Decision tree modeling for football game prediction
DOI:
https://doi.org/10.33448/rsd-v9i9.6869Keywords:
Soccer; Bookmakers; Statistic; Decision tree.Abstract
After technological advances, data analysis for sports purposes has become of fundamental importance for tactical evolution and obtaining good results. In football, the use of these analyzes has been growing and bringing numerous benefits, both for the tactical development, as well as in the physical part of the athletes. In addition to tactical and technical collaboration for football, statistics are also widely used in predictions, ranging from a penalty kick to the final result of the game. The objective of this work is to find a model for predicting the results of soccer matches. Mandante (Mandante Team wins) Draw or Visitor (Visiting Team wins) using the Decision Tree method, where, after modeling the data and analyzing the accuracy of the model, which house would be more profitable was analyzed.
References
Afonso, M. S., Barros, S. S., Koth, A. P., Rodrigues, V. L., Neves, F. B., & Lourenção, L. G. (2020). Sports physiotherapy in program of prevention of injury in professional football. Research, Society and Development, 9(3), 72932434.
Albuquerque, M. A., Lucena, S. L. L., & Barros, K. N. N. O. (2020). Comparação de modelo clássico e Bayesiano para dados de óbitos perinatais no ISEA, Campina Grande-PB. Research, Society and Development, 9(8), e464985477-e464985477.
Anderson, C., & Sally, D. (2013). Os números do jogo: porque tudo o que você sabe sobre futebol está errado. São Paulo: Paralela.
Barros, K. N. N. O., Albuquerque, M. A., Gomes, A. S., & Dantas, D. R. G. (2020). Análise de agrupamentos exploratória dos usuários do Programa Multidisciplinar de Tratamento do Tabagismo do HUAC, Campina Grande–PB. Research, Society and Development, 9(8), e825986532-e825986532.
Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. CRC press.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees (Wadsworth, Belmont, CA). ISBN-13, 978-0412048418.
Burman, P. (1989). A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods. Biometrika, 76(3), 503-514.
Carling, C., Bloomfield, J., Nelsen, L., & Reilly, T. (2008). The role of motion analysis in elite soccer. Sports medicine, 38(10), 839-862.
Grochtmann, M., & Grimm, K. (1993). Classification trees for partition testing. Software Testing, Verification and Reliability, 3(2), 63-82.
Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. 112, 18. New York: springer.
Loh, W. Y. (2011). Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1), 14-23.
Moisen, G. G. (2008). Classification and regression trees. In: Jørgensen, Sven Erik; Fath, Brian D. (Editor-in-Chief). Encyclopedia of Ecology, volume 1. Oxford, UK: Elsevier. 582-588., 582-588.
Monard, M. C., & Baranauskas, J. A. (2003). Indução de regras e árvores de decisão. Sistemas Inteligentes-Fundamentos e Aplicações, 1, 115-139.
Morgan, J. N., & Sonquist, J. A. (1963). Problems in the analysis of survey data, and a proposal. Journal of the American statistical association, 58(302), 415-434.
Oddsshark. O que são odds. 2018. Recuperado de:<https://www.oddsshark.com/br/ como-apostar/o-que-sao-odds>.
Pereira, A. S., Shitsuka, D. M., Parreira, F. J., & Shitsuka, R. (2018). Metodologia da pesquisa científica. [e-book]. Santa Maria. Ed. UAB/NTE/UFSM. Recuperado de https://repositorio.ufsm.br/bitstream/handle/1/15824/Lic_Computacao_Metodologia-Pesquisa-Cientifica.pdf?sequence=1
Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus, 5(1), 1-13.
Rokach, L., & Maimon, O. (2005). Top-down induction of decision trees classifiers-a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 35(4), 476-487.
Sehnem, R., & Frozza, R. (2019). Análise de variáveis em partidas de futebol para previsão de resultados. Anais do Salão de Ensino e de Extensão, 217.
Vogado, L. H., Veras, R. M., Araujo, F. H., Silva, R. R., & Aires, K. R. (2019, June). Rede Neural Convolucional para o Diagnóstico de Leucemia. In Anais Principais do XIX Simpósio Brasileiro de Computação Aplicada à Saúde, 46-57.
Weiss, S. M., & Indurkhya, N. (1994). Small sample decision tree pruning. In Machine Learning Proceedings 1994. 335-342. Morgan Kaufmann.
Wilkinson, L. (2004). Classification and regression trees. Systat, 11, 35-56.
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