Skip to main content

Beat the Bookmaker – Winning Football Bets with Machine Learning (Best Application Paper)

  • Conference paper
  • First Online:
Artificial Intelligence XXXV (SGAI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11311))

Abstract

Over the past decades, football (soccer) has continued to draw more and more attention from people all over the world. Meanwhile, the appearance of the internet led to a rapidly growing market for online bookmakers, companies which offer sport bets for specific odds. With numerous matches every week in dozens of countries, football league matches hold enormous potential for developing betting strategies. In this context, a betting strategy beats the bookmaker if it generates positive average profits over time. In this paper, we developed a data-driven framework for predicting the outcome of football league matches and generating meaningful profits by betting accordingly. Conducting a simulation study based on the matches of the five top European football leagues from season 2013/14 to 2017/18 showed that economically and statistically significant returns can be achieved by exploiting large data sets with modern machine learning algorithms. Furthermore, it turned out that these results cannot be reached with a linear regression model or simple betting strategies, such as always betting on the home team.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We thank http://www.football-data.co.uk/data.php for providing the data.

  2. 2.

    Without loss of generality, our model can also be used for matches without home advantage, e.g., FIFA World Cup and UEFA Euro Cup. In this case both teams would be neutral teams.

References

  1. Alexander, C., Dimitriu, A.: Indexing and statistical arbitrage. J. Portfolio Manag. 31(2), 50–63 (2005)

    Article  Google Scholar 

  2. Archontakis, F., Osborne, E.: Playing it safe? A Fibonacci strategy for soccer betting. J. Sports Econ. 8(3), 295–308 (2007)

    Article  Google Scholar 

  3. Avellaneda, M., Lee, J.H.: Statistical arbitrage in the US equities market. Quant. Financ. 10(7), 761–782 (2010)

    Article  MathSciNet  Google Scholar 

  4. Bernile, G., Lyandres, E.: Understanding investor sentiment: the case of soccer. Financ. Manag. 40(2), 357–380 (2011)

    Article  Google Scholar 

  5. Bollinger, J.: Bollinger on Bollinger bands. McGraw-Hill, New York (2001)

    Google Scholar 

  6. Choi, D., Hui, S.K.: The role of surprise: understanding overreaction and underreaction to unanticipated events using in-play soccer betting market. J. Econ. Behav. Organ. 107, 614–629 (2014)

    Article  Google Scholar 

  7. Croxson, K., Reade, J.: Information and efficiency: goal arrival in soccer betting. Econ. J. 124(575), 62–91 (2014)

    Article  Google Scholar 

  8. Endres, S., Stübinger, J.: Optimal trading strategies for Lévy-driven Ornstein-Uhlenbeck processes. FAU Discussion Papers in Economics (17). University of Erlangen-Nürnberg (2017)

    Google Scholar 

  9. Forrest, D., Simmons, R.: Sentiment in the betting market on Spanish football. Appl. Econ. 40(1), 119–126 (2008)

    Article  Google Scholar 

  10. Franck, E., Verbeek, E., Nüesch, S.: Prediction accuracy of different market structures–bookmakers versus a betting exchange. Int. J. Forecast. 26(3), 448–459 (2010)

    Article  Google Scholar 

  11. Franck, E., Verbeek, E., Nüesch, S.: Inter-market Arbitrage in Betting. Economica 80(318), 300–325 (2013)

    Article  Google Scholar 

  12. Gatev, E., Goetzmann, W.N., Rouwenhorst, K.G.: Pairs trading: performance of a relative-value arbitrage rule. Rev. Financ. Stud. 19(3), 797–827 (2006)

    Article  Google Scholar 

  13. Gil, R.G.R., Levitt, S.D.: Testing the efficiency of markets in the 2002 World Cup. J. Predict. Mark. 1(3), 255–270 (2012)

    Google Scholar 

  14. Godin, F., Zuallaert, J., Vandersmissen, B., de Neve, W., van de Walle, R.: Beating the bookmakers: leveraging statistics and Twitter microposts for predicting soccer results. In: Workshop on Large-Scale Sports Analytics (2014)

    Google Scholar 

  15. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. SSS. Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7

    Book  MATH  Google Scholar 

  16. Hogan, S., Jarrow, R., Teo, M., Warachka, M.: Testing market efficiency using statistical arbitrage with applications to momentum and value strategies. J. Financ. Econ. 73(3), 525–565 (2004)

