Data Mining in Elite Sports: A Review and a Framework |
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| Authors: | Bahadorreza Ofoghi John Zeleznikow Clare MacMahon Markus Raab |
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| Institution: | 1. Institute of Sport, Exercise, and Active Living, Victoria University Melbourne, Victoria, Australia Victorian Institute of Sport , Albert Park , Victoria , Australia;2. Victorian Institute of Sport , Albert Park , Victoria , Australia bahadorreza.ofoghi@vu.edu.au;4. Institute of Sport, Exercise, and Active Living, and School of Management and Information Systems, Victoria University , Melbourne , Victoria , Australia;5. Institute of Psychology, German Sport University Cologne , Cologne , Germany |
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| Abstract: | Sophisticated data analytical methods such as data mining, where the focus is upon exploration and developing new insights, are becoming increasingly useful tools in analysing elite sports performance data and supporting decision making that is crucial to gaining success. In this article, we investigate the different data mining demands of elite sports with respect to a number of features that describe sport competitions. The aim is to more structurally connect the sports and data mining domains through: (a) describing a framework for categorizing elite sports, and (b) understanding the analytical demands of different performance analysis problems. Therefore, we review different aspects such as sport categories and performance analysis requirements that influence each stage in sports data mining. We also present a model bringing together performance analysis requirements, data mining methods, data mining techniques, and technique characteristics. This will assist both data scientists and sport professionals to more effectively collaborate and contribute to success in elite sport events. |
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| Keywords: | data mining elite sport performance analysis |
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