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The Betting Odds Rating System: Using soccer forecasts to forecast soccer

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  • Fabian Wunderlich
  • Daniel Memmert

Abstract

Betting odds are frequently found to outperform mathematical models in sports related forecasting tasks, however the factors contributing to betting odds are not fully traceable and in contrast to rating-based forecasts no straightforward measure of team-specific quality is deducible from the betting odds. The present study investigates the approach of combining the methods of mathematical models and the information included in betting odds. A soccer forecasting model based on the well-known ELO rating system and taking advantage of betting odds as a source of information is presented. Data from almost 15.000 soccer matches (seasons 2007/2008 until 2016/2017) are used, including both domestic matches (English Premier League, German Bundesliga, Spanish Primera Division and Italian Serie A) and international matches (UEFA Champions League, UEFA Europe League). The novel betting odds based ELO model is shown to outperform classic ELO models, thus demonstrating that betting odds prior to a match contain more relevant information than the result of the match itself. It is shown how the novel model can help to gain valuable insights into the quality of soccer teams and its development over time, thus having a practical benefit in performance analysis. Moreover, it is argued that network based approaches might help in further improving rating and forecasting methods.

Suggested Citation

  • Fabian Wunderlich & Daniel Memmert, 2018. "The Betting Odds Rating System: Using soccer forecasts to forecast soccer," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-18, June.
    • Handle: RePEc:plo:pone00:0198668
    DOI: 10.1371/journal.pone.0198668
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    References listed on IDEAS

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    1. Martin Spann & Bernd Skiera, 2009. "Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipsters," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 55-72.
    2. Newton Paul K & Aslam Kamran, 2009. "Monte Carlo Tennis: A Stochastic Markov Chain Model," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(3), pages 1-44, July.
    3. Barrow Daniel & Drayer Ian & Elliott Peter & Gaut Garren & Osting Braxton, 2013. "Ranking rankings: an empirical comparison of the predictive power of sports ranking methods," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 9(2), pages 187-202, June.
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    Cited by:

    1. Kikuta,Kyosuke & Ono, Yoshikuni, 2024. "Global Evidence for the Relevance of Irrelevant Events: International Soccer Games and Leader Approval," IDE Discussion Papers 942, Institute of Developing Economies, Japan External Trade Organization(JETRO).
    2. Kikuta, Kyosuke & Uesugi, Mamoru, 2023. "Do Politically Irrelevant Events Cause Conflict? The Cross-continental Effects of European Professional Football on Protests in Africa," International Organization, Cambridge University Press, vol. 77(1), pages 179-216, January.
    3. Llorenç Badiella & Pedro Puig & Carlos Lago-Peñas & Martí Casals, 2023. "Influence of Red and Yellow cards on team performance in elite soccer," Annals of Operations Research, Springer, vol. 325(1), pages 149-165, June.
    4. Wunderlich, Fabian & Memmert, Daniel, 2020. "Are betting returns a useful measure of accuracy in (sports) forecasting?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 713-722.

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