Data Mining in Elite Sports: A Review and a Framework

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Ofoghi, Bahadorreza, Zeleznikow, John ORCID: 0000-0002-8786-2644, MacMahon, Clare and Raab, Markus (2013) Data Mining in Elite Sports: A Review and a Framework. Measurement in Physical Education and Exercise Science, 17 (3). pp. 171-186. ISSN 1091-367X (Print) 1532-7841 (Online)


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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Item type Article
DOI 10.1080/1091367X.2013.805137
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Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 0806 Information Systems
Historical > FOR Classification > 1106 Human Movement and Sports Science
Historical > Faculty/School/Research Centre/Department > School of Management and Information Systems
Historical > Faculty/School/Research Centre/Department > Institute of Sport, Exercise and Active Living (ISEAL)
Keywords data mining, elite sport, performance analysis, Victorian Institute of Sport, VIS, wisdom hierarchy, problem-solving methodology, prediction of events, cluster analysis, relationship modeling, association rule mining, sequential pattern mining
Citations in Scopus 51 - View on Scopus
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