World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model

Stanislaus Jiwandana Pinasthika(1), Dzikri Rahadian Fudholi(2*)

(1) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author


Football is one of the most popular sports in the world. The popularity makes every topic related to football interesting, for instance, the FIFA World Cup winner prediction. This topic is not only for casual discussion but could be a practical decision support for coaching staff to rate the team’s readiness. Most prediction methods use large match datasets. Since every national team has a different squad for every world cup and the FIFA World Cup is held every four years, the usage of a large match dataset is irrelevant. Therefore, there is a need for a prediction method based on the relevant data. We applied the Poisson distribution model for predicting the FIFA World Cup 2022 knockout stage match results. We calculate the probability of winning and losing based on their average goal scores and goal conceded and evaluate the difference by the actual result using de Finetti distance. The successful prediction is 8 out of 15 matches, with six inside the round of 16 games. This prediction model is also a brief example to overcome prediction problem using limited dataset. Thus, the new data attributes need to reformulate Poisson’s lambda. Further studies need to add the 3-4 prior world cup matches data to increase the acceptance of prediction.


probabilistic prediction; de Finetti distance; football match prediction

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[1] Q. Zhang, X. Zhang, H. Hu, C. Li, Y. Lin, and R. Ma, “Sport match prediction model for training and exercise using attention-based LSTM network,” Digit. Commun. Netw., vol. 8, pp. 508–515, Jan. 2021, doi:

[2] S.Subathra et al., “Economic Implications of Hosting FIFA World Cup – A Study with Social Reference to South Africa, Brazil, Russia, and Qatar,” in FIFA 2022 Qatar, The Legacy, Nova Science Publishers, Inc., 2021.

[3] A. A. Azeman, A. Mustapha, N. Razali, Nanthaamornphong, and Wahab, Mohd Helmy Abd, “Prediction of Football Matches Results: Decision Forest Against Neural Networks,” Int. Conf. Eletr. Eng. Comput. Telecommun. Inf. Technol., vol. 8.

[4] E. Tiwari, P. Sardar, and S. Jain, “Football Match Result Prediction Using Neural Network and Deep Learning,” Int. Conf. Reliab. Infocom Technol. Optim. Trends Future Dir., vol. 8, Jun. 2020.

[5] S. N. Maozad, S. N. A. M. Razali, A. Mustapha, A. Nanthaamornphong, M. H. A. Wahab, and N. Razali, “Comparative Analysis for Predicting Football Match Outcomes based on Poisson Models,” Int. Conf. Eletr. Eng. Comput. Telecommun. Inf. Technol., vol. 19, 2022.

[6] Md. Ashiqur Rahman, “A deep learning framework for football match prediction,” Springer Nat. Appl. Sci., Jan. 2020, doi:

[7] B. Siswanto, M. Y. Ricky, and J. M. Kerta, “Item Rating and Football Score Priori Prediction Algorithm,” Int. Conf. Inf. Manag. Technol. ICIMTech, 2019.

[8] Nur Fadilah and Sigit Priyanta, “Automatic Essay Scoring Using Data Augmentation in Bahasa Indonesia,” IJCCS Indones. J. Comput. Cybern. Syst., vol. 16, no. 4, pp. 401–410, Oct. 2022, doi: 76396.

[9] Dongil Kim and Seokho Kang, “Effect of Irrelevant Variables on Faulty Wafer Detection in Semiconductor Manufacturing,” MDPI Energ., vol. 12, Jul. 2019, doi: 10.3390/en12132530.

[10] Achmad Siddik Fathoni and Sri Hartati, “Knowledge-Based Systems Selection of Contraceptive Equipment for The Handling of Uncertainty,” IJCCS Indones. J. Comput. Cybern. Syst., vol. 15, pp. 111–120, Apr. 2021, doi: 10.22146/ijccs.58305.

[11] T. Kim, B. Lieberman, G. Luta, and E. A. Pena, “Prediction Regions for Poisson and Over-Dispersed Poisson Regression Models with Applications in Forecasting the Number of Deaths during the COVID-19 Pandemic,” Gruyter, Nov. 2021, doi:

[12] Y. Zhang, W. K. Cheung, and J. Liu, “A Unified Framework for Epidemic Prediction based on Poisson Regression,” IEEE Trans. Knowl. Data Eng., vol. 27, Oct. 2015, doi: Digital Object Identifier no. 10.1109/TKDE.2015.2436918.

[13] T. Inan, “Using poisson model for goal prediction in European football,” J. Hum. Sport Exerc., pp. 1–14, Apr. 2020, doi:

[14] Senol Celik, “Predicting the Number of Goals in Football Matches with the Poisson distribution: Example of Spain La Liga,” Sch. J. Phys. Math. Stat., Oct. 2021, doi: 10.36347/sjpms.2021.v08i08.002.

[15] Tobias Fritz, Tomas Gonda, and Paolo Perrone, “De Finetti’s Theorem in Categorical Probability,” Jun. 2021.


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