performance and well-being has rapidly grown ( Rice et al., 2016 ; Schaal et al., 2011 ), less attention has been paid to young and developing athletes ( Hill, MacNamara, Collins, & Rodgers, 2016 ). Yet, young elite athletes involved in talent-development (TD) environments (e.g., regional/national sport
Florence Lebrun, Àine MacNamara, Dave Collins and Sheelagh Rodgers
Jamie Taylor and Dave Collins
Sport is littered with examples of gifted youngsters who fail to realize what many perceived to be their ultimate potential. Much research supports the conceptualization of talent development (TD) as a nonlinear process, and many young “superstars” can attest that early success may not necessarily
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
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.
To compare 4 methods for early-career selection of Australia’s 2012 Olympic-qualifying swimmers.
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.
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.
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.
Rikstje Wiersma, Inge K. Stoter, Chris Visscher, Florentina J. Hettinga and Marije T. Elferink-Gemser
To provide insight on the development of pacing behavior in junior speed skaters and analyze possible differences between elite, subelite, and nonelite juniors.
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.
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).
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.
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
Fleur E.C.A. van Rens, Rebecca A. Ashley and Andrea R. Steele
.1080/10705519909540118 Ivarsson , A. , Stenling , A. , Fallby , J. , Johnson , U. , Borg , E. , & Johansson , G. ( 2015 ). The predictive ability of the talent development environment on youth elite football players’ well-being: A person-centered approach . Psychology of Sport and Exercise, 16 , 15 – 23 . doi
Beth G. Clarkson, Elwyn Cox and Richard C. Thelwell
later in their career when ingratiated within its culture through talent development and elite levels? Due to the limited nature of investigations concerning women football coaches, researchers can look to scholarly investigations of women in non-playing support roles (e.g., coordinators, secretaries
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.