World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model

Pinasthika, Stanislaus Jiwandana; Fudholi, Dzikri Rahadian

doi:10.22146/ijccs.82280

Open access Jurnal SINTA 2

World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 2 (2023) · pp. 151–160

DOI: 10.22146/ijccs.82280
ISSN: 2460-7258, 1978-1520
Published: 2023-04-30
Publisher: IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language: eng
License: creativecommons.org/licenses/by-sa/4.0

Abstract

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.

Keywords

Collaboration network of the authors

11 authors · 18 co-authorship links

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Universitas Gadjah Mada
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Co-author	Organisation	Joint articles
Muhammad Auzan	Universitas Gadjah Mada	2
Novia Arum Sari	Universitas Gadjah Mada	1
Raden Bagus Muhammad AdryanPutra Adhy Wijaya	Affiliation unknown	1
Delfia Nur Anrianti Putri	Universitas Gadjah Mada	1
Paulus Josianlie P	Universitas Gadjah Mada	1
M Ridho Fuadin	Universitas Gadjah Mada	1
Rabbani Nur Kumoro	Universitas Gadjah Mada	1
Audrey Shafira Fattima	Universitas Gadjah Mada	1
William Hilmy Susatyo	Universitas Gadjah Mada	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model</dc:title>
	<dc:creator>Pinasthika, Stanislaus Jiwandana</dc:creator>
	<dc:creator>Fudholi, Dzikri Rahadian</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">probabilistic prediction; de Finetti distance; football match prediction</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-04-30</dc:date>
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	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 2 (2023): April; 151-160</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/82280/36329</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
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