (Vrljic & Mallett, 2008).

Motivation is the foundation of sporting performance and achievement. Without it, even the most talented athlete is unlikely to reach their potential (Williams, 2006). However research has yet to identify a specific ‘sporting’ personality or an overall psychological profile that can predict successful performance in sport (Morris, 2000). Deci & Ryan (1985), self-determination model suggest that motivation is multidimensional and follows a continuum that reflects an athletes’ varying degree of self-determination. Mallett, Kawabata, Newcombe, Otero-Forero, & Jackson (2007) have developed a sports motivation scale (SMS-6) which acknowledges Deci & Ryan (1985) Self Determination model where motivation is considered 4

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multidimensional and that varying types of motivation could explain much of human behaviour. Assessing intrinsic and extrinsic motivation in sport is important because different types of motivation have been associated with varying outcomes (Mallett, et al., 2007). Along the motivation continuum the following summary will start by describing the least self-determined motivation and move towards a portrayal of more autonomous motivations (Williams, 2006);

Amotivation- No sense of personal control with respect to their sporting engagement, and there are no intrinsic or extrinsic reasons for doing the activity. Athletes who are amotivated are expected to exhibit maladaptive motivation patterns, and would have lost the desire to compete and quit the sport (Williams, 2006). Vallerand (1997), identifies amotivation as multidimensional construct with four major types; capacity/ability beliefs, strategy-beliefs, capacity-effort beliefs, and helplessness.

Extrinsic Motivation (external) is dependent on rewards, which are usually social or material. Athletes usually engage in sport as a means to an end, namely to obtain something they want or to avoid realizing something they do not desire. Athletes who manifest high levels of extrinsic motivation are less selfdetermined and for sporting engagements these individuals tend to report lower enjoyment of sport, and burnout. They are particularly prone to not trying as hard as possible or dropping out, especially when the extrinsic reinforcements and constraints that maintain their involvement begin to wane (Williams, 2006).

- External regulation- Behaviour is performed to satisfy an external demand or system from the external rewards an athlete expects to secure. “I’m only going to practice today because my scholarship depends on it� (Williams, 2006).

- Introjected regulation- Athlete participate because, inside they feel that they have to play, self imposed guilt. This motivation replaces the external source of control with an internal one (Williams, 2006).

- Identified regulation- Behaviour is undertaken by choice but as a means to an end, with the athlete not considering the behaviour pleasurable (Williams, 2006).

- Integrated Regulation- Athletes may endorse the training as being consistent with their personal beliefs about health and fitness (Mallett, 2005).

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Intrinsic Motivation (internal) for its own sake usually revolves around the inherent pleasure of doing the activity. It is highly autonomous and represents the quintessential state of self-determination (Ryan & Deci, 2000). Sport affords the possibility of accomplishment and mastery, or the opportunity to experience pleasant sensations whether they are sensory or aesthetic (Vallerand, 2001). Intrinsic athletes find sport pleasurable in and of itself and are maximally motivated both quantitatively and qualitatively. “Millionaires would be unable to sustain high levels of motivation and commitment throughout their careers if they did not have high levels of intrinsic motivation for engaging in their aport, particularly during periods of adversity, duress and poor performance” (Williams, 2006 p. 66).

Player trainability “Raw talent” is of little use if can not be actualised within the environment of top level competition. However performance can improve through training and development. The level of improvement due to training and development can vary and this is why it must be considered after the detection of a talented athlete has occurred (Regnier, n.d.). Hoare & Warr, (2000) also suggest that a genetic or innate predisposition to respond to training interventions must exist; however more talented players may have higher motivation and, therefore practice more. Relevant contributions of sociological and genetic factors are likely to vary according to the unique characteristics of the sport in question.

Recent advancements in genetic profiling have tentatively identified Genes associated with certain types of performances.

Myostatin (inhibits muscle growth), IGF-1: muscle growth and development, IGF-2: muscle growth and regulation, ACTN-3: muscle fibre protein in Type II muscle fibres ACE (Angiotensin Converting Enzyme) is associated with vascular function and has three genotypes (II, ID & DD). Gayagay (1998) identified endurance athletes more likely to carry (II, ID) of these variants.

Given the complexity of performance phenotypes, it should be obvious that we have along way to go before we have a satisfactory understanding of the role of genetic inheritance on exercise related traits and in the adaptation to a physically active lifestyle (Rankinen, Bray, Hagberg, Perusse, Roth, Wolfarth and Bouchard, 2005). However the research indicates that certain performance characteristics may be innate or subject to the effect of training. It should be noted that potential talent cannot be nurtured without appropriate environmental and support mechanisms (Richardson & Reilly, 2001).

