Bayesian approach to quantify morphological impact on performance in international elite freestyle swimming. Issue 1 (23rd October 2019)

• Record Type: Journal Article
• Authors: Pla, Robin; Leroy, Arthur; Massal, Romain; Bellami, Maxime; Kaillani, Fatima; Hellard, Philippe; Toussaint, Jean-François; Sedeaud, Adrien
• Is Part Of: BMJ open sport & exercise medicine. Volume 5:Issue 1(2019)
• Publication Date: 2019-10-23
• Subjects: morphology -- swimming -- performance -- Bayesian regression -- talent identification; Sports medicine -- Periodicals; Exercise therapy -- Periodicals; 617.102705
• DOI: 10.1136/bmjsem-2019-000543
• Languages: English
• ISSNs: 2055-7647

Abstract

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.