Modelling and predicting adversarial behaviour using large amounts of spatiotemporal data
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Xinyu Wei Thesis (PDF 27MB) |
Description
This research represents pioneering work to exploit new and rich data from tracking system to model player behaviour in sports. Novel methods for understanding and predicting player behaviour were proposed. The key contribution is the development of an algorithm that capture the “style” of players from trajectory data. Experimental results show improved prediction performance in various sports including tennis, basketball and soccer.
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| ID Code: | 101959 |
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| Item Type: | QUT Thesis (PhD) |
| Supervisor: | Sridharan, Sridha & Fookes, Clinton B. |
| Keywords: | spatiotemporal data analysis, event forecasting, style, sports, data mining, basketball tracking, hawkeye, tennis prediction, prediction, adversarial behaviour |
| Divisions: | Past > QUT Faculties & Divisions > Science & Engineering Faculty Past > Schools > School of Electrical Engineering & Computer Science |
| Institution: | Queensland University of Technology |
| Deposited On: | 12 Dec 2016 06:51 |
| Last Modified: | 08 Sep 2017 14:51 |
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