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

TL;DR

This paper introduces a multivariate latent factor distributed lag model to analyze ordinal wellness data, capturing the cumulative effects of training and recovery on athlete well-being, providing detailed insights beyond univariate summaries.

Contribution

It develops a novel joint multivariate latent factor model with distributed lag components to analyze ordinal wellness data, revealing detailed effects of training and recovery on athletes.

Findings

Model identifies the relative importance of wellness metrics.

Captures cumulative effects of training and recovery over time.

Applied successfully to professional soccer athlete data.

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 athlete daily wellness, training, and recovery data collected across two Major League Soccer seasons.