A Variance Gamma model for Rugby Union matches

Fry John (), Smart Oliver (), Jean-Philippe Serbera and Klar Bernhard ()
Additional contact information
Fry John: School of Management, University of Bradford, Bradford, West Yorkshire, BD7 1DP, UK
Smart Oliver: Department of Computing and Mathematics, Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester, M1 5GD, UK
Klar Bernhard: Karlsruhe Institute of Technology (KIT), Department of Mathematics, Englerstr. 2, 76131 Karlsruhe, Germany

Journal of Quantitative Analysis in Sports, 2021, vol. 17, issue 1, 67-75

Abstract: Amid much recent interest we discuss a Variance Gamma model for Rugby Union matches (applications to other sports are possible). Our model emerges as a special case of the recently introduced Gamma Difference distribution though there is a rich history of applied work using the Variance Gamma distribution – particularly in finance. Restricting to this special case adds analytical tractability and computational ease. Our three-dimensional model extends classical two-dimensional Poisson models for soccer. Analytical results are obtained for match outcomes, total score and the awarding of bonus points. Model calibration is demonstrated using historical results, bookmakers’ data and tournament simulations.

Keywords: football; Poisson distribution; Rugby Union; Soccer; sports analytics; Variance Gamma distribution (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1515/jqas-2019-0088 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:jqsprt:v:17:y:2021:i:1:p:67-75:n:2

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jqas/html

DOI: 10.1515/jqas-2019-0088

Access Statistics for this article

Journal of Quantitative Analysis in Sports is currently edited by Mark Glickman

More articles in Journal of Quantitative Analysis in Sports from De Gruyter
Bibliographic data for series maintained by Peter Golla ().