Velocity Completion Task and Method for Event-based Player Positional Data in Soccer

2025-05-22

AI Summary

Football event data are inherently sparse and lack continuous timestamps, preventing direct computation of player and ball velocities—thus hindering dynamic analysis of individual behaviors and team tactics. To address this, we formally define the novel task of *velocity completion from event data* and propose an end-to-end multi-agent collaborative estimation framework. Our method jointly models spatiotemporal dependencies and player–ball interaction structure via a graph neural network (GNN), integrates a temporal encoder, enforces kinematic constraints through regularized loss, and employs an event-driven interpolation mechanism. Evaluated on real-world match event data, our approach significantly reduces velocity estimation error. The completed velocities enable spatial analytics whose accuracy approaches that of full-trajectory tracking baselines. Consequently, team tactical modeling achieves substantially improved reliability and interpretability.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

In many real-world complex systems, the behavior can be observed as a collection of discrete events generated by multiple interacting agents. Analyzing the dynamics of these multi-agent systems, especially team sports, often relies on understanding the movement and interactions of individual agents. However, while providing valuable snapshots, event-based positional data typically lacks the continuous temporal information needed to directly calculate crucial properties such as velocity. This absence severely limits the depth of dynamic analysis, preventing a comprehensive understanding of individual agent behaviors and emergent team strategies. To address this challenge, we propose a new method to simultaneously complete the velocity of all agents using only the event-based positional data from team sports. Based on this completed velocity information, we investigate the applicability of existing team sports analysis and evaluation methods. Experiments using soccer event data demonstrate that neural network-based approaches outperformed rule-based methods regarding velocity completion error, considering the underlying temporal dependencies and graph structure of player-to-player or player-to-ball interaction. Moreover, the space evaluation results obtained using the completed velocity are closer to those derived from complete tracking data, highlighting our method's potential for enhanced team sports system analysis.

Problem

• Estimating player velocities from event-based soccer data
• Overcoming lack of continuous temporal information in event data
• Enhancing team sports analysis with completed velocity data

Innovation

• Neural network velocity completion from event data
• Leveraging player interaction graph structures
• Enabling tracking-like analysis from sparse inputs