Talent Identification in Sport: A Systematic Review
• examined differences in performance variables between highly skilled and less-skilled
athletes (between 1990-2015)
• high variability in factors that were found to discriminate between skilled and less-skilled
individuals & clear overrepresentation of studies that examined physical profiles of male
athletes
• useful for coaches to use in combination with sport-specific and skill-level-specific
considerations in determining what level of risk exists for their talent identification decisions
Introduction
• talent identification (TID) programs are designed to identify young athletes with potential
for success in senior elite sport
• effective TID program has the potential to detect talent early, which may act as a vital
component to increasing a nation’s chances at sporting success
◦ using evidence-based processes that can be refined through feedback and evaluation of
the system
◦ have the capacity to recognize talented athletes early, which helps to focus funding and
training opportunities on athletes with the greatest potential for success
◦ however, discrepancy between research proposes and practice
• perception of talent and ability is important (nature or nurture)
◦ misconceptions regarding what talent ‘looks like’ are widespread in high-performance
sport settings (young athletes often selected based on physical growth → not equal to
talent)
• variation in definitions of talent
◦ Brown: special, natural ability and capacity for achievement or success
◦ Howe: likelihood of becoming exceptionally competent in certain fields depends on the
presence or absence of inborn attributes
◦ Gagne: outstanding mastery of systematically developed abilities, called competencies
(knowledge and skills), in at least one field of human activity to a degree that places a
person at least among the top 10% of age peers who are or have been active in that field
• centered on measurement and subsequent comparison of characteristics that contribute to
sport-specific performance
◦ often compare different age groups and skill levels in cross-sectional design → rooted in
assumption that characteristics of future success can be extrapolated from individuals
performance at one give point in time (assume talent is static -Y ignore maturity and
relative age effects)
◦ crosss-sectional data is limited as early performance is not strongly associated with later
success and chronological age and biological maturity rarely progress at same rate
(children may be helped or hindered on performance tests by their biological maturity,
especially when compared with chronological age norms)
◦ longitudinal or retrospective designs preferred when tracking dynamic variables such as
skilled performance as have greater capacity to test for causal relationship
Method
• requirements to be considered as study in review
1. Highly skilled participants (advanced, expert or eminence included)
2. Time-based comparison (track changes in outcome variable over period of at least 12
months → compare between skill groups; longitudinal: min 12 months and 2 time point
to track changes in performance; retrospective: at least one performance variable must
have been measured at least 12 months before an assessment of success was conducted)
3. Between-group comparison (min 2 different skilled/talented groups)
, 4. removal of grey-area topics (not included: studies exploring birthplace effects, deliberate
practice, genetic predispositions, handedness, long-term athlete development, or relative
age effects)
• search of identifying articles was broken down in 3 phases
◦ phase 1: search of electronic databases
◦ phase 2: search using additional resources
◦ phase 3: collaboration with panel of experts
Results
• phase 1 identifies 1696 articles, phase 2 422 additional articles and phase 3 22 atricle
• after removal of duplicates, reviewing titles and abstracts and full-text assessment 20 article
remained (16 longitudinal design, 2 retrospective, 2 mixed design)
• Barreiros: Only one-third of the athletes who were selected to be top junior players were
also selected to be on the senior team. This demonstrates the difficulty in using early
identification as a predictor for future success
• Bottoni: The findings indicate that retrospective analysis of running and swimming
performance outcomes is not an appropriate method for predicting future triathlon success
• Brouwers: Findings demonstrated that player performances at young ages were not
correlated with later success in tennis. Additionally, this study did not find an age at which
all players should start to perform in order to be successful at the professional level
• di Cagno: Coordination and precision capabilities can be used as long-term predictors of
success in gymnastics
• Elferink-Gemser: Findings indicated that both female and male elite field hockey players
scored better on technical and tactical variables. In addition, female elite-level athletes
scored higher on interval endurance capacity and motivation compared with their sub-elite
counterparts. Contrastingly, the males in the sub-elite category scored higher in motivation
compared with their elite counterparts
• Falk: Selected water polo players were superior on a variety of swimming and motor ability
tasks, as well as in game intelligence
• Figueiredo: Elite soccer players were found to be older, both chronologically and skeletally,
larger, and they outperformed the sub-elite groups in physiological measures and motor skill
tests. The degree of goal orientation did not differ between groups
• Gil: There were notable differences found among the U14 soccer players who were asked to
play in the U15 team compared with the U14 players who were not selected. This selected
group of athletes was found to be taller and heavier than their non-selected counterparts
• Gil: The discriminant analysis showed that the selected soccer athletes were older, lighter,
and had a lower body mass index rating. The selected individuals also performed better on
the velocity and agility tests compared with the control and non-selected groups
• Gonaus: Soccer players who were drafted demonstrated superior performance in sport-
specific speed and power in the upper limbs as well as other physiological measures
compared with non-drafted players
• Huijgen: Findings indicated that the Loughborough Soccer Passing Test was able to
distinguish between players who were selected compared with those who were de-selected
• Lidor: The physiological and anthropometrical tests were not capable of discriminating
between the selected and non-selected handball players. The only test that showed a
difference between groups was the slalom dribbling test
• Pion: Only 18% of the gymnastics athletes who passed the baseline test consisting of motor
skills and physiological measures continued performing at the highest level of competition 5
years later
• Pyne: Findings showed that the 5 m, 10 m, 20 m sprint, agility test, and the multistage
shuttle run discriminated drafted athletes from non-drafted athletes. Of the drafted athletes,
those who had better running vertical jump ability and faster agility scores were more likely
