IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v18y2022i4p253-268n3.html
   My bibliography  Save this article

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.
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jqas-2021-0112
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/jqas-2021-0112?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Carlos M. Carvalho & Nicholas G. Polson & James G. Scott, 2010. "The horseshoe estimator for sparse signals," Biometrika, Biometrika Trust, vol. 97(2), pages 465-480.
    2. J E Griffin & K G Łatuszyński & M F J Steel, 2021. "In search of lost mixing time: adaptive Markov chain Monte Carlo schemes for Bayesian variable selection with very large p," Biometrika, Biometrika Trust, vol. 108(1), pages 53-69.
    3. Gao Zhenyu & Li Yixing & Wang Zhengxin, 2020. "Restoring the real world records in Men’s swimming without high-tech swimsuits," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(4), pages 291-300, December.
    4. Stephenson Alec G. & Tawn Jonathan A., 2013. "Determining the Best Track Performances of All Time Using a Conceptual Population Model for Athletics Records," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(1), pages 67-76, March.
    5. Brander James A. & Yeung Louisa & Egan Edward J., 2014. "Estimating the effects of age on NHL player performance," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 1-19, June.
    6. Gennaro Boccia & Paolo Moisè & Alberto Franceschi & Francesco Trova & Davide Panero & Antonio La Torre & Alberto Rainoldi & Federico Schena & Marco Cardinale, 2017. "Career Performance Trajectories in Track and Field Jumping Events from Youth to Senior Success: The Importance of Learning and Development," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-15, January.
    7. Santos-Fernandez Edgar & Wu Paul & Mengersen Kerrie L., 2019. "Bayesian statistics meets sports: a comprehensive review," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 289-312, December.
    8. Eduardo Ley & Mark F.J. Steel, 2009. "On the effect of prior assumptions in Bayesian model averaging with applications to growth regression This article was published online on 30 March 2009. An error was subsequently identified. This not," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 651-674.
    9. Egidi Leonardo & Gabry Jonah, 2018. "Bayesian hierarchical models for predicting individual performance in soccer," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(3), pages 143-157, September.
    10. Strand Matthew & Nelson Daniel & Grunwald Gary, 2018. "Modeling between-subject differences and within-subject changes for long distance runners by age," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(2), pages 81-90, June.
    11. Oliver G. Stevenson & Brendon J. Brewer, 2021. "Finding your feet: A Gaussian process model for estimating the abilities of batsmen in test cricket," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 481-506, March.
    12. Wimmer Valentin & Fenske Nora & Pyrka Patricia & Fahrmeir Ludwig, 2011. "Exploring Competition Performance in Decathlon Using Semi-Parametric Latent Variable Models," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(4), pages 1-21, October.
    13. Kovalchik Stephanie Ann & Stefani Ray, 2013. "Longitudinal analyses of Olympic athletics and swimming events find no gender gap in performance improvement," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(1), pages 15-24, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ley, Eduardo & Steel, Mark F.J., 2012. "Mixtures of g-priors for Bayesian model averaging with economic applications," Journal of Econometrics, Elsevier, vol. 171(2), pages 251-266.
    2. Santos-Fernandez Edgar & Wu Paul & Mengersen Kerrie L., 2019. "Bayesian statistics meets sports: a comprehensive review," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 289-312, December.
    3. Sabin R. Paul, 2021. "Estimating player value in American football using plus–minus models," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(4), pages 313-364, December.
    4. Tamal Ghosh & Malay Ghosh & Jerry J. Maples & Xueying Tang, 2022. "Multivariate Global-Local Priors for Small Area Estimation," Stats, MDPI, vol. 5(3), pages 1-16, July.
    5. Martin Feldkircher & Florian Huber & Gary Koop & Michael Pfarrhofer, 2022. "APPROXIMATE BAYESIAN INFERENCE AND FORECASTING IN HUGE‐DIMENSIONAL MULTICOUNTRY VARs," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1625-1658, November.
    6. Loaiza-Maya, Rubén & Smith, Michael Stanley & Nott, David J. & Danaher, Peter J., 2022. "Fast and accurate variational inference for models with many latent variables," Journal of Econometrics, Elsevier, vol. 230(2), pages 339-362.
    7. Doris A. Oberdabernig & Stefan Humer & Jesus Crespo Cuaresma, 2018. "Democracy, Geography and Model Uncertainty," Scottish Journal of Political Economy, Scottish Economic Society, vol. 65(2), pages 154-185, May.
    8. Roman Horvath & Marek Rusnak & Katerina Smidkova & Jan Zapal, 2014. "The dissent voting behaviour of central bankers: what do we really know?," Applied Economics, Taylor & Francis Journals, vol. 46(4), pages 450-461, February.
    9. Brander James A. & Yeung Louisa & Egan Edward J., 2014. "Estimating the effects of age on NHL player performance," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 1-19, June.
    10. Martin Guth, 2022. "Predicting Default Probabilities for Stress Tests: A Comparison of Models," Papers 2202.03110, arXiv.org.
    11. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    12. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    13. Chan, Joshua C.C., 2021. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1212-1226.
    14. Ander Wilson & Brian J. Reich, 2014. "Confounder selection via penalized credible regions," Biometrics, The International Biometric Society, vol. 70(4), pages 852-861, December.
    15. Luca Barbaglia & Lorenzo Frattarolo & Niko Hauzenberger & Dominik Hirschbuehl & Florian Huber & Luca Onorante & Michael Pfarrhofer & Luca Tiozzo Pezzoli, 2024. "Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model," Papers 2401.10054, arXiv.org.
    16. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021. "Economic Predictions With Big Data: The Illusion of Sparsity," Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
    17. Anindya Bhadra, 2022. "Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 235-239, June.
    18. Debamita Kundu & Riten Mitra & Jeremy T. Gaskins, 2021. "Bayesian variable selection for multioutcome models through shared shrinkage," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 295-320, March.
    19. Yi Nengjun & Ma Shuangge, 2012. "Hierarchical Shrinkage Priors and Model Fitting for High-dimensional Generalized Linear Models," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(6), pages 1-25, November.
    20. You Wu & Jeremy Gaskins & Maiying Kong & Susmita Datta, 2018. "Profiling the effects of short time†course cold ischemia on tumor protein phosphorylation using a Bayesian approach," Biometrics, The International Biometric Society, vol. 74(1), pages 331-341, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:jqsprt:v:18:y:2022:i:4:p:253-268:n:3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.