Abstract
The development of big data and machine learning is having a special boost in the field of innovation in sports. Football is the most popular sport in Europe with millions of players and billions of euros invested. Currently, machine learning applications in football are focused on video analysis to events detection (tracking of players, statistics matches, scouting, ...), injuries evaluation and prediction, among others. The xG metric (Expected Goals) determines the probability that a shot will result in a goal and is often displayed on screen during the most important football matches in the world. In this paper we present a new model to obtain more accurate predictions of the probability of a shot becoming a goal. The model is based on a multi-layer perceptron neural network (MLP), which allows the interaction of different variables and improves the model’s performance. Our proposal includes an evaluation of the quality and a comparison with the xG provided by statsbomb, one of the most important football data providers of the world. The results show that our model outperforms the quality of the expectations provided by statsbomb. Specifically, it is clearly better in their capability to detect actual positive cases (goals).
This work has been supported by the Spanish MINECO/FEDER project AwESOMe (PID2021-122215NB-C31) and the Region of Madrid project FORTE-CM (S2018/TCS-4314), co-funded by EIE Funds of the European Union.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Not all xG models take into account the same factors. We refer in this work to the xG model given by Statsbomb [25], which uses more contextual events and better quality data than any other provider to accurately measure the quality of chances.
References
Akan, S., Varli, S.: Use of deep learning in soccer videos analysis: survey. Multimed. Syst. 29(3), 897–915 (2023)
Iman Behravan and Seyed Mohammad Razavi: A novel machine learning method for estimating football players’ value in the transfer market. Soft. Comput. 25(3), 2499–2511 (2021)
Cacho-Elizondo, S., Álvarez, J.D.L.: Big data in the decision-making processes of football teams. J. Strateg. Innov. Sustain. 15(2), 21–44 (2020)
Dyte, D., Clarke, S.R.: A ratings based poisson model for world cup soccer simulation. J. Oper. Res. Soc. 51(8), 993–998 (2000)
Fang, L., Wei, Q., Cheng, X.: Technical and tactical command decision algorithm of football matches based on big data and neural network. Sci. Program. 1–9(04), 2021 (2021)
Footystats. https://footystats.org/es/spain/la-liga/xg. Accessed 10 Mar 2023
Gilch, L.A.: Prediction model for the Africa cup of nations 2019 via nested poisson regression. Afr. J. Appl. Stat. 6(1), 599–616 (2019)
Groll, A., Kneib, T., Mayr, A., Schauberger, G.: On the dependency of soccer scores - a sparse bivariate Poisson model for the UEFA European football championship 2016. J. Quant. Anal. Sports 14(2), 65–79 (2018)
Groll, A., Schauberger, G., Tutz, G.: Prediction of major international soccer tournaments based on team-specific regularized Poisson regression: an application to the FIFA World Cup 2014. J. Quant. Anal. Sports 11(2), 97–115 (2015)
He, X.: Application of deep learning in video target tracking of soccer players. Soft. Comput. 20(20), 10971–10979 (2022)
Herberger, T.A., Litke, C.: The impact of big data and sports analytics on professional football: a systematic literature review. In: Herberger, T.A., Dötsch, J.J. (eds.) Digitalization, Digital Transformation and Sustainability in the Global Economy. SPBE, pp. 147–171. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77340-3_12
Hossain, E., Khan, I., Un-Noor, F., Sikander, S.S., Sunny, M.S.H.: Application of big data and machine learning in smart grid, and associated security concerns: a review. IEEE Access 7, 13960–13988 (2019)
Ip, R.H., Ang, L.M., Seng, K.P., Broster, J.C., Pratley, J.E.: Big data and machine learning for crop protection. Comput. Electr. Agric. 151, 376–383 (2018)
Kim, J., Kim, H., Lee, J., Lee, J., Yoon, J., Ko, S.-K.: A deep learning approach for fatigue prediction in sports using GPS data and rate of perceived exertion. IEEE Access 10, 103056–103064 (2022)
Méndez, M., Merayo, M.G., Núñez, M.: Long-term traffic flow forecasting using a hybrid CNN-BiLSTM model. Eng. Appl. Artif. Intell. 121, 106041 (2023)
Méndez, M., Merayo, M.G., Núñez,M.: Machine learning algorithms to forecast air quality: a survey. Artif. Intell. Rev. (2023)
Meng, T., Yang, J.Y.: Intervention of football players’ training effect based on machine learning. In: 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE 2022), pp. 592–595 (2022)
Kee Yuan Ngiam and Wei Khor: Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 20(5), e262–e273 (2019)
Qiu, J., Qihui, W., Ding, G., Yuhua, X., Feng, S.: A survey of machine learning for big data processing. EURASIP J. Adv. Signal Process. 1–16, 2016 (2016)
Rodrigues, J.F., Florea, L., de Oliveira, M.C.F., Diamond, D., Oliveira, O.N.: Big data and machine learning for materials science. Discover Mater. 1(1), 1–27 (2021). https://doi.org/10.1007/s43939-021-00012-0
Roldán-Gómez, J., del Rincón, J.M., Boubeta-Puig, J., Martínez, J.L.: An automatic unsupervised complex event processing rules generation architecture for real-time IoT attacks detection. Wireless Netw., 1–18 (2023)
Rossi, A., Pappalardo, L., Cintia, P., Iaia, F.M., Fernández, J., Medina, D.: Effective injury forecasting in soccer with GPS training data and machine learning. PLoS ONE 13, 1–15 (2018)
Surender R.S.: A survey of big data and machine learning. Int. J. Electr. Comput. Eng. 10(1) (2020)
Soccermatics. fitting the \(xg\) model. https://soccermatics.readthedocs.io/en/latest/gallery/lesson2/plot_xGModelFit.html. Accessed 10 Mar 2023
Statsbomb. https://statsbomb.com. Accessed 10 Mar 2023
Stival, L., et al.: Using machine learning pipeline to predict entry into the attack zone in football. PLoS ONE 18, 1–24 (2023)
Teranishi, M., Tsutsui, K., Takeda, K., Fujii, K.: Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds.) Machine Learning and Data Mining for Sports Analytics, MLSA 2022. Communications in Computer and Information Science, vol. 1783, pp. 53–73. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-27527-2_5
Thakkar, P., Shah, M.: An assessment of football through the lens of data science. Ann. Data Sci. 8, 823–836 (2021)
Understat. https://understat.com/. Accessed 10 Mar 2023
Wang,D.: Soccer tournament simulation and analysis for south Africa world cup with Poisson model of goal probability. In: 2010 Chinese Control and Decision Conference, pp. 3654–3659 (2010)
Zhou, L., Pan, S., Wang, J., Vasilakos, A.V.: Machine learning on big data: opportunities and challenges. Neurocomputing 237, 350–361 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Méndez, M., Montero, C., Núñez, M. (2023). Improving the Expected Goal Value in Football Using Multilayer Perceptron Networks. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-031-42430-4_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-42429-8
Online ISBN: 978-3-031-42430-4
eBook Packages: Computer ScienceComputer Science (R0)