The expected possession value (EPV) of a soccer possession represents the likelihood of a team scoring or conceding the next goal at any time instance. In this work, we develop a comprehensive analysis framework for the EPV, providing soccer practitioners with the ability to evaluate the impact of observed and potential actions, both visually and analytically. The EPV expression is decomposed into a series of subcomponents that model the influence of passes, ball drives and shot actions on the expected outcome of a possession. We show we can learn from spatiotemporal tracking data and obtain calibrated models for all the components of the EPV. For the components related with passes, we produce visually-interpretable probability surfaces from a series of deep neural network architectures built on top of flexible representations of game states. Additionally, we present a series of novel practical applications providing coaches with an enriched interpretation of specific game situations. This is, to our knowledge, the first EPV approach in soccer that uses this decomposition and incorporates the dynamics of the 22 players and the ball through tracking data.

足球控球的预期控球价值 (EPV) 代表球队在任何时间点进球或失球的可能性。在这项工作中，我们为 EPV 开发了一个综合分析框架，使足球从业者能够以视觉和分析的方式评估观察到的和潜在的行为的影响。EPV 表达式被分解为一系列子组件，这些子组件模拟了传球、传球和射门动作对控球预期结果的影响。我们展示了我们可以从时空跟踪数据中学习并获得 EPV 的所有组件的校准模型。对于与通行证相关的组件，我们从一系列建立在游戏状态灵活表示之上的深度神经网络架构中生成视觉上可解释的概率表面。此外，我们展示了一系列新颖的实际应用，为教练提供了对特定比赛情况的丰富解释。据我们所知，这是第一个使用这种分解并通过跟踪数据结合 22 名球员和球的动态的足球 EPV 方法。