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.
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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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