Predizendo os Vencedores dos Playoffs: Um Estudo de Caso com Aprendizado de Máquina em Partidas de Futebol Americano
Resumo
Com o avanço no desenvolvimento de tecnologias, a análise de informações sobre esportes tornou-se uma questão cada vez mais desafiadora. Além disso, as bases de dados disponíveis para estudos de previsão de resultados são limitadas. Considerando isso, neste artigo serão estudados conceitos relacionados ao futebol americano e modelagem de algoritmos de Aprendizado de Máquina (AM), aplicados para a predição de times ganhadores ou perdedores com base nos dados das últimas temporadas. Como resultados, é possível observar que técnicas de AM, quando combinadas com uma quantidade expressiva de dados e com os devidos tratamentos, podem fornecer bons resultados na previsão de resultados de partidas esportivas.
Palavras-chave:
Aplicações/pipelines de ciência de dados, Machine learning, IA, Gerenciamento de dados e sistemas de dados, Aplicativos de fluxo de trabalho e bancos de dados
Referências
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Beal, R. J., Norman, T., and Ramchurn, S. (2020). A critical comparison of machine learning classifiers to predict match outcomes in the nfl. International Journal of Computer Science in Sport, 19(2).
Bosch, P. (2018). Predicting the winner of nfl-games using machine and deep learning. Vrije universiteit, Amsterdam.
Durand, R. B., Patterson, F. M., and Shank, C. A. (2021). Behavioral biases in the nfl gambling market: Overreaction to news and the recency bias. Journal of Behavioral and Experimental Finance, 31:100522.
Haiyun, Z. and Yizhe, X. (2020). Sports performance prediction model based on integrated learning algorithm and cloud computing hadoop platform. microprocessors and microsystems, 79:103322.
Herold, M., Goes, F., Nopp, S., Bauer, P., Thompson, C., and Meyer, T. (2019). Machine learning in men’s professional football: Current applications and future directions for improving attacking play. International Journal of Sports Science & Coaching, 14(6):798–817.
Hoog Antink, C., Braczynski, A. K., and Ganse, B. (2021). Learning from machine learning: prediction of age-related athletic performance decline trajectories. GeroScience, 43(5):2547–2559.
Horvat, T. and Job, J. (2020). The use of machine learning in sport outcome prediction: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(5):e1380.
Hsu, Y.-C. (2020). Using machine learning and candlestick patterns to predict the outcomes of american football games. Applied Sciences, 10(13):4484.
Liu, G., Luo, Y., Schulte, O., and Kharrat, T. (2020). Deep soccer analytics: learning an action-value function for evaluating soccer players. Data Mining and Knowledge Discovery, 34(5):1531–1559.
Liu, Y. (2020). Teaching effect and improvement model of college basketball sports based on big data analysis. In Journal of Physics: Conference Series, volume 1533, page 042056. IOP Publishing.
Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc.
Mataruna-Dos-Santos, L. J., Faccia, A., Helú, H. M., and Khan, M. S. (2020). Big data analyses and new technology applications in sport management, an overview. In Proceedings of the 2020 International Conference on Big Data in Management, pages 17–22.
Muazu Musa, R., Abdul Majeed, A. P., Suhaimi, M. Z., Mohd Razman, M. A., Abdullah, M. R., and Abu Osman, N. A. (2021). Performance indicators predicting medallists and non-medallists in elite men volleyball competition. In Machine Learning in Elite Volleyball, pages 43–49. Springer.
Nguyen, N. H., Nguyen, D. T. A., Ma, B., and Hu, J. (2022). The application of machine learning and deep learning in sport: predicting nba players’ performance and popularity. Journal of Information and Telecommunication, 6(2):217–235.
Patel, D., Shah, D., and Shah, M. (2020). The intertwine of brain and body: a quantitative analysis on how big data influences the system of sports. Annals of Data Science, 7(1):1–16.
Beal, R. J., Norman, T., and Ramchurn, S. (2020). A critical comparison of machine learning classifiers to predict match outcomes in the nfl. International Journal of Computer Science in Sport, 19(2).
Bosch, P. (2018). Predicting the winner of nfl-games using machine and deep learning. Vrije universiteit, Amsterdam.
Durand, R. B., Patterson, F. M., and Shank, C. A. (2021). Behavioral biases in the nfl gambling market: Overreaction to news and the recency bias. Journal of Behavioral and Experimental Finance, 31:100522.
Haiyun, Z. and Yizhe, X. (2020). Sports performance prediction model based on integrated learning algorithm and cloud computing hadoop platform. microprocessors and microsystems, 79:103322.
Herold, M., Goes, F., Nopp, S., Bauer, P., Thompson, C., and Meyer, T. (2019). Machine learning in men’s professional football: Current applications and future directions for improving attacking play. International Journal of Sports Science & Coaching, 14(6):798–817.
Hoog Antink, C., Braczynski, A. K., and Ganse, B. (2021). Learning from machine learning: prediction of age-related athletic performance decline trajectories. GeroScience, 43(5):2547–2559.
Horvat, T. and Job, J. (2020). The use of machine learning in sport outcome prediction: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(5):e1380.
Hsu, Y.-C. (2020). Using machine learning and candlestick patterns to predict the outcomes of american football games. Applied Sciences, 10(13):4484.
Liu, G., Luo, Y., Schulte, O., and Kharrat, T. (2020). Deep soccer analytics: learning an action-value function for evaluating soccer players. Data Mining and Knowledge Discovery, 34(5):1531–1559.
Liu, Y. (2020). Teaching effect and improvement model of college basketball sports based on big data analysis. In Journal of Physics: Conference Series, volume 1533, page 042056. IOP Publishing.
Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc.
Mataruna-Dos-Santos, L. J., Faccia, A., Helú, H. M., and Khan, M. S. (2020). Big data analyses and new technology applications in sport management, an overview. In Proceedings of the 2020 International Conference on Big Data in Management, pages 17–22.
Muazu Musa, R., Abdul Majeed, A. P., Suhaimi, M. Z., Mohd Razman, M. A., Abdullah, M. R., and Abu Osman, N. A. (2021). Performance indicators predicting medallists and non-medallists in elite men volleyball competition. In Machine Learning in Elite Volleyball, pages 43–49. Springer.
Nguyen, N. H., Nguyen, D. T. A., Ma, B., and Hu, J. (2022). The application of machine learning and deep learning in sport: predicting nba players’ performance and popularity. Journal of Information and Telecommunication, 6(2):217–235.
Patel, D., Shah, D., and Shah, M. (2020). The intertwine of brain and body: a quantitative analysis on how big data influences the system of sports. Annals of Data Science, 7(1):1–16.
Publicado
25/09/2023
Como Citar
BERNARDES, Danielle Regina; V. FRANCISCON, João Fernando; ARAÚJO, Fernando Rafael; DE OLIVEIRA, Marcos Paulo; P. BONIDIA, Robson.
Predizendo os Vencedores dos Playoffs: Um Estudo de Caso com Aprendizado de Máquina em Partidas de Futebol Americano. In: WORKSHOP DE TRABALHOS DE ALUNOS DA GRADUAÇÃO (WTAG) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 38. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 22-28.
DOI: https://doi.org/10.5753/sbbd_estendido.2023.232738.
