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

TL;DR

This paper introduces a comprehensive framework for evaluating the expected value of soccer possessions at a granular level, utilizing deep neural networks to analyze spatiotemporal data and assist coaching decisions.

Contribution

It develops a calibrated, multi-component model of EPV, including new methods for visualizing potential passes and analyzing game situations.

Findings

Calibrated models for all EPV components.

Deep neural networks produce interpretable probability surfaces.

Practical applications enhance coaching insights.

Abstract

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.