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Data Science advances in sports commonly involve "big data", i.e., largesport-related data sets. However, such big data sets are not always available,necessitating specialized models that apply to relatively few observations. Oneimportant area of sport-science research that features small data sets is thestudy of recovery from exercise. In this area, models are typically fitted todata collected from exhaustive exercise test protocols, which athletes canperform only a few times. Recent findings highlight that established recoverysuch as the so-called work-balance models are too simple to adequately fitobserved trends in the data. Therefore, we investigated a hydraulic model thatrequires the same few data points as work-balance models to be applied, butpromises to predict recovery dynamics more accurately.  To compare the hydraulic model to established work-balance models, weretrospectively applied them to data compiled from published studies. In total,one hydraulic model and three work-balance models were compared on dataextracted from five studies. The hydraulic model outperformed establishedwork-balance models on all defined metrics, even those that penalize modelsfeaturing higher numbers of parameters. These results incentivize furtherinvestigation of the hydraulic model as a new alternative to establishedperformance models of energy recovery.

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