The analysis of high-intensity runs (or sprints) in soccer has long been a topic of interest for sports science researchers and practitioners. In particular, recent studies suggested contextualizing sprints based on their tactical purposes to better understand the physical-tactical requirements of modern match-play. However, they have a limitation in scalability, as human experts have to manually classify hundreds of sprints for every match. To address this challenge, this paper proposes a deep learning framework for automatically classifying sprints in soccer into contextual categories. The proposed model covers the permutation-invariant and sequential nature of multi-agent trajectories in soccer by deploying Set Transformers and a bidirectional GRU. We train the model with category labels made through the collaboration of human annotators and a rule-based classifier. Experimental results show that our model classifies sprints in the test dataset into 15 categories with the accuracy of 77.65%, implying the potential of the proposed framework for facilitating the integrated analysis of soccer sprints at scale.

翻译：足球中的高强度跑动（或冲刺）分析长期以来一直是体育科学研究者和从业者关注的话题。特别是，近期研究建议根据冲刺的战术目的对其进行情境化分类，以更好地理解现代比赛对体能-战术的综合要求。然而，这些方法在可扩展性上存在局限，因为每场比赛都需要由专家手动分类数百次冲刺。为应对这一挑战，本文提出了一种深度学习框架，用于将足球比赛中的冲刺自动分类至情境化类别。所提出的模型通过部署集合变换器（Set Transformers）和双向门控循环单元（GRU），覆盖了足球比赛中多智能体轨迹的置换不变性与时序特性。我们使用由人工标注员与基于规则的分类器协作生成的类别标签对模型进行训练。实验结果表明，我们的模型在测试数据集中将冲刺分类至15个类别的准确率达到77.65%，这证明了所提框架在推动大规模足球冲刺综合分析方面的潜力。