Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data
نویسندگان
چکیده
Abstract Subjective wellness data can provide important information on the well-being of athletes and be used to maximize player performance detect prevent against injury. Wellness data, which are often ordinal multivariate, include metrics relating physical, mental, emotional status athlete. Training recovery have significant short- long-term effects athlete wellness, these vary across individual. We develop a joint multivariate latent factor model for response investigate training wellness. use distributed lag capture cumulative through time. Current efforts using subjective averaged over create univariate summary however this approach mask in data. Our leverages each variable identify relative importance monitoring The is applied professional referee daily training, collected two Major League Soccer seasons.
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ژورنال
عنوان ژورنال: Journal of Quantitative Analysis in Sports
سال: 2021
ISSN: ['2194-6388', '1559-0410']
DOI: https://doi.org/10.1515/jqas-2020-0051