Supporting athlete selection and strategic planning in track cycling omnium: A statistical and machine learning approach
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Ofoghi, Bahadorreza, Zeleznikow, John 
ORCID: 0000-0002-8786-2644, MacMahon, Clare and Dwyer, Dan
(2013)
Supporting athlete selection and strategic planning in track cycling omnium: A statistical and machine learning approach.
Information Sciences, 233.
pp. 200-213.
ISSN 0020-0255
Abstract
We demonstrated an in-depth data analytical process to facilitate the decision-making procedure in track cycling omnium that includes utilization of statistical, machine learning-based, and probabilistic approaches. We considered both interomnium and intra-omnium procedures.
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| Item type | Article |
| URI | https://vuir.vu.edu.au/id/eprint/22254 |
| DOI | 10.1016/j.ins.2012.12.050 |
| Official URL | http://www.sciencedirect.com/science/article/pii/S... |
| Subjects | Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing Historical > FOR Classification > 1106 Human Movement and Sports Science Historical > Faculty/School/Research Centre/Department > School of Management and Information Systems Historical > Faculty/School/Research Centre/Department > Institute of Sport, Exercise and Active Living (ISEAL) Current > Division/Research > College of Sports and Exercise Science |
| Keywords | ResPubID26708, Leave-One-Out Cross-Validation, LOOCV, decision support, track cycling omnium, statistical analysis, decision support, Bayesian network, machine learning |
| Citations in Scopus | 17 - View on Scopus |
| Download/View statistics | View download statistics for this item |
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