Interpretable Athlete Performance Modelling in Collegiate Basketball: A Review of Machine Learning and Computer Vision Methods

Abstract

Rapid advances in strategic decision-making in sports analytics, particularly in basketball, have been driven by data-centric computer vision and machine learning (CV-ML) techniques that aim to optimise player performance, reduce injury risk, and support training and competition strategies. This extensive review synthesises recent advancements in ML and deep learning (DL) models applied to strategic decision-making within the context of collegiate basketball. Following PRISMA guidelines, we systematically reviewed 106 peer-reviewed studies published between 2010 and 2025. Each study was assessed across multiple dimensions-model complexity (e.g., classical ML vs. black-box DL models), feature-set exhaustiveness (e.g., only game statistics vs. multimodal data including internal and external stressors), and outcome utility, encompassing both predictive accuracy and the availability of explainable AI (xAI) outputs. The review concluded that athletic performance modelling in collegiate basketball is most effective when interpretable classical ML models are applied to structured and multimodal datasets, and further enhanced through XAI techniques that improve interpretability, build trust, and offer actionable insights for coaching and decision-making.

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