接下来发生了什么？使用深度学习评估足球事件数据中的防守动作
What Happened Next? Using Deep Learning to Value Defensive Actions in Football Event-Data
摘要：
客观地量化足球运动员行为的价值是一个具有挑战性的问题。到目前为止，足球分析的研究主要集中在比赛的进攻方，而对评估防守行动的事件驱动指标的研究较少（例如，拦截和拦截）。因此，在本文中，我们使用深度学习技术来定义一个新的度量标准，通过研究之前的游戏段落的威胁来评估这种防御行为。通过这样做，我们能够根据他们在游戏中阻止发生的事情来评估防御行为。我们的防守行动预期威胁（DAxT）模型已经使用2017/2018和2018/2019英超联赛赛季的真实事件数据进行了验证，我们将模型输出与其他功能相结合，以获得球员防守能力的整体评级。总的来说，我们发现我们的模型能够预测防御行为的影响，使我们能够更好地使用事件数据来评估防御者。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
