Data science approach to simulating the FIFA World Cup Qatar 2022 at a website in tribute to Maradona

Alejandro Álvarez (), Alejandro Cataldo (), Guillermo Durán (), Manuel Durán (), Pablo Galaz (), Iván Monardo () and Denis Sauré ()
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Alejandro Álvarez: Universidad de Buenos Aires
Alejandro Cataldo: Pontificia Universidad Católica de Chile
Guillermo Durán: Universidad de Buenos Aires
Manuel Durán: Universidad de Buenos Aires
Pablo Galaz: Universidad de Chile
Iván Monardo: Universidad de Buenos Aires
Denis Sauré: Universidad de Chile

Computational Statistics, 2025, vol. 40, issue 4, No 26, 2223-2247

Abstract: 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 effects. 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 significant 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.

Keywords: Sports analytics; Match results prediction; Simulation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01557-3

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