Machine Learning for Soccer Match Result Prediction
Rory Bunker Calvin Yeung Keisuke Fujii
Machine learning has become a common approach to predicting the outcomes of
soccer matches, and the body of literature in this domain has grown
substantially in the past decade and a half. This chapter discusses available
datasets, the types of models and features, and ways of evaluating model
performance in this application domain. The aim of this chapter is to give a
broad overview of the current state and potential future developments in
machine learning for soccer match results prediction, as a resource for those
interested in conducting future studies in the area. Our main findings are that
while gradient-boosted tree models such as CatBoost, applied to soccer-specific
ratings such as pi-ratings, are currently the best-performing models on
datasets containing only goals as the match features, there needs to be a more
thorough comparison of the performance of deep learning models and Random
Forest on a range of datasets with different types of features. Furthermore,
new rating systems using both player- and team-level information and
incorporating additional information from, e.g., spatiotemporal tracking and
event data, could be investigated further. Finally, the interpretability of
match result prediction models needs to be enhanced for them to be more useful
for team management.