A Scientific Basis for Talent Identification and Elite Player Development October 2006

A Scientific Basis for Talent Identification and Elite Player DevelopmentOctober 2006

Questions we asked….. • Is there any evidence in the scientific literature on the ability (or inability) to identify sporting giftedness/talent? • Are there any known indicators of potential (but which are invariant to age and training)? • Are there specific age-related developmental stages in developing players? • What time and/or effort is required to master a task (in this instance, football)? • Are we making appropriate use of the entire talent pool available to us?

Source:National Athlete Development Survey (2002). Gagné, F. & Gulbin, J.P. Australian Sports Commission. Gagné’s Differentiated Model of Giftedness and Talent (DMGT) Under the partial influence of genetic endowment

Gagné’s Differentiated Model of Giftedness and Talent (DMGT) • Giftedness is defined as “the possession and use of untrained and spontaneously expressed natural abilities (called aptitudes or gifts), in at least one ability domain, to a degree that places an individual at least amongst the top 10% of age peers” • Talent is defined as “the superior mastery of systematically developed abilities (or skills) and knowledge in at least one field of human activity to a degree that places an individual at least amongst the top 10% of age peers who are or have been active in that field or fields” • Where giftedness is the beginning of the learning process, talent is the result of it.

Gagné’s Differentiated Model of Giftedness and Talent (DMGT) • Three components influence the learning/practice process that changes natural athletic ability into systematically developed sporting skills (talent) • Intrapersonal catalysts (physical and psychological factors) • Environmental catalysts • Chance factors • There is an element of both nature and nurture in this model • Without an innate ability no amount of training will create a top elite player, and • Without the appropriate quantity and quality of training a player will not develop into a top elite player

Ability or inability to identify or predict giftedness/talent • Is it possible to identify a priori which persons might become talented footballers, and which not?

Talent Prediction • A number of studies have been undertaken to discriminate between elite and non-elite soccer players on the basis of anthropomorphic, physiological, psychological and soccer-specific skills tests • Univariate differences • Endomorphy (fatness), skinfolds (7 areas) and body fat • Sprint speed, speed endurance, aerobic power, agility, standing vertical jump • Task-orientation, anticipation, somatic anxiety • Dribbling skill • Multivariate (discriminant) analysis • Agility, 30m sprint time, ego orientation and 1 vs 1 anticipation were the set of variables that provided the best predictors of talent. • Results indicate that • Advanced biological maturity status is associated with slightly better technical performance on the tests; specifically, older, more mature players perform better in dribbling and in ball control

Talent Prediction • Leaves us wondering if • these variables/model are only useful in talent discrimination rather than talent identification/prediction • these measures are able to discriminate among players already selected and exposed to systemised training, because the sensitivity of the tests tends to decrease once players reach the elite level

Talent Prediction • No consensus about the relative importance of physical, psychological and physiological qualities in predicting football talent (Williams and Reilly, 2000) • Talent ID programs around the globe are not firmly based on scientific rationale (Williams and Franks, 1998) and rely heavily on intuition or ‘eye’ of expert coaches and talent scouts to identify talented performers

Talent Prediction “It is probable that talent detection, identification and development are not amenable to a reductionist process that can permit ultimate potential to be defined with much degree of certainty. The problem is especially complex in sports such as soccer where performance itself is multifactorial. At present anthropomorphic and physiological profiling is best viewed as an objective means of monitoring young players, while emphasis should be placed on technical skills and engagement in teamwork.” (Reilly, T., Bangsbo, J. and Franks, A. (2000). Anthropometric and physiological predispositions for elite soccer. Journal of Sports Sciences, 18, 669-683.) “A coaches’ judgement is the best solution for identifying talent, not some scientific detection model.” (Tranckle, P. (2004). Understanding giftedness and talent in sport. The Coach, 21, March/April 2004, 61-73.)

Talent Prediction • “Psychological factors are what distinguishes successful elite players from their non-elite counterparts” (Morgan 1979). Little work done on psychological factors in talent ID, or even which psychological factors, or how to measure them • Cognitive factors and game intelligence – perceptual skill in soccer – is a promising area for talent ID • Skilled players are better than lesser skilled players at recognising structured patterns of play because their knowledge allows them to ‘chunk’ perceptual information into larger and more meaningful units (cf. Ross (2006) regarding expert chess players). This enables them to recognise emergent features of a pattern of play early in its initiation, thereby facilitating anticipation • Skilled players also use ‘advance cue utilization’ (i.e. ability to make accurate predictions based on information arising from an opponent’s posture and bodily orientation). This has been tested extensively in the penalty situation.

Talent Prediction • It is conjectured that skilled players use their expert knowledge to dismiss many potential situational events as being ‘highly improbable’ and attach a hierarchy of probabilities to remaining events, i.e. likelihood of occurrence. • Can perceptual skill be developed through training? • Teach young players about important features/structures of play. Set pieces are obvious starting points. • Focus on postural cues, and the relationship between these and subsequent performance (via video simulation). • Decision making skills can be taught from as young as 7 years, i.e. earlier than expected.