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Talent Identification is defined by Hoare (2002), as “the screening of young athletes currently participating in sport using competition results, experienced coaches and/or physical, physiological and skills test in order to identify those most likely to succeed in that sport”. In young athletes, a years difference can lead to significant variations in; anthropometric variables (height, weight, etc), development of physical condition (speed and agility), cognitive skills (reflected in games analysis, perception and tactical ability), and psychological/emotional maturity (Simmons & Paull, 2001). These characteristics have to be evaluated in relation to their level of maturation age as opposed to chronological age suggest MacPhail (2005).

Adolescent variations in skeletal age can differ by more than two years within a group with the same year of birth (Jimenez & Pain, 2008), while Wood (1985), suggests a skeletal age difference of up to six years can exist. Thus it is very important to consider biological maturation when dealing with talent identification.

Team Ball sports are complex in nature, with Williams & Franks (1998) identifying anthropometric, physiological, psychological, perceptual and technical ability contributing to performance. Including game specific knowledge in the assessment of talent identification can assist detectors in deciding who has talent and who is experienced in the specific sport, as players need time within the sport of choice to develop strategies and a ‘game sense’ or awareness (Williams & Franks, 1998). Skilled players are more accurate at recognising and recalling patterns of play; better at anticipating visual cues; more effective and appropriate visual search behaviours; and more accurate in their expectation of what is likely to happen given a particular set of circumstances (Williams & Reilly, 2000). Gabbett, Georgieff, and Domrow (2007) differentiated volleyball players’ performance levels with sport-specific skills test. The volleyball study was better at distinguishing player abilities and performance levels than physiological, anthropometric measures.

This result may have implications for future talent identification testing protocols.

Invasion games have a lot in common when viewed from a tactical perspective. The aim of invasion games is to move into an opponent’s territory in order to score while maintaining possession of an object (usually a ball), create and use space, and attack a goal. Participants must also learn to defend space and a goal (Mitchell, 1996). The similarities between invasion games may result in a transfer of “game sense” and “awareness” between ball sports and consequently, any attempt to identify early talent must take into account players’ previous sporting experiences including the specific sport, those within the same category of sport i.e. invasion games and other less related sporting pursuits such as athletics, and gymnastics across the development cycle (Hoare & Warr, 2000).

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Football Talent Identification Multivariate analysis has previously been used with success in discriminating first-team professional players from those in the reserve teams. The test battery included anthropometric, physiological and psychological measures (Reilly & Thomass, 1977). Reilly, Williams, Nevill, & Franks (2000), identified that elite football players have had an advantage over their sub-elite counterparts in relation to body composition, speed, speed endurance, vertical jumping, agility, motivation orientation, control and perception of anxiety, anticipation and technical skill.

Physical performances associated with elite youth football players’ include; aerobic fitness such as Vo2max and heart volume (Reilly et al 2000). Jankovic, Matkovic, & Matkovic (1997) also found that these aerobic performance indicators differentiated novices from elite players. Williams & Reilly (2000) also identified maximal oxygen uptake as being a successful differentiator between expert and intermediate young players, however the maximal oxygen uptake test administered may not have been sensitive enough to distinguish players already exposed to systematized training programs.

Anaerobic fitness and power associated with elite football performance include sprints ranging in distances of 5-40 meters and vertical jumps. Panfil, Naglak, Bober, and Zaton (19997), revealed that 16-year-old elite players recorded better running and jumping performances than less elite counter parts. These test allowed previous researchers to distinguish elite level athletes form novices (Jankovic et al., 1997). Reilly et al., (2000) suggest that agility was the best way to distinguish normal individuals from elite players in relation to anaerobic fitness testing.

Repeated sprints have been identified as crucial to football performance and may even determine the outcome of a game. Repeated-sprint bouts are defined by Spencer (2004) as a minimum of three sprints with an average recovery time between sprints of less than 21 seconds. Repeat sprint bouts occurred on average 1.0 ± 1.0 times during women’s national league games, while International women competitions produced on average 4.8 ± 2.8 sprint bouts per game (Gabbett & Mulvey, 2008). There was no difference amongst positions for repeated-sprint activity with respect to number of sprints, and average sprint duration, however the recovery between sprints was shorter for defenders than other field players (Gabbett & Mulvey, 2008).

