Learning Betting Tips from Users’ Bet Selections

  • Erik Štrumbelj
  • Marko Robnik Šikonja
  • Igor Kononenko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5632)


In this paper we address the problem of using bet selections of a large number of mostly non-expert users to improve sports betting tips. A similarity based approach is used to describe individual users’ strategies and we propose two different scoring functions to evaluate them. The information contained in users’ bet selections improves on using only bookmaker odds. Even when only bookmaker odds are used, the approach gives results comparable to those of a regression-based forecasting model.


Machine learning data mining nearest neighbors forecasting sports betting 


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  1. 1.
    Andersson, P., Edman, J., Ekman, M.: Predicting the world cup 2002 in soccer: Performance and confidence of experts and non-experts. International Journal of Forecasting 21(3), 565–576 (2005)CrossRefGoogle Scholar
  2. 2.
    Andersson, P., Memmert, D., Popowicz, E.: Forecasting outcomes of the world cup 2006 in football: Performance and confidence of bettors and laypeople. Psychology of Sport and Exercise 10(1), 116–123 (2009)CrossRefGoogle Scholar
  3. 3.
    Boulier, B.L., Stekler, H.O.: Predicting the outcomes of national football league games. International Journal of Forecasting 19(2), 257–270 (2003)CrossRefGoogle Scholar
  4. 4.
    Brier, G.W.: Verification of forecasts expressed in terms of probability. Monthly Weather Review 75, 1–3 (1950)CrossRefGoogle Scholar
  5. 5.
    Dixon, M.J., Pope, P.F.: The value of statistical forecasts in the uk association football betting market. International Journal of Forecasting 20, 697–711 (2004)CrossRefGoogle Scholar
  6. 6.
    Efron, B., Tibshirani, R.: Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical Science 1(1), 54–75 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Forrest, D., Goddard, J., Simmons, R.: Odds-setters as forecasters: The case of english football. International Journal of Forecasting 21(3), 551–564 (2005)CrossRefGoogle Scholar
  8. 8.
    Forrest, D., Simmons, R.: Forecasting sport: the behaviour and performance of football tipsters. International Journal of Forecasting 16, 317–331 (2000)CrossRefGoogle Scholar
  9. 9.
    Goddard, J., Asimakopoulos, I.: Forecasting football results and the efficiency of fixed-odds betting. Journal of Forecasting 23, 51–66 (2004)CrossRefGoogle Scholar
  10. 10.
    Scheibehenne, B., Broderb, A.: Predicting wimbledon 2005 tennis results by mere player name recognition. International Journal of Forecasting 23(3), 415–426 (2007)CrossRefGoogle Scholar
  11. 11.
    Song, C., Boulier, B.L., Stekler, H.O.: The comparative accuracy of judgmental and model forecasts of american football games. International Journal of Forecasting 23(3), 405–413 (2007)CrossRefGoogle Scholar
  12. 12.
    Ziegler, C., Lausen, G., Kostan, J.A.: On exploiting classification taxonomies in recommender systems. AI Communications 21(2-3), 97–125 (2008)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Erik Štrumbelj
    • 1
  • Marko Robnik Šikonja
    • 1
  • Igor Kononenko
    • 1
  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia

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