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

Schliep Erin M. (), Schafer Toryn L. J. () and Hawkey Matthew ()
Additional contact information
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

Journal of Quantitative Analysis in Sports, 2021, vol. 17, issue 3, 241-254

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 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jqas-2020-0051 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:jqsprt:v:17:y:2021:i:3:p:241-254:n:6

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jqas/html

DOI: 10.1515/jqas-2020-0051

Access Statistics for this article

Journal of Quantitative Analysis in Sports is currently edited by Mark Glickman

More articles in Journal of Quantitative Analysis in Sports from De Gruyter
Bibliographic data for series maintained by Peter Golla ().