论文标题
分布式滞后模型，以使用多元序数健康数据来确定运动员训练和恢复的累积效应
Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data
论文作者
论文摘要
主观健康数据可以提供有关运动员福祉的重要信息，并用于最大程度地提高球员性能，检测和防止伤害。保健数据通常是序数和多变量，包括与运动员的身体，心理和情感状况有关的指标。培训和恢复可能会对运动员健康产生重大的短期和长期影响，这些影响在各个人之间可能会有所不同。我们为序数响应数据开发了一个联合多元潜在因子模型，以研究训练和恢复对运动员健康的影响。我们使用潜在因子分布式滞后模型来捕获随着时间的推移训练和恢复的累积影响。当前使用主观健康数据的努力对这些指标进行了平均，以创建保健单变量摘要，但是这种方法可以掩盖数据中的重要信息。我们的多元模型利用每个序数变量，可用于确定每个序列在监测运动员健康中的相对重要性。该模型适用于在两个大联盟足球赛季收集的运动员日常健康，训练和恢复数据。
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.
