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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Glickman, PhD, Mark / Rigdon, Steve

4 Issues per year

CiteScore 2016: 0.44

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

Anthony Costa Constantinou
  • Corresponding author
  • Electronic Engineering and Computer Science, Queen Mary, University of London, CS332, RIM GROUP, EECS, Mile End, London E1 4NS, UK
  • Email:
/ Norman Elliott Fenton
  • Electronic Engineering and Computer Science, Queen Mary, University of London, CS435, RIM GROUP, EECS, Mile End, London E1 4NS, UK
Published Online: 2013-03-30 | DOI: https://doi.org/10.1515/jqas-2012-0036


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


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About the article

Corresponding author: Anthony Costa Constantinou, Electronic Engineering and Computer Science, Queen Mary, University of London, CS332, RIM GROUP, EECS, Mile End, London E1 4NS, UK

Published Online: 2013-03-30

It might also worth mentioning that the ELO rating algorithm was featured prominently in the popular movie The Social Network (also known as the Facebook movie), whereby during a scene Eduardo Saverin writes the mathematical formula for the ELO rating system on Zuckerberg’s dorm room window.

If the rating is applied to a single league competition, the average team in that league will have a rating of 0. If the rating is applied to more than one league in which adversaries between the different leagues (or cup competitions) play against each other, the average team over all leagues will have a rating of 0.

If the prediction is +4 in favour of the home side then an actual result of 5–0 will give you an error of approximately 1. But if the prediction is 0 in favour of the home side and the actual result is 1–0, then this also gives you the same error as above.

The learning parameters could have been optimised based on predictions of type {H, D, A} (corresponding to home win, draw and away win), based on profitability, based on scoring rules, or based on many other different accuracy measurements and metrics. We have chosen score difference for optimising the learning parameters since the pi-ratings themselves are exclusively determined by that information.

The first five EPL seasons (1992/1993 to 1996/1997) are solely considered for generating the initial ratings for the competing teams. This is important because training the model on ignorant team ratings (i.e., starting from 0) will negatively affect the training procedure. Thus, learning parameters λ and γ are trained during the subsequent ten seasons; 1997/1998 to 2006/2007 inclusive.

For the pi-rating system the ratings are segregated into intervals of 0.10 (from ≤–1.1 to >1.6), for ELOb the ratings are segregated into intervals of 25 (from ≤–330 to >345), and for ELOg the ratings are segregated into intervals of 35 (from ≤–475 to >470).

Assumes a profit margin of 5%.

For the newly promoted team Wolves the development of the ratings start at match instance 760 since no performances have been recorded relative to the residual EPL teams during the two preceding seasons.

Where the pi-ratings of the home and away team follow ∼Normal (x, y) distributions for capturing rating uncertainty, where x is the pi-rating value (RαH or RβA) and y is the pi-rating variance, which can be measured over n preceding match instances.

Citation Information: Journal of Quantitative Analysis in Sports, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2012-0036.

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