Looking Beyond the Past: Analyzing the Intrinsic Playing Style of Soccer Teams
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-26422-1_23
Reference23 articles.
1. Bialkowski, A., Lucey, P., Carr, P., Yue, Y., Sridharan, S., Matthews, I.: Identifying team style in soccer using formations learned from spatiotemporal tracking data. In: IEEE International Conference on Data Mining Workshop, pp. 9–14 (2014)
2. Castellano, J., Aguilar Pic, M.: Identification and preference of game styles in Laliga associated with match outcomes. Int. J. Environ. Res. Publ. Health 16(24), 5090 (2019)
3. Cho, H., Ryu, H., Song, M.: Pass2vec: Analyzing soccer players’ passing style using deep learning. Int. J. Sports Sci. Coach. 17(2), 355–365 (2021)
4. Decroos, T., Bransen, L., Van Haaren, J., Davis, J.: Actions speak louder than goals: Valuing player actions in soccer. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1851–1861 (2019)
5. Decroos, T., Davis, J.: Player vectors: Characterizing soccer players’ playstyle from match event streams. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 569–584 (2019)
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