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。

足球比赛结果预测的机器学习

Machine Learning for Soccer Match Result Prediction

Competitive online games use rating systems for matchmaking;
progression-based algorithms that estimate the skill level of players with
interpretable ratings in terms of the outcome of the games they played.
However, the overall experience of players is shaped by factors beyond the sole
outcome of their games. In this paper, we engineer several features from
in-game statistics to model players and create ratings that accurately
represent their behavior and true performance level. We then compare the
estimating power of our behavioral ratings against ratings created with three
mainstream rating systems by predicting rank of players in four popular game
modes from the competitive shooter genre. Our results show that the behavioral
ratings present more accurate performance estimations while maintaining the
interpretability of the created representations. Considering different aspects
of the playing behavior of players and using behavioral ratings for matchmaking
can lead to match-ups that are more aligned with players' goals and interests,
consequently resulting in a more enjoyable gaming experience.

研究比较了行为分级系统和三种主流分级系统对玩家实际表现的预测能力，并证明了使用行为分级系统进行配对可以实现更好的游戏体验。

竞技在线射击游戏中的行为玩家评级

Behavioral Player Rating in Competitive Online Shooter Games

The majority of online reviews consist of plain-text feedback together with a
single numeric score. However, there are multiple dimensions to products and
opinions, and understanding the `aspects' that contribute to users' ratings may
help us to better understand their individual preferences. For example, a
user's impression of an audiobook presumably depends on aspects such as the
story and the narrator, and knowing their opinions on these aspects may help us
to recommend better products. In this paper, we build models for rating systems
in which such dimensions are explicit, in the sense that users leave separate
ratings for each aspect of a product. By introducing new corpora consisting of
five million reviews, rated with between three and six aspects, we evaluate our
models on three prediction tasks: First, we use our model to uncover which
parts of a review discuss which of the rated aspects. Second, we use our model
to summarize reviews, which for us means finding the sentences that best
explain a user's rating. Finally, since aspect ratings are optional in many of
the datasets we consider, we use our model to recover those ratings that are
missing from a user's evaluation. Our model matches state-of-the-art approaches
on existing small-scale datasets, while scaling to the real-world datasets we
introduce. Moreover, our model is able to `disentangle' content and sentiment
words: we automatically learn content words that are indicative of a particular
aspect as well as the aspect-specific sentiment words that are indicative of a
particular rating.

本文提出了基于多层次评价维度的评分系统，并通过介绍由包含 500 万个评论的新语料库建立的三个预测任务的数据集，证明了该模型的可行性及在现实世界数据上的可扩展性。此外，该模型能够自动学习与特定方面相关的内容词和情感词，从而将评论文本中的内容和情感信息分离开来。