Predicting Football Team Performance with Explainable AI: Leveraging SHAP to Identify Key Team-Level Performance Metrics

Serafeim Moustakidis, Spyridon Plakias, Christos Kokkotis, Themistoklis Tsatalas and Dimitrios Tsaopoulos ()
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Serafeim Moustakidis: AIDEAS OÜ, 10117 Tallinn, Estonia
Spyridon Plakias: Department of Physical Education and Sport Science, University of Thessaly, 42100 Trikala, Greece
Christos Kokkotis: Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece
Themistoklis Tsatalas: Department of Physical Education and Sport Science, University of Thessaly, 42100 Trikala, Greece
Dimitrios Tsaopoulos: Institute for Bio-Economy and Agri-Technology, Center for Research and Technology Hellas, 38333 Volos, Greece

Future Internet, 2023, vol. 15, issue 5, 1-18

Abstract: Understanding the performance indicators that contribute to the final score of a football match is crucial for directing the training process towards specific goals. This paper presents a pipeline for identifying key team-level performance variables in football using explainable ML techniques. The input data includes various team-specific features such as ball possession and pass behaviors, with the target output being the average scoring performance of each team over a season. The pipeline includes data preprocessing, sequential forward feature selection, model training, prediction, and explainability using SHapley Additive exPlanations (SHAP). Results show that 14 variables have the greatest contribution to the outcome of a match, with 12 having a positive effect and 2 having a negative effect. The study also identified the importance of certain performance indicators, such as shots, chances, passing, and ball possession, to the final score. This pipeline provides valuable insights for coaches and sports analysts to understand which aspects of a team’s performance need improvement and enable targeted interventions to improve performance. The use of explainable ML techniques allows for a deeper understanding of the factors contributing to the predicted average team score performance.

Keywords: soccer; machine learning; explainability; performance metrics (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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