title: Modelling and analysing track cycling Omnium performances using statistical and machine learning techniques.
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.
authors: Victoria University, Institute of Sport , Exercise, and Active Living, Room G.03, Land Titles Office, Melbourne 3000, Australia. bahadorreza.ofoghi@vu.edu.au
authors: Bahadorreza
authors: B
authors: Ofoghi
authors: John
authors: J
authors: Zeleznikow
authors: Dan
authors: D
authors: Dwyer
authors: Clare
authors: C
authors: Macmahon
mesh_terms: D001185:Artificial Intelligence; D054874:Athletic Performance / Q000706:statistics & numerical data*; D001642:Bicycling / Q000706:statistics & numerical data*; D003657:Decision Making; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D018709:Statistics, Nonparametric