Towards Expected Counter - Using Comprehensible Features to Predict Counterattacks-Reference-Cited by-同舟云学术

Towards Expected Counter - Using Comprehensible Features to Predict Counterattacks

Published:2023 Issue: Volume: Page:3-13
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Biermann Henrik^ORCID,Wieland Franz-Georg^ORCID,Timmer Jens^ORCID,Memmert Daniel^ORCID,Phatak Ashwin^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-27527-2_1

Reference16 articles.

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2. Bauer, P., Anzer, G.: Data-driven detection of counterpressing in professional football: a supervised machine learning task based on synchronized positional and event data with expert-based feature extraction. Data Min. Knowl. Disc. 35(5), 2009–2049 (2021). https://doi.org/10.1007/s10618-021-00763-7. https://link.springer.com/10.1007/s10618-021-00763-7

3. Bauer, P., Anzer, G.: A goal scoring probability model for shots based on synchronized positional and event data in football (soccer). Front. Sports Active Living 3, 53 (2021). https://doi.org/10.3389/fspor.2021.624475

4. Fernandes, T., Camerino, O., Garganta, J., Pereira, R., Barreira, D.: Design and validation of an observational instrument for defence in soccer based on the Dynamical Systems Theory. Int. J. Sports Sci. Coach. 14(2), 138–152 (2019). https://doi.org/10.1177/1747954119827283. http://journals.sagepub.com/doi/10.1177/1747954119827283

5. Fernandez, J., Bornn, L.: Wide Open Spaces: a statistical technique for measuring space creation in professional soccer. In: Sloan sports analytics conference, vol. 2018 (2018)

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