  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 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
such as the so-called work-balance 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 work-balance models to be applied, but
promises to predict recovery dynamics more accurately.
  To compare the hydraulic model to established work-balance models, we
retrospectively applied them to data compiled from published studies. In total,
one hydraulic model and three work-balance models were compared on data
extracted from five studies. The hydraulic model outperformed established
work-balance 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.
