Football Match Prediction: A Monte Carlo Simulation Approach and Perspectives with Neural Networks
Resumo
Introduction: The unpredictability and low scoring in football make predictive modeling a high-variance challenge. The use of statistical databases, such as Football Manager, opens new avenues for sports analysis. Objective: This paper presents preliminary results of a heuristic Monte Carlo simulation applied to two knockout scenarios: the Campeonato Goiano (two-legged) and the 2026 World Cup playoffs (single-elimination), treated as independent case studies. Methodology: The Poisson distribution was used within 10,000 Monte Carlo iterations, with a dynamically modeled λ parameter based on each team’s relative strength from Football Manager 2026. Two pipelines were developed: an additive linear approach for the Goiano and a proportional ratio approach for the World Cup playoffs. Preliminary Results: The model achieved 83.3% accuracy (5/6) for the World Cup playoffs and 71.4% (5/7) for the Goiano, with Brier Scores of 0.2269 and 0.1229, respectively — both below the 0.25 randomguess threshold. These results support the progression towards a comparative study with Artificial Neural Networks and multiple linear regression in the full 2026 World Cup simulation.
Palavras-chave:
Football, Monte Carlo Simulation, Artificial Neural Networks, Predictive Modeling
Referências
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Bandara, I., Shelyag, S., Rajasegarar, S., Dwyer, D., Kim, E.-J., e Angelova, M. (2024). Winning with chaos in association football: Spatiotemporal event distribution randomness metric for team performance evaluation. IEEE Access, 12:83363–83376.
Constantinou, A. C., Fenton, N. E., e Neil, M. (2012). pi-football: A bayesian network model for forecasting association football match outcomes. Knowledge-Based Systems, 36:322–339.
Costa, A. Q. d. e Rocco Jr., A. J. (2015). Jogos digitais e a gestão do esporte: o caso football manager. In Anais do XIX Congresso Brasileiro de Ciências do Esporte e VI Congresso Internacional de Ciências do Esporte, Vitória. Colégio Brasileiro de Ciências do Esporte.
da Silva, L. P., Schincariol, T., e Wack, M. (2026). Football empires: Simulating the effects of colonialism on the 2026 world cup. Technical report, Center for Open Science.
Dixon, M. J. e Coles, S. G. (1997). Modelling association football scores and inefficiencies in the football betting market. Journal of the Royal Statistical Society: Series C (Applied Statistics), 46(2):265–280.
Elstak, I., Salmon, P., e McLean, S. (2024). Artificial intelligence applications in the football codes: A systematic review. Journal of sports sciences, 42(13):1184–1199.
FM Scout (2025). Fmscout editor 2026. FMScout.com, Software Tool.
FMInside (2025). Football manager database and player ratings.
Goodfellow, I., Bengio, Y., e Courville, A. (2016). Deep Learning. MIT Press.
Hocquet, A. (2016). Football manager: Mutual shaping between game, sport, and community. Journal of Media Studies and Popular Culture = Revue d’études des médias et de culture populaire, 6. Special Issue.
Klaiber, M. e Rössle, M. (2026). Uncovering data-driven football: Topic modeling from a data analytics perspective. Journal of Sports Sciences, pages 1–35.
Luiz, L. E., Fialho, G., e Teixeira, J. P. (2024). Is football unpredictable? predicting matches using neural networks. Forecasting, 6(4):1152–1168.
Maher, M. J. (1982). Modelling association football scores. Statistica Neerlandica, 36(3):109–118.
Memmert, D. e Raabe, D. (2018). Data Analytics in Football: Positional Data Collection, Modelling and Analysis. Routledge.
Metropolis, N. e Ulam, S. (1949). The monte carlo method. Journal of the American Statistical Association, 44(247):335–341.
Moroney, M. J. (1956). Facts from figures, volume 236. Penguin books Baltimore, MD.
Nunes, F. A. d. S. F. (2025). Modelos preditores de contratação de profissionais do futebol baseado em Inteligência Artificial por mecanismos Machine Learning. Tese (doutorado em administração), Universidade Federal de Minas Gerais, Belo Horizonte.
Pál, J. G. e Bíró, C. (2025). Evaluating profitability in sports betting using probabilistic models and betting strategies. In Annales Mathematicae et Informaticae, volume 61, pages 202–214.
