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Bayesian modelling of elite sporting performance with large databases

Griffin, Jim E., Hinoveanu, Laurentiu, Hopker, James G. (2023) Bayesian modelling of elite sporting performance with large databases. Journal of Quantitative Analysis in Sports, 18 (4). pp. 253-268. ISSN 1559-0410. (KAR id:98209)

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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.

Item Type: Article
Uncontrolled keywords: Bayesian variable selection; longitudinal models; Markov chain Monte Carlo; performance monitoring; skew t distribution
Subjects: G Geography. Anthropology. Recreation > GV Recreation. Leisure > Sports sciences
Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Natural Sciences > Sport and Exercise Sciences
Funders: [135] University of Kent
Depositing User: James Hopker
Date Deposited: 21 Nov 2022 13:48 UTC
Last Modified: 11 Apr 2023 15:22 UTC
Resource URI: (The current URI for this page, for reference purposes)
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