VERSA: Verified Event Data Format for Reliable Soccer Analytics

TL;DR

Versa addresses the reliability problem in soccer event streams by formalizing valid event sequences through a state-transition model and enabling automatic detection and correction of anomalies. The framework unifies heterogeneous data sources into a single Versa representation, improving cross-provider consistency and safeguarding downstream tasks such as VAEP. Empirical results show reduced logical inconsistencies, stronger cross-provider alignment (via Normalized Edit Similarity and VAEP correlations), and superior predictive performance across multiple datasets. The work demonstrates that rigorous data verification can substantially boost the robustness and interpretability of data-driven soccer analytics in practice, across leagues and providers.

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.

Figures (2)

• Figure 1: Event stream verification model using state-transition diagram
• Figure 2: This case illustrates an incorrect event order scenario in which a Block event appears before a Shot event