Journal of Quantitative Analysis in Sports
An official journal of the American Statistical Association
Editor-in-Chief: Mark Glickman PhD
SCImago Journal Rank (SJR) 2015: 0.288
Source Normalized Impact per Paper (SNIP) 2015: 0.358
Impact per Publication (IPP) 2015: 0.250
Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries
1Electronic Engineering and Computer Science, Queen Mary, University of London, CS332, RIM GROUP, EECS, Mile End, London E1 4NS, UK
2Electronic Engineering and Computer Science, Queen Mary, University of London, CS435, RIM GROUP, EECS, Mile End, London E1 4NS, UK
Citation Information: Journal of Quantitative Analysis in Sports. Volume 9, Issue 1, Pages 37–50, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2012-0036, March 2013
- Published Online:
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.
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