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

2026-05-25

AI Summary

This study addresses the complexity of pass decision-making in the dynamic adversarial environment of professional football by proposing a dynamic graph-based approach for predicting passing targets. Players are modeled as nodes and potential passing lanes as edges, integrating features such as position, angle, and defensive pressure. A message-passing neural network (MPNN) is employed to predict the most likely receiver while simultaneously quantifying both the threat and creativity of each pass—a novel contribution. The method innovatively fuses tracking and event data, aligning them precisely via an optimized Needleman–Wunsch algorithm. Experimental results demonstrate state-of-the-art performance in pass target identification, achieving high top-three recommendation accuracy and enabling real-time analysis at over one thousand passes per second, thereby significantly enhancing tactical evaluation efficiency.

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.

Problem

• Receiver Selection
• Passing Decision-Making
• Football
• Graph Neural Network
• Player Interaction

Innovation

• Message-Passing Neural Network
• Receiver Selection
• Dynamic Graph Modeling
• Football Decision-Making
• Needleman-Wunsch Synchronization