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Darren J. Burgess and Geraldine A. Naughton

Traditional talent development pathways for adolescents in team sports follow talent identification procedures based on subjective games ratings and isolated athletic assessment. Most talent development models are exclusive rather than inclusive in nature. Subsequently, talent identification may result in discontentment, premature stratification, or dropout from team sports. Understanding the multidimensional differences among the requirements of adolescent and elite adult athletes could provide more realistic goals for potential talented players. Coach education should include adolescent development, and rewards for team success at the adolescent level should reflect the needs of long-term player development. Effective talent development needs to incorporate physical and psychological maturity, the relative age effect, objective measures of game sense, and athletic prowess. The influences of media and culture on the individual, and the competing time demands between various competitions for player training time should be monitored and mediated where appropriate. Despite the complexity, talent development is a worthy investment in professional team sport.

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Richard Bailey and David Collins

Despite evident differences between approaches to talent development, many share a set of common characteristics and presumptions. We call this the Standard Model of Talent Development (SMTD). This model is articulated and the relevant literature drawn out to highlight the model's strengths and weaknesses. The SMTD has been enormously influential, in terms of both policy documentation and practice, and it retains an obvious common sense appeal. However, we will argue that not only is its attractiveness illusionary and inconsistent to the emerging evidence base from research, but it is also undesirable from a variety of perspectives and desired outcomes. In short, we suggest that the most common system for identifying talent is unsubstantiated from both a process and an outcome perspective.

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Marije T. Elferink-Gemser and Florentina J. Hettinga

Pacing has been characterized as a multifaceted goal-directed process of decision making in which athletes need to decide how and when to invest their energy during the race, a process essential for optimal performance. Both physiological and psychological characteristics associated with adequate pacing and performance are known to develop with age. Consequently, the multifaceted skill of pacing might be under construction throughout adolescence, as well. Therefore, the authors propose that the complex skill of pacing is a potential important performance characteristic for talented youth athletes that needs to be developed throughout adolescence. To explore whether pacing is a marker for talent and how talented athletes develop this skill in middle-distance and endurance sports, they aim to bring together literature on pacing and literature on talent development and self-regulation of learning. Subsequently, by applying the cyclical process of self-regulation to pacing, they propose a practical model for the development of performance in endurance sports in youth athletes. Not only is self-regulation essential throughout the process of reaching the long-term goal of athletic excellence, but it also seems crucial for the development of pacing skills within a race and the development of a refined performance template based on previous experiences. Coaches and trainers are advised to incorporate pacing as a performance characteristic in their talent-development programs by stimulating their athletes to reflect, plan, monitor, and evaluate their races on a regular basis to build performance templates and, as such, improve their performance.

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Fleur E.C.A. van Rens, Erika Borkoles, Damian Farrow and Remco C.J. Polman

A holistic perspective on talent development in sport is important to facilitate a developmentally appropriate approach to cultivating sporting expertise ( Henriksen, 2010a ; b ; Miller & Kerr, 2002 ; Wylleman & Lavallee, 2004 ). Understanding the personal, environmental, and organizational

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Sian V. Allen, Tom J. Vandenbogaerde, David B. Pyne and Will G. Hopkins

Talent identification and development typically involve allocation of resources toward athletes selected on the basis of early-career performance.

Purpose:

To compare 4 methods for early-career selection of Australia’s 2012 Olympic-qualifying swimmers.

Methods:

Performance times from 5738 Australian swimmers in individual Olympic events at 101 competitions from 2000 to 2012 were analyzed as percentages of world-record times using 4 methods that retrospectively simulated early selection of swimmers into a talent-development squad. For all methods, squad-selection thresholds were set to include 90% of Olympic qualifiers. One method used each swimmer’s given-year performance for selection, while the others predicted each swimmer’s 2012 performance. The predictive methods were regression and neural-network modeling using given-year performance and age and quadratic trajectories derived using mixed modeling of each swimmer’s annual best career performances up to the given year. All methods were applied to swimmers in 2007 and repeated for each subsequent year through 2011.

Results:

The regression model produced squad sizes of 562, 552, 188, 140, and 93 for the years 2007 through 2011. Corresponding proportions of the squads consisting of Olympic qualifiers were 11%, 11%, 32%, 43%, and 66%. Neural-network modeling produced similar outcomes, but the other methods were less effective. Swimming Australia’s actual squads ranged from 91 to 67 swimmers but included only 50−74% of Olympic qualifiers.

Conclusions:

Large talent-development squads are required to include most eventual Olympic qualifiers. Criteria additional to age and performance are needed to improve early selection of swimmers to talent-development squads.

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Rikstje Wiersma, Inge K. Stoter, Chris Visscher, Florentina J. Hettinga and Marije T. Elferink-Gemser

Purpose:

To provide insight on the development of pacing behavior in junior speed skaters and analyze possible differences between elite, subelite, and nonelite juniors.

