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

  • Erin M. Schliep ORCID logo EMAIL logo , Toryn L. J. Schafer and Matthew Hawkey
Published/Copyright: May 12, 2021
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Journal of Quantitative Analysis in Sports
From the journal Journal of Quantitative Analysis in Sports Volume 17 Issue 3

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.

Keywords: Bayesian hierarchical model; latent factor models; MCMC; memory; probit regression
Corresponding author: Erin M. Schliep, Department of Statistics, University of Missouri, Columbia, USA, E-mail:

  1. Author contribution: Schliep led the theoretical development, model specification, and implementation and was the lead author on writing the manuscript. Schafer assisted in exploratory data analysis and preliminary model specification. Hawkey provided the data, as well as the original motivation and context for the work. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/jqas-2020-0051).

Received: 2020-04-25
Revised: 2020-11-29
Accepted: 2021-04-26
Published Online: 2021-05-12
Published in Print: 2021-09-27

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/jqas-2020-0051
Keywords for this article
Bayesian hierarchical model; latent factor models; MCMC; memory; probit regression

Articles in the same Issue

  1. Frontmatter
  2. Research articles
  3. Winning and losing streaks in the National Hockey League: are teams experiencing momentum or are games a sequence of random events?
  4. The middle-seed anomaly: why does it occur in some sports tournaments but not others?
  5. A Skellam regression model for quantifying positional value in soccer
  6. How to extend Elo: a Bayesian perspective
  7. A mixed effects multinomial logistic-normal model for forecasting baseball performance
  8. Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data
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