VERSA: Verified Event Data Format for Reliable Soccer Analytics

TL;DR

This paper introduces VERSA, a verification framework that ensures the integrity of soccer event data by detecting and correcting inconsistencies, thereby improving the reliability of analytics and downstream tasks like player contribution evaluation.

Contribution

VERSA is the first systematic verification framework for soccer event data that uses a state-transition model to automatically detect and correct logical inconsistencies.

Findings

18.81% of recorded events had logical inconsistencies

VERSA improves data consistency across providers

Refined data enhances the robustness of player contribution analysis

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.