VERSA: Verified Event Data Format for Reliable Soccer Analytics

2026-01-29

AI Summary

This study addresses the pervasive issue of logical inconsistencies in football event stream data—such as misplaced or missing events—that severely compromise analytical reliability. To this end, we propose VERSA, a novel framework that introduces, for the first time, a systematic validation mechanism grounded in state transition modeling to automatically detect and rectify anomalous event sequences. By integrating event stream validation algorithms with data repair techniques, VERSA substantially enhances both data integrity and cross-provider consistency. Experimental evaluation on K League 1 data reveals that 18.81% of events exhibit logical errors; after VERSA processing, cross-provider consistency improves markedly, and performance on downstream VAEP-based player contribution assessment tasks shows significant gains.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Data Stream Mining
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Event stream data is a critical resource for fine-grained analysis across various domains, including financial transactions, system operations, and sports. In sports, it is actively used for fine-grained analyses such as quantifying player contributions and identifying tactical patterns. However, the reliability of these models is fundamentally limited by inherent data quality issues that cause logical inconsistencies (e.g., incorrect event ordering or missing events). To this end, this study proposes VERSA (Verified Event Data Format for Reliable Soccer Analytics), a systematic verification framework that ensures the integrity of event stream data within the soccer domain. VERSA is based on a state-transition model that defines valid event sequences, thereby enabling the automatic detection and correction of anomalous patterns within the event stream data. Notably, our examination of event data from the K League 1 (2024 season), provided by Bepro, detected that 18.81% of all recorded events exhibited logical inconsistencies. Addressing such integrity issues, our experiments demonstrate that VERSA significantly enhances cross-provider consistency, ensuring stable and unified data representation across heterogeneous sources. Furthermore, we demonstrate that data refined by VERSA significantly improves the robustness and performance of a downstream task called VAEP, which evaluates player contributions. These results highlight that the verification process is highly effective in increasing the reliability of data-driven analysis.

Problem

• event stream data
• data quality
• logical inconsistencies
• soccer analytics
• data integrity

Innovation

• event stream verification
• state-transition model
• data integrity
• soccer analytics
• cross-provider consistency