A machine learning and explainability-driven methodology for identifying winning strategies in Rugby Union

Abstract

Interest in predicting sports match outcomes has grown significantly, driven by advancements in machine learning techniques and widespread adoption. However, the utilization of these predictive models in enhancing tactical team performance remains relatively limited. We propose a methodology that combines machine learning and algorithm explainability techniques, which were demonstrated through a case study on Rugby Union. Our study unfolds in two phases: first, we identify the most suitable modeling approach for our data by establishing a prediction model based on performance indicators observed during games. Subsequently, we applied an analysis based on SHapley Additive exPlanations (SHAP) values to interpret the predictions of this model. Our findings serve three primary purposes: (i) from a global standpoint, identifying performance indicators that primarily determine match outcomes; (ii) from an aggregated point of view highlighting strengths and weaknesses of any given team; and (iii) from a local perspective, offering technical staff diagnostic analyses of past games.