Descoberta de Conhecimento em Dados de Scout do Campeonato Brasileiro de Futebol
Resumo
A análise de dados esportivos, conhecida como scout, tem ganhado grande atenção graças aos resultados positivos de sua aplicação em uma grande variedade de esportes. Devido à popularidade do futebol, há diversos trabalhos que buscam aplicar o Aprendizado de Máquina em dados de scout nesse esporte. Esses dados compreendem contagem de eventos, como passes e finalizações, e podem auxiliar o processo de tomada de decisão por parte de equipes técnicas. No entanto, esses esforços se concentram no futebol europeu. Neste trabalho, investigamos a obtenção de conhecimento por algoritmos de Aprendizado de Máquina em dados de scout obtidos no futebol brasileiro. Com isso, mostramos o potencial e as limitações atuais dessa abordagem.
Palavras-chave:
Análise Esportiva, Aprendizado de Máquina, Aprendizado não supervisionado, Scouts
Referências
Alamar, B. C. (2013). Sports analytics: A guide for coaches, managers, and other decision makers. Columbia University Press.
Arndt, C. and Brefeld, U. (2016). Predicting the future performance of soccer players. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9(5):373–382.
Berrar, D., Lopes, P., Davis, J., and Dubitzky, W. (2019). Guest editorial: special issue on machine learning for soccer. Machine Learning, 108(1):1–7.
Brefeld, U. and Zimmermann, A. (2017). Guest editorial: Special issue on sports analytics. Data Mining and Knowledge Discovery, 31(6):1577–1579.
Mota, E., Coimbra, D., and Peixoto, M. (2018). Cartola fc data analysis: A simulation, analysis, and visualization tool based on cartola fc fantasy game. In Proceedings of the XIV Brazilian Symposium on Information Systems, pages 1–8.
Pappalardo, L., Cintia, P., Rossi, A., Massucco, E., Ferragina, P., Pedreschi, D., and Giannotti, F. (2019). A public data set of spatio-temporal match events in soccer competitions. Scientific data, 6(1):1–15.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011). Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830.
Pollard, R. (1986). Home advantage in soccer: A retrospective analysis. Journal of sports sciences, 4(3):237–248.
Rossi, A., Pappalardo, L., Cintia, P., Iaia, F. M., Fernández, J., and Medina, D. (2018). Effective injury forecasting in soccer with gps training data and machine learning. PloS one, 13(7):e0201264.
Santos, J. M. A. d. (2019). Previsões de resultados em partidas do campeonato brasileiro de futebol. PhD thesis, Fundação Getúlio Vargas.
Schumaker, R. P., Solieman, O. K., and Chen, H. (2010). Sports data mining, volume 26. Springer Science & Business Media.
Alamar, B. C. (2013). Sports analytics: A guide for coaches, managers, and other decision makers. Columbia University Press.
Arndt, C. and Brefeld, U. (2016). Predicting the future performance of soccer players. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9(5):373–382.
Berrar, D., Lopes, P., Davis, J., and Dubitzky, W. (2019). Guest editorial: special issue on machine learning for soccer. Machine Learning, 108(1):1–7.
Brefeld, U. and Zimmermann, A. (2017). Guest editorial: Special issue on sports analytics. Data Mining and Knowledge Discovery, 31(6):1577–1579.
Mota, E., Coimbra, D., and Peixoto, M. (2018). Cartola fc data analysis: A simulation, analysis, and visualization tool based on cartola fc fantasy game. In Proceedings of the XIV Brazilian Symposium on Information Systems, pages 1–8.
Pappalardo, L., Cintia, P., Rossi, A., Massucco, E., Ferragina, P., Pedreschi, D., and Giannotti, F. (2019). A public data set of spatio-temporal match events in soccer competitions. Scientific data, 6(1):1–15.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011). Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830.
Pollard, R. (1986). Home advantage in soccer: A retrospective analysis. Journal of sports sciences, 4(3):237–248.
