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Presented By Carlton Cooke (BSc, PGCE, PhD, FBASES) Co-authored

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1 “Searching for Sporting Excellence: Talent Identification and Development”
Presented By Carlton Cooke (BSc, PGCE, PhD, FBASES) Co-authored Steve Cobley, Kevin Till and Nicholas Wattie (Carnegie Research Centre for Sports Performance)

2 The presentation Defining terms and the UK approach
Talent Identification and development – evidence An example study – UK Rugby League Some frameworks and models Some general remarks Key points

3 UK Sport Responsibilities for the nation’s Olympic and Paralympic performance potential through: The World Class Performance Programme, working closely with NGBs. Supporting our leading athletes, in coaching, talent identification, sports science and medicine and Performance Lifestyle.

4 World Class Performance Programme
Covers all summer Olympic and Paralympic sports & high-performing winter Olympic sports at three levels: Podium - athletes with medal winning capabilities (i.e. a max of 4 years)  Development – athletes with realistic medal winning capabilities for 2012 and in newly funded competitive sports for 2012  Talent - identification and confirmation of athletes with the potential to progress

5 World Class Performance Programme
Started 1997 Lessons learned from Sydney and Athens Funding targeted at athletes via their sport's governing body 1,200 athletes at Podium and Development levels benefit from an annual investment of around £100 million Many more involved at the Talent level.

6 UK Talent Team A collaboration between UK Sport and the English Institute of Sport supporting the National Governing Bodies of Sport with:  Talent Identification Talent Confirmation Talent Development Talent Transfer

7 Talent Identification
Screening of athletes - physical, physiological, psychological and skill attributes - identify potential for international success Athletes selected through talent identification -no previous involvement in the sport identified for (raw latent talent)

8 Example: Sporting Giants
February 2007 First appeal of its kind Potential athletes to make themselves known – criteria: tall (minimum 190cm men & 180cm women), young (between 16 and 25), with some sort of athletic background. Possible outcome - join performance programme Olympic sports of rowing, handball or volleyball. Registration closed with 4,800 applications – about 4,000 met all 3 criteria.

9 Talent Confirmation Extended assessment phase where athletes’ talent characteristics are verified. This could include coachability, trainability, adaptability to a high performance environment. Can last 3 to 12 months. Gives athletes insight into life high performance sport.

10 Talent Development Athletes are immersed in a highly specialised environment to enable them to develop into high performance athletes. Exposure to expert coaching, training and competition, access to excellent facilities, specialist equipment and support services.

11 Talent Transfer Structured re-assignment of athletes to sports with similar and transferable characteristics. Athletes transferred often show development in their new sport in short timescales, having already developed many key aspects from their original or “donor” sports.

12 Talent Transfer ↑ dropout following de-selection in popular sports (e.g., soccer) may have most potential for UK male transfer (different in India, Australia..). Athletes familiar with ↑ training loads/regulation & in similar perceptual-cognitive tasks may show > potential for transfer. Relies on physical & perceptual/cognitive similarity between sport tasks. Cognitive transfer possible (Smeeton et al., 2004). Transfer paths can be planned/suggested (e.g., Rowing-Cycling). ↑ potential for less mature or popular sports (e.g., Female contexts). Strategic targeting/planning & eventual deliberate practice still required. Those not selected to be given prof contracts may drop out of sport altogether. Deliberate talent transfer progs give them a second chance Common elements transfer well : phys aerobic fitness Rowing to cycling Games players transfer game intelligence well to different games

13 Example: UK Sport - Pitch2Podium
Created with football and rugby. Provides young football and rugby players unsuccessful in securing a professional contract with a second opportunity to succeed in a new Olympic sport

14 Pitch2Podium High profile athletes successfully transferred, including: Darren Campbell: Football for Plymouth Argyle, returned to athletics in 1995 going on to win Olympic gold. Sir Steve Redgrave: Britain’s greatest ever Olympian - early involvement in rugby before rowing.

15 Example: Girls4Gold June 2008, search for sportswomen
Potential Olympic champions cycling, skeleton, canoeing, modern pentathlon, rowing and sailing Most extensive female sporting talent recruitment drive ever in GB Applicants - female, aged 17 to 25, competing in any sport at county/regional level

16 Girls4Gold Women - new Olympic sport relatively late age – medals in short timeframes include: Shelley Rudman: former hurdler - silver medal at the 2006 Winter Olympics in bob skeleton, < four years after trying the sport aged 21. Rebecca Romero: a former Olympic medallist rower - transferred to track cycling aged 26 - Olympic Champion in 2008, < 3 years after taking up cycling

17 Talent Transfer: Bullock et al. (2009)
Aim: Develop an Australian athlete for Torino 2006. Public campaign to attract potential athletes (2004). 30m sprint (explosive leg speed) used to identify 26 potentials. Physical test battery & dryland sled push used to select/predict. 10 athletes transferred from state/international level. Surf-life saving, track 100m sprinters or Heptathlon. (De)selection after 1st competitive exposure. Remaining exposed to dryland prep, off-season training, & 5-month competition circuit. 1 athlete competed at Torino after 300 approx sled simulations, 220 sled runs over 14 months – offered the term “deliberate programming” Good paper in j sp sciences on how much time goes into transfer of aussie athlete to skeleton bob

18 Reflections on Talent Identification (TID), Selection & Development
What does current evidence tell us about best practice?

19 TID Issues: Physically Based Sports (e.g., Rowing)
Performance predictors are narrow/specific. Kramer et al. (1994) VO2 Max consistently > correlate across field/lab tests. Anthropometric (e.g., height) + physiological (e.g., V02) > predict ergometer performance in year olds (Mikulic & Ruzic, 2008) Cosgrove et al., (1999) VO2 Max & lean body mass represented 72% of variance in average speed of adult club level rowers. Power at V02 Max, VO2 Max, O2 Consumption at blood lactate threshold accounted for 98% variance in 2000m ergometer task with elite rowers. (Ingham et al., 2002). Predictors suggested to modify somewhat with the length/duration of rowing event, number of crew & skill level. Note difference rowing ergos and on water

20 Talent ID in British Rowing: World Class Start Programme
Looking for the extreme of the population distribution Assessment based on normative data for tests Tests include: Height Arm span Rowing specific leg and arm strength Cardiovascular fitness (arm/leg cycle not rowing) Prediction of potential easier based on research GB rowing – short time from ID or transfer to success

21 Gymnastics (early specialisation and technical sport – biomechanics key)
General description Implication for the gymnast Physical Maturation Fusion of growth plates occurs early in early maturers. Conversely, late maturers have open growth plates for a longer time and thus are at risk to growth plate injuries for a longer time. There is a much higher ratio of late maturers in Canadian male gymnasts than in the non-gymnast population (Russell, 1994). Growth plates are particularly vulnerable to shear forces. Rapidly growing gymnasts gain mass before strength and thus are weak relative to their weight. These two factors make pubertal gymnasts susceptible to debilitating injury from under-rotated twists and somersaults. Coaches beware. This is not the time to add another twist or salto unless the gymnast has sufficient air time to complete it well before landing. Table 1. Extract from phase 1 of the FIG development programme for the early pubertal stage (age years). Early specilistaion so transfer in not really possible Technical aspects key, linked to size, str & cond and maturation World gove body very detailed development prog and advice for gymnasts, coaches and parents – relatively few sports have this level of detailed guidance available for all world wide. Arkayev and Suchilin’s book on how to create gymnastics champs is also excellent – not just for gymnastics Individual changes with time in growth, maturation and development are key.

22 TID Issues: Team Sports (e.g., Falk et al., 2004)
Aim: Examine physical, technical, & tactical performance variables to assist selection in junior (14-15) water polo. Selected players performed better on: - Field-based physical swimming sprints. - Technical control of dribbling & ball handling. - Game intelligence (subjective assessment of tactical positioning, movement, decision making, & passing). 67% of players were correctly selected based on findings. Game intelligence (tactical components) deemed important discriminators for present & higher levels of play.

23 Case Study: Rugby Football League
Project: Evaluation of Player Performance Pathway Rugby Football League (RFL)

24 Focus on some acknowledged TID Problems in sports
Age Grouping & Relative Age Effects Early v Late Maturers Effects of rate of maturation on performance characteristics (position and fitness) (Vaeyens et al., 2008)

25 RFL Pathway – selection 2007
Sept - May April July September October Community Game Service Area Regional Camp National Carnival National Camp Local amateur clubs Local district e.g. Leeds, Wakefield, etc. 4 Regions – Yorks, North-West, Cumbria, Other National tournament with teams from Regions Squads selected from National Carnival Under 7s – Under 18s (n=14,390) Under 13s (n=425) Under 14s (n=435) Under 15s (n=438) Under 13s (n=138) Under 14s (n=139) Under 15s (n=140) Under 13s (n=75) Under 14s (n=80) Under 15s (n=79) Under 13s (n=40) Under 14s (n=24) Under 15s (n=24)

