A Game of Runs: Extracting Initiation Rules for Basketball Scoring Runs through Focused Sequence Mining
By: Ioannis Sevrisarianos and Ioannis Katakis
References
- Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. Proceedings of the Eleventh International Conference on Data Engineering, 3–14.
https://doi.org/10.1109/ICDE.1995.380415 - Bunker, R., & Susnjak, T. (2022). The application of machine learning techniques for predicting match results in team sport: A review. Journal of Artificial Intelligence Research, 73, 1285–1322.
https://doi.org/10.1613/jair.1.13509 - Bunker, R. P., & Thabtah, F. (2019). A machine learning framework for sport result prediction. Applied Computing and Informatics, 15(1), 27–33.
https://doi.org/10.1016/j.aci.2017.09.005 - Cheng, H., Yan, X., Han, J., & Yu, P. S. (2008). Direct discriminative pattern mining for effective classification. 2008 IEEE 24th International Conference on Data Engineering, 169–178.
https://doi.org/10.1109/ICDE.2008.4497425 - Claudino, J. G., Capanema, D. D. O., de Souza, T. V., Serrão, J. C., Machado Pereira, A. C., & Nassis, G. P. (2019). Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: a systematic review. Sports medicine-open, 5(1), 28.
https://doi.org/10.1186/s40798-019-0202-3 - Decroos, T., Bransen, L., Van Haaren, J., & Davis, J. (2019). Actions speak louder than goals: Valuing player actions in soccer. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1851–1861.
https://doi.org/10.1145/3292500.3330758 - Fernández, J., Bornn, L., & Cervone, D. (2021). A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions. Machine Learning, 110(6), 1389–1427.
https://doi.org/10.1007/s10994-021-05989-6 - Han, J., Pei, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., & Hsu, M. (2001). Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth. Proceedings of the 17th International Conference on Data Engineering, 215–224.
https://doi.org/10.1109/ICDE.2001.914830 - Horvat, T., & 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.
https://doi.org/10.1002/widm.1380 - Iso-Ahola, S. E., & Dotson, C. O. (2014). Psychological momentum: Why success breeds success. Review of General Psychology, 18(1), 19–33.
https://doi.org/10.1037/a0036406 - Ji, Y., Ye, G., & Cheng, H. (2014). Interactive body part contrast mining for human interaction recognition. 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), 1–6.
https://doi.org/10.1109/icmew.2014.6890714 - Karthikeyan, V., & Priyadharsini, S. S. (2024). A stacked convolutional neural network framework with multi-scale attention mechanism for text-independent voiceprint recognition. Pattern Anal. Appl., 27(2), 48.
https://doi.org/10.1007/S10044-024-01278-9 - Klaassen, F., & Magnus, J. R. (2014). Analyzing Wimbledon: The power of statistics. Oxford University Press.
https://doi.org/10.1093/acprof:oso/9780199355952.001.0001 - Li, C., Zhang, H., Zhang, Y., Shen, J., & An, R. (2025). The application of artificial intelligence techniques in predicting game outcomes of professional basketball league: A systematic review. Plos one, 20(6), e0326326.
https://doi.org/10.1371/journal.pone.0326326 - Livieris, I. E., Pintelas, E., & Pintelas, P. (2020). A CNN–LSTM model for gold price time-series forecasting. Neural Computing and Applications, 32, 17351–17360.
https://doi.org/10.1007/s00521-020-04867-x - Mannila, H., Toivonen, H., & Inkeri Verkamo, A. (1997). Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1(3), 259–289.
https://doi.org/10.1023/A:1009748302351 - Maymin, P. Z. (2017). The automated general manager: Can an algorithmic system for drafts, trades, and free agency outperform human front offices? Journal of Global Sport Management, 2(4), 234–249.
https://doi.org/10.1080/24704067.2017.1389248 - Meng, M., Zhang, Y., Ma, Y., Gao, Y., & Kong, W. (2023). EEG-based emotion recognition with cascaded convolutional recurrent neural networks. Pattern Anal. Appl., 26(2), 783–795.
