ELASTIC：足球中事件数据与追踪数据的基于轨迹的同步方法
ELASTIC: Trajectory-Based Synchronization of Event and Tracking Data in Soccer
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- KAIST（韩国科学技术院）
- Fitogether Inc.（Fitogether公司）
- AFC Ajax（阿贾克斯足球俱乐部）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对足球事件与追踪数据对齐差的问题，提出仅用轨迹推断事件时间戳的ELASTIC框架，通过插入虚拟终止事件、改进Needleman-Wunsch算法实现同步，在公开基准上优于现有方法，可提升足球分析效果。
中文摘要 AI 辅助
结合事件数据与追踪数据是现代足球分析的基础，但两类数据极少能良好对齐：人工标注的事件时间戳常错过动作发生的真实时刻，扭曲了下游模型依赖的时空上下文。现有同步方法依赖含噪声的人工标注事件位置，且无法检测球员接球动作，模糊了每位球员获得球权的时刻。为解决这些局限，我们提出ELASTIC（Event-Location-AgnoSTIC同步器），这一框架仅从球员与球的轨迹推断事件的起止时间戳，无需依赖标注的事件位置。为恢复接球动作，ELASTIC在连续事件间插入虚拟终止事件以丰富事件序列，使每个事件的结束能与其开始一同被检测。随后，它提取一组球触合理的稀疏候选帧，并用改进的Needleman-Wunsch算法对齐插入终止事件的序列与候选帧序列。为实现可复现的评估，我们通过在Sportec Open DFL数据集上标注真实时间戳构建了公开基准，在该基准上ELASTIC显著优于现有方法。通过下游任务评估，我们进一步表明，改进的同步效果可转化为足球分析中可测量的提升。源代码与基准可在此https URL获取。
英文摘要
Combining event and tracking data is fundamental to modern soccer analytics, yet the two sources are rarely well aligned: event timestamps recorded by human annotators often miss the true moment of the action, distorting the spatiotemporal context that downstream models rely on. Existing synchronization methods depend on noisy human-annotated event locations and fail to detect ball receptions, obscuring when each player gains ball possession. To address these limitations, we propose ELASTIC (Event-Location-AgnoSTIC synchronizer), a framework that infers the start and end timestamps of events solely from player and ball trajectories, without relying on annotated event locations. To recover ball receptions, ELASTIC enriches the event sequence by inserting virtual termination events between consecutive events, so that the end of each event is detected jointly with its start. It then extracts a sparse set of candidate frames where ball touches are physically plausible, and aligns the termination-inserted event sequence with the candidate-frame sequence using an extended Needleman-Wunsch algorithm. For reproducible evaluation, we construct a publicly available benchmark by annotating ground-truth timestamps on the Sportec Open DFL Dataset, on which ELASTIC substantially outperforms existing methods. Through downstream task evaluation, we further show that improved synchronization translates into measurable gains in soccer analytics. The source code and benchmark are available at https://github.com/hyunsungkim-ds/elastic.git.
