Abstract
The purpose of this research was to determine the expected progression of adolescent female swimming performances using a longitudinal approach. The performances of 514 female swimmers (12–19 year olds) who participated in one or more FINA-regulated annual international schools’ swimming championships over an eight-year period were analysed. Quadratic functions for each of the seven individual events (50, 100, 200 m freestyle, 100 m backstroke, breaststroke, butterfly, 200 m individual medley) were determined using mixed linear models. The predicted threshold of peak performance ranged from 16.8 ± 0.2 (200 m individual medley) to 20.6 ± 0.1 (100 m butterfly) years of age, preceded by gradual rates of improvement (mean rate of 1.6% per year). However, following cross validation, only three events (100 m backstroke, 200 m individual medley and 200 m freestyle) produced reliable models. Identifying the factors that contribute to the progression of female performance in this transitory period of life remains challenging, not least since the onset of puberty is likely to have occurred prior to reaching 12 years of age, the minimum competition age for this championship.
Highlights
Based on the increasing pressure for nations to develop talented athletes and win medals at the highest level, many sporting bodies have directed strategies and resources to increasing performance levels in all sports; swimming is no exception [1,2,3]
Resultsin reduced confidence in those models. This included the full model of the fixed quadraticMany for the
Despite the poor fit of some of the models generated, the novel analysis of individual events allows for some interesting comparisons to be made
Summary
Based on the increasing pressure for nations to develop talented athletes and win medals at the highest level, many sporting bodies have directed strategies and resources to increasing performance levels in all sports; swimming is no exception [1,2,3]. Trying to separate the performance gains that are made by athletes due to training as opposed to natural growth and development has been one of the most important challenges to overcome. There have been a number of approaches to predictive modelling in a variety of different sports, including physiological, mathematical or probability strategies [5]. These authors suggest that until all factors such as biomechanical, physiological and psychological parameters that influence human performance are fully understood and accounted for, modelling will continue to lack sufficient accuracy to meaningfully predict future performance. Numerous studies have considered how changes in physical, physiological and biomechanical parameters affect performance during adolescence [6,7]
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