Understanding the most extreme demands of football competition allows testers to develop game-specific tests which may improve the probability of predicting performance. This knowledge also assist coaches in developing game-specific conditioning programs, which accurately depict and prepare players for requirements of high level competition (Gabbett & Mulvey, 2008).

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Maturation and age effects have been identified as benefits for many players, as players born earlier in the football season are more likely to become elite players than those born in the later half of the season (Helsen, Van Winckel, & Williams, 2005). Elite football clubs have a tendency to select players with advanced biological maturation suggest Malina, Chamorro, Serratosa, & Morate, (2007). This could be due to the pressures of winning tournaments at differing age levels i.e. U/17 talent identification carnivals, where winning at the youth level is generally rated well above who has the greatest potential to develop into a world class athlete. When excessive emphasis is placed on winning, it is easy to lose sight of the needs and interest of the young athlete (Williams, 2006). This selection bias benefits individuals born in the early part of the academic year and is more evident with goalkeepers and defenders, who tend to be the tallest and heaviest players within the adult game (Williams & Reilly, 2000).

Consistent and effective execution of advanced ball skills such as kicking and heading are required for successful performance in football (Vrljic & Mallett, 2008). Reilly et al, (2000) used specific ball skill performance tests and reported differences between expert and novice-performers. Forwards could be differentiated on the basis of shooting from other football players where as elite youth players were better at dribbling the ball than their novice counters parts; however no differences were observed in their kicking ability. Test for passing, shooting, controlling and dribbling the ball have been identified by Reilly & Holmes (1983) as the principal components in assessment of skilled football play.

Possible psychological predictors of football performance have been identified and separated by Reilly et al, (2000) into two distinct categorises;

- Personality traits such as self-confidence, self efficacy, anxiety control, motivation, and concentration. According to Duda (1993) task mastery will lead to increased work ethic, persistence in the face of failure and optimal performance. Work done by Ericsson & Lehmann, (1996), argues that the level of performance is directly related to accumulated practice and that, regardless of natural abilities or genetic predisposition traits, at leat 10 years or 10000 hours of intensive practice is necessary to acquire the skill and experience required to become an expert. Elite football players are also less likely to experience somatic anxiety and have higher levels of self efficacy (Reilly et al., 2000). Sports research has shown that self-efficacy is a positive predictor of motor skill acquisition, execution, and competitive sport performance (Williams, 2006).

Anxiety and self-confidence are

more likely to be perceived by elite players as being helpful to performance rather than detrimental (Jones & Swain, 1995) and (Reilly et al., 2000).

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- Perceptuo-cognitive skill identified by Reilly et al., (2000) that differentiate playing levels include; attention, anticipation, decision making, game intelligence and creative thinking. Williams and Reilly (2000), identified anticipation and decision making as consistent indicators between skilled and less skilled players

Identifying future football talent through testing must take into account the specific demands of the sport suggest Regnier (n.d.). The purpose of this study is to identify similarities and differences in football players’ attributes at the national, state, regional and club level. Attributes such as speed, agility, power, ball skill and motivation will be tested as they have been identified by previous research (Hoare & Warr, 2000), (Reilly et al., 2000) and football coaches in a study conducted by Vrljic & Mallett (2008) as key indicators of football success.

It is quite common to use regression analysis techniques on data collected from elite athletes to identify the best performance predictors. It has been suggested that factors found to predict performance at the elite level can be used as talent detection tools on populations of younger athletes. It should be noted that each variable from a set of discriminating variables will not have the same degree of stability as others, and it has been suggested by Regnier, (n.d.), that each variable be weighted by its coefficient of hereditary, thus stabilising discriminating variables and improving the regression analysis accuracy.

The regression analysis is used to (1) determine the variables that best predict performance amongst the target population, and (2) produce the regression equation that will be used in conjunction with the results from the discriminating variables to evaluate the probability of success of an athlete taken from a new sample of the pool population (Regnier, n.d.).

This study protocol was approved by the University Of Queensland School Of Human Movement Studies.

Schools, parents and participants were all provided with written and verbal explanations of the testing protocols and an information sheet regarding the study was provided along with an informed consent sheet obtained from all parties. All information obtained will remain in a secure location and confidential.

The hypothesis of this study is that the level of competition a football player is currently playing at is related to the scores achieved in the administered tests. Players at a higher performance level compared to lower performance level players exhibit unique qualities that make them suited to the game of football (eg speed and agility). While sporting selection is highly subjective especially within team sports, it is reasonable to assume that players selected at higher levels will be chosen on overall athletic abilities rather than personal agendas.

