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.

翻译：对足球运动中流行的预期进球（xG）指标的分析表明，（略）少数高质量射门尝试比大量低质量射门更有可能产生更多进球。这一观察结果已促使射门行为发生改变。球队开始放弃禁区外的射门，以期随后在更靠近球门的位置创造更好的射门机会。本文评估了这种减少远距离射门的做法是否合理。为此，我们提出了一种新颖的通用框架，通过结合机器学习和人工智能（AI）技术来推理足球比赛中的决策制定。首先，我们通过从事件流数据中学习马尔可夫决策过程（MDP），建模一支球队在两个赛季中的进攻行为。其次，我们利用人工智能验证文献中的推理技术对每支球队的MDP进行分析。通过向MDP提出反事实问题，我们能够推理某些潜在决策的有效性。我们的核心结论是：若球队在少数特定区域更频繁地从禁区外射门，将能取得更多进球。所提出的框架可轻松扩展并应用于分析比赛的其他方面。