The underlying theme of this paper is to show how large-scale cluster analysis can be applied to discover the best pacing strategy and the optimum combination of race components that will help athletes achieve their best performance at each particular race distance, which may not be observed easily otherwise. Taking into consideration the dependence of the repeated measurements made over athletes` entire race record times over each of the i-th laps, we report three main findings: Firstly, based on the resultant tree-like dendrograms from cluster analysis, we find that male and female swimmers may use similar patterns on stroke lengths in their races whereas the swimming speeds of males are generally somewhat faster. Secondly, we demonstrate a potential application of large-scale cluster analysis to find the optimum relationship between stroke length and swimming speed that will allow German national swimmer, Stefan Herbst, and French national swimmer, Amaury Leveaux to reproduce fast swims in the 200m freestyle race distance events. Thirdly, we demonstrate a potential application of large-scale cluster analysis to find the best pacing strategy that will allow British national swimmer, Paul Palmer, Australian national swimmer, Ian Thorpe, American national swimmer, Joshua Davis, Canadian national swimmer, Rick Say, Italian national swimmers, Emiliano Brembilla and Massimiliano Rosolino, to improve their times in the 200m freestyle race distance events.
© Copyright 2008 International Journal of Sports Science and Engineering. World Academic Press. Alle Rechte vorbehalten.