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Stacked-Based Ensemble Machine Learning Model for Positioning Footballer

  • Research Article-Computer Engineering and Computer Science
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Abstract

Due to the high performance of machine learning (ML) methods in different disciplines, these methods have been used frequently in various sports fields, especially in the last decade. Researchers have used ML algorithms in football on various subjects such as match result prediction, estimation of factors affecting match results, prediction of league standings, and analysis of the performances of football players. However, there has not been enough work on determining the position of the football player, which is one of the leading problems for coaches in football. Therefore, this study aims to classify footballer positions employing a stacked ensemble ML model using the FIFA’19 game dataset. To achieve this aim, a two-stage application is followed. In the first stage, 10 features are selected using four different feature selection algorithms. In the second stage, Deep Neural Networks, Random Forest, and Gradient Boosting were used as single-based algorithms, and Logistic Regression was employed as a meta-learner in the stacked model. The results show that the combination of the Chi-square feature selection technique and the stacked-based ensemble learning model yielded the best accuracy (83.9%). The findings emphasize the validity and robustness of our stacked ensemble learning model to determine the positions of footballers.

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Notes

  1. https://www.kaggle.com/karangadiya/fifa19.

References

  1. Tümer, A.E.; Koçer, S.: Prediction of team league’s rankings in volleyball by artificial neural network method. Int. J. Perform. Anal. Sport 17(3), 202–211 (2017). https://doi.org/10.1080/24748668.2017.1331570

    Article  Google Scholar 

  2. Özdemir, E.; Ballı, S.: Türkiye erkekler basketbol ligi maç sonuçlarının makine Öğgrenmesi yöntemleri ile tahmini. Mühendislik Bilimleri ve Tasarım Dergisi 8(3), 740–752 (2020)

    Article  Google Scholar 

  3. Fernandes, C.J.; Yakubov, R.; Li, Y.; Prasad, A.K.; Chan, T.C.Y.: Predicting plays in the national football league. J. Sports Anal. 6(1), 35–43 (2020). https://doi.org/10.3233/JSA-190348

    Article  Google Scholar 

  4. Razali, N.; Mustapha, A.; Yatim, F.A.; Aziz, R.A.: Predicting player position for talent identification in association football. In: IOP Conference Series: Materials Science and Engineering, vol. 226, p. 012087. IOP Publishing (2017)

  5. Maulany, G.J.; et al.: Application of case based reasoning and nearest neighbor algorithm for positioning football players. Int. J. Mech. Eng. Technol. (IJMET) 9(13), 258–265 (2018)

    Google Scholar 

  6. Abualigah, L.; Diabat, A.; Mirjalili, S.; Elaziz, M.A.; Gandomi, A.H.: The arithmetic optimization algorithm. Comput. Methods Appl. Mech. Eng. 376, 113609 (2021a)

    Article  MathSciNet  MATH  Google Scholar 

  7. Abualigah, L.; Yousri, D.; Elaziz, M.A.; Ewees, A.A.; Al-qaness, M.A.A.; Gandomi, A.H.: Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput. Ind. Eng. 157, 107250 (2021b)

    Article  Google Scholar 

  8. Abualigah, L.; Elaziz, M.A.; Sumari, P.; Geem, Z.W.; Gandomi, A.H.: Reptile search algorithm (RSA): a nature-inspired meta-heuristic optimizer. Expert Syst. Appl. 191, 116158 (2022)

    Article  Google Scholar 

  9. Agushaka, J.O.; Ezugwu, A.E.; Abualigah, L.: Dwarf mongoose optimization algorithm. Comput. Methods Appl. Mech. Eng. 391, 114570 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  10. Oyelade, O.N.; Ezugwu, A.E.-S.; Mohamed, T.I.A.; Abualigah, L.: Ebola optimization search algorithm: a new nature-inspired metaheuristic optimization algorithm. IEEE Access 10, 16150–16177 (2022). https://doi.org/10.1109/ACCESS.2022.3147821

    Article  Google Scholar 

  11. Savaş, S.: Detecting the stages of Alzheimer’s disease with pre-trained deep learning architectures. Arab. J. Sci. Eng. 47, 2201–2218 (2022). https://doi.org/10.1007/s13369-021-06131-3

  12. Karakış, R.; Güler, İ.; Işık, A.H.: Feature selection in pulmonary function test data with machine learning methods. In: 2013 21st Signal Processing and Communications Applications Conference (SIU), pp. 1–4 (2013). https://doi.org/10.1109/SIU.2013.6531578

  13. Buyrukoğlu, S.: Early detection of Alzheimer’s disease using data mining: comparison of ensemble feature selection approaches. Konya Mühendislik Bilimleri Dergisi 9(1), 50–61 (2021)

    Google Scholar 

  14. Abualigah, L.M.Q.; et al.: Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering. Springer (2019)

  15. Too, E.C.; Yujian, L.; Njuki, S.; Yingchun, L.: A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric. 161, 272–279 (2019)

    Article  Google Scholar 

  16. Porkodi, R.: Comparison of filter based feature selection algorithms: an overview. Int. J. Innov. Res. Technol. Sci. 2(2), 108–113 (2014)

    Google Scholar 

  17. Vanaja, S.; Kumar, K.R.: Analysis of feature selection algorithms on classification: a survey. Int. J. Comput. Appl. 96(17), 28–35 (2014)

    Google Scholar 

  18. Tang, J.; Alelyani, S.; Liu, H.: Feature Selection for Classification: A Review, pp. 37–64. CRC Press (2014). ISBN 9781466586741. https://doi.org/10.1201/b17320

  19. Canziani, A.; Paszke, A.; Culurciello, E.: An analysis of deep neural network models for practical applications. CoRR (2016). arxiv:1605.07678

  20. Calp, M.H.: Use of Deep Learning Approaches in Cancer Diagnosis, pp. 249–267. Springer, Singapore, 2021. ISBN 978-981-15-6321-8. https://doi.org/10.1007/978-981-15-6321-8_15

  21. Bütüner, R.; Calp, M.H.: Covid-19 detection from lung tomography images using deep learning and machine learning methods. Technical report, EasyChair (2020)

  22. Savaş, S.; Topaloğlu, N.; Kazcı, Ö.; Koşar, P.N.: Classification of carotid artery intima media thickness ultrasound images with deep learning. J. Med. Syst. 43(8), 1–12 (2019). https://doi.org/10.1007/s10916-019-1406-2

    Article  Google Scholar 

  23. Liu, Y.; Wang, Y.; Zhang, J.: New machine learning algorithm: random forest. In: Liu, B., Ma, M., Chang, J. (eds.) Information Computing and Applications, pp. 246–252. Springer, Berlin (2012). ISBN 978-3-642-34062-8

  24. Natekin, A.; Knoll, A.: Gradient boosting machines, a tutorial. Front. Neurorobotics 7, 21 (2013). ISSN 1662-5218. https://doi.org/10.3389/fnbot.2013.00021

  25. Buyrukoğlu, S.: Improvement of machine learning models’ performances based on ensemble learning for the detection of Alzheimer disease. In: 2021 6th International Conference on Computer Science and Engineering (UBMK), pp. 102–106 (2021). https://doi.org/10.1109/UBMK52708.2021.9558994

  26. Aurélien, G.: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media (2019)

  27. Hossin, M.; Sulaiman., M.N.: A review on evaluation metrics for data classification evaluations. Int. J. Data Min. Knowl. Manag. Process 5(2), 1 (2015)

    Article  Google Scholar 

  28. Awadh, K.; Akbaş, A.: Intrusion detection model based on TF.IDF and C4. 5 algorithms. Politeknik Dergisi 24(4), 1691–1698 (2021). https://doi.org/10.2339/politeknik.693221

    Article  Google Scholar 

  29. Hung, C.-Y.; Chen, W.-C.; Lai, P.-T.; Lin, C.-H.; Lee, C.-C.: Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3110–3113. IEEE (2017)

  30. Ul Haq, A., Li, J., Memon, M.H., Khan, J., Ud Din, S., Ahad, I., Sun, R., Lai, Z.: Comparative analysis of the classification performance of machine learning classifiers and deep neural network classifier for prediction of Parkinson disease. In: 2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 101–106. IEEE (2018)

  31. Buyrukoglu, S.; Batmaz, F.; Lock, R.: Improving marking efficiency for longer programming solutions based on a semi-automated assessment approach. Comput. Appl. Eng. Educ. 27(3), 733–743 (2019). https://doi.org/10.1002/cae.22094

  32. South, C.; Egros, E.: Forecasting college football game outcomes using modern modeling techniques. J. Sports Anal. 6(1), 25–33 (2020)

  33. Geurkink, Y.; Boone, J.; Verstockt, S.; Bourgois, J.G.: Machine learning-based identification of the strongest predictive variables of winning and losing in Belgian professional soccer. Appl. Sci. 11(5) (2021). ISSN 2076-3417. https://doi.org/10.3390/app11052378

  34. Hasan, A.; Aktuğ, Z.B.; Kılıç., F.: Türkiye süper lig sezon sonu takım sıralamasının geliştirilen yapay sinir ağları modeli ile tahmin edilmesi. Spor ve Performans Araştırmaları Dergisi 11(3), 258–268 (2020)

    Google Scholar 

  35. Hasan, A.; Serkan, İ; Aktuğ, Z.B.; Kılıç, F.: Yapay sinir ağları modeli ile İspanya futbol ligi (la lıga) sezon sonu takım sıralamasının tahmin edilmesi. SPORMETRE Beden Eğitimi ve Spor Bilimleri Dergisi 19(1), 147–155 (2021)

    Google Scholar 

  36. Kılıç, F.; Hasan, A.; Aktuğ., Z.B.: Futbolda yapay sinirağları modeli ile lig sıralaması tahmini. Uluslararası Güncel Eğitim Araştırmaları Dergisi 6(2), 379–391 (2020)

    Google Scholar 

  37. Almulla, J.; Alam, T.: Machine learning models reveal key performance metrics of football players to win matches in Qatar stars league. IEEE Access 8, 213695–213705 (2020). https://doi.org/10.1109/ACCESS.2020.3038601

    Article  Google Scholar 

  38. Ćwiklinski, B.; Giełczyk, A.; Choraś, M.: Who will score? A machine learning approach to supporting football team building and transfers. Entropy 23(1), 90 (2021)

    Article  Google Scholar 

  39. Khan, M.A.; Habib, M.; Saqib, S.; Alyas, T.; Khan, K.M.; Ghamdi, M.A.A.; Almotiri, S.H.: Analysis of the smart player’s impact on the success of a team empowered with machine learning. Comput. Mater. Continua 66(1), 691–706 (2021). https://doi.org/10.32604/cmc.2020.012542. ISSN 1546-2226.

    Article  Google Scholar 

  40. Hubáček, O.; Šourek, G.; Železnỳ, F.: Learning to predict soccer results from relational data with gradient boosted trees. Mach. Learn. 108(1), 29–47 (2019)

    Article  MathSciNet  Google Scholar 

  41. Baboota, R., Kaur, H.: Predictive analysis and modelling football results using machine learning approach for English premier league. Int. J. Forecast. 35(2), 741–755 (2019). ISSN 0169-2070. https://doi.org/10.1016/j.ijforecast.2018.01.003

  42. Karabıyık, A., Zaim, A.H.: Futbol maç sonuçlarına etki eden faktörlerin tespiti ve faktörlerin etki paylarının hesaplanarak maç sonuçlarının tahmin edilmesi. II. Mühç Araştırmacılar Kongresi (2005)

  43. Bilek, G.; Ulas, E.: Predicting match outcome according to the quality of opponent in the English premier league using situational variables and team performance indicators. Int. J. Perform. Anal. Sport 19(6), 930–941 (2019). https://doi.org/10.1080/24748668.2019.1684773

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Çankırı Karatekin University, Çankırı, 18100, Turkey

    Selim Buyrukoğlu & Serkan Savaş

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  1. Selim Buyrukoğlu
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  2. Serkan Savaş
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Correspondence to Serkan Savaş.

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Buyrukoğlu, S., Savaş, S. Stacked-Based Ensemble Machine Learning Model for Positioning Footballer. Arab J Sci Eng 48, 1371–1383 (2023). https://doi.org/10.1007/s13369-022-06857-8

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