Physics-Based Modeling of Pass Probabilities in Soccer

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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 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.

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