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<p>The purpose of this study was to identify intra- and inter-individual variabilities during short course 50&#xa0;m sprints. Swimming velocity (<i>SV</i>), stroke frequency (<i>SF</i>), and stroke length (<i>SL</i>) for each stroke cycle in 189 male and 160 female swimmers’ 50&#xa0;m time trials (with their specialised stroke) were analysed. The inter-individual variability for each kinematic variable was analysed using the inter-individual standard deviation of the Gaussian Process regression. Intra-participant variability was analysed using k-means clustering with kinematic data extracted from the first, mid-, and last strokes. In all strokes and both sexes, swimmers showed large inter-individual kinematic variabilities at the first and last strokes, which justified the need to separate these strokes from the clean-swimming segment in race analyses. Intra-individual kinematic patterns were categorised into four clusters with different within-lap <i>SV</i> patterns. Particularly, many front crawl and backstroke swimmers showed a faster velocity in mid-pool than in the transition, while many butterfly swimmers showed the fastest <i>SV</i> in the transition. This might suggest a greater difficulty in the transition technique in alternating strokes than in butterfly. Race analyses should focus on not only the overall trend but also individual variabilities to investigate the swimmers’ behaviour during swimming races.</p>

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Variabilities in the stroking parameters during short course 50 m time trials in all four competitive swimming strokes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月2日

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  • Tomohiro Gonjo,
  • Santiago Veiga,
  • Francisco Hermosilla-Perona,
  • Bjørn Harald Olstad

摘要

The purpose of this study was to identify intra- and inter-individual variabilities during short course 50 m sprints. Swimming velocity (SV), stroke frequency (SF), and stroke length (SL) for each stroke cycle in 189 male and 160 female swimmers’ 50 m time trials (with their specialised stroke) were analysed. The inter-individual variability for each kinematic variable was analysed using the inter-individual standard deviation of the Gaussian Process regression. Intra-participant variability was analysed using k-means clustering with kinematic data extracted from the first, mid-, and last strokes. In all strokes and both sexes, swimmers showed large inter-individual kinematic variabilities at the first and last strokes, which justified the need to separate these strokes from the clean-swimming segment in race analyses. Intra-individual kinematic patterns were categorised into four clusters with different within-lap SV patterns. Particularly, many front crawl and backstroke swimmers showed a faster velocity in mid-pool than in the transition, while many butterfly swimmers showed the fastest SV in the transition. This might suggest a greater difficulty in the transition technique in alternating strokes than in butterfly. Race analyses should focus on not only the overall trend but also individual variabilities to investigate the swimmers’ behaviour during swimming races.