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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. In: Artificial Intelligence in Theory and Practice III : Third IFIP TC 12 International Conference on Artificial Intelligence, IFIP AI 2010, Held as Part of WCC 2010, Brisbane, Australia, September 20-23, 2010. Proceedings. Bramer, Max, ed. IFIP Advances in Information and Communication Technology (331). Springer, Berlin, pp. 67-76.

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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.

Item Type: Book Section
ISBN: 9783642152856 (print), 9783642152863 (online)
Uncontrolled Keywords: ResPubID20891, cycling races, cyclists
Subjects: Current > FOR Classification > 0104 Statistics
Historical > SEO Classification > 970111 Expanding Knowledge in the Medical and Health Sciences
Historical > Faculty/School/Research Centre/Department > Institute of Sport, Exercise and Active Living (ISEAL)
Depositing User: VUIR
Date Deposited: 16 May 2013 02:56
Last Modified: 14 Jan 2015 05:36
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Citations in Scopus: 13 - View on Scopus

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