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.

机器学习在足球比赛结果预测中已成为一种常见方法，本章讨论了该领域中可用的数据集、模型类型和特征以及评估模型性能的方法。研究发现，目前基于渐变提升树模型（如 CatBoost）和特定于足球比赛的评分（如 pi - 评分）的机器学习模型在只包含进球作为比赛特征的数据集上表现最佳，但需要更详细比较深度学习模型和随机森林在不同类型特征数据集上的性能。此外，可以进一步研究新的评分系统，其中包括球员和团队级别信息，并融合来自时空追踪和事件数据的附加信息。最后，需要提高比赛结果预测模型的可解释性，以使其对团队管理更有用。