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English Information

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Authors
# Name
1 Ana Gabriela de Araújo(ana.araujo.4@aluno.cefet-rj.br)
2 Matheus Melo(matheus.melo@aluno.cefet-rj.br)
3 Lucas Tavares(lucas.tavares@aluno.cefet-rj.br)
4 Jorge Soares(jorge.soares@cefet-rj.br)

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Reference
# Reference
1 Bifet, A. and Gavaldà, R. (2007). Learning from Time-Changing Data with Adaptive Windowing, pages 443–448.
2 Capobianco, G., Giacomo, U. D., Mercaldo, F., Nardone, V., and Santone, A. (2019). Can machine learning predict soccer match results? In ICAART 2019 - Proceedings of the 11th International Conference on Agents and Artificial Intelligence, volume 2, pages 458–465. SciTePress.
3 Dutta, A., Saikia, H., Gogoi, J., and Bhattacharjee, D. (2024). Forecasting the opening goal in second-half of a football match: Bayesian and frequentist perspectives. Computational Statistics.
4 Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A. (2014). A survey on concept drift adaptation.
5 Hinder, F., Vaquet, V., and Hammer, B. (2024). One or two things we know about concept drift—a survey on monitoring in evolving environments. Part A: detecting concept drift. Frontiers in Artificial Intelligence, 7:1330257.
6 Kore, A., Abbasi Bavil, E., Subasri, V., Abdalla, M., Fine, B., Dolatabadi, E., and Abdalla, M. (2024). Empirical data drift detection experiments on real-world medical imaging data. Nature Communications, 15:1887.
7 Lang, S., Wimmer, T., Erben, A., and Link, D. (2025). Which indicators matter? using performance indicators to predict in-game success-related events in association football. International Journal of Computer Science in Sport, 24:16–44.
8 Mendes-Neves, T., Meireles, L., and Mendes-Moreira, J. (2024). Towards a foundation large events model for soccer. Machine Learning, 113:8687–8709.
9 Page, E. S. (1954). Continuous inspection schemes. Biometrika, 41(1/2):100–115.
10 Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hr´objartsson, A., Lalu, M. M., Li, T., Loder, E.W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., and Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372:n71.
11 Raab, C., Heusinger, M., and Schleif, F.-M. (2020). Reactive soft prototype computing for concept drift streams. Neurocomputing, 416.
12 Salles, R., Lima, J., Reis, M., Coutinho, R., Pacitti, E., Masseglia, F., Akbarinia, R., Chen, C., Garibaldi, J., Porto, F., and Ogasawara, E. (2024). SoftED: Metrics for soft evaluation of time series event detection. Computers and Industrial Engineering, 198.
13 Sarmento, H., Figueiredo, A., Lago-Pe˜nas, C., Milanovic, Z., Barbosa, A., Tadeu, P., and Bradley, P. S. (2018). Influence of tactical and situational variables on offensive sequences during elite football matches. Technical report.
14 Simpson, I., Beal, R. J., Locke, D., and Norman, T. J. (2022). Seq2event: Learning the language of soccer using transformer-based match event prediction. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 3898–3908. Association for Computing Machinery.
15 Tavares, L. G., Lima, J., Melo, M., Chen, C., Garibaldi, J., Scatena, G. d. S., Costa, A. H. R., Gomi, E. S., Salles, R., Pacitti, E., Santos, I., Siqueira, I. G. a., Carvalho, D., Coutinho, R., Porto, F., and Ogasawara, E. (2025). Fuzzy-based ensemble method for robust concept drift detection in multivariate time series. In 2025 International Joint Conference on Neural Networks (IJCNN).
16 Yao, W., Wang, Y., Zhu, M., Cao, Y., and Zeng, D. (2022). Goal or miss? a bernoulli distribution for in-game outcome prediction in soccer. Entropy, 24.