A scoping review of explainable artificial intelligence in sports science

Abstract

Abstract Artificial intelligence (AI) and machine learning (ML) are increasingly being applied in sports science to predict performance, assess injury risk, and support tactical decision-making. However, the opacity of many machine learning (ML) models has led to a growing interest in explainable AI (XAI) methods, which aim to make the decisions of "black box" algorithms more transparent and interpretable. The result of this is an increase in trust, facilitation of model validation, and enablement of more informed decision-making for practitioners and analysts. While XAI is well established in domains like healthcare or finance, its application in sports science remains fragmented and underexplored. This scoping review follows the PRISMA-Sc approach and systematically examines how XAI and interpretable ML techniques have been applied in the context of sports science. Between 2014 and June 2024, a total of 19 studies were identified through a multi-database search and analyzed in terms of sport type, ML model, XAI method, dependent variable, limitations, challenges and future research. The results reveal a clear dominance of SHapley Additive Explanations across multiple disciplines, while other tools such as Gradient-weighted Class Activation Mapping, Individual Conditional Expectation Plots, or domain-specific approaches are rarely used. Visual and rule-based explanation methods were largely absent. The findings highlight a lack of methodological diversity and limited validation of explanations with domain experts or practitioners. To increase real-world impact, future work should explore comparative evaluations of XAI techniques, domain-specific explanation frameworks, and user-centered approaches that align with the needs of coaches, athletes, and analysts.

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