We investigate how to learn functions that rate game situations on a soccer pitch according to their potential to lead to successful attacks. We follow a purely data-driven approach using techniques from deep reinforcement learning to valuate multiplayer positionings based on positional data. Empirically, the predicted scores highly correlate with dangerousness of actual situations and show that rating of player positioning without expert knowledge is possible.

中文翻译：

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学习评估足球运动员的位置。

我们研究如何学习根据足球场上导致成功进攻的可能性对比赛情况进行评分的功能。我们采用纯粹的数据驱动方法，使用深度强化学习中的技术根据位置数据评估多人游戏的位置。根据经验，预测分数与实际情况的危险性高度相关，并表明在没有专家知识的情况下对球员位置进行评级是可能的。