Data-driven quantification of the effect of wind on athletics performance

M. Moinat, O. Fabius, K. S. Emanuel*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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−1 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−1 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.

Original languageEnglish
Pages (from-to)1185-1190
Number of pages6
JournalEuropean Journal of Sport Science
Volume18
Issue number9
DOIs
Publication statusPublished - 21 Oct 2018

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