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Methods Recruitment Participants were selected on the basis of enrolment in two School of Excellence programs, and the third school had its open youth boys teams tested. This was non-random theoretical sampling and infers that all players in this study have football experience at differing performance levels, but the participants are other wise as heterogenous as possible (Walliman, 2005).

Testing Participation Information Section: This section was able to be completed by students prior to testing day however the height and weight test was done on testing day. This will ensure there is no bias or miscalculation.

Surveys: This section of the test required participants to fill out the attached questionnaires as honestly possible; subjects must complete the SMS 6 Motivation inventory during the testing times allocated to schools. This ensured all subjects had the same amount of time to respond to the questionnaire.

Physical Test: Juggles: - In three minutes the participant juggled the ball and need to achieve the highest number of consecutive juggles as they possibly could. The observer must count the number of times that the subject drops the ball.

-No hands were allowed -The ball started on the ground.

Vertical Jump: - The participants were required to jump as high as possible and knock the aligned sticks to get a score/value for the jump. Each subject had two attempts and the best score was used.

20m sprint: - Subjects were required to run as fast as possible over a 20 meter distance, all participants will received two trails and the lowest time was recorded via timing gates.

Illinois agility test: - The length of the course was 10 metres and the width (distance between the start and finish points) was 10 metres. Four cones were used to mark the start, finish and the two turning points. Each cone in the centre was spaced 3.3 meters apart.

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The Illinois Agility Run Test was conducted as follows: 

The athlete stands at the starting point



When ready the athlete negotiates the course around the cones to the finish



The assistant records the total time taken from their command to the athlete completing the course

Figure 1. The circuit for the Illinois Agility Running test Analysis of results The test results will be compared to the players current playing level; this is to see if a relationship exists between certain playing levels and their test results. Regression analysis will be conducted to evaluate the relationship between the independent variables (IV) and the dependant variables (DV) - Examples of IV’s are: experience, month born, 20 meter sprint, shuttle run, Illinois agility test, vertical jump height, juggling, motivation, training age - The DV is the players’ performance level, gender Results 97 participants from three schools in the Brisbane region were sampled, with three players being 14 years of age and 11 surveys were not legibly completed or had missing data.

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S40956523 Descriptive Statistics

Age 15

16

17

N

Maximum 16

Mean 4.49

Std. Deviation 3.565

30

455

170.76

128.674

3.03

3.77

3.3184

.20603

41

Juggles

41

Sprint

19

Jump

43

39

64

50.86

5.480

Agility

18

16.69

20.76

17.7883

.97731

Height

28

150

183

171.54

9.524

Valid N (listwise)

16

Drops

25

0

18

4.72

4.659

Juggles

25

10

483

175.12

136.213

Sprint

24

2.89

3.1879

2.61489

Jump

25

39

Agility

16

16.90

Height

21

Valid N (listwise)

12

Drops

21

Juggles

21

Sprint Jump

21 22

36

70

51.59

9.772

Agility

12

15.50

19.19

17.3658

1.07348

Height

18 9

157

189

176.00

9.852

Drops

2

0

5

2.50

3.536

Juggles

2

91

425

258.00

236.174

Sprint Jump

2

2.99

3.28

3.1350

.20506

2 1

57 16.38

66 16.38

61.50 16.3800

6.364 .

1 0

182

182

182.00

Valid N (listwise) 18

Minimum 0

Drops

Agility Height Valid N (listwise)

3.33 80

54.28

9.405

18.81

17.0369

3.56613

162

187

176.43

7.236

0

15

3.29

4.051

20

330

173.95

102.703

2.98

3.83

3.2614

.24375

Table2. Mean scores of physical test in each age group 15 Years of age study consisted of 38 boys and 5 girls

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S40956523 Descriptive Statistics

1=male, 2=female 1

2

N Drops Juggles Sprint Jump Agility Valid N (listwise) Drops Juggles Sprint Jump Agility Valid N (listwise)

38 38 15 38 15 15 3 3 4 5 3 3

Minimum 0 30 3.03 41 16.69

Maximum 16 455 3.42 64 18.00

Mean 4.47 165.76 3.2333 51.84 17.4340

Std. Deviation 3.585 126.697 .12437 4.924 .37881

0 120 3.52 39 18.34

7 425 3.77 47 20.76

4.67 234.00 3.6375 43.40 19.5600

4.041 166.442 .10275 3.507 1.21012

Table3. Mean scores of physical test for boys and girls aged 15 years

(Boys aged 15, Performance level 5) Descriptive Statistics N

Minimum

Maximum

Mean

Std. Deviation

Drops

4

0

1

.25

.500

Juggles

4

275

428

352.75

69.048

Sprint

2

3.11

3.16

3.1350

.03536

Jump

4

47

56

51.25

3.775

Agility

2

16.69

16.82

16.7550

.09192

Valid N (list wise)

