  We address the problem of unsupervised classification of players in a team
sport according to their team affiliation, when jersey colours and design are
not known a priori. We adopt a contrastive learning approach in which an
embedding network learns to maximize the distance between representations of
players on different teams relative to players on the same team, in a purely
unsupervised fashion, without any labelled data. We evaluate the approach using
a new hockey dataset and find that it outperforms prior unsupervised approaches
by a substantial margin, particularly for real-time application when only a
small number of frames are available for unsupervised learning before team
assignments must be made. Remarkably, we show that our contrastive method
achieves 94% accuracy after unsupervised training on only a single frame, with
accuracy rising to 97% within 500 frames (17 seconds of game time). We further
demonstrate how accurate team classification allows accurate team-conditional
heat maps of player positioning to be computed.
