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A Machine Learning Approach to Predicting Winning Patterns in Track Cycling Omnium

Abstract : 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.
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Bahadorreza Ofoghi, John Zeleznikow, Clare Macmahon, Dan Dwyer. A Machine Learning Approach to Predicting Winning Patterns in Track Cycling Omnium. Third IFIP TC12 International Conference on Artificial Intelligence (AI) / Held as Part of World Computer Congress (WCC), Sep 2010, Brisbane, Australia. pp.67-76, ⟨10.1007/978-3-642-15286-3_7⟩. ⟨hal-01054582⟩



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