Methods:

Season-best times (SBTs) in the 1500-m and corresponding pacing behavior were obtained longitudinally for 104 Dutch male speed skaters at age 13–14 (U15), 15–16 (U17), and 17–18 (U19) y. Based on their U19 SBT, skaters were divided into elite (n = 17), subelite (n = 64), and nonelite (n = 23) groups. Pacing behavior was analyzed using the 0- to 300-m, 300- to 700-m, 700- to 1100-m, and 1100- to 1500-m times, expressed as a percentage of final time. Mixed analyses of variance were used for statistical analyses.

Results:

With age, pacing behavior generally developed toward a slower 0- to 300-m and 1100- to 1500-m and a faster midsection relative to final time. While being faster on all sections, the elite were relatively slower on 0- to 300-m (22.1% ± 0.27%) than the subelite and nonelite (21.5% ± 0.44%) (P < .01) but relatively faster on 300- to 700-m (24.6% ± 0.30%) than the nonelite (24.9% ± 0.58%) (P = .002). On 700- to 1100-m, the elite and subelite (26.2% ± 0.25%) were relatively faster than the nonelite (26.5% ± 0.41%) (P = .008). Differences in the development of pacing behavior were found from U17 to U19, with relative 700- to 1100-m times decreasing for the elite and subelite (26.2% ± 0.31% to 26.1% ± 0.27%) but increasing for the nonelite (26.3% ± 0.29% to 26.5% ± 0.41%) (P = .014).

Conclusions:

Maintaining high speed into 700 to 1100 m, accompanied by a relatively slower start, appears crucial for high performance in 1500-m speed skating. Generally, juniors develop toward this profile, with a more pronounced development toward a relatively faster 700- to 1100-m from U17 to U19 for elite junior speed skaters. The results of the current study indicate the relevance of pacing behavior for talent development.

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Greg Doncaster, John Iga and Viswanath Unnithan

talent development of young soccer players ( 38 ). This has resulted in research that aims to identify, examine, and analyze the key physical and physiological characteristics of elite youth soccer players who are associated with superior soccer performance ( 38 ). However, confounding variables of

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Stephen Seiler

An Olympic Games is a measurable test of a nation´s sporting power. Medal counts are the object of intense scrutiny after every Olympiad. Most countries celebrate any medal with national glee, since 60% of competing countries will win none. In 2012, 10% of the competing countries won 75% of all medals. Despite this concentration among a few countries, more countries are winning more medals now than 20 years ago, thanks in part to athlete-support and -development programs arising around the globe. Small strong sporting countries like Norway are typified by fairly large variation in medal results from Olympiad to Olympiad and a high concentration of results in a few sports. These are important factors to consider when evaluating national performance and interpreting the medal count. Medal conversion, podium placements relative to top 8 placements, may provide a measure of the competitiveness of athlete-support programs in this international zero sum game where the cost of winning Olympic gold keeps rising whether measured in dollars or human capital.

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Darren Burgess, Geraldine Naughton and Kevin Norton

Purpose:

The understanding of the gap between Under 18 y (U18) and senior-level competition and the evolution of this gap in Australian Football lack a strong evidence base. Despite the multimillion dollars invested in recruitment, scientific research on successful transition is limited. No studies have compared individual players’ movement rate, game statistics and ball speed in U18 and senior competition of the Australian Football League across time. This project compared differences in player movement and ball speed between matches from senior AFL competitive matches and U18 players in the 2003 and 2009 seasons.

Methods:

TrakPerformance Software and Global Positioning System (GPS) technology were used to analyze the movement of players, ball speed and game statistics. ANOVA compared the two levels of competition over time.

Results:

Observed interactions for distance traveled per minute of play (P = .009), number of sprints per minute of play (P < .001), time spent at sprint speed in the game (P < .001), time on field (P < .001), and ball speed (P < .001) were found. Subsequent analysis identified increases in movement patterns in senior AFL competition in 2009 compared with the same level of competition in 2003 and U18 players in 2003 and 2009.

Conclusions:

Senior AFL players in 2009 were moving further, sprinting relatively more frequently, playing less time and playing at game speeds significantly greater than the same senior competition in 2003 as well as compared with both cohorts of U18 players.

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Craig A. Williams, Jon L. Oliver and James Faulkner

Purpose:

The aim of the study was to longitudinally assess speed and jump performance characteristics of youth football players over a 3 y period.

Methods:

Two hundred players across five age squads (U12–U16) from an English Football League academy participated. Sprint performance (10 and 30 m) and countermove-ment jump height were assessed at 6 mo intervals. Pairwise analyses determined the level of change in performance between consecutive intervals.

Results:

Sprint performance changes tended to be greatest during the early teenage years, with observed changes exceeding the smallest worthwhile effect (1.0% for 10 and 30 m sprints). Changes in jump performance were above the smallest worthwhile effect of 1.8% for all but one interval. Large individual variability in the magnitude of change in sprint and jump performance, perhaps due to the confounding effect of growth and maturation, revealed few significant differences across the 6 mo intervals. Cumulative changes in performance demonstrated strong linear relationships, with a yearly rate of change of 6.9% for jump height, and 3.1 and 2.7% for 10 m and 30 m sprint time respectively. The magnitude of change in performance tended not to differ from one interval to another.

Conclusions:

The results of this study may primarily be used to monitor and predict the rate of progression of youth football players. In addition, these results may be used as a benchmark to evaluate the effectiveness of a current training program.