Forecasting the FIFA World Cup – Combining Result- and Goal-Based Team Ability Parameters

  • Pieter RobberechtsEmail author
  • Jesse Davis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11330)


In this study we compare result-based Elo ratings and goal-based ODM (Offense Defense Model) ratings as covariates in an ordered logit regression and bivariate Poisson model to generate predictions for the outcome of the 2018 FIFA World Cup. To this end, we first estimate probabilities of match results between all competing nations. With an evaluation on the four previous World Cups between 2002 and 2014, we show that an ordered logit model with Elo ratings as a single covariate achieves the best performance. Secondly, via Monte Carlo simulations we compute each team’s probability of advancing past a given stage of the tournament. Additionally, we apply our models on the Open International Soccer Database and show that our approach leads to good predictions for domestic league football matches as well.


Football match outcome prediction Tournament simulation 



PR is supported by Interreg V A project NANO4Sports. JD is partially supported by KU Leuven Research Fund (C14/17/070 and C22/15/015), FWO-Vlaanderen (SBO-150033) and Interreg V A project NANO4Sports.


  1. 1.
    Baio, G., Blangiardo, M.: Bayesian hierarchical model for the prediction of football results. J. Appl. Stat. 37(2), 253–264 (2010). Scholar
  2. 2.
    Baxter, M., Stevenson, R.: Discriminating between the poisson and negative binomial distributions: an application to goal scoring in association football. J. Appl. Stat. 15(3), 347–354 (1988). Scholar
  3. 3.
    Berrar, D., Dubitzky, W., Lopes, P.: Incorporating domain knowledge in machine learning for soccer outcome prediction. Mach. Learn. 108(1), 97–126 (2019)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Boulier, B.L., Stekler, H.O.: Predicting the outcomes of National Football League games. Int. J. Forecast. 19(2), 257–270 (2003). Scholar
  5. 5.
    Byrd, R.H., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SISC 16(5), 1190–1208 (1995). Scholar
  6. 6.
    Constantinou, A.C.: Dolores: a model that predicts football match outcomes from all over the world. Mach. Learn. 108(1), 49–75 (2018). Scholar
  7. 7.
    Dixon, M.J., Coles, S.G.: Modelling association football scores and inefficiencies in the football betting market. J. Royal Stat. Soc. Ser. C (Appl. Stat.) 46(2), 265–280 (1997)CrossRefGoogle Scholar
  8. 8.
    Dubitzky, W., Lopes, P., Davis, J., Berrar, D.: The open international soccer database for machine learning. Mach. Learn. 108(1), 9–28 (2019)MathSciNetCrossRefGoogle Scholar
  9. 9.
    EA Sports: EA Sport predicts France to win the FIFA World Cup, May 2018.
  10. 10.
    Elo, A.E.: The Rating of Chess Players, Past and Present. Arco Pub., New York (1978)Google Scholar
  11. 11.
    Epstein, E.S.: A scoring system for probability forecasts of ranked categories. J. Appl. Meteorol. 8(6), 985–987 (1969).\(<\)0985:ASSFPF\(>\)2.0.CO;2CrossRefGoogle Scholar
  12. 12.
    FiveThirtyEight: 2014 World Cup Predictions, June 2014.
  13. 13.
    FiveThirtyEight: 2018 World Cup Predictions, June 2018.
  14. 14.
    Forrest, D., Goddard, J., Simmons, R.: Odds-setters as forecasters: the case of English football. Int. J. Forecast. 21(3), 551–564 (2005). Scholar
  15. 15.
    Forrest, D., Simmons, R.: Outcome uncertainty and attendance demand in sport: the case of English soccer. J. Royal Stat. Soc. 51(2), 229–241 (2002). Scholar
  16. 16.
    Glickman, M.E.: Parameter estimation in large dynamic paired comparison experiments. J. Royal Stat. Soc. Ser. C (Appl. Stat.) 48(3), 377–394 (2002). Scholar
  17. 17.
    Goddard, J.: Regression models for forecasting goals and match results in association football. Int. J. Forecast. 21(2), 331–340 (2005). Scholar
  18. 18.
    Goddard, J., Asimakopoulos, I.: Forecasting football results and the efficiency of fixed-odds betting. J. Forecast. 23(1), 51–66 (2004). Scholar
  19. 19.
    Govan, A.Y., Langville, A.N., Meyer, C.D.: Offense-defense approach to ranking team sports. J. Q. Anal. Sports 5(1) (2009).
  20. 20.