26 Relative Age Effects (RAE)
Community no selection – turn up and try – already a step response over quartiles 30% Q1 v 21% Q4 Each level of selection more bias towards older children in year Selection for talent id and dev or performance

27 Body Size & Maturation National Players (n=208) > 50th > 97th
Chronological Age Stature (cm) Body Mass (kg) Age at PHV (years) Years From PHV National Players (n=208) > 50th > 97th PHV – 14.1yrs 14.46±0.87 174.09± 7.39 95.3% 32.1% 69.45± 11.38 97.4% 38.3% 13.52± 0.58 t= p<0.001 1.20±2.02 Regional Players (n=473) 14.49±0.86 173.95± 7.91 92.4% 33.3% 68.82± 12.62 96.0% 30.2% 13.62±0.6 t= p<0.001 0.87±0.95 No diff chron age between nat and reg but nat sig more mature 1.2 years past phv compared with 0.87 for regionals, so picking older and more mature!

28 Sum of skinfolds Longitudinal tracking shows sig diff between performance levels (sig less fat) Significant Time Effect (P=0.017); Significant Selection Level Effect (P=0.03)

29 Predicted VO2max ( (20m MSST)
National s here are to p graph – sig higher pred vo2max which tracks longitudinally Significant Time Effect (P<0.001); Significant Selection Level Effect (P=0.041)

30 RAE Position Results (400 regional players)
Regional players (n about 400) Describe position differences

31 Anthropometric & Maturational Results
Outside Backs Halves and Hookers Props Back row Age at PHV (years) 13.66 ± 0.54 14.00 ± 0.59 13.29 ± 0.43 13.41 ± 0.49 Stature (cm) ± 7.70 ± 7.96 ± 5.9 ± 5.33 Body Mass (kg) 65.93 ± 10.64 62.32 ± 9.53 79.22 ± 11.79 73.11 ± 9.9 Sum of 4 Skinfolds 33.57 ± 12 33.82 ± 12.35 51.35 ± 19.25 41.65 ± 15.98 Props age at phv sig less than halves & hookers and outside backs, sig taller, Sig heavier than all positions Sig more body fat

32 Performance Characteristics
Outside Backs Pivots Props Back Rowers Vertical Jump (cm) 42.19 ± 5.65 39.47 ± 5.27 38.74 ± 5.45 40.21 ± 4.9 MB Throw (m) 5.79 ± 0.84 5.51 ± 0.78 6.05 ± 6.02 ± 0.74 10m Sprint (s) 1.88 ± 0.14 0.13 1.94 ± 0.16 1.91 ± 0.11 60m Sprint (s) 8.39 ± 0.51 8.55 ± 0.59 8.76 ± 0.53 8.54 ± 0.48 Agility 505 (s) 2.48 ± 2.49 ± 2.57 ± 2.51 ± VO2 Max ( 49.07 ± 4.90 49.88 ± 4.6 46.52 ± 5.73 49.44 ± 5.12 Now look at performance Outside backs sig better on vj than others but props are worst (but not sig diff to piv and BR) Props no sig better on med ball throw than 2 others - only better than piv Props sig worse on sprinting, agility and aerobic power So props are eldest, most mature, but least able physically – suggests selection to stereotype – size matters more than anything else in selection Fewer selected props make it to prof contracts – BR move to props

33 Summary of Rugby League findings
Participation and Selection inequalities in RL – RAE is a ‘Problem!’ Physical size and maturation = increased selection opportunities Playing Position interaction with RAE Differences in anthropometric and fitness characteristics amongst playing positions ‘Props’ – Earliest maturers but score lowest on Physical Fitness Pathway Selection for Performance not Talent ID and Development Measurement and evaluation did not inform selection for pathway Selection criteria subjective assessment by “experts” Research has informed RFL leading to changes to the Player Performance Pathway

34 Development issues: (Ericsson et al.,1993).
Examined current activity & developmental history of musicians at the Berlin music school. Structure, content & volume of training discriminated skill level. Deliberate Practice Framework est. Highly specific deliberate practice (DP) required. Accumulation of DP hours necessary (i.e., 10 years) Early specialization promoted. Practicing Alone: A form of DP Piano Experts: hrs Amateurs: 1606 hrs Violin Experts: hrs Good: hrs Amateur (MT): 3420 hrs In Wrestling (Starkes et al., 1996) DP = Sparring, Mat-Work, One-One work with Coach (These differentiated skill levels.)

