Objectively quantifying the value of player actions in football (soccer) is a challenging problem. To date, studies in football analytics have mainly focused on the attacking side of the game, while there has been less work on event-driven metrics for valuing defensive actions (e.g., tackles and interceptions). Therefore in this paper, we use deep learning techniques to define a novel metric that values such defensive actions by studying the threat of passages of play that preceded them. By doing so, we are able to value defensive actions based on what they prevented from happening in the game. Our Defensive Action Expected Threat (DAxT) model has been validated using real-world event-data from the 2017/2018 and 2018/2019 English Premier League seasons, and we combine our model outputs with additional features to derive an overall rating of defensive ability for players. Overall, we find that our model is able to predict the impact of defensive actions allowing us to better value defenders using event-data.

本文通过采用深度学习技术，利用相关比赛数据定义了一种新的度量方式——Defensive Action Expected Threat (DAxT)模型, 该模型能够通过研究防守动作前的比赛进攻路径危险度，从而对防守动作进行评估，提高防守球员的价值。我们通过在实际英超联赛中的数据验证，以及与其他特征的结合，得出了防守球员的综合评分。总之，本研究在防守行动的价值评估方面提供了新的方法和技术。

深度学习在足球比赛事件数据中价值防守动作的应用