    Graham, I., Stott, H.: Predicting bookmaker odds and efficiency for UK football. Appl. Econ. 40(1), 99–109 (2008). Scholar
  21. 21.
    Groll, A., Ley, C., Schauberger, G., Van Eetvelde, H.: Prediction of the FIFA World Cup 2018 - a random forest approach with an emphasis on estimated team ability parameters. arXiv:1806.03208 [stat], June 2018
  22. 22.
    Herbrich, R., Minka, T., Graepel, T.: TrueSkill™ : a Bayesian skill rating system. In: Schölkopf, B., Platt, J.C., Hoffman, T. (eds.) Advances in Neural Information Processing Systems, vol. 19, pp. 569–576. MIT Press (2007)Google Scholar
  23. 23.
    Hubáček, O., Šourek, G., Železný, F.: Learning to predict soccer results from relational data with gradient boosted trees. Mach. Learn. 108(1), 29–47 (2019). Scholar
  24. 24.
    Hvattum, L.M., Arntzen, H.: Using ELO ratings for match result prediction in association football. Int. J. Forecast. 26(3), 460–470 (2010). Scholar
  25. 25.
    Joy, B., Weil, E., Giulianotti, R.C., Alegi, P.C., Rollin, J.: Football.
  26. 26.
    Karlis, D., Ntzoufras, I.: Analysis of sports data by using bivariate Poisson models. J. Royal Stat. Soc. 52(3), 381–393 (2003). Scholar
  27. 27.
    Keener, J.: The Perron–Frobenius theorem and the ranking of football teams. SIAM Rev. 35(1), 80–93 (1993). Scholar
  28. 28.
    Kuypers, T.: Information and efficiency: an empirical study of a fixed odds betting market. Appl. Econ. 32(11), 1353–1363 (2000). Scholar
  29. 29.
    Langville, A.N., Meyer, C.D.: Who’s #1?: The Science of Rating and Ranking. Princeton University Press, Princeton (2012)CrossRefGoogle Scholar
  30. 30.
    Lasek, J., Szlávik, Z., Bhulai, S.: The predictive power of ranking systems in association football. Int. J. Appl. Pattern Recogn. 1(1), 27–46 (2013). Scholar
  31. 31.
    Lee, A.J.: Modeling scores in the premier league: is Manchester united really the best? Chance 10(1), 15–19 (1997). Scholar
  32. 32.
    Leitner, C., Zeileis, A., Hornik, K.: Forecasting sports tournaments by ratings of (prob)abilities: a comparison for the EURO 2008. Int. J. Forecast. 26(3), 471–481 (2010). Scholar
  33. 33.
    Ley, C., Van de Wiele, T., Van Eetvelde, H.: Ranking soccer teams on basis of their current strength: a comparison of maximum likelihood approaches. eprint arXiv:1705.09575, May 2017
  34. 34.
    Maher, M.J.: Modelling association football scores. Stat. Neerl. 36(3), 109–118 (1982). Scholar
  35. 35.
    McCullagh, P.: Regression models for ordinal data. J. Royal Stat. Soc. 42(2), 109–142 (1980)MathSciNetzbMATHGoogle Scholar
  36. 36.
    Park, J., Newman, M.E.J.: A network-based ranking system for American college football. J. Stat. Mech. Theory Exp. 2005(10), P10014–P10014 (2005). Scholar
  37. 37.
    Pope, P.F., Peel, D.A.: Information, prices and efficiency in a fixed-odds betting market. Economica 56(223), 323–341 (1989). Scholar
  38. 38.
    Rue, H., Salvesen, O.: Prediction and retrospective analysis of soccer matches in a league. J. Royal Stat. Soc. Ser. D (Stat.) 49(3), 399–418 (2000). Scholar
  39. 39.
    Spann, M., Skiera, B.: Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipsters. J. Forecast. 28(1), 55–72 (2008). Scholar
  40. 40.
    Stefani, R.T.: Improved least squares football, basketball, and soccer predictions. IEEE Trans. Syst. Man Cybern. 10(2), 116–123 (1980). Scholar
  41. 41.
    Tsokos, A., Narayanan, S., Kosmidis, G.I.B., Cucuringu, M., Whitaker, G., Kiraly, F.: Modeling outcomes of soccer matches. Mach. Learn. 108(1), 77–95 (2019)MathSciNetCrossRefGoogle Scholar
  42. 42.
    UBS AG: and the winner is.... investing in emerging markets (special edition, 2018 World Cup in Russia), May 2018Google Scholar
  43. 43.
    Van Haaren, J., Davis, J.: Predicting the final league tables of domestic football leagues. In: Proceedings of the 5th International Conference on Mathematics in Sport, pp. 202–207 (2015)Google Scholar
  44. 44.
    Zeileis, A., Leitner, C., Hornik, K.: Probabilistic forecasts for the 2018 FIFA World Cup based on the bookmaker consensus model, p. 19Google Scholar
  45. 45.
    Zyga, L.: New algorithm ranks sports teams like Google’s PageRank, December 2009.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceKU LeuvenLeuvenBelgium

Personalised recommendations