2025-12-09 –, Ernst-Curie
The process of decision-making in football is characterized by a complex interplay between spatial positioning, opponent pressure, and player intent. In this research, we introduce xReceiver, a real-time Graph Neural Network (GNN) framework designed to predict 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. We have developed a Message-Passing Neural Network (MPNN) that is trained using a combination of tracking data and event data from professional matches. Our model achieves 65.22% accuracy in identifying the actual chosen receiver and 95.65% accuracy within its top three suggestions. xReceiver further offers quantification of each option's likelihood, threat, and creativity, enabling performance analysts to evaluate over 1,000 passes in seconds.
During my internship at the Royal Belgian FA, my supervisor received a request from the performance analyst: how can we analyse the quality of passes using AI?
We generally consider a player to be a good passer if most of their passes are successful, but sometimes passing the ball to that player is not the right option if there is someone else who is less marked and in a better position! Can players really identify the right teammate in the right situation?
Outline of what will be discussed during the talk:
- Introduction (2–3 minutes).
- From data to graph through synchronization (5 minutes).
- Graph Neural Networks (5 minutes)
- The xReceiver model (7-8 minutes)
- Applications in Opponent Analysis and Scouting (5 minutes)
Questions and feedback will be welcomed at the end, since these can really help with the improvements of this project.
Fotball Data Scientist/AI Engineer @SportAnalytics
Data Science and AI's Master Student @UNITS
Former Data Science Intern @RBFA