Supporting athlete selection and strategic planning in track cycling omnium: A statistical and machine learning approach
Download
Download
Download
Full text for this resource is not available from the Research Repository.
Export
Ofoghi, Bahadorreza, Zeleznikow, John, 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.
Dimensions Badge
Altmetric Badge
| Item type | Article |
| URI | https://vuir.vu.edu.au/id/eprint/22254 |
| DOI | https://doi.org/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 | 14 - View on Scopus |
| Download/View statistics | View download statistics for this item |
CORE (COnnecting REpositories)