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

Abstract

The expected possession value (EPV) of a soccer possession represents the likelihood of a team scoring or conceding the next goal at any time instance. In this work, we develop a comprehensive analysis framework for the EPV, providing soccer practitioners with the ability to evaluate the impact of observed and potential actions, both visually and analytically. The EPV expression is decomposed into a series of subcomponents that model the influence of passes, ball drives and shot actions on the expected outcome of a possession. We show we can learn from spatiotemporal tracking data and obtain calibrated models for all the components of the EPV. For the components related with passes, we produce visually-interpretable probability surfaces from a series of deep neural network architectures built on top of flexible representations of game states. Additionally, we present a series of novel practical applications providing coaches with an enriched interpretation of specific game situations. This is, to our knowledge, the first EPV approach in soccer that uses this decomposition and incorporates the dynamics of the 22 players and the ball through tracking data.

Valuing Actions and Ranking Hockey Players With Machine Learning

“…The impact metric approach has also been validated in other sports, such as soccer [Decroos et al, 2019, Liu et al, 2020a, Fernández et al, 2021 and basketball [Cervone et al, 2014].…”

Deep Reinforcement Learning in a Racket Sport for Player Evaluation With Technical and Tactical Contexts

“…For example, the state-action value function (Q-function) of a player was estimated using a recurrent neural network [10], [17], which was interpreted using a linear model tree [18]. To evaluate the shooting action of players, researchers investigated the expected possession value [19]- [21], and the value of the space [22], [23] by extending a Voronoi diagram [24]. In team games, DRL was used for estimating the quality of defensive actions in ball-screen defense in basketball [25].…”

Deep Generative Multi-Agent Imitation Model as a Computational Benchmark for Evaluating Human Performance in Complex Interactive Tasks: A Case Study in Football

“…The limitation of the VAEP is that the model results heavily rely on the patterns learned from training data, which causes the potential subjectivity and inaccuracy when analysing player performance in new contexts considering the complexity of football game. The other state-of-the-art metric for evaluating human performance in playing football is the EPV(Expected Possession Value) [Fernández et al, 2019]. For a given game state in the football game, they model the potential value in terms of contribution to scoring from passing, dribbling and shooting.…”