Transforming Football Data into Object-centric Event Logs with Spatial Context Information

2025-07-16

AI Summary

Conventional event logs struggle to capture multi-agent coordination and spatiotemporal dynamics in team sports. Method: This paper proposes an object-centric event log modeling framework tailored to football, mapping raw match data—including player/ball tracking trajectories and discrete event records—onto structured logs where players, the ball, and teams serve as first-class objects. Spatial contextual features (e.g., positions, inter-object distances, movement directions) are explicitly embedded to enable fine-grained, process-oriented representation of team coordination. Contribution/Results: It introduces the first systematically constructed and empirically validated spatially enhanced object-centric event log framework in sports analytics, accompanied by the first real-world football object-centric event log dataset—built from multi-season tracking and event data. Experiments demonstrate that this representation significantly improves mining capabilities for tactical patterns, collaborative pathways, and anomalous behaviors, establishing a novel paradigm for process mining in complex, dynamic domains.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Multiagent Systems: Teamwork

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Object-centric event logs expand the conventional single-case notion event log by considering multiple objects, allowing for the analysis of more complex and realistic process behavior. However, the number of real-world object-centric event logs remains limited, and further studies are needed to test their usefulness. The increasing availability of data from team sports can facilitate object-centric process mining, leveraging both real-world data and suitable use cases. In this paper, we present a framework for transforming football (soccer) data into an object-centric event log, further enhanced with a spatial dimension. We demonstrate the effectiveness of our framework by generating object-centric event logs based on real-world football data and discuss the results for varying process representations. With our paper, we provide the first example for object-centric event logs in football analytics. Future work should consider variant analysis and filtering techniques to better handle variability

Problem

• Transform football data into object-centric event logs
• Enhance event logs with spatial context information
• Test usefulness of object-centric logs in sports analytics

Innovation

• Transforms football data into object-centric logs
• Enhances logs with spatial dimension information
• Uses real-world football data for validation