https://doi.org/10.1007/S10044-023-01136-0 - Morgulev, E., Voslinsky, A., Azar, O. H., & Bar-Eli, M. (2020). Biased perceptions about momentum: Do comeback teams have higher chances to win in basketball overtimes? Judgment and Decision Making, 15(4), 545–560.
https://doi.org/10.1017/S1930297500007488 - Ötting, M., Langrock, R., & Maruotti, A. (2023). A copula-based multivariate hidden markov model for modelling momentum in football. AStA Advances in Statistical Analysis, 107(1), 9–27.
https://doi.org/10.1007/s10182-021-00395-8 - Perin, C., Vuillemot, R., Stolper, C. D., Stasko, J. T., Wood, J., & Carpendale, S. (2018). State of the art of sports data visualization. Computer Graphics Forum, 37, 663–686.
https://doi.org/10.1111/cgf.13447 - Rico-González, M., Pino-Ortega, J., Méndez, A., Clemente, F., & Baca, A. (2023). Machine learning application in soccer: a systematic review. Biology of sport, 40(1), 249–263.
https://doi.org/10.5114/biolsport.2023.112970 - Ryall, E., & Edgar, A. (2022). Watching sport during COVID-19. In Philosophy, sport and the pandemic (pp. 139–151). Routledge.
https://doi.org/10.4324/9781003214243-12 - Severini, T. A. (2020). Analytic methods in sports: Using mathematics and statistics to understand data from baseball, football, basketball, and other sports. Crc Press.
https://doi.org/10.1201/9780367252090 - Stival, L., Pinto, A., Andrade, F. D. S. P. D., Santiago, P. R. P., Biermann, H., Torres, R. D. S., & Dias, U. (2023). Using machine learning pipeline to predict entry into the attack zone in football. PloS one, 18(1), e0265372.
https://doi.org/10.1371/journal.pone.0265372 - Sun, C., Wu, G., Pan, G., Zhang, T., Li, J., Jiao, S., Liu, Y.-C., Chen, K., Liu, K., Xin, D., & Gao, G. (2024). Convolutional neural network-based pattern recognition of partial discharge in high-speed electric-multiple-unit cable termination. Sensors, 24(8), 2660.
https://doi.org/10.3390/S24082660 - Tang, Q., Wei, X., & Tan, B. (2026). The Role of Machine Learning in Talent Identification for Team Sports: A Systematic Review. Journal of Sports Science and Medicine, 25(1), 58–83.
https://doi.org/10.52082/jssm.2026.58 - Xarles, A., Escalera, S., Moeslund, T. B., & Clapés, A. (2025). Action Valuation in Sports: A Survey. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 6132–6142).
https://doi.org/10.48550/arXiv.2504.06163 - Xu, X., Liu, P., & Guo, M. (2023). Drainage pattern recognition of river network based on graph convolutional neural network. ISPRS Int. J. Geo Inf., 12(7), 253.
https://doi.org/10.3390/IJGI12070253 - Yin, W., Kann, K., Yu, M., & Schütze, H. (2017). Comparative study of CNN and RNN for natural language processing. arXiv Preprint arXiv:1702.01923.
https://doi.org/10.48550/arXiv.1702.01923 - Zaki, M. J. (2001). SPADE: An efficient algorithm for mining frequent sequences. Machine Learning, 42(1), 31–60.
https://doi.org/10.1023/A:1007652502315
DOI: https://doi.org/10.2478/ijcss-2026-0006 | Journal eISSN: 1684-4769
Language: English
Page range: 88 - 110
Published on: Aug 7, 2026
Published by: International Association of Computer Science in Sport
In partnership with: Paradigm Publishing Services
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© 2026 Ioannis Sevrisarianos, Ioannis Katakis, published by International Association of Computer Science in Sport
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.