IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v40y2025i4d10.1007_s00180-024-01557-3.html
   My bibliography  Save this article

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

Author

Listed:
  • Alejandro Álvarez

    (Universidad de Buenos Aires)

  • Alejandro Cataldo

    (Pontificia Universidad Católica de Chile)

  • Guillermo Durán

    (Universidad de Buenos Aires
    Universidad de Chile
    Consejo Nacional Investigaciones Científicas y Técnicas
    Instituto Sistemas Complejos de Ingeniería)

  • Manuel Durán

    (Universidad de Buenos Aires)

  • Pablo Galaz

    (Universidad de Chile)

  • Iván Monardo

    (Universidad de Buenos Aires)

  • Denis Sauré

    (Universidad de Chile
    Instituto Sistemas Complejos de Ingeniería)

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.

Suggested Citation

  • Alejandro Álvarez & Alejandro Cataldo & Guillermo Durán & Manuel Durán & Pablo Galaz & Iván Monardo & Denis Sauré, 2025. "Data science approach to simulating the FIFA World Cup Qatar 2022 at a website in tribute to Maradona," Computational Statistics, Springer, vol. 40(4), pages 2223-2247, April.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-024-01557-3
    DOI: 10.1007/s00180-024-01557-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-024-01557-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-024-01557-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Boshnakov, Georgi & Kharrat, Tarak & McHale, Ian G., 2017. "A bivariate Weibull count model for forecasting association football scores," International Journal of Forecasting, Elsevier, vol. 33(2), pages 458-466.
    2. Jean-Marc Falter & Christophe Perignon, 2000. "Demand for football and intramatch winning probability: an essay on the glorious uncertainty of sports," Applied Economics, Taylor & Francis Journals, vol. 32(13), pages 1757-1765.
    3. Forrest, David & Goddard, John & Simmons, Robert, 2005. "Odds-setters as forecasters: The case of English football," International Journal of Forecasting, Elsevier, vol. 21(3), pages 551-564.
    4. Baboota, Rahul & Kaur, Harleen, 2019. "Predictive analysis and modelling football results using machine learning approach for English Premier League," International Journal of Forecasting, Elsevier, vol. 35(2), pages 741-755.
    5. Gianluca Baio & Marta Blangiardo, 2010. "Bayesian hierarchical model for the prediction of football results," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 253-264.
    6. Audas, Rick & Dobson, Stephen & Goddard, John, 2002. "The impact of managerial change on team performance in professional sports," Journal of Economics and Business, Elsevier, vol. 54(6), pages 633-650.
    7. N Hirotsu & M Wright, 2003. "Determining the best strategy for changing the configuration of a football team," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 878-887, August.
    8. D Dyte & S R Clarke, 2000. "A ratings based Poisson model for World Cup soccer simulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(8), pages 993-998, August.
    9. Tim Kuypers, 2000. "Information and efficiency: an empirical study of a fixed odds betting market," Applied Economics, Taylor & Francis Journals, vol. 32(11), pages 1353-1363.
    10. Siem Jan Koopman & Rutger Lit, 2015. "A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 167-186, January.
    11. Hvattum, Lars Magnus & Arntzen, Halvard, 2010. "Using ELO ratings for match result prediction in association football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 460-470, July.
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. repec:fth:geneec:00.04 is not listed on IDEAS
    14. Constantinou Anthony Costa & Fenton Norman Elliott, 2012. "Solving the Problem of Inadequate Scoring Rules for Assessing Probabilistic Football Forecast Models," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-14, March.
    15. Forrest, David & Simmons, Robert, 2000. "Forecasting sport: the behaviour and performance of football tipsters," International Journal of Forecasting, Elsevier, vol. 16(3), pages 317-331.
    16. Marcio Alves Diniz & Rafael Izbicki & Danilo Lopes & Luis Ernesto Salasar, 2019. "Comparing probabilistic predictive models applied to football," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(5), pages 770-782, May.
    17. Gunther Schauberger & Andreas Groll & Gerhard Tutz, 2018. "Analysis of the importance of on-field covariates in the German Bundesliga," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(9), pages 1561-1578, July.
    18. Ian G. McHale & Łukasz Szczepański, 2014. "A mixed effects model for identifying goal scoring ability of footballers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 177(2), pages 397-417, February.
    19. A K Suzuki & L E B Salasar & J G Leite & F Louzada-Neto, 2010. "A Bayesian approach for predicting match outcomes: The 2006 (Association) Football World Cup," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(10), pages 1530-1539, October.