    Article  Google Scholar 

  17. Jegadeesh, N., Titman, S.: Returns to buying winners and selling losers: implications for stock market efficiency. J. Financ. 48(1), 65–91 (1993)

    Article  Google Scholar 

  18. Knoll, J., Stübinger, J., Grottke, M.: Exploiting social media with higher-order factorization machines: statistical arbitrage on high-frequency data of the S&P 500. Quanitative Finance, Forthcoming (2018)

    Google Scholar 

  19. Levitt, S.D.: Why are gambling markets organised so differently from financial markets? Econ. J. 114(495), 223–246 (2004)

    Article  Google Scholar 

  20. Lisi, F., Zanella, G.: Tennis betting: can statistics beat bookmakers? Electron. J. Appl. Stat. Anal. 10(3), 790–808 (2017)

    MathSciNet  Google Scholar 

  21. Liu, B., Chang, L.B., Geman, H.: Intraday pairs trading strategies on high frequency data: the case of oil companies. Quant. Financ. 17(1), 87–100 (2017)

    Article  MathSciNet  Google Scholar 

  22. Luckner, S., Schröder, J., Slamka, C.: On the forecast accuracy of sports prediction markets. In: Gimpel, H., Jennings, N.R., Kersten, G.E., Ockenfels, A., Weinhardt, C. (eds.) Negotiation, Auctions, and Market Engineering. LNBIP, vol. 2, pp. 227–234. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-77554-6_17

    Chapter  Google Scholar 

  23. Palomino, F., Renneboog, L., Zhang, C.: Information salience, investor sentiment, and stock returns: the case of British soccer betting. J. Corp. Financ. 15(3), 368–387 (2009)

    Article  Google Scholar 

  24. Pole, A.: Statistical Arbitrage: Algorithmic Trading Insights and Techniques. Wiley, Hoboken (2011)

    Google Scholar 

  25. Rue, H., Salvesen, O.: Prediction and retrospective analysis of soccer matches in a league. J. Roy. Stat. Soc.: Ser. D (Stat.) 49(3), 399–418 (2000)

    Article  Google Scholar 

  26. 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 (2009)

    Article  MathSciNet  Google Scholar 

  27. Stefani, R.T.: Improved least squares football, basketball, and soccer predictions. IEEE Trans. Syst. Man Cybern. 10(2), 116–123 (1980)

    Article  Google Scholar 

  28. Steinwart, I., Christmann, A.: Support Vector Machines. Springer, New York (2008). https://doi.org/10.1007/978-0-387-77242-4

    Book  MATH  Google Scholar 

  29. Stekler, H.O., Sendor, D., Verlander, R.: Issues in sports forecasting. Int. J. Forecast. 26(3), 606–621 (2010)

    Article  Google Scholar 

  30. Stübinger, J.: Statistical arbitrage with optimal causal paths on high-frequency data of the S&P 500. Quant. Financ. (2018, forthcoming)

    Google Scholar 

  31. Stübinger, J., Endres, S.: Pairs trading with a mean-reverting jump-diffusion model on high-frequency data. Quant. Financ. 18, 1735–1751 (2018)

    Article  MathSciNet  Google Scholar 

  32. Stübinger, J., Mangold, B., Krauss, C.: Statistical arbitrage with vine copulas. Quant. Financ. 18, 1831–1849 (2018)

    Article  MathSciNet  Google Scholar 

  33. Tax, N., Joustra, Y.: Predicting the Dutch football competition using public data: a machine learning approach. Trans. Knowl. Data Eng. 10(10), 1–13 (2015)

    Google Scholar 

  34. Zeileis, A., Leitner, C., Hornik, K.: Predictive Bookmaker Consensus Model for the UEFA Euro 2016 (2016)

    Google Scholar 

  35. Zeileis, A., Leitner, C., Hornik, K.: Probabilistic forecasts for the 2018 FIFA World Cup based on the bookmaker consensus model. Working Papers in Economics and Statistics - Universität Insbruck (2018)

    Google Scholar 

  36. Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms. Chapman and Hall, Boca Raton (2012)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Johannes Stübinger or Julian Knoll .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Stübinger, J., Knoll, J. (2018). Beat the Bookmaker – Winning Football Bets with Machine Learning (Best Application Paper). In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science(), vol 11311. Springer, Cham. https://doi.org/10.1007/978-3-030-04191-5_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04191-5_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04190-8

  • Online ISBN: 978-3-030-04191-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics