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Subjective wellness data can provide important information on the well-beingof athletes and be used to maximize player performance and detect and preventagainst injury. Wellness data, which are often ordinal and multivariate,include metrics relating to the physical, mental, and emotional status of theathlete. Training and recovery can have significant short- and long-termeffects on athlete wellness, and these effects can vary across individual. Wedevelop a joint multivariate latent factor model for ordinal response data toinvestigate the effects of training and recovery on athlete wellness. We use alatent factor distributed lag model to capture the cumulative effects oftraining and recovery through time. Current efforts using subjective wellnessdata have averaged over these metrics to create a univariate summary ofwellness, however this approach can mask important information in the data. Ourmultivariate model leverages each ordinal variable and can be used to identifythe relative importance of each in monitoring athlete wellness. The model isapplied to athlete daily wellness, training, and recovery data collected acrosstwo Major League Soccer seasons.

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