VERSA: Verified Event Data Format for Reliable Soccer Analytics
Geonhee Jo, Mingu Kang, Kangmin Lee, Minho Lee, Pascal Bauer, Sang-Ki Ko

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSoftware System Performance and Reliability · Time Series Analysis and Forecasting · Sports Analytics and Performance
