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Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data

Author

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  • Schliep Erin M.

    (Department of Statistics, University of Missouri, Columbia, USA)

  • Schafer Toryn L. J.

    (Department of Statistics, University of Missouri, Columbia, USA)

  • Hawkey Matthew

    (Victoria University Institute of Sport Exercise and Active Living, Melbourne, Australia)

Abstract

Subjective wellness data can provide important information on the well-being of athletes and be used to maximize player performance and detect and prevent against injury. Wellness data, which are often ordinal and multivariate, include metrics relating to the physical, mental, and emotional status of the athlete. Training and recovery can have significant short- and long-term effects on athlete wellness, and these effects can vary across individual. We develop a joint multivariate latent factor model for ordinal response data to investigate the effects of training and recovery on athlete wellness. We use a latent factor distributed lag model to capture the cumulative effects of training and recovery through time. Current efforts using subjective wellness data have averaged over these metrics to create a univariate summary of wellness, however this approach can mask important information in the data. Our multivariate model leverages each ordinal variable and can be used to identify the relative importance of each in monitoring athlete wellness. The model is applied to professional referee daily wellness, training, and recovery data collected across two Major League Soccer seasons.

Suggested Citation

  • Schliep Erin M. & Schafer Toryn L. J. & Hawkey Matthew, 2021. "Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(3), pages 241-254, September.
    • Handle: RePEc:bpj:jqsprt:v:17:y:2021:i:3:p:241-254:n:6
    DOI: 10.1515/jqas-2020-0051
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    References listed on IDEAS

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    1. Higgs, Megan Dailey & Hoeting, Jennifer A., 2010. "A clipped latent variable model for spatially correlated ordered categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1999-2011, August.
    2. Silvia Cagnone & Cinzia Viroli, 2018. "Multivariate latent variable transition models of longitudinal mixed data: an analysis on alcohol use disorder," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1399-1418, November.
    3. Li C. Liu & Donald Hedeker, 2006. "A Mixed-Effects Regression Model for Longitudinal Multivariate Ordinal Data," Biometrics, The International Biometric Society, vol. 62(1), pages 261-268, March.
    4. Chaubert, F. & Mortier, F. & Saint André, L., 2008. "Multivariate dynamic model for ordinal outcomes," Journal of Multivariate Analysis, Elsevier, vol. 99(8), pages 1717-1732, September.
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