A hydraulic model outperforms work-balance models for predicting recovery kinetics from intermittent exercise.

Data Science advances in sports commonly involve "big data", i.e., large sport-related data sets. However, such big data sets are not always available, necessitating specialized models that apply to relatively few observations. One important area of sport-science research that features small data sets is the study of energy recovery from exercise. In this area, models are typically fitted to data collected from exhaustive exercise test protocols, which athletes can perform only a few times. Recent findings highlight that established recovery models like W' balance (W'bal) models are too simple to adequately fit observed trends in the data. Therefore, we investigated a hydraulic model that requires the same few data points as W'bal models to be applied, but promises to predict recovery dynamics more accurately. To compare the hydraulic model to established W'bal models, we retrospectively applied them to a compilation of data from published studies. In total, one hydraulic model and three W'bal models were compared on data extracted from five studies. The hydraulic model outperformed established W'bal models on all defined metrics, even those that penalize models featuring higher numbers of parameters. These results incentivize further investigation of the hydraulic model as a new alternative to established performance models of energy recovery.