{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T01:48:24Z","timestamp":1778118504514,"version":"3.51.4"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031275265","type":"print"},{"value":"9783031275272","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-27527-2_1","type":"book-chapter","created":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T07:02:54Z","timestamp":1677222174000},"page":"3-13","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Towards Expected Counter - Using Comprehensible Features to\u00a0Predict Counterattacks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5660-9876","authenticated-orcid":false,"given":"Henrik","family":"Biermann","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5629-8622","authenticated-orcid":false,"given":"Franz-Georg","family":"Wieland","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4517-1383","authenticated-orcid":false,"given":"Jens","family":"Timmer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3406-9175","authenticated-orcid":false,"given":"Daniel","family":"Memmert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1772-8949","authenticated-orcid":false,"given":"Ashwin","family":"Phatak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,25]]},"reference":[{"key":"1_CR1","doi-asserted-by":"publisher","unstructured":"Anzer, G., Bauer, P.: Expected passes: determining the difficulty of a pass in football (soccer) using spatio-temporal data. Data Min. Knowl. Disc. 36(1), 295\u2013317 (2022). https:\/\/doi.org\/10.1007\/s10618-021-00810-3. https:\/\/link.springer.com\/10.1007\/s10618-021-00810-3","DOI":"10.1007\/s10618-021-00810-3"},{"key":"1_CR2","doi-asserted-by":"publisher","unstructured":"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\u20132049 (2021). https:\/\/doi.org\/10.1007\/s10618-021-00763-7. https:\/\/link.springer.com\/10.1007\/s10618-021-00763-7","DOI":"10.1007\/s10618-021-00763-7"},{"key":"1_CR3","doi-asserted-by":"publisher","unstructured":"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","DOI":"10.3389\/fspor.2021.624475"},{"key":"1_CR4","doi-asserted-by":"publisher","unstructured":"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\u2013152 (2019). https:\/\/doi.org\/10.1177\/1747954119827283. http:\/\/journals.sagepub.com\/doi\/10.1177\/1747954119827283","DOI":"10.1177\/1747954119827283"},{"key":"1_CR5","unstructured":"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)"},{"key":"1_CR6","doi-asserted-by":"publisher","unstructured":"Fern\u00e1ndez, J., Bornn, L., Cervone, D.: A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions. Mach. Learn. 110(6), 1389\u20131427 (2021). https:\/\/doi.org\/10.1007\/s10994-021-05989-6. https:\/\/link.springer.com\/10.1007\/s10994-021-05989-6","DOI":"10.1007\/s10994-021-05989-6"},{"key":"1_CR7","doi-asserted-by":"publisher","unstructured":"Groll, A., Schauberger, G., Tutz, G.: Prediction of major international soccer tournaments based on team-specific regularized Poisson regression: an application to the FIFA World Cup 2014. J. Quant. Anal. Sports 11(2), 97\u2013115 (2015). https:\/\/doi.org\/10.1515\/jqas-2014-0051. https:\/\/www.degruyter.com\/document\/doi\/10.1515\/jqas-2014-0051\/html","DOI":"10.1515\/jqas-2014-0051"},{"issue":"1","key":"1_CR8","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1080\/24748668.2016.11868892","volume":"16","author":"A Hewitt","year":"2016","unstructured":"Hewitt, A., Greenham, G., Norton, K.: Game style in soccer: what is it and can we quantify it? Int. J. Perform. Anal. Sport 16(1), 355\u2013372 (2016)","journal-title":"Int. J. Perform. Anal. Sport"},{"key":"1_CR9","unstructured":"Hockeyviz: Smoothing: how to (2022). https:\/\/hockeyviz.com\/howto\/smoothing"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Lago-Ballesteros, J., Lago-Pe\u00f1as, C., Rey, E.: The effect of playing tactics and situational variables on achieving score-box possessions in a professional soccer team. J. Sports Sci. 30(14), 1455\u20131461 (2012)","DOI":"10.1080\/02640414.2012.712715"},{"issue":"5","key":"1_CR11","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1007\/s10618-020-00705-9","volume":"34","author":"G Liu","year":"2020","unstructured":"Liu, G., Luo, Y., Schulte, O., Kharrat, T.: Deep soccer analytics: learning an action-value function for evaluating soccer players. Data Min. Knowl. Disc. 34(5), 1531\u20131559 (2020). https:\/\/doi.org\/10.1007\/s10618-020-00705-9","journal-title":"Data Min. Knowl. Disc."},{"key":"1_CR12","unstructured":"LLC, S.: Playing Styles Definition by StatsPerform (2022). https:\/\/www.statsperform.com\/resource\/stats-playing-styles-introduction\/"},{"key":"1_CR13","doi-asserted-by":"publisher","unstructured":"Memmert, D., Raabe, D.: Data analytics in football: positional data collection, modelling and analysis. Routledge, Abingdon, Oxon; 1 edn. New York, NY : Routledge (2018). https:\/\/doi.org\/10.4324\/9781351210164. https:\/\/www.taylorfrancis.com\/books\/9781351210157","DOI":"10.4324\/9781351210164"},{"key":"1_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/978-3-030-91608-4_36","volume-title":"Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2021","author":"L Raudonius","year":"2021","unstructured":"Raudonius, L., Allmendinger, R.: Evaluating football player actions during counterattacks. In: Yin, H., et al. (eds.) IDEAL 2021. LNCS, vol. 13113, pp. 367\u2013377. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-91608-4_36"},{"key":"1_CR15","doi-asserted-by":"publisher","unstructured":"Robberechts, P., Van Haaren, J., Davis, J.: A Bayesian approach to in-game win probability in soccer. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3512\u20133521 (2021). https:\/\/doi.org\/10.1145\/3447548.3467194. http:\/\/arxiv.org\/abs\/1906.05029. arXiv: 1906.05029","DOI":"10.1145\/3447548.3467194"},{"key":"1_CR16","unstructured":"Spearman, W.R., Basye, A.T., Dick, G.J., Hotovy, R., Hudl, P.P.: Physics-based modeling of pass probabilities in soccer (2017)"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Data Mining for Sports Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-27527-2_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T07:03:10Z","timestamp":1677222190000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-27527-2_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031275265","9783031275272"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-27527-2_1","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"25 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning and Data Mining for Sports Analytics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlsa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dtai.cs.kuleuven.be\/events\/MLSA22\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"10","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"56% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}