Análise de Variáveis em Partidas de Futebol: Previsão de Resultados com Naïve Bayes e Poisson
Abstract
The objective of this work is to analyze the set of variables that can have more influence on the prediction of the result of a soccer match, using techniques such as probability calculations and prediction algorithms, with the intention of obtaining profits in betting. The techniques used for the development were Bayesian networks, with the Naïve Bayes algorithm, and probability networks, based on the Poisson calculus. The data used for training were from the Brazilian Championship (between 2010 and 2017), considering data from the years 2018 and 2019 for tests. The main results achieved were 53% correctness of the result of a match and the main variables involved were attacking strength and defense strength.
References
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Barbosa, R. M.; Nacano, L. R.; Freitas, R.; Batista, B. L.; Barbosa Jr, F. The use of decision trees, naïve Bayes algorithms, and trace element patterns for controlling the authenticity of free-range-pastured hens’ eggs. Journal of food science, Wiley Online Library, v. 79, n. 9, p. C1672–C1677, 2014.
Carpita, M.; Sandri, M.; Simonetto, A.; Zuccolotto, P. Discovering the Drivers of Football Match Outcomes with Data Mining, Quality Technology & Quantitative Management, Vol. 12:4, p. 561-577, 2015.
Chagas, J. M. A (im)possibilidade de regulamentação das apostas esportivas no ordenamento jurídico brasileiro. Florianópolis: UFSC. 2016. (Trabalho de Conclusão) Constantinou, A. C; Fenton, N. E. Towards smart-data: Improving predictive accuracy in long-term football team performance, Knowledge-Based Systems, 124 (2017), p. 93104, 2017.
Esme, E.; Kiran, M. S. Prediction of Football Match Outcomes Based on Bookmaker Odds by Using k-Nearest Neighbor Algorithm, International Journal of Machine Learning and Computing, Vol. 8, No. 1, 2018.
Fayyad, U.; Piatetsky-Shapiro, G.; Smyth P. From Data Mining to Knowledge Discovery in Databases. AI Magazine, Vol 17, No. 3, p. 37-54. 1996.
Keogh, F., Rose, G. Football betting the global gambling industry worth billions, BBC, 2013. Disponível em: http://www.bbc.com/sport/football/24354124. Acessado em: 12/03/2019.
Macedo, P. A. P.; Silva, C. D.; Predição de resultados no Campeonato brasileiro 2012 série A, Revista Brasileira de Futebol, p.35-41, 2014.
Rahman, M. H. A. A.; Mustapha, A.; Fauzi, R.; Razali, N. Bayesian Approach to Classification of Football Match Outcome, International Journal of Integrated Engineering: Special Issue 2018: Data Information Engineering, Vol. 10 No. 6 (2018) p. 155-158, 2018.
Rezende, Solange Oliveira. Sistemas Inteligentes – Fundamentos e Aplicações. São Paulo: Manole, 2003.
Zhang, H. The Optimality of Naïve Bayes, Faculty of Computer Science, University of New Brunswick, Canada, 2004.
Published
2021-11-29
How to Cite
SEHNEM, Rodrigo; FROZZA, Rejane; BAGATINI, Daniela Duarte da Silva; PERANCONI, Daniela Saccol.
Análise de Variáveis em Partidas de Futebol: Previsão de Resultados com Naïve Bayes e Poisson. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 18. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 13-24.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2021.18237.