2

Table4. National level boys aged 15

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16 Years of age 25 boys and 2 girls

Descriptive Statistics 1=male, 2=female 1 Drops Juggles Sprint Jump Agility Valid N (listwise) 2 Drops Juggles Sprint Jump Agility Valid N (listwise)

N 23 23 22 23 16 15 2 2 2 2 0 0

Minimum Maximum Mean Std. Deviation 0 11 3.87 3.622 27 483 189.04 133.061 2.89 3.33 3.105 2.73620 41 80 55.52 8.733 16.90 18.81 17.0369 3.56613 11 10 3.62 39

18 20 3.68 41

14.50 15.00 3.6500 40.00

Table5. Mean scores of physical test for boys and girls aged 16 years

(Boys aged 16, Performance level 5) Descriptive Statistic Drops

N 4

Minimum 0

Maximum 3

Mean 1.25

Std. Deviation 1.500

Juggles Sprint

4

237

370

299.25

55.877

4

2.89

3.1

3.00

Jump

.48053

4

60

71

63.00

5.354

Agility

4

16.90

17.86

17.1050

.81636

Valid N (list wise)

3

Table6. National level boys aged 15

15

4.950 7.071 .04243 1.414

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17 & 18 years of age 19 boys and 5 girls Descriptive Statistics 1=male, 2=female 1

2

N Drops Juggles Sprint Jump Agility Valid N (listwise) Drops Juggles Sprint Jump Agility Valid N (listwise)

81 81 57 81 41 40 9 9 10 12 7 6

Minimum 0 27 2.96 37 2.89

Maximum 16 483 16.03 80 18.81

Mean 3.75 177.37 3.4305 53.49 16.7702

Std. Deviation 3.341 119.597 1.70534 7.128 2.30965

0 10 3.52 36 17.58

18 425 3.83 47 20.76

8.44 127.22 3.6440 41.08 18.9100

6.327 140.710 .09582 3.753 1.05407

Table7. Mean scores of physical test for boys and girls aged 17 & 18 years

(Boys aged 17 Performance levels 5) Descriptive Statistics

Drops

N 8

Juggles

8

Sprint

8

Jump

8

Agility

3

Valid N (listwise)

3

Minimum 0

Maximum 3

Mean 1.63

Std. Deviation 1.408

136

330

226.13

81.000

3.02

3.30

3.1450

.08701

48

65

56.38

7.029

15.90

16.85

16.4767

.50659

Table8. National level boys aged 17 & 18

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Performance level and Motivation Correlations

Spearman's rho

1=School,2=Distric,3= Regional,4=State,5= National Amotivation

ExReg

IntorReg

IdReg

InterReg

Intrinsic

Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N

1=School,2= Distric,3= Regional,4= State,5= National 1.000 .

97 .024 .814 97 .109 .289 97 .085 .410 97 -.037 .723 97 .165 .106 97 .133 .195 97

Amotivation .024 .814 97 1.000 .

97 .052 .611 97 .165 .107 97 .027 .792 97 -.444** .000 97 -.283** .005 97

ExReg IntorReg .109 .085 .289 .410 97 97 .052 .165 .611 .107 97 97 1.000 .547** . .000 97 97 .547** 1.000 .000 .

97 97 .532** .447** .000 .000 97 97 .385** .207* .000 .042 97 97 .419** .195 .000 .056 97 97

IdReg InterReg -.037 .165 .723 .106 97 97 .027 -.444** .792 .000 97 97 .532** .385** .000 .000 97 97 .447** .207* .000 .042 97 97 1.000 .445** . .000 97 97 .445** 1.000 .000 .

97 97 .508** .637** .000 .000 97 97

Intrinsic .133 .195 97 -.283** .005 97 .419** .000 97 .195 .056 97 .508** .000 97 .637** .000 97 1.000 .

97

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table9. Shows the relationship between performance level and athlete motivations

There is no relationship between motivation levels and performance levels, however as this group has been selected to train and play in top schooling sides, most students will be motivated to play football.

Amotivation had a negative relationship to Integrated (p > 0.001) and Intrinsic (p > 0.005) motivation.

Identified regulation had a relationship to all extrinsic and intrinsic motivations.

