  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.