Talent identification – as it currently happens • Vrljic and Mallet interviewed five youth coaches responsible for selecting and coaching Under 15/16 Queensland boys teams at the national championships. • Results show that four categories are considered: • Physical skills • Running speed (over short distances, say 30-40 meters) [most important] • Physical strength • Skill-based (i.e. being able to win the ball) • Physical (i.e. physical appearance of athleticism) • Aerobic fitness • Technical skills • Ball control at speed • Ball control under unpredictable circumstances • Ball control with various body parts

Talent identification – as it currently happens • Cognitive-perceptual skills • Reading the game / anticipating play • Decision making / choosing the best option / “vision” (usually have to be taught this in the development process, so should not be part of the identification process) • Personal qualities • Hunger to succeed • Mental toughness • Unselfish, i.e. team player rather than individual • Coaches preferred to identify talent in authentic playing situations (i.e. a match) than in skill/drill tests.

Body shape • Is there a particular body shape that is best suited to elite football?

Body shape (somatotype) 3 components: • Endomorphy (fatness) • Measured by skinfolds, adjusted for height • Ectomorphy (linearity/tallness) • Measured by height/weight ratio • Mesomorphy (skeletal/muscular) • Measured by limb circumferences corrected for fatness, skeletal breadths/widths, height An individual’s physique includes all 3 components e.g. A typical adult elite player might have [Endo / Meso / Ecto 2.8 / 5.3 / 3.3] Each component is expressed numerically from 1 (low) to 7 (high) NB. Components change with growth and maturation during adolescence

Body shape (somatotype) • Youth soccer players tend to have physiques similar to those of elite adult players, with adult elite players more mesomorphic, probably reflecting growth of muscle and skeletal mass • Potential effects of training on muscularity at more elite levels • Youth soccer players tend to have more fat and be relatively speaking taller than elite adult players • None of this research is predictive, i.e. the shape of body that might indicate a potential elite player

Invariant indicators of potential Is it possible to identify the subset of the population that are gifted in the domains that are relevant to sporting ability? i.e. have the right “nature” in the nature/nurture debate? The measure would have to be invariant to age and to training (If so, we might concentrate our talent identification/development process on that subset, assuming that very few others will ever become “talented”) Proviso: Any procedure we use will have to be practical and easy enough to implement

Measuring innate ability in sport • Ability in sports is highly related to male physical competitiveness • It is well researched that the ratio of the length of the 2nd to the 4th fingers (2D:4D) is a negative correlate of prenatal and adult testosterone • Men with lower 2D:4D ratios reported higher attainment in a range of sports and had higher mental rotation scores (a measure of visual-spatial ability) than those with high 2D:4D ratios. • Implication: Testosterone promotes the development and maintenance of traits which are useful in sports, and the 2D:4D ratio is a way to measure this ability, independent from previous exposure to or training in the sport. • Recent research (Spector, T. (2006). British Journal of Sports Medicine) shows that this holds in the case of females as well. It is conjectured that genetic factors play a major role.

Measuring innate ability in sport • This is promising research, but a little impractical at this stage • Could there be other such measures?

Age-related development stages • When is the best age to concentrate training?

Age-related development • Coaches worldwide currently design long and short-term athlete training models as well as competition and recovery programs based on their athletes’ chronological age. • Research has shown that chronological age is not a good indicator on which to base athlete development models for athletes between the ages of 10 to 16. There is a wide variation in the physical, cognitive and emotional development of athletes within this age group. • Ideally, coaches should determine the biological age of their athletes and use this information as the foundation for athlete development models. Unfortunately, there is no reliable procedure to identify biological age non-invasively.

Age-related development • Use the onset of Peak Height Velocity (PHV) as a reference point for the design of optimal individual programs with relation to “critical” or “sensitive” windows of trainability during the maturation process. • Prior to the onset of PHV, boys and girls can train together and chronological age can be used to determine training, competition and recovery programs. • The average age for the onset of PHV is 12 and 14 years for females and males respectively. • The onset of PHV is influenced by both genetic and environmental factors, including climate, cultural influences, and social environment.

Age-related development • Using simple measurements, PHV can be monitored and training can be related to and optimised to exploit the critical periods of trainability. • All energy systems are always trainable, but during the so-called “critical” periods accelerated adaptation will take place if the proper volume, intensity and frequency of exercise are implemented.

Time and effort to develop mastery in football • How much / long does it take to master a sport (i.e. to attain elite level)?

