  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.
