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Temporal Graph Network Framework for Quantifying Pass Reception Probabilities Against Defensive Structures
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DOI：10.1007/s10994-025-06935-6.png)
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Abstract
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Passing decisions in soccer are heavily influenced by the opposing team’s defensive organization. Existing approaches often decompose the problem into pass selection and success probabilities, sometimes incorporating pressure-related features to account for defensive constraints. In this study, we propose a framework for evaluating passing decisions against defensive structures using temporal graph networks (TGNs). Rather than separately modeling selection and success, we estimate the probability of a pass being received by each teammate or intercepted by an opponent, leveraging temporal, spatial, and relational data to capture dynamic interactions. We focus on forward passes originating in the middle third of the pitch, where teams frequently encounter structured defensive shapes. Specifically, we analyze passes that (1) bypass defensive lines, (2) reach teammates inside defensive structure, or (3) penetrate, exit, and reach teammates outside defensive structure. Our evaluation compares pass reception prediction accuracy against baselines, examines receiver availability against defensive structure, and assesses the situational value of passing options. The results suggest that TGNs improve pass probability estimates while offering practical insights for decision-making against organized defenses.
Keywords:
Soccer analytics
Temporal graph network
Deep learning
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