• examined differences in performance variables between highly skilled and less-skilled
athletes (between 1990-2015)
• high variability in factors that were found to discriminate between skilled and less-skilled
individuals & clear overrepresentation of studies that examined physical profiles of male
athletes
• useful for coaches to use in combination with sport-specific and skill-level-specific
considerations in determining what level of risk exists for their talent identification decisions
Introduction
• talent identification (TID) programs are designed to identify young athletes with potential
for success in senior elite sport
• effective TID program has the potential to detect talent early, which may act as a vital
component to increasing a nation’s chances at sporting success
◦ using evidence-based processes that can be refined through feedback and evaluation of
the system
◦ have the capacity to recognize talented athletes early, which helps to focus funding and
training opportunities on athletes with the greatest potential for success
◦ however, discrepancy between research proposes and practice
• perception of talent and ability is important (nature or nurture)
◦ misconceptions regarding what talent ‘looks like’ are widespread in high-performance
sport settings (young athletes often selected based on physical growth → not equal to
talent)
• variation in definitions of talent
◦ Brown: special, natural ability and capacity for achievement or success
◦ Howe: likelihood of becoming exceptionally competent in certain fields depends on the
presence or absence of inborn attributes
◦ Gagne: outstanding mastery of systematically developed abilities, called competencies
(knowledge and skills), in at least one field of human activity to a degree that places a
person at least among the top 10% of age peers who are or have been active in that field
• centered on measurement and subsequent comparison of characteristics that contribute to
sport-specific performance
◦ often compare different age groups and skill levels in cross-sectional design → rooted in
assumption that characteristics of future success can be extrapolated from individuals
performance at one give point in time (assume talent is static -Y ignore maturity and
relative age effects)
◦ crosss-sectional data is limited as early performance is not strongly associated with later
success and chronological age and biological maturity rarely progress at same rate
(children may be helped or hindered on performance tests by their biological maturity,
especially when compared with chronological age norms)
◦ longitudinal or retrospective designs preferred when tracking dynamic variables such as
skilled performance as have greater capacity to test for causal relationship
Method
• requirements to be considered as study in review
1. Highly skilled participants (advanced, expert or eminence included)
2. Time-based comparison (track changes in outcome variable over period of at least 12
months → compare between skill groups; longitudinal: min 12 months and 2 time point
to track changes in performance; retrospective: at least one performance variable must
have been measured at least 12 months before an assessment of success was conducted)
3. Between-group comparison (min 2 different skilled/talented groups)
, 4. removal of grey-area topics (not included: studies exploring birthplace effects, deliberate
practice, genetic predispositions, handedness, long-term athlete development, or relative
age effects)
• search of identifying articles was broken down in 3 phases
◦ phase 1: search of electronic databases
◦ phase 2: search using additional resources
◦ phase 3: collaboration with panel of experts
Results
• phase 1 identifies 1696 articles, phase 2 422 additional articles and phase 3 22 atricle
• after removal of duplicates, reviewing titles and abstracts and full-text assessment 20 article
remained (16 longitudinal design, 2 retrospective, 2 mixed design)
• Barreiros: Only one-third of the athletes who were selected to be top junior players were
also selected to be on the senior team. This demonstrates the difficulty in using early
identification as a predictor for future success
• Bottoni: The findings indicate that retrospective analysis of running and swimming
performance outcomes is not an appropriate method for predicting future triathlon success
• Brouwers: Findings demonstrated that player performances at young ages were not
correlated with later success in tennis. Additionally, this study did not find an age at which
all players should start to perform in order to be successful at the professional level
• di Cagno: Coordination and precision capabilities can be used as long-term predictors of
success in gymnastics
• Elferink-Gemser: Findings indicated that both female and male elite field hockey players
scored better on technical and tactical variables. In addition, female elite-level athletes
scored higher on interval endurance capacity and motivation compared with their sub-elite
counterparts. Contrastingly, the males in the sub-elite category scored higher in motivation
compared with their elite counterparts
• Falk: Selected water polo players were superior on a variety of swimming and motor ability
tasks, as well as in game intelligence
• Figueiredo: Elite soccer players were found to be older, both chronologically and skeletally,
larger, and they outperformed the sub-elite groups in physiological measures and motor skill
tests. The degree of goal orientation did not differ between groups
• Gil: There were notable differences found among the U14 soccer players who were asked to
play in the U15 team compared with the U14 players who were not selected. This selected
group of athletes was found to be taller and heavier than their non-selected counterparts
• Gil: The discriminant analysis showed that the selected soccer athletes were older, lighter,
and had a lower body mass index rating. The selected individuals also performed better on
the velocity and agility tests compared with the control and non-selected groups
• Gonaus: Soccer players who were drafted demonstrated superior performance in sport-
specific speed and power in the upper limbs as well as other physiological measures
compared with non-drafted players
• Huijgen: Findings indicated that the Loughborough Soccer Passing Test was able to
distinguish between players who were selected compared with those who were de-selected
• Lidor: The physiological and anthropometrical tests were not capable of discriminating
between the selected and non-selected handball players. The only test that showed a
difference between groups was the slalom dribbling test
• Pion: Only 18% of the gymnastics athletes who passed the baseline test consisting of motor
skills and physiological measures continued performing at the highest level of competition 5
years later
• Pyne: Findings showed that the 5 m, 10 m, 20 m sprint, agility test, and the multistage
shuttle run discriminated drafted athletes from non-drafted athletes. Of the drafted athletes,
those who had better running vertical jump ability and faster agility scores were more likely
