Skip to main content

Goal-Oriented Classification of Football Results

  • Conference paper
  • First Online:
Computational Collective Intelligence (ICCCI 2023)

Abstract

In this article, we propose identifying and analyzing the problem of relatively poor classification results related to a single decision class in sports data. First, we preprocess the data to obtain the decision class. Then, we implement a goal-oriented approach to the football data to improve the results for algorithms like ACDT (Ant Colony Decision Tree) and ACDF (Ant Colony Decision Forrest). The main difference in the case of the goal-oriented approach is the focus on particular classification measures like precision and recall. These measures are adapted to mentioned algorithms, and the whole approach is compared with the original algorithms based on the accuracy measure. Finally, numerical experiments are performed on the initially preprocessed real-world data set based on nine seasons of the German football Bundesliga.

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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

References

  1. Ahmad, M.A., Eckert, C., Teredesai, A.: Interpretable machine learning in healthcare. In: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 559–560 (2018)

    Google Scholar 

  2. Arabzad, S.M., Tayebi Araghi, M.E., Sadi-Nezhad, S., Ghofrani, N.: Football match results prediction using artificial neural networks: the case of Iran pro league. J. Appl. Res. Ind. Eng. 1, 159–179 (2014)

    Google Scholar 

  3. Babota, 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)

    Article  Google Scholar 

  4. Boulier, B.L., Stekler, H.O.: Neural network prediction of NFL football games. Int. J. Forecast. 19(2), 257–270 (2003)

    Article  Google Scholar 

  5. Breiman, L., Friedman, J., Stone, C., Olshen, R.: Classification and regression trees. Chapman & Hall, New York (1984)

    Google Scholar 

  6. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996). https://doi.org/10.1007/BF00058655

    Article  MATH  Google Scholar 

  7. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  8. Bunker, R.P., Thabtah, F.: A machine learning framework for sport result prediction. Appl. Comput. Inform. 15(1), 27–33 (2019)

    Article  Google Scholar 

  9. De Prado, M.L.: Advances in Financial Machine Learning. Wiley, Hoboken (2018)

    Google Scholar 

  10. Delen, D., Cogdell, D., Kasap, N.: A comparative analysis of data mining methods in predicting NCAA bowl outcomes. Int. J. Forecast. 28(2), 543–552 (2012). https://doi.org/10.1016/j.ijforecast.2011.05.002

    Article  Google Scholar 

  11. Dorigo, M.: Optimization, learning and natural algorithms (in Italian). Ph.D. thesis, vol. 192, pp. 1573–1582 (1992)

    Google Scholar 

  12. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008). https://doi.org/10.1023/B:STCO.0000035301.49549.88

    Article  MATH  Google Scholar 

  13. Fernandez, M., Ulmer, B.: Predicting soccer match results in the English premier league (2014)

    Google Scholar 

  14. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997). https://doi.org/10.1006/jcss.1997.1504

    Article  MathSciNet  MATH  Google Scholar 

  15. Freund, Y., Schapire, R.E., et al.: Experiments with a new boosting algorithm. In: ICML, vol. 96, pp. 148–156. Citeseer (1996)

    Google Scholar 

  16. Głowania, S., Kozak, J., Juszczuk, P.: New voting schemas for heterogeneous ensemble of classifiers in the problem of football results prediction. Procedia Comput. Sci. 207, 3393–3402 (2022)

    Article  Google Scholar 

  17. Hasan, A., Moin, S., Karim, A., Shamshirband, S.: Machine learning-based sentiment analysis for twitter accounts. Math. Comput. Appl. 23(1), 11 (2018)

    Google Scholar 

  18. Hastie, T., Tibshirani, R., Friedman, J.H., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, vol. 2. Springer, New York (2009). https://doi.org/10.1007/978-0-387-21606-5

  19. Joseph, A., Fenton, N.E., Neil, M.: Predicting football results using Bayesian nets and other machine learning techniques. Knowl.-Based Syst. 19(7), 544–553 (2006)

    Article  Google Scholar 

  20. Juszczuk, P., Kozak, J., Dziczkowski, G., Głowania, S., Jach, T., Probierz, B.: Real-world data difficulty estimation with the use of entropy. Entropy 23(12), 1621 (2021). https://doi.org/10.3390/e23121621

    Article  Google Scholar 

  21. Kozak, J.: Decision Tree and Ensemble Learning Based on Ant Colony Optimization. SCI, vol. 781. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-93752-6

    Book  Google Scholar 

  22. Kozak, J., Boryczka, U.: Multiple boosting in the ant colony decision forest meta-classifier. Knowl.-Based Syst. 75, 141–151 (2015)

    Article  Google Scholar 

  23. Kozak, J., Głowania, S.: Heterogeneous ensembles of classifiers in predicting Bundesliga football results. Procedia Comput. Sci. 192, 1573–1582 (2021). https://doi.org/10.1016/j.procs.2021.08.161

    Article  Google Scholar 

  24. Kozak, J., Głowania, S.: Bundesliga football results (2021). https://www.ue.katowice.pl/index.php?id=20435

  25. Maszczyk, A., Gołaś, A., Pietraszewski, P., Roczniok, R., Zając, A., Stanula, A.: Application of neural and regression models in sports results prediction. Procedia Soc. Behav. Sci. 117, 482–487 (2014). https://doi.org/10.1016/j.sbspro.2014.02.249

    Article  Google Scholar 

  26. McCabe, A., Trevathan, J.: Artificial intelligence in sports prediction. In: Fifth International Conference on Information Technology: New Generations (ITNG 2008), pp. 1194–1197. IEEE (2008). https://doi.org/10.1109/ITNG.2008.203

  27. Men, Y.: Intelligent sports prediction analysis system based on improved gaussian fuzzy algorithm. Alex. Eng. J. 61(7), 5351–5359 (2022)

    Article  Google Scholar 

  28. Nguyen, N.H., Nguyen, D.T.A., Ma, B., Hu, J.: The application of machine learning and deep learning in sport: predicting NBA players’ performance and popularity. J. Inf. Telecommun. 6(2), 217–235 (2022)

    Google Scholar 

  29. Pai, P.F., ChangLiao, L.H., Lin, K.P.: Analyzing basketball games by a support vector machines with decision tree model. Neural Comput. Appl. 28(12), 4159–4167 (2017). https://doi.org/10.1007/s00521-016-2321-9

    Article  Google Scholar 

  30. Pająk, G., Krutz, P., Patalas-Maliszewska, J., Rehm, M., Pająk, I., Dix, M.: An approach to sport activities recognition based on an inertial sensor and deep learning. Sens. Actuators, A 345(1), 113773 (2022)

    Article  Google Scholar 

  31. Qiu, S., et al.: Multi-sensor information fusion based on machine learning for real applications in human activity recognition: state-of-the-art and research challenges. Physica A: Stat. Mech. Appl. 528, 121461 (2019)

    Google Scholar 

  32. Rue, H., Salvesen, O.: Prediction and retrospective analysis of soccer matches in a league. J. Royal Stat. Soc. Ser. D (2000)

    Google Scholar 

  33. Schauberger, G., Groll, A., Tutz, G.: Modeling football results in the German Bundesliga using match-specific covariates. Technical report number 197 (2016)

    Google Scholar 

  34. Shen, H.: Prediction simulation of sports injury based on embedded system and neural network. Microprocess. Microsyst. 82, 103900 (2021)

    Article  Google Scholar 

  35. Zhang, Q., Zhang, X., Hu, H., Li, C., Lin, Y., Ma, R.: Sports match prediction model for training and exercise using attention-based LSTM network. Digi. Commun. Netw. 8(4), 508–515 (2022)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Szymon Głowania .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Głowania, S., Kozak, J., Juszczuk, P. (2023). Goal-Oriented Classification of Football Results. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_59

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-41456-5_59

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41455-8

  • Online ISBN: 978-3-031-41456-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics