Supporting athlete selection and strategic planning in track cycling omnium: A statistical and machine learning approach

Ofoghi, Bahadorreza, Zeleznikow, John, MacMahon, Clare and Dwyer, Dan 2013, Supporting athlete selection and strategic planning in track cycling omnium: A statistical and machine learning approach, Information Sciences, vol. 233, pp. 200-213.

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Title Supporting athlete selection and strategic planning in track cycling omnium: A statistical and machine learning approach
Author(s) Ofoghi, Bahadorreza
Zeleznikow, John
MacMahon, Clare
Dwyer, Dan
Journal name Information Sciences
Volume number 233
Start page 200
End page 213
Total pages 14
Publisher Elsevier Inc
Place of publication Philadelphia, PA
Publication date 2013-06
ISSN 0020-0255
1872-6291
Keyword(s) Decision support
Track cycling omnium
Statistical analysis
Machine learning
Bayesian network
Summary This article describes the implementation of machine learning techniques that assist cycling experts in the crucial decision-making processes for athlete selection and strategic planning in the track cycling omnium. The omnium is a multi-event competition that was included in the Olympic Games for the first time in 2012. Presently, selectors and cycling coaches make decisions based on experience and opinion. They rarely have access to knowledge that helps predict athletic performances. The omnium presents a unique and complex decision-making challenge as it is not clear what type of athlete is best suited to the omnium (e.g., sprint or endurance specialist) and tactical decisions made by the coach and athlete during the event will have significant effects on the overall performance of the athlete. In the present work, a variety of machine learning techniques were used to analyze omnium competition data from the World Championships since 2007. The analysis indicates that sprint events have slightly more influence in determining the medalists, than endurance-based events. Using a probabilistic analysis, we created a model of performance prediction that provides an unprecedented level of supporting information that assists coaches with strategic and tactical decisions during the omnium.
Language eng
Field of Research 110699 Human Movement and Sports Science not elsewhere classified
Socio Economic Objective 970111 Expanding Knowledge in the Medical and Health Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2013, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058764

Document type: Journal Article
Collections: Faculty of Health
School of Exercise and Nutrition Sciences
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Created: Thu, 05 Dec 2013, 10:39:55 EST by Dan Dwyer

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