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

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 co...

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Bibliographic Details
Published in:Decision analytics journal Vol. 15; p. 100568
Main Authors: Odet, Arnaud, Bechard, Thomas, Moretto, Pierre, Dejean, Sebastien, Pasquaretta, Cristian
Format: Journal Article
Language:English
Published: Elsevier Inc 01.06.2025
Elsevier
Subjects:
Cognitive science
Engineering Sciences
Explainable artificial intelligence
Life Sciences
Machine learning
Match prediction
Performance analytics
Physics
Team evaluation
Performance analytics
Explainable artificial intelligence
Match prediction
Team evaluation
Machine learning
ISSN:2772-6622, 2772-6622
Online Access:Get full text
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Summary: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. •Develop a machine learning model to predict Rugby Union match outcomes.•Apply explainability techniques to interpret model predictions.•Identify key performance indicators influencing match results.•Assess team strengths and weaknesses using data analytics.•Provide diagnostic insights to support coaching decisions.
ISSN:2772-6622
2772-6622
DOI:10.1016/j.dajour.2025.100568