a Department of Orthopedic Surgery , VU University Medical Center, Amsterdam Movement Sciences , Amsterdam , Netherlands.
b Faculty of Science , University of Amsterdam , Amsterdam , Netherlands.
Eur J Sport Sci. 2018 Oct;18(9):1185-1190. doi: 10.1080/17461391.2018.1480062. Epub 2018 Jun 11.
So far, the relationship between wind and athletics performance has been studied mainly for 100 m sprint, based on simulation of biomechanical models, requiring several assumptions. In this study, this relationship is quantified empirically for all five horizontal jump and sprint events where wind is measured, with freely available competition results. After systematic scraping several elite and sub-elite results sites, the obtained results (n = 150,169) were filtered and matched to athletes. A quadratic mixed effects model with athlete and season as random effects was applied to express the influence of wind velocity on performance in each event. Whether this effect differs with performance level was investigated by applying the model on subgroups based on performance level. In the fitted quadratic model, the linear coefficients were significant (p < .001) for all events; the quadratic coefficients were significant for all events (p < .001) except long jump (p = .138). A 2.0 m s tail wind provides an average advantage of 0.125, 0.140 and 0.146-s for the 100, 200 and 100/110 m hurdles, respectively, and an advantage of 0.058 and 0.102 m for long jump and triple jump, respectively. Performance level had a significant effect on the wind influence only for 100 m (p < .001). Amateur athletes (∼13 s) benefit 69% more from a 2.0 m s tail wind than elite athletes (∼10 s). Practical formulas are presented for each event. These can easily be used correct results for wind speed, allowing better talent scouting and championship selection. This study demonstrates the efficacy of answering scientific questions empirically, through freely available data.
迄今为止，主要基于生物力学模型模拟，针对 100 米短跑研究了风与运动表现的关系，该研究需要进行多项假设。本研究通过可自由获取的比赛结果，对所有五项水平跳跃和短跑项目中进行了实证量化研究，测量了风的影响。通过系统地从多个精英和次精英成绩网站刮取数据，对所获得的结果（n=150169）进行筛选和匹配运动员。应用包含运动员和季节为随机效应的二次混合效应模型，以表达每个项目中风速对表现的影响。通过基于表现水平的子组应用模型，研究了这种影响是否因表现水平而异。在拟合的二次模型中，所有事件的线性系数均具有统计学意义（p<.001）；除跳远外（p=.138），所有事件的二次系数均具有统计学意义（p<.001）。一个 2.0 m s 的顺风为 100 米、200 米和 100/110 米栏分别提供 0.125、0.140 和 0.146 秒的平均优势，跳远和三级跳远分别提供 0.058 和 0.102 米的优势。仅 100 米的表现水平对风的影响具有统计学意义（p<.001）。业余运动员（约 13 s）比精英运动员（约 10 s）从 2.0 m s 的顺风中获益更多，获益比例为 69%。为每个项目提供了实用公式。这些公式可以方便地用于校正风速，从而更好地进行人才选拔和锦标赛选拔。本研究通过可自由获取的数据证明了通过实证回答科学问题的有效性。
