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Modelling and analysing track cycling Omnium performances using statistical and machine learning techniques
journal contribution
posted on 2013-05-01, 00:00 authored by Bahadorreza OfoghiBahadorreza Ofoghi, J Zeleznikow, Dan DwyerDan Dwyer, C MacMahonThis article describes the utilisation of an unsupervised machine learning technique and statistical approaches (e.g., the Kolmogorov-Smirnov test) that assist cycling experts in the crucial decision-making processes for athlete selection, training, and strategic planning in the track cycling Omnium. The Omnium is a multi-event competition that will be included in the summer Olympic Games for the first time in 2012. Presently, selectors and cycling coaches make decisions based on experience and intuition. They rarely have access to objective data. We analysed both the old five-event (first raced internationally in 2007) and new six-event (first raced internationally in 2011) Omniums and found that the addition of the elimination race component to the Omnium has, contrary to expectations, not favoured track endurance riders. We analysed the Omnium data and also determined the inter-relationships between different individual events as well as between those events and the final standings of riders. In further analysis, we found that there is no maximum ranking (poorest performance) in each individual event that riders can afford whilst still winning a medal. We also found the required times for riders to finish the timed components that are necessary for medal winning. The results of this study consider the scoring system of the Omnium and inform decision-making toward successful participation in future major Omnium competitions.
History
Journal
Journal of sports sciencesVolume
31Issue
9Pagination
954 - 962Publisher
RoutledgeLocation
Oxon, EnglandPublisher DOI
ISSN
0264-0414eISSN
1466-447XLanguage
engPublication classification
C1 Refereed article in a scholarly journal; C Journal articleCopyright notice
2013, Taylor & FrancisUsage metrics
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