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.

翻译：事件流数据是金融交易、系统运行及体育等多个领域进行细粒度分析的关键资源。在体育领域，它被积极用于量化球员贡献、识别战术模式等细粒度分析。然而，这些模型的可靠性从根本上受到固有数据质量问题的限制，这些问题会导致逻辑不一致（例如事件顺序错误或事件缺失）。为此，本研究提出VERSA（面向可靠足球分析的可验证事件数据格式），这是一个系统化的验证框架，旨在确保足球领域内事件流数据的完整性。VERSA基于一个状态转移模型，该模型定义了有效的事件序列，从而能够自动检测并纠正事件流数据中的异常模式。值得注意的是，我们对Bepro提供的K League 1（2024赛季）事件数据的检查发现，所有记录事件中有18.81%表现出逻辑不一致。针对此类完整性问题，我们的实验表明，VERSA显著提升了跨数据提供方的一致性，确保了异构来源间稳定且统一的数据表示。此外，我们证明，经VERSA精炼的数据显著提升了名为VAEP的下游任务（用于评估球员贡献）的鲁棒性与性能。这些结果突出表明，该验证过程对于提高数据驱动分析的可靠性极为有效。