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Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J., e Kattan, M. W. (2010). Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology, 21(1):128–138.
Sumpter, D. (2016). Soccermatics: Mathematical Adventures in the Beautiful Game. Bloomsbury Publishing.
Bandara, I., Shelyag, S., Rajasegarar, S., Dwyer, D., Kim, E.-J., e Angelova, M. (2024). Winning with chaos in association football: Spatiotemporal event distribution randomness metric for team performance evaluation. IEEE Access, 12:83363–83376.
Constantinou, A. C., Fenton, N. E., e Neil, M. (2012). pi-football: A bayesian network model for forecasting association football match outcomes. Knowledge-Based Systems, 36:322–339.
Costa, A. Q. d. e Rocco Jr., A. J. (2015). Jogos digitais e a gestão do esporte: o caso football manager. In Anais do XIX Congresso Brasileiro de Ciências do Esporte e VI Congresso Internacional de Ciências do Esporte, Vitória. Colégio Brasileiro de Ciências do Esporte.
da Silva, L. P., Schincariol, T., e Wack, M. (2026). Football empires: Simulating the effects of colonialism on the 2026 world cup. Technical report, Center for Open Science.
Dixon, M. J. e Coles, S. G. (1997). Modelling association football scores and inefficiencies in the football betting market. Journal of the Royal Statistical Society: Series C (Applied Statistics), 46(2):265–280.
Elstak, I., Salmon, P., e McLean, S. (2024). Artificial intelligence applications in the football codes: A systematic review. Journal of sports sciences, 42(13):1184–1199.
FM Scout (2025). Fmscout editor 2026. FMScout.com, Software Tool.
FMInside (2025). Football manager database and player ratings.
Goodfellow, I., Bengio, Y., e Courville, A. (2016). Deep Learning. MIT Press.
Hocquet, A. (2016). Football manager: Mutual shaping between game, sport, and community. Journal of Media Studies and Popular Culture = Revue d’études des médias et de culture populaire, 6. Special Issue.
Klaiber, M. e Rössle, M. (2026). Uncovering data-driven football: Topic modeling from a data analytics perspective. Journal of Sports Sciences, pages 1–35.
Luiz, L. E., Fialho, G., e Teixeira, J. P. (2024). Is football unpredictable? predicting matches using neural networks. Forecasting, 6(4):1152–1168.
Maher, M. J. (1982). Modelling association football scores. Statistica Neerlandica, 36(3):109–118.
Memmert, D. e Raabe, D. (2018). Data Analytics in Football: Positional Data Collection, Modelling and Analysis. Routledge.
Metropolis, N. e Ulam, S. (1949). The monte carlo method. Journal of the American Statistical Association, 44(247):335–341.
Moroney, M. J. (1956). Facts from figures, volume 236. Penguin books Baltimore, MD.
Nunes, F. A. d. S. F. (2025). Modelos preditores de contratação de profissionais do futebol baseado em Inteligência Artificial por mecanismos Machine Learning. Tese (doutorado em administração), Universidade Federal de Minas Gerais, Belo Horizonte.
Pál, J. G. e Bíró, C. (2025). Evaluating profitability in sports betting using probabilistic models and betting strategies. In Annales Mathematicae et Informaticae, volume 61, pages 202–214.
Sports Interactive (2025). Football manager 2026. PC/Mac/Mobile/Console.
Steyerberg, E. W., Vickers, A. J., Cook, N. R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M. J., e Kattan, M. W. (2010). Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology, 21(1):128–138.
Sumpter, D. (2016). Soccermatics: Mathematical Adventures in the Beautiful Game. Bloomsbury Publishing.
Publicado
29/09/2026
Como Citar
CARDOSO JUNIOR, Warner Pereira; REZENDE, Críscilla Maia Costa; AGUIAR JUNIOR, Dióscoros Brito.
Football Match Prediction: A Monte Carlo Simulation Approach and Perspectives with Neural Networks. In: TRILHA DE COMPUTAÇÃO – ARTIGOS CURTOS - SIMPÓSIO BRASILEIRO DE JOGOS E ENTRETENIMENTO DIGITAL (SBGAMES), 25. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 171-177.
DOI: https://doi.org/10.5753/sbgames_estendido.2026.26372.
