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Match Outcomes Prediction of Six Top English Premier League Clubs via Machine Learning Technique

  • Rabiu Muazu Musa
  • Anwar P. P. Abdul MajeedEmail author
  • Mohd Azraai Mohd Razman
  • Mohd Ali Hanafiah Shaharudin
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1015)

Abstract

The English Premier League (EPL) is one of the most widely covered league in the world. The prediction of football matches, particularly EPL has received due attention over the past two decades by means of both conventional statistical and machine learning approaches. More often than not, the predictions reported in the literature have rather been dissatisfactory in forecasting the outcome of the matches. This work offers a unique approach in predicting EPL match outcomes, i.e., win, lose or draw by considering top six teams in the league namely Manchester United, Manchester City, Liverpool, Arsenal, Chelsea and Tottenham Hotspur over the span of four consecutive seasons from 2013 to 2016. Fifteen features were selected based on their relevance to the game. Six different Support Vector Machine (SVM) model variations viz. linear, quadratic, cubic, fine radial basis function (RBF), medium RBF, as well as course RBF were developed to predict the match outcomes. A five-fold cross-validation technique was employed whilst, a separate fresh data was supplied to the best model developed in evaluating the predictive efficacy of the model. It was demonstrated from the study that the linear SVM model provided an excellent prediction accuracy of 100% on both the trained as well as untrained data. Therefore, it could be concluded that the selection of the relevant features, as well as the methodology employed, could yield a reliable prediction of top six EPL clubs match outcomes.

Keywords

Support Vector Machine Football Match outcome Feature selection 

Notes

Acknowledgement

The authors would like to gratefully acknowledge Universiti Malaysia Pahang for funding this study via RDU 180321.

