Physics-Based Modeling of Pass Probabilities in Soccer
Physics-Based Modeling of Pass Probabilities in Soccer
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基于物理的足球传球概率建模
DOI：
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发表时间：
2017
期刊：
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通讯作者：
Paul Pop Hudl
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作者：
W. Spearman;A. Basye;G. Dick;Ryan Hotovy;Paul Pop Hudl
在本文中，我们提出了一个模型，足球中的控球的基础上的概念，需要多长时间的球员到达球（时间控制）和需要多长时间的球员控制球（时间控制）。我们使用这个模型来量化给定传球成功的可能性，我们使用2015-2016英超赛季的跟踪和事件数据来确定模型的自由参数。在一个保留的测试集上，该模型正确地预测了接收队的准确率为81%，具体的接收球员的准确率为68%。虽然基于简单的数学概念，但各种现象都是涌现的，例如压力对接球的影响。使用传球概率模型，我们得到了一些创新的新指标，可以用来量化的价值传递和技术的接收器和后卫。在38场比赛的数据集上计算每支球队，发现这些指标与赛季结束时的联赛排名密切相关。我们相信，这个模型和衍生的指标将是有用的赛后分析和球员球探。最后，我们使用应用的方法来计算通过概率来计算一个球场控制功能，可以用来量化和可视化的区域，由每个团队控制的球场。
In this paper, we present a model for ball control in soccer based on the concepts of how long it takes a player to reach the ball (time-to-control) and how long it takes a player to control the ball (time-to-control). We use this model to quantify the likelihood that a given pass will succeed we determine the free parameters of the model using tracking and event data from the 2015-2016 Premier League season. On a reserved test set, the model correctly predicts the receiving team with an accuracy of 81% and the specific receiving player with an accuracy of 68%. Though based on simple mathematical concepts, various phenomena are emergent such as the effect of pressure on receiving a pass. Using the pass probability model, we derive a number of innovative new metrics around passing that can be used to quantify the value of passes and the skill of receivers and defenders. Computed per-team over a 38-game dataset, these metrics are found to correlate strongly with league standing at the end of the season. We believe that this model and derived metrics will be useful for both post-match analysis and player scouting. Lastly, we use apply the approach used to computing passing probabilities to calculate a pitch control function that can be used to quantify and visualize regions of the pitch controlled by each team.