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Bayesian modelling of elite sporting performance with large databases

Author

Listed:
  • 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)

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.

Suggested Citation

  • Griffin Jim E. & Hinoveanu Laurenţiu C. & Hopker James G., 2022. "Bayesian modelling of elite sporting performance with large databases," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 18(4), pages 253-268, December.
    • Handle: RePEc:bpj:jqsprt:v:18:y:2022:i:4:p:253-268:n:3
    DOI: 10.1515/jqas-2021-0112
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    References listed on IDEAS

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