Skip to main content

Advertisement

SpringerLink
Log in

Introducing an expert system for prediction of soccer player ranking using ensemble learning

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Soccer is one of the most played sports in the world with many individuals involved. Recognizing talented players and team selection is a challenging task for coaches. Coaches need to employ different methods in order to rank soccer players and select them by their corresponding rank. In this paper, we propose a new web-based approach for ranking soccer players by using information available from online sources. The first step to do this task is collecting information about players. This information is fetched from the Internet and will be preprocessed or augmented by professional users at a web-based expert system. Information is highly dynamic in a sense that data change constantly. To build a ranking system for players, machine learning approaches are employed. We use different classification algorithms on prepared data and choose the best model from applied methods to rank new players in each state of the dataset. To improve classification results, a weighted ensemble method using a genetic algorithm for optimizing weights is proposed. We used this model to predict players’ rank. The ranking is done separately for different types of ranks with two, three, or four number of rankings. Experiments were done in the Persian premier league and have shown promising results for predicting player ranks with improvement in accuracy for four-, three-, and two-class predictions. The results show that (1) achieving higher performance will be harder with each level of granularity that is added to ranking classes of system. (2) A web-based system can be useful in order to develop a ranking system in sports. (3) The new ensemble method is able to improve classification models by improving the best model. We believe that using our innovative system, challenges for team selection and talent recognition can be solved. This assumption is proved with final results of the system and feedbacks from professionals.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. Data set of all players are freely available for academic applications at http://nlp.guilan.ac.ir/datasets.

References

  1. Ali A (2011) Measuring soccer skill performance: a review. Scand J Med Sci Sports 21(2):170–183

    Article  Google Scholar 

  2. Bachrach Y, Graepel T, Kasneci G, Kosinski M, Van Gael J (2012) Crowd IQ: aggregating opinions to boost performance. In: Proceedings of the 11th international conference on autonomous agents and multiagent systems, vol 1, pp 535–542

  3. Bialkowski A, Lucey P, Carr P, Yue Y, Sridharan S, Matthews I (2014) Large-scale analysis of soccer matches using spatiotemporal tracking data. In: Proceedings of the 2014 IEEE international conference on data mining, IEEE, IEEE Computer Society, Washington, DC, USA, ICDM ’14, pp 725–730. https://doi.org/10.1109/ICDM.2014.133

  4. Choi YS, Moon BR, Seo SY (2005) Genetic fuzzy discretization with adaptive intervals for classification problems. In: Proceedings of the 7th annual conference on genetic and evolutionary computation. ACM, pp 2037–2043

  5. Daud A, Muhammad F, Dawood H, Dawood H (2015) Ranking cricket teams. Inf Process Manag 51(2):62–73. https://doi.org/10.1016/j.ipm.2014.10.010

    Article  Google Scholar 

  6. De Stefano C, Della Cioppa A, Marcelli A (2002) An adaptive weighted majority vote rule for combining multiple classifiers. In: 16th International conference on pattern recognition, 2002. Proceedings. IEEE, vol 2, pp 192–195

  7. Dietterich TG (2000) Ensemble methods in machine learning. In: International workshop on multiple classifier systems. Springer, pp 1–15

  8. Fearnhead P, Taylor BM (2011) On estimating the ability of NBA players. J Quant Anal Sports. https://doi.org/10.2202/1559-0410.1298

    Article  Google Scholar 

  9. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 11(1):10–18

    Article  Google Scholar 

  10. Holland JH (1975) Adaptation in natural and artificial systems. MIT press, Cambridge. https://doi.org/10.1137/1018105

    Book  Google Scholar 

  11. Hu YH, Chen YL, Chou HL (2017) Opinion mining from online hotel reviews—a text summarization approach. Inf Process Manag 53(2):436–449. https://doi.org/10.1016/j.ipm.2016.12.002

    Article  Google Scholar 

  12. Irani K, Fayyad U (1993) Multi-interval discretization of continuous-valued attributes for classification learning. Proc Natl Acad Sci USA 90:1022–1027. https://doi.org/10.1109/TKDE.2011.181

    Article  Google Scholar 

  13. Ishibuchi H, Yamamoto T (2003) Deriving fuzzy discretization from interval discretization. In: The 12th IEEE international conference on fuzzy systems, 2003. FUZZ’03. IEEE, vol 1, pp 749–754

  14. Jalilian G, Khabiri M (2005) Describing the status of Iran’s Football Premier League clubs and clubs in major leagues, comparison with China, Malaysia and the United Kingdom (In Persian). J Strateg Manag Rev 5(1):41–54

    Google Scholar 

  15. Jiawei H, Kamber M, Han J, Kamber M, Pei J (2006) Data mining: concepts and techniques. Morgan Kaufmann, Burlington. https://doi.org/10.1016/B978-0-12-381479-1.00001-0

    Book  MATH  Google Scholar 

  16. Karsak EE (2000) A fuzzy multiple objective programming approach for personnel selection. In: 2000 IEEE international conference on systems, man, and cybernetics. IEEE, vol 3, pp 2007–2012

