Predicting a nation`s Olympic-qualifying swimmers
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.
© Copyright 2015 International Journal of Sports Physiology and Performance. All rights reserved.
| Subjects: | |
|---|---|
| Notations: | junior sports |
| Tagging: | Qualifikation |
| Published in: | International Journal of Sports Physiology and Performance |
| Language: | English |
| Published: |
2015
|
| Online Access: | http://doi.org/10.1123/ijspp.2014-0314 |
| Volume: | 10 |
| Issue: | 4 |
| Pages: | 431-435 |
| Document types: | article |
| Level: | advanced |