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Bayesian approach to quantify morphological impact on performance in international elite freestyle swimming
  1. Robin Pla1,2,
  2. Arthur Leroy2,3,
  3. Romain Massal2,3,
  4. Maxime Bellami2,
  5. Fatima Kaillani2,3,
  6. Philippe Hellard1,2,
  7. Jean-François Toussaint2,3,
  8. Adrien Sedeaud2,3
  1. 1French Swimming Federation, Clichy, France
  2. 2'Institut de Recherche bio-Médicale et d'Epidémiologie du Sport, Paris, France
  3. 3Université Paris Descartes, Paris, Île-de-France, France
  1. Correspondence to Dr Robin Pla; robinpla38{at}gmail.com

Abstract

Objectives The purpose of this study was to quantify the impact of morphological characteristics on freestyle swimming performance by event and gender.

Design Height, mass, body mass index (BMI) and speed data were collected for the top 100 international male and female swimmers from 50 to 1500 m freestyle events for the 2000–2014 seasons.

Methods Several Bayesian hierarchical regressions were performed on race speed with height, mass and BMI as predictors. Posterior probability distributions were computed using Markov chain Monte Carlo algorithms.

Results Regression results exhibited relationships between morphology and performance for both genders and all race distances. Height was always positively correlated with speed with a 95% probability. Conversely, mass plays a different role according to the context. Heavier profiles seem favourable on sprint distances, whereas mass becomes a handicap as distance increases. Male and female swimmers present several differences on the influence of morphology on speed, particularly about the mass. Best morphological profiles are associated with a gain of speed of 0.7%–3.0% for men and 1%–6% for women, depending on race distance. BMI has been investigated as a predictor of race speed but appears as weakly informative in this context.

Conclusion Morphological indicators such as height and mass strongly contribute to swimming performance from sprint to distance events, and this contribution is quantified for each race distance. These profiles may help swimming federations to detect athletes and drive them to compete in specific distances according to their morphology.

  • morphology
  • swimming
  • performance
  • Bayesian regression
  • talent identification

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Footnotes

  • Twitter @RobiinRoad

  • Contributors AL, AS and J-FT conceived the idea. AL developed the theory and performed the computations. FK, MB and RM helped with the data collection and the statistical analysis. AS encouraged RP to investigate the findings of this work. RP took the lead in the manuscript and was helped by PH to supervise in the Introduction and Discussion sections. AL wrote the Methods and Results sections. J-FT and AS supervised the project. RP and AL discussed the research direction. All authors discussed the results and commented on the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available upon reasonable request.

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