Although player and ball tracking data is fast becoming the norm in professional sports, large-scale mining of spatiotemporal data has yet to surface. In this paper, given an entire season’s worth of player and ball tracking data from a professional soccer league (≈400,000,000 data points), we present a method which can conduct both individual player and team analysis. Due to the dynamic, continuous and multi-player/team nature of team sports like soccer, a major issue is aligning player positions over time. We show that our “role- based” representation that dynamically updates each player’s relative position at each frame captures the short-term context to enable both individual player and team analysis, in addition to providing extremely accurate and quick retrieval and clustering performance compared to density-based, greedy and exhaustive methods. We discover role directly from data by utilizing a minimum entropy data partitioning method.
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