Skip to main content
SpringerLink
Log in

Forecasting Goal Performance for Top League Football Players: A Comparative Study

  • Conference paper
  • First Online:
Artificial Intelligence Applications and Innovations (AIAI 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 676))

  • 711 Accesses

Abstract

In this paper, we review the literature on Sports Analytics (SA) and predict football players’ scoring performance. Based on previous years’ performance, we predict the number of goals that players scored during the 2021–22 season. To achieve this, we collected advanced statistics for players from five major European Leagues: the English Premier League, the Spanish La Liga, the German Bundesliga, the French Ligue1 and the Italian Serie A, for seasons from 2017–18 up to 2021–22. Additionally, we used one-season lag features, and three supervised Machine Learning (ML) algorithms for experimental benchmarking: Linear Regression (LR), Random Forest (RF) and Multilayer Perceptron (MLP). Furthermore, we compared these models based on their performance. All models’ results are auspicious and comparable to each other. LR was the best performing model with Mean Absolute Error (MAE) 1.60, Mean Squared Error (MSE) 7.06 and Root Mean Square Error (RMSE) 2.66. Based on feature importance analysis, we established that every player’s upcoming scoring performance is strongly associated with previous season’s goals (Gls) and expected goals (xG).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover 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. Al-Asadi, M.A., Tasdemir, S.: Predict the value of football players using FIFA video game data and machine learning techniques. IEEE Access 10, 22631–22645 (2022). https://doi.org/10.1109/ACCESS.2022.3154767

  2. Acharya, M.S., Armaan, A., Antony, A.S.: A comparison of regression models for prediction of graduate admissions. In: 2019 International Conference on Computational Intelligence in Data Science (ICCIDS) (2019). https://doi.org/10.1109/iccids.2019.8862140

  3. Apostolou, K., Tjortjis, C.: Sports analytics algorithms for performance prediction. In: 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–4 (2018). https://doi.org/10.1109/IISA.2019.8900754

  4. Babbar, M., Rakshit, S.K.: A systematic review of sports analytics. Int. Conf. Bus. Manage. (2019)

    Google Scholar 

  5. Calleja, P., Muscat, A., Decelis, A.: The effects of audience behaviour on football players’ performance. J. New Stud. Sport Manage. 3(1), 336–353 (2022). https://doi.org/10.22103/JNSSM.2022.18890.1055

  6. Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)?- arguments against avoiding RMSE in the literature. Geoscientific Model Dev. 7, 1247–1250 (2014). https://doi.org/10.5194/gmd-7-1247-2014

    Article  Google Scholar 

  7. Cintia, P., Pappalardo, L., Rinzivillo, S.: A network-based approach to evaluate the performance of football teams. In: Machine Learning and Data Mining for Sports Analytics Workshop (MLSA 2015) (2015)

    Google Scholar 

  8. Frey, M., Murina, E., Rohrabach, J., Walser, M., Haas, P., Dettling, M.: Machine learning for position detection in football. In: 2019 6th Swiss Conference on Data Science (SDS), pp. 111–112 (2019). https://doi.org/10.1109/SDS.2019.00009

  9. Ghafari, S.M., Tjortjis, C.: A survey on association rules mining using heuristics. Wiley Interdisc. Rev.: Data Min Knowl. Disc. 9(4), (2019). https://doi.org/10.1002/widm.1307

  10. Gupta, S., Gupta, A.: Dealing with noise problem in machine learning datasets: a systematic review. Procedia Comput. Sci. 161, 466–474 (2019)

    Article  Google Scholar 

  11. Gyarmati, L., Hefeeda, M.: Competition-wide evaluation of individual and team movements in soccer. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 144–151 (2016). https://doi.org/10.1109/icdmw.2016.0028

  12. Hinton, G.E.: How neural networks learn from experience. Sci. Am. 267(3), 144–151 (1992). https://doi.org/10.1038/scientificamerican0992-144

    Article  Google Scholar 

  13. Iqbal, M.A.: Application of regression techniques with their advantages and disadvantages. Elektron Magazine 4, 11–17 (2021)

    Google Scholar 

  14. Jackson, E., Agrawal, R.: Performance evaluation of different feature encoding schemes on cybersecurity logs. In: 2019 SoutheastCon (2019). https://doi.org/10.1109/SoutheastCon42311.2019.9020560

  15. Kapoteli, E., Koukaras, P., Tjortjis, C.: Social media sentiment analysis related to COVID-19 vaccines: case studies in English and Greek language. Artif. Intell. Appl. Innov. (2022). https://doi.org/10.1007/978-3-031-08337-2_30

