Sports injury surveillance programs have been vital in advancing the understanding of injury epidemiology across various athlete populations. Surveillance-based epidemiological measures of injury occurrence are ubiquitous in the sports medicine literature, and the injury rate is one such commonly used measure. Traditional approaches to calculating injury rates have predominantly relied on frequentist methods, which, while informative, have limitations in addressing certain practical questions. We explore an alternative Bayesian framework for analyzing injury rates, highlighting its potential to enhance sports medicine practice. We delineate the practical implications of adopting a Bayesian approach, contrasting key analytical outputs such as credible intervals with their frequentist counterparts. Through simulated and real-world examples, we demonstrate the types of analyses and inferences that are only possible within this framework. We particularly discuss how Bayesian methods allow for direct calculation of probabilities for specific outcomes and provide intuitive interpretations of uncertainty. We discuss the computational and inferential advantages of the Bayesian approach, illustrating how it can offer more nuanced insights into injury incidence in sport injury epidemiology.
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