Bayesian modelling of elite sporting performance with large databases

Griffin Jim E. (), Hinoveanu Laurenţiu C. () and Hopker James G. ()
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Griffin Jim E.: Department of Statistical Science, University College London, London, UK
Hinoveanu Laurenţiu C.: School of Sport and Exercise Sciences, University of Kent, Canterbury, UK
Hopker James G.: School of Sport and Exercise Sciences, University of Kent, Canterbury, UK

Journal of Quantitative Analysis in Sports, 2022, vol. 18, issue 4, 253-268

Abstract: The availability of large databases of athletic performances offers the opportunity to understand age-related performance progression and to benchmark individual performance against the World’s best. We build a flexible Bayesian model of individual performance progression whilst allowing for confounders, such as atmospheric conditions, and can be fitted using Markov chain Monte Carlo. We show how the model can be used to understand performance progression and the age of peak performance in both individuals and the population. We apply the model to both women and men in 100 m sprinting and weightlifting. In both disciplines, we find that age-related performance is skewed, that the average population performance trajectories of women and men are quite different, and that age of peak performance is substantially different between women and men. We also find that there is substantial variability in individual performance trajectories and the age of peak performance.

Keywords: Bayesian variable selection; longitudinal models; Markov chain Monte Carlo; performance monitoring; skew t distribution (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/jqas-2021-0112

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