Transcript No Slide Title

“Searching for Sporting Excellence:
Talent Identification and Development”
Presented
By
Carlton Cooke (BSc, PGCE, PhD, FBASES)
Co-authored
By
Steve Cobley, Kevin Till and
Nicholas Wattie
(Carnegie Research Centre for Sports Performance)
The presentation
1.
Defining terms and the UK approach
2.
Talent Identification and development – evidence
3.
An example study – UK Rugby League
4.
Some frameworks and models
5.
Some general remarks
6.
Key points
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.
www.uksport.gov.uk/
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
www.uksport.gov.uk/
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.
www.uksport.gov.uk/
UK Talent Team

A collaboration between UK Sport and the
English Institute of Sport supporting the
National Governing Bodies of Sport with:

Talent
Talent
Talent
Talent



Identification
Confirmation
Development
Transfer
www.uksport.gov.uk/
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)
www.uksport.gov.uk/
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.
Talent Confirmation

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
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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.
www.uksport.gov.uk/
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.
www.uksport.gov.uk/
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.
www.uksport.gov.uk/
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 perceptualcognitive 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).
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Strategic targeting/planning & eventual deliberate practice still required.
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
www.uksport.gov.uk/
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.
www.uksport.gov.uk/
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
www.uksport.gov.uk/
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.
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Rebecca Romero: a former Olympic medallist
rower - transferred to track cycling aged 26 Olympic Champion in 2008, < 3 years after
taking up cycling
Talent Transfer: Bullock et al. (2009)
Aim: Develop an Australian athlete for Torino 2006.
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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.
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10 athletes transferred from state/international level.
Surf-life saving, track 100m sprinters or Heptathlon.
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(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”
Reflections on Talent Identification (TID),
Selection & Development
What does current evidence tell us about best practice?
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 12-13 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.
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
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
to shear forces.

Rapidly growing gymnasts gain mass
growth plates for a longer time and thus
before strength and thus are weak
are at risk to growth plate injuries for a
relative to their weight.
longer time.

Growth plates are particularly vulnerable

These two factors make pubertal
There is a much higher ratio of late
gymnasts susceptible to debilitating
maturers in Canadian male gymnasts
injury from under-rotated twists and
than in the non-gymnast population
somersaults.
(Russell, 1994).

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 11 -13 years).
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.
Case Study:
Rugby Football League
Project: Evaluation of Player
Performance Pathway
Rugby Football League (RFL)
Focus on some acknowledged TID
Problems in sports

Age Grouping & Relative Age Effects
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Early v Late Maturers

Effects of rate of maturation on performance
characteristics (position and fitness)
(Vaeyens et al., 2008)
RFL Pathway – selection 2007
Sept - May
April
Community
Game
Service
Area
Local amateur
clubs
Local district
e.g. Leeds,
Wakefield, etc.
July
Regional
Camp
4 Regions –
Yorks, NorthWest, Cumbria,
Other
September
National
Carnival
October
National
Camp
National
tournament
with teams from
Regions
Squads selected
from National
Carnival
Under 7s –
Under 13s (n=425)
Under 13s (n=138)
Under 13s (n=75)
Under 13s (n=40)
Under 18s
Under 14s (n=435)
Under 14s (n=139)
Under 14s (n=80)
Under 14s (n=24)
(n=14,390)
Under 15s (n=438)
Under 15s (n=140)
Under 15s (n=79)
Under 15s (n=24)
Relative Age Effects (RAE)
60.00
50.00
% of Players
40.00
Q1 %
Q2 %
Q3 %
Q4 %
30.00
20.00
10.00
0.00
Community Service Area
Regional
Selection Level
National
Carnival
National
Body Size & Maturation
National Players
(n=208)
> 50th
 > 97th
 PHV – 14.1yrs
Chronological
Age
Stature (cm)
Body Mass
(kg)
Age at PHV
(years)
Years From
PHV
14.46±0.87
174.09±
7.39
95.3%
32.1%
69.45±
11.38
97.4%
38.3%
13.52±
0.58
1.20±2.02
t=-13.887
p<0.001
Regional Players
(n=473)
> 50th
 > 97th
 PHV – 14.1yrs
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=-15.81
p<0.001
0.87±0.95
Sum of skinfolds
Sum of 4 Skinfolds (mm)
50
45
40
35
30
Regional
National
U13s
38.6
31.3
U14s
41
31.6
U15s
45.3
36.8
Significant Time Effect (P=0.017); Significant Selection Level Effect (P=0.03)
VO2max (ml.kg-1.min-1)
Predicted VO2max
-1.min-1)
54
(ml.kg
53
52
51
50
49
48
47
46
45
44
Regional
National
U13s
46.2
49.9
(20m MSST)
U14s
49.2
52.5
U15s
50.1
53.8
Significant Time Effect (P<0.001); Significant Selection Level Effect (P=0.041)
RAE Position Results
(400 regional players)
60
% of Players
50
40
Q1
Q2
Q3
Q4
30
20
10
0
All
Outside
Backs
Pivots
Props
Backrowers
%
%
%
%
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)
172.85 ±
7.70
169.42 ±
7.96
177.73 ±
5.9
176.92 ±
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
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 ±
0.84
6.02 ±
0.74
10m Sprint (s)
1.88 ±
0.14
1.88 ±
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 ±
0.13
2.49 ±
0.14
2.57 ±
0.16
2.51 ±
0.16
VO2 Max (ml.kg
49.07 ±
4.90
49.88 ±
4.6
46.52 ±
5.73
49.44 ±
5.12
-1.min-1)
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
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Differences in anthropometric and fitness characteristics amongst
playing positions
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‘Props’ – Earliest maturers but score lowest on Physical Fitness
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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
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.
Practicing Alone: A form of DP
In Wrestling (Starkes et al., 1996)
Violin
Experts:
7410 hrs DP = Sparring, Mat-Work,
Good:
5301 hrs
One-One work with Coach
Amateur (MT): 3420 hrs
(These differentiated skill levels.)
Deliberate Practice Framework est.
 Highly specific deliberate practice (DP) required.
 Accumulation of DP hours necessary (i.e., 10 years)
 Early specialization promoted.
Piano
Experts: 7606 hrs
Amateurs: 1606 hrs
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, socioeconomic & resource variables.
Talent Development:
-Relevant to mature & perceptualcognitive 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.
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.
Sport context analysis: key performance
variables according to developmental stage?
Height: Tall (Basketball, Volleyball)
Short (Gymnastics, Diving)
Memory: (Chess; Ballet).
Perceptual: (F1 Driving; Racquet Sports)
Weight: Heavy (Throws, Weightlifting)
Light (Dist. running; Jockey).
Upper Limb Length: Long (Swimming)
Short (Powerlifting)
Decision Making: (Yachting; Orienteering)
Technical: (Golf, Shooting)
Aesthetic Technique: (Dance)
Sitting Height: Long (Hurdles)
Short (Wrestling)
Aerobic Capacity: (Cycling)
Multi-component sports/tasks
- Soccer, Rugby, Cricket, Volleyball, Hockey etc
Anaerobic Power: (Sprinting)
Within sport/task breakdown
-Cricket Batting, Bowling, Keeping
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)
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)
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).
Key Points
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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.
Key Points

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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
Remember who is on the receiving end!
Thanks for listening!