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Performance level compared to number of juggles Bars show Means

250

Juggles

200

150

100

50

1

2

3

4

5

1=School,2=Distric,3=Regiona l,4=Sta te ,5=National

Figure2. Performances levels and mean juggling scores

Correlations between juggle and performance level 1=School,2=Di stric,3=Region al,4=State,5=N ational

Juggles Spearman's rho

Juggles

Correlation Coefficient

1.000

.459(**)

.

.000

92

92

.459(**)

1.000

.000

.

92

97

Sig. (2-tailed) N 1=School,2=Distric,3=Regi onal,4=State,5=National

Correlation Coefficient Sig. (2-tailed) N

Table10. Correlation between juggling scores and performance Juggling has a relationship to performance level (P > 0.001).

Boys and Girls descriptive statistics 1=School,2=Distric,3=Regi onal,4=State,5=National 1

Mean Drops Juggles

2

Drops Juggles

3

Drops Juggles

4

Drops Juggles

5

Drops Juggles

18

Std. Deviation

N

4.75

2.435

8

102.50

60.982

8

6.11

3.628

18

106.22

61.282

18

4.90

3.881

30

156.83

128.012

30

3.47

5.113

17

219.00

148.199

17

1.63

2.006

19

256.21

100.388

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Performance levels vertical jump, 20 meter sprint, agility test and juggles Correlations

Spearman's rho

1=School,2=Distric,3= Regional,4=State,5= National Juggles

Sprint

Jump

Agility

Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed) N

1=School,2= Distric,3= Regional,4= State,5= National 1.000 .

97 .459** .000 92 -.097 .429 69 .198 .054 95 -.313* .029 49

Juggles .459** .000 92 1.000 .

92 -.165 .177 68 .247* .018 92 -.288* .049 47

Sprint -.097 .429 69 -.165 .177 68 1.000 .

69 -.629** .000 69 .486** .001 47

Jump .198 .054 95 .247* .018 92 -.629** .000 69 1.000 .

95 -.365* .011 48

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table12. Performance levels and the interrelationships between performance predictors

Agility (p > 0.05) and juggling (p >0.01) are predictors of performance level. Sprint and Vertical Jump test did not reveal any relationship to the participants’ performance level.

19

Agility -.313* .029 49 -.288* .049 47 .486** .001 47 -.365* .011 48 1.000 .

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Overall performance indicators utilised in liner regression Model Summary Model 1

R .684a

R Square .468

Adjusted R Square .373

Std. Error of the Estimate 1.029

a. Predictors: (Constant), Intrinsic, 1=male, 2=female, Sprint, Age, Juggles, Jump, Agility

Table13. Linear Regression (R square) scores

Coefficientsa

Model 1

(Constant) Age 1=male, 2=female Juggles Sprint Jump Agility Intrinsic

Unstandardized Coefficients B Std. Error 6.263 6.235 .096 .186 2.437 .701 .004 .001 -.502 .251 .045 .021 -.521 .221 .010 .156

Standardized Coefficients Beta .068 .633 .385 -.725 .296 -.921 .008

t 1.004 .514 3.475 3.021 -1.999 2.100 -2.359 .065

Sig. .321 .610 .001 .004 .053 .042 .023 .949

a. Dependent Variable: 1=School,2=Distric,3=Regional,4=State,5=National

Table14. Performance predictors

Juggling is an independent predictor of performance (p > .004)

Model Summary Model 1

R .577a

R Square .333

Adjusted R Square .269

Std. Error of the Estimate 1.111

a. Predictors: (Constant), Agility, Age, Juggles, 1=male, 2=female

Table15. (R Square) scores in relation to identified predictors of performance

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Model 1

(Constant) Age 1=male, 2=female Juggles Agility

Unstandardized Coefficients B Std. Error -.869 3.376 .253 .181 .958 .517 .005 .001 -.121 .079

Standardized Coefficients Beta .180 .249 .445 -.215

t -.257 1.396 1.855 3.373 -1.544

Sig. .798 .170 .071 .002 .130

a. Dependent Variable: 1=School,2=Distric,3=Regional,4=State,5=National

Table16. Coefficients of performance prediction

Juggling is an independent predictor of performance when compared to agility, age and sex Model Summary Model 1

R .568a

R Square .322

Adjusted R Square .258

Std. Error of the Estimate 1.119

a. Predictors: (Constant), Log of agility, Age, 1=male, 2=female, Log of juggles

Table17. (R Square) scores in relation to identified predictors of performance adjusted (log)