Rossi, A., Pappalardo, L., Cintia, P., Iaia, F. M., Fernández, J., and Medina, D. (2018). Effective injury forecasting in soccer with gps training data and machine learning. PloS one, 13(7):e0201264.
Santos, J. M. A. d. (2019). Previsões de resultados em partidas do campeonato brasileiro de futebol. PhD thesis, Fundação Getúlio Vargas.
Schumaker, R. P., Solieman, O. K., and Chen, H. (2010). Sports data mining, volume 26. Springer Science & Business Media.
Arndt, C. and Brefeld, U. (2016). Predicting the future performance of soccer players. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9(5):373–382.
Berrar, D., Lopes, P., Davis, J., and Dubitzky, W. (2019). Guest editorial: special issue on machine learning for soccer. Machine Learning, 108(1):1–7.
Brefeld, U. and Zimmermann, A. (2017). Guest editorial: Special issue on sports analytics. Data Mining and Knowledge Discovery, 31(6):1577–1579.
Mota, E., Coimbra, D., and Peixoto, M. (2018). Cartola fc data analysis: A simulation, analysis, and visualization tool based on cartola fc fantasy game. In Proceedings of the XIV Brazilian Symposium on Information Systems, pages 1–8.
Pappalardo, L., Cintia, P., Rossi, A., Massucco, E., Ferragina, P., Pedreschi, D., and Giannotti, F. (2019). A public data set of spatio-temporal match events in soccer competitions. Scientific data, 6(1):1–15.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011). Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830.
Pollard, R. (1986). Home advantage in soccer: A retrospective analysis. Journal of sports sciences, 4(3):237–248.
Rossi, A., Pappalardo, L., Cintia, P., Iaia, F. M., Fernández, J., and Medina, D. (2018). Effective injury forecasting in soccer with gps training data and machine learning. PloS one, 13(7):e0201264.
Santos, J. M. A. d. (2019). Previsões de resultados em partidas do campeonato brasileiro de futebol. PhD thesis, Fundação Getúlio Vargas.
Schumaker, R. P., Solieman, O. K., and Chen, H. (2010). Sports data mining, volume 26. Springer Science & Business Media.
Alamar, B. C. (2013). Sports analytics: A guide for coaches, managers, and other decision makers. Columbia University Press.
Arndt, C. and Brefeld, U. (2016). Predicting the future performance of soccer players. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9(5):373–382.
Berrar, D., Lopes, P., Davis, J., and Dubitzky, W. (2019). Guest editorial: special issue on machine learning for soccer. Machine Learning, 108(1):1–7.
Brefeld, U. and Zimmermann, A. (2017). Guest editorial: Special issue on sports analytics. Data Mining and Knowledge Discovery, 31(6):1577–1579.
Mota, E., Coimbra, D., and Peixoto, M. (2018). Cartola fc data analysis: A simulation, analysis, and visualization tool based on cartola fc fantasy game. In Proceedings of the XIV Brazilian Symposium on Information Systems, pages 1–8.
Pappalardo, L., Cintia, P., Rossi, A., Massucco, E., Ferragina, P., Pedreschi, D., and Giannotti, F. (2019). A public data set of spatio-temporal match events in soccer competitions. Scientific data, 6(1):1–15.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al. (2011). Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830.
Pollard, R. (1986). Home advantage in soccer: A retrospective analysis. Journal of sports sciences, 4(3):237–248.
Rossi, A., Pappalardo, L., Cintia, P., Iaia, F. M., Fernández, J., and Medina, D. (2018). Effective injury forecasting in soccer with gps training data and machine learning. PloS one, 13(7):e0201264.
Santos, J. M. A. d. (2019). Previsões de resultados em partidas do campeonato brasileiro de futebol. PhD thesis, Fundação Getúlio Vargas.
Schumaker, R. P., Solieman, O. K., and Chen, H. (2010). Sports data mining, volume 26. Springer Science & Business Media.
Publicado
20/10/2020
Como Citar
ORTOLAN, Luís; SILVA, Diego.
Descoberta de Conhecimento em Dados de Scout do Campeonato Brasileiro de Futebol. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 626-637.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2020.12165.
