Document Detail

Modelling and analysing track cycling Omnium performances using statistical and machine learning techniques.
MedLine Citation:
PMID:  23320948     Owner:  NLM     Status:  Publisher    
Abstract This 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.
Bahadorreza Ofoghi; John Zeleznikow; Dan Dwyer; Clare Macmahon
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-16
Journal Detail:
Title:  Journal of sports sciences     Volume:  -     ISSN:  1466-447X     ISO Abbreviation:  J Sports Sci     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-16     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8405364     Medline TA:  J Sports Sci     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
a Victoria University, Institute of Sport , Exercise, and Active Living, Room G.03, Land Titles Office, 283 Queen Street , Melbourne , 3000 , Australia.
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