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

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conference contribution
posted on 2010-09-01, 00:00 authored by Bahadorreza OfoghiBahadorreza Ofoghi, J Zeleznikow, C MacMahon, Dan DwyerDan Dwyer
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.

History

Volume

331

Issue

Part III

Pagination

67 - 76

Publisher

Springer

Location

Berlin, Germany

ISSN

1868-4238

Language

eng

Publication classification

E1.1 Full written paper - refereed; E Conference publication

Copyright notice

2010, Springer

Title of proceedings

Artificial intelligence in theory and practice III : proceedings of the third conference on artificial intelligence, IFIP AI 2010