在球场上留下目标：评估足球决策
Leaving Goals on the Pitch: Evaluating Decision Making in Soccer
摘要：
对足球中流行的预期进球（xG）指标的分析表明，（略）少量的高质量尝试可能会比大量的低质量尝试产生更多的进球。这一观察结果推动了射击行为的变化。球队放弃禁区外的射门，希望在以后更接近球门的地方产生更好的射门。本文评估了这种远距离射门的减少是否有道理。因此，我们提出了一个新的通用框架，原因在足球决策结合技术，从机器学习和人工智能（AI）。首先，我们通过从事件流数据中学习马尔可夫决策过程（MDP）来模拟一支球队在两个赛季中的进攻行为。其次，我们使用的推理技术，从人工智能文献验证每个团队的MDP。这使我们能够通过向MDP提出反事实问题来推理某些潜在决策的有效性。我们的主要结论是，如果球队在禁区外的少数特定位置射门次数更多，他们会进更多的球。所提出的框架可以很容易地扩展和应用到分析游戏的其他方面。
英文摘要：
Analysis of the popular expected goals (xG) metric in soccer has determined that a (slightly) smaller number of high-quality attempts will likely yield more goals than a slew of low-quality ones. This observation has driven a change in shooting behavior. Teams are passing up on shots from outside the penalty box, in the hopes of generating a better shot closer to goal later on. This paper evaluates whether this decrease in long-distance shots is warranted. Therefore, we propose a novel generic framework to reason about decision-making in soccer by combining techniques from machine learning and artificial intelligence (AI). First, we model how a team has behaved offensively over the course of two seasons by learning a Markov Decision Process (MDP) from event stream data. Second, we use reasoning techniques arising from the AI literature on verification to each team's MDP. This allows us to reason about the efficacy of certain potential decisions by posing counterfactual questions to the MDP. Our key conclusion is that teams would score more goals if they shot more often from outside the penalty box in a small number of team-specific locations. The proposed framework can easily be extended and applied to analyze other aspects of the game.
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。