Coefficientsa

Model 1

(Constant) Age 1=male, 2=female Log of juggles Log of agility

Unstandardized Coefficients B Std. Error -5.110 4.056 .332 .182 1.044 .515 .855 .246 -.899 .645

Standardized Coefficients Beta .236 .271 .472 -.184

t -1.260 1.825 2.027 3.480 -1.394

Sig. .215 .075 .049 .001 .171

a. Dependent Variable: 1=School,2=Distric,3=Regional,4=State,5=National

Table18. Coefficients of performance prediction adjusted (Log) When the juggles and agility test were adjusted to normalise the data via log index, juggling was still the only independent predictor of performance level

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Maturation effect Correlations

Spearman's rho

1=School,2=Distric,3= Regional,4=State,5= National 1=Jan,2=Feb,3=Mar,4= Apr,5=May,6=Jun,7= Jul,8=Aug,9=Sep,10= Oct,11=Nov,12=Dec

Correlation Coefficient Sig. (2-tailed) N Correlation Coefficient Sig. (2-tailed)

1=School,2= Distric,3= Regional,4= State,5= National 1.000 .

97 -.073

1=Jan,2= Feb,3= Mar,4= Apr,5=May,6= Jun,7=Jul,8= Aug,9= Sep,10= Oct,11= Nov,12=Dec -.073 .477 97 1.000

.477

.

97

97

N

Table19. Performance level and month of birth

There was no maturation effect observed in this study with players born in all parts of the year at the higher performance levels.

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Defenders and goal keepers taller than midfield

Histogram

12

10

Frequency

8

6

4

2 Mean =174.39 Std. Dev. =9.048 N =69 0 150

160

170

180

190

Height

Figure3. The distribution and range of participant’s heights Group Statistics

Height

defenders & keepers 0 1

N

Mean 174.53 174.26

34 35

Std. Deviation 8.396 9.760

Std. Error Mean 1.440 1.650

Table20. Height in relation to positions selected in Independent Samples Test Levene's Test for Equality of Variances

F Height

Equal variances assumed Equal variances not assumed

1.694

Sig. .198

t-test for Equality of Means

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference Lower Upper

.124

67

.902

.272

2.195

-4.108

4.653

.124

66.045

.901

.272

2.190

-4.100

4.644

Table21. Independent t test for height and position relationships 23

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There is no relationship between positions and height, with p >.902

Boys and Girls training ages 1

2

12.5

SoccerExp

10.0

7.5



5.0



2.5

0.00

0.25

0.50

0.75

1.00

0.00

0.25

Sex=1 (FILTER)

0.50

0.75

1.00

Sex=1 (FILTER)

Figure4. Boys and girls training age (Boys =1, Girls = 2) Test Statisticsa SoccerExp 226.500 304.500 -3.157 .002

Mann-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed)

a. Grouping Variable: 1=male, 2=female

Table22. Difference between male and female training ages There was a difference between boys and girls training ages with p > 0.002 independent t test Percentiles 25 Weighted Average(Definition 1)

SoccerExp

Tukey's Hinges

SoccerExp

50

75

8.50

10.00

11.00

9.00

10.00

11.00

Table23. Boy’s median training age is 10 (8.5 – 11) Percentiles(a) 25 Weighted Average(Definition 1)

SoccerExp

Tukey's Hinges

SoccerExp

50

5.00

6.50

9.75

5.00

6.50

9.50

Table24. Girl’s median training age is 6.5 (5 – 9.8)

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Discussion Table 9 reveals that there is no relationship between performance levels and specific forms of motivation identified by Deci & Ryan (1985). This may be due to the participants being a homogenous group as all players’ selected for the study are heavily involved in football programs run through their school.

Amotivation had a negative relationship with intrinsic and integrated motivations which implies that most participants involved in the study were involved in football for specific reasons and they had an inherent desire to learn more about the game and improve their football skills.

Linear Regression was not able to significantly predict performance levels in relation to the physical test administered. This study identified juggling (p >0.002) as a moderate independent predictor of performance level with agility (p > 0.05) a weak predictor of performance level.

Juggles have been identified in Table10 as a predictor of performance level, with Figure 2 revealing that the participants in the higher performance levels on average scored more juggles and as a consequence dropped the ball less frequently in the three minute period than the lower performance level participants. This finding is similar to the results of Gabbett et al, (2007) study and may imply that once individuals are playing or selected to play a specific sport, skills test are better predictors of performance than general talent detection measures such as a twenty meter sprints and vertical jump test.

