Despite recent advances in AI, event data collection in soccer still relies heavily on labor-intensive manual annotation. Although prior work has explored automatic event detection using player and ball trajectories, ball tracking also remains difficult to scale due to high infrastructural and operational costs. As a result, comprehensive data collection in soccer is largely confined to top-tier competitions, limiting the broader adoption of data-driven analysis in this domain. To address this challenge, this paper proposes PathCRF, a framework for detecting on-ball soccer events using only player tracking data. We model player trajectories as a fully connected dynamic graph and formulate event detection as the problem of selecting exactly one edge corresponding to the current possession state at each time step. To ensure logical consistency of the resulting edge sequence, we employ a Conditional Random Field (CRF) that forbids impossible transitions between consecutive edges. Both emission and transition scores dynamically computed from edge embeddings produced by a Set Attention-based backbone architecture. During inference, the most probable edge sequence is obtained via Viterbi decoding, and events such as ball controls or passes are detected whenever the selected edge changes between adjacent time steps. Experiments show that PathCRF produces accurate, logically consistent possession paths, enabling reliable downstream analyses while substantially reducing the need for manual event annotation. The source code is available at https://github.com/hyunsungkim-ds/pathcrf.git.

翻译：尽管人工智能领域近期取得了显著进展，足球赛事中的事件数据采集仍严重依赖劳动密集型的人工标注。虽然已有研究探索利用球员与足球轨迹进行自动事件检测，但足球追踪技术因高昂的基础设施与运营成本仍难以规模化应用。因此，足球领域的全面数据采集主要局限于顶级赛事，限制了数据驱动分析方法在该领域的广泛普及。为应对这一挑战，本文提出PathCRF框架，该框架仅使用球员追踪数据即可检测持球足球事件。我们将球员轨迹建模为全连接动态图，并将事件检测问题转化为在每个时间步选择恰好对应当前持球状态的一条边。为确保生成边序列的逻辑一致性，我们采用条件随机场（CRF）来禁止连续边之间不可能发生的状态转移。通过基于集合注意力的主干架构生成的边嵌入动态计算发射分数与转移分数。在推理过程中，通过维特比解码获取最可能的边序列，当相邻时间步间选择的边发生变化时，即可检测到控球或传球等事件。实验表明，PathCRF能生成准确且逻辑一致的持球路径，在显著减少人工事件标注需求的同时，支持可靠的下游分析。源代码已发布于https://github.com/hyunsungkim-ds/pathcrf.git。