Evaluating passing decision-making in professional football: An enhanced MPNN approach to Receiver Selection

• URL: http://arxiv.org/abs/2605.25696v1
• Date: Mon, 25 May 2026 10:53:47 GMT
• Title: Evaluating passing decision-making in professional football: An enhanced MPNN approach to Receiver Selection
• Abstract summary: This work introduces a Graph Neural Network framework designed to predict Receiver Selection.<n>A Message-Passing Neural Network (MPNN) has been developed and trained using a combination of tracking data and event data from professional matches.<n>MPNN achieves competitive accuracy in identifying the actual chosen receiver and state-of-the-art accuracy within its top three suggestions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The process of decision-making in football is characterized by a complex interplay between spatial positioning, opponent pressure, and player intent. This work introduces a Graph Neural Network (GNN) framework designed to predict Receiver Selection, the optimal passing target, by modeling on-field interactions as dynamic graphs. Each player is represented as a node with positional and contextual features, while potential passing lines form weighted edges characterized by distance, angle, and pressure metrics. A Message-Passing Neural Network (MPNN) has been developed and trained using a combination of tracking data and event data from professional matches, synchronized through a robust pipeline based on an optimized version of the Needleman-Wunsch Algorithm. The model achieves competitive accuracy in identifying the actual chosen receiver and state-of-the-art accuracy within its top three suggestions. Our model further offers quantification of each option's likelihood, threat, and creativity, enabling performance analysts to evaluate over 1,000 passes in seconds.

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