A machine learning approach to predicting winning patterns in track cycling omnium

Ofoghi, Bahadorreza, Zeleznikow, John, MacMahon, Clare and Dwyer, Dan 2010, A machine learning approach to predicting winning patterns in track cycling omnium, Artificial intelligence in theory and practice III : proceedings of the third conference on artificial intelligence, IFIP AI 2010, vol. 331, no. Part III, pp. 67-76.

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Title A machine learning approach to predicting winning patterns in track cycling omnium
Author(s) Ofoghi, Bahadorreza
Zeleznikow, John
MacMahon, Clare
Dwyer, Dan
Journal name Artificial intelligence in theory and practice III : proceedings of the third conference on artificial intelligence, IFIP AI 2010
Volume number 331
Issue number Part III
Start page 67
End page 76
Total pages 10
Publisher Springer
Place of publication Berlin, Germany
Publication date 2010-09
ISSN 1868-4238
Keyword(s) cycling
machine-learning
olympic games
performance patterns
artificial intelligence
thermal conductivity
Summary This paper presents work on using Machine Learning approaches for predicting performance patterns of medalists in Track Cycling Omnium championships. The omnium is a newly introduced track cycling competition to be included in the London 2012 Olympic Games. It involves six individual events and, therefore, requires strategic planning for riders and coaches to achieve the best overall standing in terms of the ranking, speed, and time in each individual component. We carried out unsupervised, supervised, and statistical analyses on the men’s and women’s historical competition data in the World Championships since 2008 to find winning patterns for each gender in terms of the ranking of riders in each individual event. Our results demonstrate that both sprint and endurance capacities are required for both men and women to win a medal in the omnium. Sprint ability is shown to have slightly more influence in deciding the medalists of the omnium competitions.
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 C1.1 Refereed article in a scholarly journal
Copyright notice ©2010, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058762

Document type: Journal Article
Collection: School of Exercise and Nutrition Sciences
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Created: Thu, 05 Dec 2013, 10:35:19 EST by Dan Dwyer

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