  17. Kianmehr K, Alshalalfa M, Alhajj R (2008) Effectiveness of fuzzy discretization for class association rule-based classification. In: International symposium on methodologies for intelligent systems. Springer, pp 298–308

  18. Kim MJ, Min SH, Han I (2006) An evolutionary approach to the combination of multiple classifiers to predict a stock price index. Expert Syst Appl 31(2):241–247

    Article  Google Scholar 

  19. Kohavi R, Provost F (1998) Glossary of terms. Mach Learn 30(2–3):271–274. https://doi.org/10.1023/A:1017181826899

    Article  Google Scholar 

  20. Kotsiantis S, Kanellopoulos D, Pintelas P et al (2006) Handling imbalanced datasets: a review. GESTS Int Trans Comput Sci Eng 30(1):25–36

    Google Scholar 

  21. Kubatko J, Oliver D, Pelton K, Rosenbaum DT et al (2007) A starting point for analyzing basketball statistics. J Quant Anal Sports 3(3):1–22

    MathSciNet  Google Scholar 

  22. Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley, New York

    Book  Google Scholar 

  23. Kusiak A (2001) Feature transformation methods in data mining. IEEE Trans Electron Packag Manuf 24(3):214–221

    Article  Google Scholar 

  24. Li Y, Zhang Y (2012) Application of data mining techniques in sports training. In: 2012 5th International conference on biomedical engineering and informatics. IEEE, pp 954–958. https://doi.org/10.1109/BMEI.2012.6513050

  25. Louzada F, Maiorano AC, Ara A (2016) ISports: a web-oriented expert system for talent identification in soccer. Expert Syst Appl 44:400–412. https://doi.org/10.1016/j.eswa.2015.09.007

    Article  Google Scholar 

  26. Mohammad Kazemi R, Tondnevis F, Khabiri M (2008) Analysis of price of sports marketing in the Iranian professional soccer league, comparing the current situation with South Korean and Japanese League (In Persian). J Strateg Manag Rev 6(12):121–132

    Google Scholar 

  27. Orso V, Ruotsalo T, Leino J, Gamberini L, Jacucci G (2017) Overlaying social information: the effects on users’ search and information-selection behavior. Inf Process Manag 53(6):1269–1286. https://doi.org/10.1016/j.ipm.2017.06.001

    Article  Google Scholar 

  28. Reilly T, Williams AM, Nevill A, Franks A (2000) A multidisciplinary approach to talent identification in soccer. J Sports Sci 18(May 2013):695–702. https://doi.org/10.1080/02640410050120078

    Article  Google Scholar 

  29. Ruta D, Gabrys B (2005) Classifier selection for majority voting. Inf Fusion 6(1):63–81

    Article  Google Scholar 

  30. Schumaker RP, Solieman OK, Chen H (2010) Sports data mining, integrated series in information systems. Springer, Boston. https://doi.org/10.1007/978-1-4419-6730-5

    Book  Google Scholar 

  31. Tavana M, Azizi F, Azizi F, Behzadian M (2013) A fuzzy inference system with application to player selection and team formation in multi-player sports. Sport Manag Rev 16(1):97–110. https://doi.org/10.1016/j.smr.2012.06.002

    Article  Google Scholar 

  32. Torabi T, Ghorbani M, Bagheri M, Zarifi S (2015) New methods of financing of football clubs in developed countries and Its compatibility with developing countries (A case study in professional football clubs in the Premier League Iran and the United Kingdom), in Persian. Invest Knowl 4:217–232

    Google Scholar 

  33. Trawinski K (2010) A fuzzy classification system for prediction of the results of the basketball games. In: International conference on fuzzy systems, pp 1–7. https://doi.org/10.1109/FUZZY.2010.5584399

  34. Štrumbelj E, Šikonja MR (2010) Online bookmakers’ odds as forecasts: the case of European soccer leagues. Int J Forecast 26(3):482–488. https://doi.org/10.1016/j.ijforecast.2009.10.005

    Article  Google Scholar 

  35. Witten IH, Frank E, Ma Hall (2011) Data mining: practical machine learning tools and techniques, vol 54, 3rd edn. Morgan Kaufmann, Burlington. https://doi.org/10.1002/1521-3773(20010316)40:6<9823::AID-ANIE9823>3.3.CO;2-C

    Google Scholar 

  36. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Mr. Majid Rezaei and other coaches and national soccer players for their expert opinions and comments and their involvements in making our dataset.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, University of Guilan, Rasht, Iran

    Reza Maanijou

  2. Department of Computer Engineering, University of Guilan, P.O. Box: 1841, Rasht, Iran

    Seyed Abolghasem Mirroshandel

Authors
  1. Reza Maanijou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seyed Abolghasem Mirroshandel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seyed Abolghasem Mirroshandel.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interests. The authors alone are responsible for the content and writing of the paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maanijou, R., Mirroshandel, S.A. Introducing an expert system for prediction of soccer player ranking using ensemble learning. Neural Comput & Applic 31, 9157–9174 (2019). https://doi.org/10.1007/s00521-019-04036-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04036-9

Keywords

Search

Navigation