The 10 year / 10,000 hours rule • The Theory of Deliberate Practice is predicated on the notion that it is not simply training of any type, but rather a minimum of 10 years engagement in deliberate practice that is the necessary condition for the attainment of expertise. Deliberate practice refers to practice activities done with the specific instrumental goal of improving performance, and which • Are performed in a daily, work-like manner • Require effort and attention • Do not lead to immediate social or financial rewards • Are frequently not enjoyable to perform (i.e. the “hard yards”) • First suggested by Simon and Chase (1973) in relation to skill in chess • Subsequent work has shown this to apply to expertise in other domains, such as music, mathematics, swimming, distance running, tennis, soccer, hockey etc

The 10 year / 10,000 hours rule • Subsequent work tested subjects (experts and non-experts) in hockey, netball and basketball in Australia. • This research concludes that international standard players in almost all sports took between 10 and 13 years to develop fully to international level, with around 6,000 hours of sport-specific training. The balance of hours could be attributed to • Maintaining the level of expertise • Other sports training (both before attaining expert status in the chosen sport, and non-specific sports training, e.g. running to maintain fitness). • This implies a strong leaning towards the “nurture” side of the nature/nurture debate, i.e. it’s all in the training

The 10 year / 10,000 hours rule • Other studies have shown a negative correlation to exist between hours of sport-specific training required to reach international expertise and number of prior sporting activities experienced, i.e. participation in other sporting activities may aid development of expert decision-making skills, and transfer of learning may take place from one sport to another. • Significantly, the results point to an implication that early specialisation may not be a necessary requirement for expert level performance in decision-making sports.

“Effortful study” is a prerequisite for success • A recent study (in Scientific American) has looked at how chess grand-masters are able to assimilate so much information so quickly and accurately. • Concludes (based on academic research) that “effortful study is the key to achieving success” where “effortful study…. entails tackling challenges that lie just beyond one’s competence”, i.e. to get good you must extend yourself. This explains • why you can only be the best at competitive teams sports if you are playing with the best; and • why a very strong RAE among youth may be somewhat counterbalanced at an older age (if the youngest in an age group have to engage in more effortful study than others of their age in order to excel and if those differences become habits then persist into the professional years while the early innate benefits of being slightly older disappear) • the characteristics of a developmental pathway (i.e. it must provide substantial opportunity for “effortful study” at all ages)

The 10 year / 10,000 hours rule Generally accepted categorisation of sports participation: • Sampling years – deliberate play (6 to 12 years) • Broad range of sporting activities • Emphasis on fun • Specializing years (13 to 15 years) • Decrease in other sporting activity, starting to specialize in one sport • Growing emphasis on skill development • Investment years (16 and over) • Devotion to one primary activity • Strategic and competitive skills training Teachers become more demanding as one moves through the stages

Squandering the talent pool – the Relative Age Effect • Systematic exclusion of groups of players, some of which might be highly talented

Relative Age Effect (RAE) • The relative age effect (RAE) in sport was first noted among elite level ice hockey players in the USA. These findings demonstrated that for major junior leagues and the NHL player birth dates decreased in frequency from January through December. • The RAE is strikingly evident in activities that are competitive and where performance is highly correlated with age and maturity, (e.g. football in Australia) • It was theorized that the RAE arose from the consequences of grouping young boys for entry into organized sport, thereby producing a one-year age range for the participants. As size, speed, and coordination are highly correlated with age, older players within the age-group will, on average, show superior performance.

Relative Age Effect (RAE) • Thus maturity has been mistaken for ability by coaches, peers and the individuals themselves. The age-advantaged children are imbued with greater self-confidence and regard by others. The opposite will likely hold for those younger than their group-mates, who are likely to suffer from lowered self confidence and self-esteem. • A consequence is an increased drop-out rate for those disadvantaged by age. • Predictably, the RAE has also been found in a host of other competitive sports such as baseball, basketball, soccer, rugby, American football, cricket etc etc.

Relative Age Effect (RAE) • RAE has been observed in activities other than sport as well – the obvious one is the cut-off for school entering in all schools, but also musical ability, youth suicide etc • There are some sports and activities in which no RAE is evident (e.g. dance, table tennis, gymnastics to name a few). These sports depend heavily on the technical ability (or motor skill) of the participant.

Relative Age Effect (RAE) – Australian data Boys Under 14 and 15 – NYC 2006 (n=351) Very highly statistically significant trend

Relative Age Effect (RAE) – Australian data Under 17 and 20 national teams – 2006 (n=47) Statistically significant trend

Relative Age Effect (RAE) – Australian data HAL and overseas professionals (includes Socceroos and Futsalroos) (n=278) Statistically significant trend

Relative Age Effect (RAE) – Australian data HAL and overseas professionals (includes Socceroos and Futsalroos) (n=278) Nearly statistically significant trend

Relative Age Effect (RAE) – Australian data Socceroos 2006 FIFA World Cup squad (n=23) Not statistically significant (sample size)

Relative Age Effect (RAE) – Australian data Girls NTC U15 and U17 – 2006 (n=268) Not statistically significant

Relative Age Effect (RAE) – Australian data Matildas and Young Matildas 2006 (n=52) Nearly statistically significant trend

A Scientific Basis for Talent Identification and Elite Player Development October 2006