Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries

Constantinou Anthony Costa () and Fenton Norman Elliott
Additional contact information
Constantinou Anthony Costa: Electronic Engineering and Computer Science, Queen Mary, University of London, CS332, RIM GROUP, EECS, Mile End, London E1 4NS, UK
Fenton Norman Elliott: Electronic Engineering and Computer Science, Queen Mary, University of London, CS435, RIM GROUP, EECS, Mile End, London E1 4NS, UK

Journal of Quantitative Analysis in Sports, 2013, vol. 9, issue 1, 37-50

Abstract: A rating system provides relative measures of superiority between adversaries. We propose a novel and simple approach, which we call pi-rating, for dynamically rating Association Football teams solely on the basis of the relative discrepancies in scores through relevant match instances. The pi-rating system is applicable to any other sport where the score is considered as a good indicator for prediction purposes, as well as determining the relative performances between adversaries. In an attempt to examine how well the ratings capture a team’s performance, we have a) assessed them against two recently proposed football ELO rating variants and b) used them as the basis of a football betting strategy against published market odds. The results show that the pi-ratings outperform considerably the widely accepted ELO ratings and, perhaps more importantly, demonstrate profitability over a period of five English Premier League seasons (2007/2008–2011/2012), even allowing for the bookmakers’ built-in profit margin. This is the first academic study to demonstrate profitability against market odds using such a relatively simple technique, and the resulting pi-ratings can be incorporated as parameters into other more sophisticated models in an attempt to further enhance forecasting capability.

Keywords: dynamic sports rating; ELO rating; football betting; football prediction; football ranking (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://doi.org/10.1515/jqas-2012-0036 (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:9:y:2013:i:1:p:37-50:n:4

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

DOI: 10.1515/jqas-2012-0036

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 ().