论文标题
足球分析中的可解释的预期目标模型
Explainable expected goal models for performance analysis in football analytics
论文作者
论文摘要
预期的进球提供了对球队和球员表现的更具代表性的衡量标准，这也适合足球的低分性质，而不是现代足球比赛中得分。比赛的得分涉及随机性，并且通常不能代表球队和球员的表现，因此近年来使用替代统计数据（例如目标，球控球和演练）是很受欢迎的。为了衡量射击的可能性是预期目标的目标，使用了几个功能来训练基于事件并跟踪足球数据的预期目标模型。这些功能的选择，数据的大小和日期以及可能影响模型性能的参数。使用Black-Box机器学习模型来提高模型的预测性能，可降低其可解释性，从而导致可以从模型中收集的信息丢失。本文提出了一个准确的预期目标模型，该模型由2014 - 15年至2020 - 21年欧洲前五名的欧洲足球联赛的七个赛季中的315,430张训练。此外，通过使用可解释的人工智能工具来获得可解释的预期目标模型来评估团队或球员性能的可解释的预期目标模型来解释该模型。据我们所知，这是第一篇论文，该论文展示了可解释的人工智能工具汇总的实际应用，以解释一组观察结果，以监视团队和球员绩效的准确预期目标模型。此外，这些方法可以推广到其他运动分支。
The expected goal provides a more representative measure of the team and player performance which also suit the low-scoring nature of football instead of score in modern football. The score of a match involves randomness and often may not represent the performance of the teams and players, therefore it has been popular to use the alternative statistics in recent years such as shots on target, ball possessions, and drills. To measure the probability of a shot being a goal by the expected goal, several features are used to train an expected goal model which is based on the event and tracking football data. The selection of these features, the size and date of the data, and the model which are used as the parameters that may affect the performance of the model. Using black-box machine learning models for increasing the predictive performance of the model decreases its interpretability that causes the loss of information that can be gathered from the model. This paper proposes an accurate expected goal model trained consisting of 315,430 shots from seven seasons between 2014-15 and 2020-21 of the top-five European football leagues. Moreover, this model is explained by using explainable artificial intelligence tool to obtain an explainable expected goal model for evaluating a team or player performance. To the best of our knowledge, this is the first paper that demonstrates a practical application of an explainable artificial intelligence tool aggregated profiles to explain a group of observations on an accurate expected goal model for monitoring the team and player performance. Moreover, these methods can be generalized to other sports branches.
