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Modelling and analysing track cycling Omnium performances using statistical and machine learning techniques

Ofoghi, Bahadorreza and Zeleznikow, John and Dwyer, Dan and MacMahon, Clare (2013) Modelling and analysing track cycling Omnium performances using statistical and machine learning techniques. Journal of Sports Sciences, 31 (9). pp. 954-962. ISSN 0264-0414

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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.

Item Type: Article
Uncontrolled Keywords: ResPubID25457, International Cycling Union, UCI, track cycling Omnium, Kolmogorov-Smirnov statistical test, KS statistical test, Pearson correlation analysis, machine learning, statistical analysis
Subjects: FOR Classification > 0801 Artificial Intelligence and Image Processing
FOR Classification > 0806 Information Systems
FOR Classification > 1106 Human Movement and Sports Science
Faculty/School/Research Centre/Department > School of Sport and Exercise Science
Faculty/School/Research Centre/Department > School of Management and Information Systems
Faculty/School/Research Centre/Department > Institute of Sport, Exercise and Active Living (ISEAL)
Depositing User: VUIR
Date Deposited: 25 Sep 2013 23:06
Last Modified: 14 Jan 2015 05:34
URI: http://vuir.vu.edu.au/id/eprint/10699
DOI: 10.1080/02640414.2012.757344
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