  16. Koukaras, P., Tjortjis, C., Rousidis, D.: Mining association rules from COVID-19 related twitter data to discover word patterns, topics and inferences. Inf. Syst. 109, 1–21 (2022). https://doi.org/10.1016/j.is.2022.102054

    Article  Google Scholar 

  17. Krogh, A.: What are artificial neural networks? Nat. Biotechnol. 26(2), 195–197 (2008). https://doi.org/10.1038/nbt1386

    Article  Google Scholar 

  18. Kursa, M., Rudnicki, W.: The all relevant feature selection using random forest (2011)

    Google Scholar 

  19. Manish, S., Bhagat, V., Pramila, R.: Prediction of football players performance using machine learning and deep learning algorithms. In: 2021 2nd International Conference for Emerging Technology (INCET). pp. 1–5 (2021). https://doi.org/10.1109/INCET51464.2021.9456424

  20. Pantzalis, V.C., Tjortjis, C.: Sports analytics for football league table and player performance prediction. In: 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA) (2020). https://doi.org/10.1109/iisa50023.2020.9284352

  21. Pariath, R., Shah, S., Surve, A., Mittal, J.: Player performance prediction in football game. In: Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1148–1153 (2018). https://doi.org/10.1109/ICECA.2018.8474750

  22. Sarlis, V., Chatziilias, V., Tjortjis, C., Mandalidis, D.: A data science approach analyzing the impact of injuries on basketball player and team performance. Inf. Syst. 99, 101750 (2021). https://doi.org/10.1016/j.is.2021.101750

    Article  Google Scholar 

  23. Singh, J.: Random Forest: Pros and Cons. Medium (2020). https://medium.datadriveninvestor.com/random-forest-pros-and-cons-c1c42fb64f04

  24. Sports Reference, https://www.sports-reference.com/. Accessed 1 Sept 2022

  25. Srinivasan, B.: A social network analysis of football – evaluating player and team performance. In: 2017 Ninth International Conference on Advanced Computing (ICoAC), pp. 242–246 (2017). https://doi.org/10.1109/ICoAC.2017.8441301

  26. Subramanya, T., Harutyunyan, D., Riggio, R.: Machine learning-driven service function chain placement and scaling in MEC-enabled 5G networks. Comput. Netw. 1–20 (2019). https://doi.org/10.1016/j.comnet.2019.106980

  27. Tzirakis, P., Tjortjis, C.: T3C: improving a decision tree classification algorithm’s interval splits on continuous attributes. Adv. Data Anal. Classif. 11(2), 353–370 (2016). https://doi.org/10.1007/s11634-016-0246-x

    Article  MathSciNet  MATH  Google Scholar 

  28. Willmott, C., Matsuura, K.: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res. 30, 79–82 (2005). https://doi.org/10.3354/cr030079

    Article  Google Scholar 

  29. Yang, J.B., Shen, K.Q., Ong, C.J., Li, X.P.: Feature selection for MLP neural network: the use of random permutation of probabilistic outputs. IEEE Trans. Neural Netw. 20(12), 1911–1922 (2009). https://doi.org/10.1109/tnn.2009.2032543

    Article  Google Scholar 

Download references

Acknowledgments

This research is co-financed by Greece and the European Union (European Social Fund-SF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning 2014–2020» in the context of the project “Support for International Actions of the International Hellenic University”, (MIS 5154651).

Author information

Authors and Affiliations

  1. The Data Mining and Analytics Research Group, School of Science and Technology, International Hellenic University, Thessaloniki, Greece

    Nikolaos Giannakoulas, George Papageorgiou & Christos Tjortjis

Authors
  1. Nikolaos Giannakoulas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George Papageorgiou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christos Tjortjis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christos Tjortjis .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  3. University of Sunderland, Sunderland, UK

    John MacIntyre

  4. University of Leon, León, Spain

    Manuel Dominguez

Appendix

Appendix

We include here both basic and advanced features for forecasting experiments.

Appendix Basic and Advanced Features Dataset Glossary
Full size table

Rights and permissions

Reprints and permissions

Copyright information

© 2023 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Giannakoulas, N., Papageorgiou, G., Tjortjis, C. (2023). Forecasting Goal Performance for Top League Football Players: A Comparative Study. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 676. Springer, Cham. https://doi.org/10.1007/978-3-031-34107-6_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-34107-6_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34106-9

  • Online ISBN: 978-3-031-34107-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation