Bayesian approaches for critical velocity modelling of data from intermittent efforts

Abstract

In sports science, critical power and related critical velocity models have been widely investigated, and are being increasingly applied to field-based team sports. A challenge associated with these models is that laboratory experiments that yield accurate measurements of maximal sustainable velocity are expensive. Alternatively, inexpensive field data (from training and matches) are being used to fit such models. However, the intermittent nature of efforts in field-based sports implies that the dependent variable concerning maximum sustainable velocity is reliably calibrated only for short time durations. This paper develops methods where field data based on short time durations is combined with prior knowledge to fit the three-parameter critical velocity model. This is accomplished in a Bayesian framework for which Markov chain methods are required for model fitting and inference.