References

  1. 1.
    Brooks, J., Kerr, M., Guttag, J.: Using machine learning to draw inferences from pass location data in soccer. Stat. Anal. Data Min. ASA Data Sci. J. 9, 338–349 (2016).  https://doi.org/10.1002/sam.11318MathSciNetCrossRefGoogle Scholar
  2. 2.
    Razali, N., Mustapha, A., Yatim, F.A., Ab Aziz, R.: Predicting football matches results using Bayesian networks for english premier league (EPL). IOP Conf. Ser. Mater. Sci. Eng. 226, 012099 (2017).  https://doi.org/10.1088/1757-899X/226/1/012099CrossRefGoogle Scholar
  3. 3.
    Igiri, C.P.: Support vector machine-based prediction system for a football match result. IOSR J. Comput. Eng. 17, 2278–2661 (2015).  https://doi.org/10.9790/0661-17332126CrossRefGoogle Scholar
  4. 4.
    Peace, C., Okechukwu, E.: An improved prediction system for football a match result. IOSR J. Eng. 04, 2250–3021 (2014)Google Scholar
  5. 5.
    Cortis, D.: Expected values and variances in bookmaker payouts: a theoretical approach towards setting limits on odds. J. Predict. Mark. 9, 1–14 (2015)Google Scholar
  6. 6.
    Min, B., Kim, J., Choe, C., et al.: A compound framework for sports results prediction: a football case study. Knowl.-Based Syst. 21, 551–562 (2008).  https://doi.org/10.1016/j.knosys.2008.03.016CrossRefGoogle Scholar
  7. 7.
    Constantinou, A.C., Fenton, N.E.: Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries. J. Quant. Anal. Sport 9.  https://doi.org/10.1515/jqas-2012-0036CrossRefGoogle Scholar
  8. 8.
    Moroney, M.J.: Facts from Figures, 2nd edn. Penllllin Book Ltd, Harmondsworth (1953)zbMATHGoogle Scholar
  9. 9.
    Reep, C., Benjamin, B.: Skill and chance in association football. J. R. Stat. Soc. Ser. A 131, 581 (1968).  https://doi.org/10.2307/2343726CrossRefGoogle Scholar
  10. 10.
  11. 11.
    Martins, R.G., Martins, A.S., Neves, L.A., et al.: Exploring polynomial classifier to predict match results in football championships. Expert Syst. Appl. 83, 79–93 (2017).  https://doi.org/10.1016/J.ESWA.2017.04.040CrossRefGoogle Scholar
  12. 12.
    Joseph, A., Fenton, N.E., Neil, M.: Predicting football results using Bayesian nets and other machine learning techniques. Knowl.-Based Syst. 19, 544–553 (2006).  https://doi.org/10.1016/J.KNOSYS.2006.04.011CrossRefGoogle Scholar
  13. 13.
    Carmichael, F., Thomas, D.: Home-field effect and team performance. J. Sports Econ. 6, 264–281 (2005).  https://doi.org/10.1177/1527002504266154CrossRefGoogle Scholar
  14. 14.
    Palomino, F.A., Rigotti, L., Rustichini, A.: Skill, strategy and passion : an empirical analysis of soccer. Discussion Paper (1998)Google Scholar
  15. 15.
    Miljkovic, D., Gajic, L., Kovacevic, A., Konjovic, Z.: The use of data mining for basketball matches outcomes prediction. In: IEEE 8th International Symposium on Intelligent Systems and Informatics, pp. 309–312. IEEE (2010)Google Scholar
  16. 16.
    Aranda-Corral, G.A., Borrego-Díaz, J., Galán-Páez, J.: Complex concept lattices for simulating human prediction in sport. J. Syst. Sci. Complex. 26, 117–136 (2013).  https://doi.org/10.1007/s11424-013-2288-xMathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Baio, G., Blangiardo, M.: Bayesian hierarchical model for the prediction of football results. J. Appl. Stat. 37, 253–264 (2010).  https://doi.org/10.1080/02664760802684177MathSciNetCrossRefGoogle Scholar
  18. 18.
    Tax, N., Joustra, Y.: Predicting the Dutch football competition using public data: a machine learning approach. Trans. Knowl. Data Eng. 10, 1–13 (2015).  https://doi.org/10.13140/RG.2.1.1383.4729CrossRefGoogle Scholar
  19. 19.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)zbMATHGoogle Scholar
  20. 20.
    Taha, Z., Musa, R.M., Abdul Majeed, A.P.P., et al.: The identification of high potential archers based on fitness and motor ability variables: a support vector machine approach. Hum. Mov. Sci. 57, 184–193 (2018).  https://doi.org/10.1016/j.humov.2017.12.008CrossRefGoogle Scholar
  21. 21.
    Akay, M.F., Abut, F., Daneshvar, S., Heil, D.: Prediction of upper body power of cross-country skiers using support vector machines. Arab. J. Sci. Eng. 40, 1045–1055 (2015).  https://doi.org/10.1007/s13369-015-1588-yCrossRefGoogle Scholar
  22. 22.
    Muazu Musa, R., Taha, Z., Abdul Majeed, A.P.P., Abdullah, M.R.: Machine Learning in Sports. SAST. Springer, Singapore (2019).  https://doi.org/10.1007/978-981-13-2592-2CrossRefGoogle Scholar
  23. 23.
    Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977)CrossRefGoogle Scholar
  24. 24.
    Goddard, J.: Who wins the football? Significance 3, 16–19 (2006).  https://doi.org/10.1111/j.1740-9713.2006.00145.xMathSciNetCrossRefGoogle Scholar
  25. 25.
    Heuer, A., Rubner, O.: Fitness, chance, and myths: an objective view on soccer results. Eur. Phys. J. B 67, 445–458 (2009).  https://doi.org/10.1140/epjb/e2009-00024-8CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Rabiu Muazu Musa
    • 1
  • Anwar P. P. Abdul Majeed
    • 1
    Email author
  • Mohd Azraai Mohd Razman
    • 1
  • Mohd Ali Hanafiah Shaharudin
    • 1
  1. 1.Innovative Manufacturing, Mechatronics and Sports, Faculty of Manufacturing EngineeringUniversiti Malaysia PahangPekanMalaysia

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