35 Deliberate Practice Framework
General Commentary: General support for premise of DP. Hard to test without long-term tracking. Studies yet to show causal relationship, based on correlation methods. Questioned on extrapolation without direct testing on sport contexts. Difficult to account for inter-individual motivation & psychological dispositions toward training. Fails to account for contextual, socio-economic & resource variables. Talent Development: Relevant to mature & perceptual-cognitive based skills (e.g., chess, gymnastics, cricket-batting). Risks and benefits with early sport specialization (Wiersma, 2000). Diversified approaches to training have been advocated (Baker et al., 2009). Retrospective analyses of elite players in team sports suggests many do not specialize until mid/late teenage years.

36 Developmental Model of Sport Participation (DMSP) (Côté 1999; Côté, Baker & Abernethy 2003)
Based on Canadian & Australian elite team & ind. sport athletes. Retrospective interviews, assessment of diaries & training logs conducted. Suggests early play underpins participation. Suggests DP is not necessary, unless in particular contexts (e.g., Rhythmic Gymnasts) Later specialization identified in elite athletes. Identifies parent, peer, & coach roles across developmental stages. Social climate & environmental changes also identified.

37 Sport context analysis: key performance variables according to developmental stage?
Height: Tall (Basketball, Volleyball) Short (Gymnastics, Diving) Weight: Heavy (Throws, Weightlifting) Light (Dist. running; Jockey). Upper Limb Length: Long (Swimming) Short (Powerlifting) Sitting Height: Long (Hurdles) Short (Wrestling) Aerobic Capacity: (Cycling) Anaerobic Power: (Sprinting) Memory: (Chess; Ballet). Perceptual: (F1 Driving; Racquet Sports) Decision Making: (Yachting; Orienteering) Technical: (Golf, Shooting) Aesthetic Technique: (Dance) Multi-component sports/tasks Soccer, Rugby, Cricket, Volleyball, Hockey etc Within sport/task breakdown Cricket Batting, Bowling, Keeping

38 RAE across sports Maturation problem reflected in selection within developmental systems. Magnitude of selection bias inequality (RAE) associated with: - Early adolescent period onwards & ↑ with skill level. - High participation/competitive team sports with stringent developmental structure (e.g., soccer, ice-hockey). - First appeared in 70’s/80’s for particular contexts, now growing! potential link with growth in TID/selection. - Questions raised on utility of early/benefits of early (de)selection. (Cobley et al., 2009)

39 Interpretation That said………
Anthropometric & physical variables appear better to identify potential athletes when compared to normative populations/low skill levels. Anthropometric & physical variables less likely to discriminate at higher skill levels (i.e., homogenous group) for team or multi-component sport tasks. One-off cross-section assessments are poor indicators, due to dynamic nature of individual growth, & change of performance context across development. Longitudinal tracking is necessary for multi-factorial sport tasks. Are we measuring the right variables? (e.g., Training History; Psychological characteristics, Trainability)

40 Interpretation A ‘standard pack’ of attributes may not differentiate at elite levels. Inter- and intra-individual variations offer uniqueness! Hard to perceive ‘read’ compared to previous experience. Novelty and new problems are presented (e.g., Left-Handers in Tennis). Combinations of physical attributes, technical skill, strategy, tactical decision-making, & deception may play a more important role. Compensation phenomenon (Williams & Ericsson, 2005). Example: Controlled variation in spin bowling. Direct manipulation of angle, grip, release point, rotation speed, flight speed, flight time, pitch to reduce predictability (consistency of approach).

41 Key Points Predicting talent has better success in some sports compared to others. Selection processes are relatively unknown. RAE bias evident. Developmental frameworks identify behaviours & structure of training necessary for long-term success. A sport specific developmental framework identifies stages of change, social & resource support change. Talent transfer between sport contexts is possible. Maturation appears to be a consistent confounder in early talent identification & selection. Test-retest reliabilities are problematic during & pre-adolescence (even within 12 months). Maturation influences performance on many physical & motor skill tests.

42 Key Points Complexity of talent prediction emerges from the nature & diversity of sport task demands - No one model fits within & across all sport tasks! Predicting variables change across development (stages of competition). Cross-sectional assessment limited in utility. Multi-disciplinary assessment & capture of variables is required. Frameworks offer methods & strategies to build a sport context model & evaluate athlete development. Sport is only 1 dimension of a young persons development Consider holistic development needs on an individual basis Working in talent identification and development requires an interdisciplinary approach and multidisciplinary teams

43 Remember who is on the receiving end! Thanks for listening!

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