Probabilistic modelling to give advice about rowing split measures to support strategy and pacing in race planning

Full text for this resource is not available from the Research Repository.

Ofoghi, Bahadorreza, Zeleznikow, John ORCID: 0000-0002-8786-2644 and MacMahon, Clare (2011) Probabilistic modelling to give advice about rowing split measures to support strategy and pacing in race planning. International Journal of Performance Analysis in Sport, 11 (2). pp. 239-254. ISSN 1474-8185

Abstract

A probabilistic and optimization approach was used in this research to find the combination of rankings and finish times in the different sectors of rowing races that could potentially result in certain final standings. In doing this, competition results were collected from 1996 to 2009 for men's and women's standard 2000-m rowing races. In particular, data was gathered to M4- and W2- races corresponding to men's four and women's pair races, respectively. Only the top six teams' results were considered. The probabilistic modelling was carried out using Bayesian Belief Networks (BBNs) that encoded the four rankings and four finish times in the 500-m sectors as well as the final standing of the boats. Both cumulative and absolute measures pertaining to each sector of the race were considered. It was shown how the BBNs that were constructed for rowing can be used for the two types of reasoning, the diagnostic and predictive reasonings.

Item type Article
URI https://vuir.vu.edu.au/id/eprint/9097
Official URL http://www.ingentaconnect.com/content/uwic/ujpa/20...
Subjects Historical > FOR Classification > 1106 Human Movement and Sports Science
Historical > Faculty/School/Research Centre/Department > Institute of Sport, Exercise and Active Living (ISEAL)
Keywords ResPubID23496, Bayesian networks, combinatorial optimization, racing patterns, probabilistic modelling, country profiles
Citations in Scopus 2 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login