Abstract We consider the task of determining the number of chances a soccer team creates, along with the composite nature of each chance—the players involved and the locations on the pitch of the assist and the chance. We infer this information using data consisting solely of attacking events, which the authors believe to be the first approach of its kind. We propose an interpretable Bayesian inference approach and implement a Poisson model to capture chance occurrences, from which we infer team abilities. We then use a Gaussian mixture model to capture the areas on the pitch a player makes an assist/takes a chance. This approach allows the visualization of differences between players in the way they approach attacking play (making assists/taking chances). We apply the resulting scheme to the 2016/2017 English Premier League, capturing team abilities to create chances, before highlighting key areas where players have most impact.

中文翻译：

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通过进攻事件可视化球队在足球比赛中的进球机会：贝叶斯推理方法。

摘要我们考虑确定足球队创造的机会数的任务，以及每种机会的综合性质-所涉及的球员以及助攻和机会在球场上的位置。我们使用仅由攻击事件组成的数据来推断此信息，作者认为这是同类方法中的第一种。我们提出了一种可解释的贝叶斯推理方法，并实现了泊松模型来捕获偶然事件，由此我们可以推断出团队的能力。然后，我们使用高斯混合模型来捕获玩家协助/抓住机会的球场区域。这种方法可以可视化玩家在进攻比赛中的差异（助攻/抓住机会）。我们将得出的方案应用于2016/2017英超联赛，