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

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  • 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)

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.

Suggested Citation

  • Constantinou Anthony Costa & Fenton Norman Elliott, 2013. "Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(1), pages 37-50, March.
  • Handle: RePEc:bpj:jqsprt:v:9:y:2013:i:1:p:37-50:n:4
    DOI: 10.1515/jqas-2012-0036
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    References listed on IDEAS

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    1. Ioannis Asimakopoulos & John Goddard, 2004. "Forecasting football results and the efficiency of fixed-odds betting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(1), pages 51-66.
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    Cited by:

    1. Kovalchik, Stephanie, 2020. "Extension of the Elo rating system to margin of victory," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1329-1341.
    2. Hvattum Lars Magnus, 2015. "Playing on artificial turf may be an advantage for Norwegian soccer teams," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(3), pages 183-192, September.
    3. Wheatcroft, Edward, 2020. "A profitable model for predicting the over/under market in football," LSE Research Online Documents on Economics 103712, London School of Economics and Political Science, LSE Library.
    4. Kovalchik, Stephanie & Reid, Machar, 2019. "A calibration method with dynamic updates for within-match forecasting of wins in tennis," International Journal of Forecasting, Elsevier, vol. 35(2), pages 756-766.
    5. Baboota, Rahul & Kaur, Harleen, 2019. "Predictive analysis and modelling football results using machine learning approach for English Premier League," International Journal of Forecasting, Elsevier, vol. 35(2), pages 741-755.

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