Large-scale analysis of soccer matches using spatiotemporal tracking data
(Großumfängliche Analyse von Fußballspielen mithilfe von räumlich-zeitlichen Trackingdaten)
Although the collection of player and ball tracking data is fast becoming the norm in professional sports, large-scale mining of such 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 nature of team sports like soccer, a major issue is aligning player positions over time. We present a "role-based" representation that dynamically updates each player's relative role at each frame and demonstrate how this captures the short-term context to enable both individual player and team analysis. We discover role directly from data by utilizing a minimum entropy data partitioning method and show how this can be used to accurately detect and visualize formations, as well as analyze individual player behavior.
© Copyright 2014 IEEE International Conference on Data Mining (ICDM). Veröffentlicht von IEEE. Alle Rechte vorbehalten.
Schlagworte: | mathematisch-logisches Modell Simulation Fußball Spielhandlung Analyse Motion Capturing |
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Notationen: | Spielsportarten Naturwissenschaften und Technik |
Tagging: | Big Data |
DOI: | 10.1109/ICDM.2014.133 |
Veröffentlicht in: | IEEE International Conference on Data Mining (ICDM) |
Veröffentlicht: |
Shenzhen
IEEE
2014
|
Seiten: | 725-730 |
Dokumentenarten: | Kongressband, Tagungsbericht |
Sprache: | Englisch |
Level: | hoch |