Agility was identified in Table12 as a predictor of performance level, however the relationship was weak.

Regnier (n.d.) suggest that linking two specific tests together into a single criterion will be more representative of the real task a player has to perform in game situations thus increasing the relevance of the test as an actual performance indicator. This concept could be achieved through a modified agility (Illinois) test where the player is now required to dribble the ball through the middle set of markers thus incorporating speed, agility, ball control and dribbling ability into the one test. These modifications to the current agility test create a more game-specific assessment which should enhance the tests ability to differentiate players’ performance levels.

Maturation effect was not observed in this study with players from higher performance levels, birth dates evenly distributed throughout the year. This studies assessment of maturation did not consider bone development as its key indicator of maturation. The study only considered the players month of birth in relation to maturation. So a true maturation effect might still have taken place with early developers not adequately identified by this assessment method of this study.

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Defenders and goal keepers have previously been identified by research as being taller than other players.

Table 21 reveals that in this study there was no relationship between height and position. This result could imply that height differences occur in the older age groups where players have completely matured. Coaches may also play a part in this phenomenon as identified by pervious research with a personal preference for taller people defending the goal.

Boys and girls training ages were identified in Table 22 as significantly different from each other. This interesting finding could be attributed to many factors such, as boys being given more opportunities to participate within their sport. Also a view might be held that girls are not as interested in sport which is an issue for women’s sport and specifically football, as there was a large difference in enrolment numbers for girls participating in the soccer school excellence programs.

This unforseen result means that females generally get selected for representative teams with less experience than the male football players. Females’ years of experience had no correlation with performance level; this was also a surprising result as the players with more experience would have an advantage over there less experienced counterparts. Further research into female football participation levels could provide reasons for gender differences observed in this study.

Participant and environmental variations are likely to have produced limitations on this study with participant’s prior activity levels will have an impact on the results as some participants played the day before testing and others were tired from club training the night before. Current training programs administered by schools, clubs and other representative teams have limited the ability to test aerobic capacity maximally as it will take too much out of the players’. Further validity concerns could arise due to player attire with great variations between clothing worn during testing times occurring. Result differences are likely to have occurred as environmental conditions for the tests administered impacted performance with differing testing times, weather conditions and the state of the field/pitch. The only way to reduce these variables is to standardise the test into a specific time frames, specify player attire, and have at a designated field/pitch for all field tests.

The study had difficulty getting parental permission and limited participant numbers. In future it would be recommended that the schools be approached for permission. Then information provided to parents and selected participants should occur as it did in this study, however if at this point rather than the study gain permission from the guardian or parents the study should be able to use implied consent i.e. those who do not want their child (ren) to participate are free to with draw and they could write in and formally withdraw from the study, this approach will reduce paper work and increase participant numbers.

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Summary and Conclusion Challenges for future talent identification are always going to revolve around developing new and innovative testing procedures that more readily identify talented players. Following the talent identification process the athlete must be provided with adequate infrastructure to enable them to develop to their full potential. This should include appropriate coaching, training and competition programs along with access to facilities, equipment, sport science findings/recommendations and medical support.

Attempting to predict inherently variable human behaviour in such a complex environment as sport, one would not expect a simple recipe as a solution (Reigner, n.d.), especially as soccer is ultimately dependent on a host of external factors, including opportunities to practise, remaining free of injury, the nature of mentorship and coaching provided during development years and, finally, personal, social and cultural factors (Reilly et al., 2000), and this is an explanation of why one-shot long term predictions are unreliable, especially for athletes during the pre-pubertal and pubertal periods suggest Havlicek, Komadel, Komarik, & Simkova (1982).

The judgement of qualified coaches is still the preferred methodology (Bartmus, Neumann, de Marees, 1987), so attempting to incorporate Jarvis (1981) suggestion of a carefully planned system of selection over a period of several years which gathers information on a number of key performance indicators will help scientist confirm coaches initial intuition with regard to players’ strengths and weaknesses suggest Williams & Reilly (2000). The possibility of further football data collection and assessment will only enhance the knowledge base of researchers, coaches and players but will also identify physical, mental, social and environmental factors which contribute to an athletes sporting excellence. A selection of research identified Subjective and Objective measures will increase the likelihood of predicting future talent.

Acknowledgements I would like to take this opportunity to thank all schools, teachers and students involved in this study.

Cavendish Road State High School J. Smith & M. Andreatta Kelvin Grove College  I. Milligan Marist College Ashgrove  M. Poole

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