UNLABELLED: 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.
UNLABELLED: 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.
Authors: Karla de Jesus; Helon V H Ayala; Kelly de Jesus; Leandro Dos S Coelho; Alexandre I A Medeiros; José A Abraldes; Mário A P Vaz; Ricardo J Fernandes; João Paulo Vilas-Boas Journal: J Hum Kinet Date: 2018-03-23 Impact factor: 2.193
Authors: Inmaculada Yustres; Jesús Santos Del Cerro; Raúl Martín; Fernando González-Mohíno; Oliver Logan; José María González-Ravé Journal: PLoS One Date: 2019-06-20 Impact factor: 3.240
Authors: Inmaculada Yustres; Jesús Santos Del Cerro; Fernando González-Mohíno; Michael Peyrebrune; José María González-Ravé Journal: Front Psychol Date: 2020-01-22
Authors: Inmaculada Yustres; Jesús Santos Del Cerro; Stelios Psycharakis; Fernando González-Mohíno; José María González-Ravé Journal: Int J Environ Res Public Health Date: 2021-01-30 Impact factor: 3.390