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Bayesian modelling of elite sporting performance with large databases

Griffin, James; Hinoveanu, Laurentiu; Hopker, James; (2022) Bayesian modelling of elite sporting performance with large databases. Journal of Quantitative Analysis in Sports , 18 (4) pp. 253-267. 10.1515/jqas-2021-0112. Green open access

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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.

Type: Article
Title: Bayesian modelling of elite sporting performance with large databases
Open access status: An open access version is available from UCL Discovery
DOI: 10.1515/jqas-2021-0112
Publisher version: https://doi.org/10.1515/jqas-2021-0112
Language: English
Additional information: © 2022 the author(s), published by De Gruyter, Berlin/Boston This work is licensed under the Creative Commons Attribution 4.0 International License.
Keywords: Bayesian variable selection; longitudinal models; Markov chain Monte Carlo; performance monitoring; skew t distribution
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10160007
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