Decomposing the Immeasurable Sport: A deep learning expected possession value framework for soccer

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Abstract: What is the right way to think about analytics in soccer? Is the sport about measured events such as passes and goals, possession percentages and traveled distance? While analytical work to date has focused primarily on these isolated aspects of the sport, coaches tend to focus on the broader tactical interplay of all 22 players on the pitch. Specifically, soccer analytics lacks a comprehensive approach that can start to address performance-related questions that are closer to the language of the game. This language pose questions such as “who adds more value?”, “how and where is this value added?”, “are teammates creating valuable space?”, “when and how should a backward pass be taken?”, “how risky is a team attacking strategy?”, “what is a player’s decision-making profile?” – questions currently unanswered in the soccer analytics literature.

We present a model that quantifies the expected outcome of a soccer possession at any time during the possession, driven by a fine-grained evaluation of the full spatio-temporal characteristics of the 22 players and the ball. The model is designed in a decoupled way which provides great interpretative power for both visual and quantitative analysis of game situations, allowing to inspect the potential value of ball drives, shots, or passes to any location. Deep learning-based component models are built to capture the complex intricacies of spatiotemporal tactics, while a high-level stochastic process model fuses each component model together in a cohesive, interpretable way.

Throughout this paper we present a wide set of practical applications that showcase the interpretation capacity of this model.

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