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

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.

Automated summary

Subjective wellness data is crucial for understanding the well-being of athletes and optimizing their performance, especially in terms of training and recovery effects. A multivariate latent factor model was developed to study the impact of training and recovery on athlete wellness. The study found that individual responses to training and recovery can vary, highlighting the importance of using a multivariate approach to monitor athlete wellness.