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The expected possession value (EPV) of a soccer possession represents thelikelihood of a team scoring or receiving the next goal at any time instance.By decomposing the EPV into a series of subcomponents that are estimatedseparately, we develop a comprehensive analysis framework providing soccerpractitioners with the ability to evaluate the impact of both observed andpotential actions. We show we can obtain calibrated models for all thecomponents of EPV, including a set of yet-unexplored problems in soccer. Weproduce visually-interpretable probability surfaces for potential passes from aseries of deep neural network architectures that learn from low-levelspatiotemporal data. Additionally, we present a series of novel practicalapplications providing coaches with an enriched interpretation of specific gamesituations.

A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions