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dc.contributor.author
Álvarez, Alejandro  
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Cataldo, Alejandro  
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Duran, Guillermo Alfredo  
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Durán, Manuel  
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Galaz, Pablo  
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Monardo, Iván  
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Sauré, Denis  
dc.date.available
2025-04-15T10:32:52Z  
dc.date.issued
2024-09  
dc.identifier.citation
Álvarez, Alejandro; Cataldo, Alejandro; Duran, Guillermo Alfredo; Durán, Manuel; Galaz, Pablo; et al.; Data science approach to simulating the FIFA World Cup Qatar 2022 at a website in tribute to Maradona; Springer Heidelberg; Computational Statistics (zeitschrift); 9-2024; 1-25  
dc.identifier.issn
0943-4062  
dc.identifier.uri
http://hdl.handle.net/11336/258765  
dc.description.abstract
This article documents the authors’ experience developing an Argentinean website in tribute to Diego Maradona (301060.exactas.uba.ar) that leverages the popularity of football in South America (and the world) to illustrate the application of data science models in sports analytics. In particular, we demonstrate their use in computing probabilities associated with various events (winning matches, advancing rounds, and becoming champions) of the FIFA World Cup Qatar 2022. Building on Dixon and Cole’s 1997 seminal model, we develop a competing Poisson model that incorporates for each participating team its attack and defense strengths as well as home-advantage efects. The calibration of the model considers match importance levels and emphasizes the recency of a team’s performance. Evaluations of the model’s results on various prediction accuracy and error metrics indicate that its performance equals or betters the traditional Poisson model and is similar to established betting sites. Our website featuring the model received over 30,000 visits from 11,000 users across 10 countries during the 2022 World Cup and garnered signifcant media coverage in Argentina. This successful endeavor underlines the potential of mathematics for predicting football match outcomes but also showcases its potential for countless practical applications and its ability to capture the attention and interest of a wide audience.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Springer Heidelberg  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
data science  
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soccer  
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prediction  
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mathematics  
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Matemática Aplicada  
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Matemáticas  
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CIENCIAS NATURALES Y EXACTAS  
dc.title
Data science approach to simulating the FIFA World Cup Qatar 2022 at a website in tribute to Maradona  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2025-04-14T10:34:48Z  
dc.journal.pagination
1-25  
dc.journal.pais
Alemania  
dc.description.fil
Fil: Álvarez, Alejandro. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; Argentina  
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Fil: Cataldo, Alejandro. Pontificia Universidad Católica de Chile; Chile  
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Fil: Duran, Guillermo Alfredo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; Argentina  
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Fil: Durán, Manuel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Cálculo; Argentina  
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Fil: Galaz, Pablo. Pontificia Universidad Católica de Chile; Chile  
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Fil: Monardo, Iván. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina  
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Fil: Sauré, Denis. Universidad de Chile; Chile  
dc.journal.title
Computational Statistics (zeitschrift)  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/10.1007/s00180-024-01557-3  
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info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s00180-024-01557-3