Factors associated with match outcomes in elite European football – insights from machine learning models

AIM To examine the factors affecting European Football match outcomes using machine learning models. METHODS Fixtures of 269 teams competing in the top seven European leagues were extracted (2001/02 to 2021/22, total >61,000 fixtures). We used eXtreme Gradient Boosting (XGBoost) to assess the relationship between result (win, draw, loss) and the explanatory variables. RESULTS The top contributors to match outcomes were travel distance, between-team differences in Elo (with a contribution magnitude to the model half of that of travel distance and match location), and recent domestic performance (with a contribution magnitude of a fourth to a third of that of travel distance and match location), irrespective of the dataset and context analyzed. Contextual factors such as rest days between matches, the number of matches since the managers have been in charge, and match-to-match player rotations were also shown to influence match outcomes; however, their contribution magnitude was consistently 4–8 times smaller than that of the three main contributors mentioned above. CONCLUSIONS Machine learning has proven to provide insightful results for coaches and supporting staff who may use their results to set expectations and adjust their practices in relation to the different contexts examined here.