Bayesian Model Averaging for Predicting Maximal Oxygen Uptake in Athletes with Non-Exercise Data

Conventionally, non-exercise models to predict maximal oxygen uptake (VO2max) have been built using the classical linear regression approach and frequentist techniques for model selection. However, uncertainty exists in the model selection process. The aim of this study was to develop a non-exercise model to predict VO2max in athletes, considering model uncertainty by means of Bayesian Model Averaging (BMA). A further aim was to evaluate the predictive performance of the BMA in comparison to models derived from standard variable selection techniques. The data comprised 272 observations of the response variable, and records of Sex, Sport, Age, Weight, Height and Body mass index. A categorization of sports was also proposed for inclusion in the model-building process. BMA was applied based on two recognized methods: Occam’s window and Markov Chain Monte Carlo Model Composition. Discordance was evident in variable selection among frequentist procedures. The two BMA strategies yielded comparable results. In agreement with the literature, the BMA showed better out-of-sample predictive performance than the models selected by standard techniques. The categorization of sports revealed consistent results.