    20. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    21. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
    22. Matthew J Penn & Christl A Donnelly, 2022. "Analysis of a double Poisson model for predicting football results in Euro 2020," PLOS ONE, Public Library of Science, vol. 17(5), pages 1-23, May.
    23. Goddard, John, 2005. "Regression models for forecasting goals and match results in association football," International Journal of Forecasting, Elsevier, vol. 21(2), pages 331-340.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Koopman, Siem Jan & Lit, Rutger, 2019. "Forecasting football match results in national league competitions using score-driven time series models," International Journal of Forecasting, Elsevier, vol. 35(2), pages 797-809.
    2. Fry, John & Serbera, Jean-Philippe & Wilson, Rob, 2021. "Managing performance expectations in association football," Journal of Business Research, Elsevier, vol. 135(C), pages 445-453.
    3. da Costa, Igor Barbosa & Marinho, Leandro Balby & Pires, Carlos Eduardo Santos, 2022. "Forecasting football results and exploiting betting markets: The case of “both teams to score”," International Journal of Forecasting, Elsevier, vol. 38(3), pages 895-909.
    4. Lasek, Jan & Gagolewski, Marek, 2021. "Interpretable sports team rating models based on the gradient descent algorithm," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1061-1071.
    5. Giovanni Angelini & Luca De Angelis, 2017. "PARX model for football match predictions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(7), pages 795-807, November.
    6. J. James Reade & Carl Singleton & Alasdair Brown, 2021. "Evaluating strange forecasts: The curious case of football match scorelines," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(2), pages 261-285, May.
    7. Raffaele Mattera, 2023. "Forecasting binary outcomes in soccer," Annals of Operations Research, Springer, vol. 325(1), pages 115-134, June.
    8. Rebeggiani, Luca & Gross, Johannes, 2018. "Chance or Ability? The Efficiency of the Football Betting Market Revisited," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181563, Verein für Socialpolitik / German Economic Association.
    9. Baboota, Rahul & Kaur, Harleen, 2019. "Predictive analysis and modelling football results using machine learning approach for English Premier League," International Journal of Forecasting, Elsevier, vol. 35(2), pages 741-755.
    10. Stekler, H.O. & Sendor, David & Verlander, Richard, 2010. "Issues in sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 606-621, July.
      • Herman O. Stekler & David Sendor & Richard Verlander, 2009. "Issues in Sports Forecasting," Working Papers 2009-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    11. Singleton, Carl & Reade, J. James & Brown, Alasdair, 2020. "Going with your gut: The (In)accuracy of forecast revisions in a football score prediction game," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 89(C).
    12. Groll Andreas & Kneib Thomas & Mayr Andreas & Schauberger Gunther, 2018. "On the dependency of soccer scores – a sparse bivariate Poisson model for the UEFA European football championship 2016," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(2), pages 65-79, June.
    13. Hvattum, Lars Magnus & Arntzen, Halvard, 2010. "Using ELO ratings for match result prediction in association football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 460-470, July.
    14. J Reade & C Singleton & L Vaughan Williams, 2020. "Betting Markets for English Premier League Results and Scorelines: Evaluating a Simple Forecasting Model," Economic Issues Journal Articles, Economic Issues, vol. 25(1), pages 87-106, March.
    15. Federico Fioravanti & Fernando Delbianco & Fernando Tohmé, 2023. "The relative importance of ability, luck and motivation in team sports: a Bayesian model of performance in the English Rugby Premiership," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 715-731, September.
    16. Wheatcroft Edward, 2021. "Evaluating probabilistic forecasts of football matches: the case against the ranked probability score," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(4), pages 273-287, December.
    17. Szczecinski Leszek, 2022. "G-Elo: generalization of the Elo algorithm by modeling the discretized margin of victory," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 18(1), pages 1-14, March.
    18. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
    19. O'Leary, Daniel E., 2017. "Crowd performance in prediction of the World Cup 2014," European Journal of Operational Research, Elsevier, vol. 260(2), pages 715-724.
    20. Marc Garnica-Caparrós & Daniel Memmert & Fabian Wunderlich, 2022. "Artificial data in sports forecasting: a simulation framework for analysing predictive models in sports," Information Systems and e-Business Management, Springer, vol. 20(3), pages 551-580, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:40:y:2025:i:4:d:10.1007_s00180-024-01557-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.