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An optimized ratings-based model for forecasting Australian Rules football

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  • Ryall, Richard
  • Bedford, Anthony

Abstract

Building a ratings model for forecasting the success of a sporting team requires the careful consideration of many factors, such as the home ground advantage and opponent quality. In this research, we build an optimized Elo ratings model for forecasting Australian Rules football (AFL), which incorporates the home ground advantage (ground familiarity and travel fatigue) and seasonal decay (initial ratings); ratings are then updated between games based on the difference between the expected and actual margins of victory. Match information gathered from the 2000 and 2001 seasons was used as a training set for the forward prediction of the 2002 to 2009 seasons. The model is then evaluated based on the number of predicted winners, the Average Absolute margin of Error (AAE) and the Return on Investment (ROI).

Suggested Citation

  • Ryall, Richard & Bedford, Anthony, 2010. "An optimized ratings-based model for forecasting Australian Rules football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 511-517, July.
    • Handle: RePEc:eee:intfor:v:26:y::i:3:p:511-517
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    2. Song, Kai & Gao, Yiran & Shi, Jian, 2020. "Making real-time predictions for NBA basketball games by combining the historical data and bookmaker’s betting line," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    3. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
    4. Angelini, Giovanni & Candila, Vincenzo & De Angelis, Luca, 2022. "Weighted Elo rating for tennis match predictions," European Journal of Operational Research, Elsevier, vol. 297(1), pages 120-132.
    5. Roberto Gásquez & Vicente Royuela, 2016. "The Determinants of International Football Success: A Panel Data Analysis of the Elo Rating," Social Science Quarterly, Southwestern Social Science Association, vol. 97(2), pages 125-141, June.
    6. Vaughan Williams Leighton & Liu Chunping & Dixon Lerato & Gerrard Hannah, 2021. "How well do Elo-based ratings predict professional tennis matches?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(2), pages 91-105, June.
    7. Ryall Richard & Bedford Anthony, 2011. "The Intra-Match Home Advantage in Australian Rules Football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(2), pages 1-14, May.
    8. Adi Schnytzer, 2011. "The Prediction Market for the Australian Football League," Working Papers 2011-15, Bar-Ilan University, Department of Economics.

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