A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions
This framework provides soccer practitioners with a more detailed understanding of game situations and the impact of actions, offering an incremental improvement in sports analytics.
This paper introduces a framework to evaluate the instantaneous expected possession value (EPV) in soccer by decomposing it into subcomponents. The authors developed calibrated models for all EPV components, including previously unexplored problems in soccer, and generated visually-interpretable probability surfaces for potential passes using deep neural networks.
The expected possession value (EPV) of a soccer possession represents the likelihood of a team scoring or receiving the next goal at any time instance. By decomposing the EPV into a series of subcomponents that are estimated separately, we develop a comprehensive analysis framework providing soccer practitioners with the ability to evaluate the impact of both observed and potential actions. We show we can obtain calibrated models for all the components of EPV, including a set of yet-unexplored problems in soccer. We produce visually-interpretable probability surfaces for potential passes from a series of deep neural network architectures that learn from low-level spatiotemporal data. Additionally, we present a series of novel practical applications providing coaches with an enriched interpretation of specific game situations.