{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T03:05:16Z","timestamp":1769569516661,"version":"3.49.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032151643","type":"print"},{"value":"9783032151650","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-15165-0_4","type":"book-chapter","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T14:22:28Z","timestamp":1769523748000},"page":"48-61","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond Outcome Bias: Incorporating Action Completion Probability and\u00a0Risk-Return Into Soccer Evaluation Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7931-2579","authenticated-orcid":false,"given":"Yannik","family":"Paul","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4466-8136","authenticated-orcid":false,"given":"Maximilian","family":"Klemp","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"}]}],"member":"297","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","unstructured":"Anzer, G., Bauer, P.: Expected passes. Data Min. Knowl. Discov. 36(1), 295\u2013317 (2022). https:\/\/doi.org\/10.1007\/s10618-021-00810-3","DOI":"10.1007\/s10618-021-00810-3"},{"key":"4_CR2","doi-asserted-by":"publisher","unstructured":"Bernoulli, D.: Exposition of a new theory on the measurement of risk. Econometrica 22(1), 23 (1954). https:\/\/doi.org\/10.2307\/1909829","DOI":"10.2307\/1909829"},{"key":"4_CR3","unstructured":"Bransen, L., Robberechts, P., Van\u00a0Haaren, J., Davis, J.: Choke or shine? Quantifying soccer players\u2019 abilities to perform under mental pressure. In: Proceedings of the 13th MIT Sloan Sports Analytics Conference, pp. 1\u201325 (2019). https:\/\/www.semanticscholar.org\/paper\/bc2c2545f4d25cda7b362e3e93069abc86306b38"},{"key":"4_CR4","doi-asserted-by":"publisher","unstructured":"Brechot, M., Flepp, R.: Dealing with randomness in match outcomes: how to rethink performance evaluation in European club football using expected goals. J. Sports Econ. 21(4), 335\u2013362 (2020). https:\/\/doi.org\/10.1177\/1527002519897962","DOI":"10.1177\/1527002519897962"},{"key":"4_CR5","doi-asserted-by":"publisher","unstructured":"Brier, G.W.: Verification of forecasts expressed in terms of probability. Monthly Weather Rev. 78(1), 1\u20133 (1950). https:\/\/doi.org\/10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2"},{"key":"4_CR6","doi-asserted-by":"publisher","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794. KDD \u201916, ACM (2016). https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"4_CR7","unstructured":"Davis, J., Decroos, T., Robberechts, P.: Introducing atomic SPADL: A new way to represent event stream data (2020). https:\/\/dtai.cs.kuleuven.be\/sports\/blog\/introducing-atomic-spadl:-a-new-way-to-represent-event-stream-data\/. Accessed 11 June 2025"},{"key":"4_CR8","doi-asserted-by":"publisher","unstructured":"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. KDD \u201919, ACM (2019). https:\/\/doi.org\/10.1145\/3292500.3330758","DOI":"10.1145\/3292500.3330758"},{"key":"4_CR9","doi-asserted-by":"publisher","unstructured":"Feather, N.T.: Expectancy-Value Approaches: present Status and Future Directions, pp. 395\u2013420. Routledge (2021). https:\/\/doi.org\/10.4324\/9781003150879-21","DOI":"10.4324\/9781003150879-21"},{"key":"4_CR10","doi-asserted-by":"publisher","unstructured":"Ferri, C., Hern\u00e1ndez-Orallo, J., Modroiu, R.: An experimental comparison of performance measures for classification. Pattern Recogn. Lett. 30(1), 27\u201338 (2009). https:\/\/doi.org\/10.1016\/j.patrec.2008.08.010","DOI":"10.1016\/j.patrec.2008.08.010"},{"key":"4_CR11","doi-asserted-by":"publisher","unstructured":"Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29\u201336 (1982). https:\/\/doi.org\/10.1148\/radiology.143.1.7063747","DOI":"10.1148\/radiology.143.1.7063747"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Heckhausen, J., Heckhausen, H. (eds.): Motivation and Action. Springer eBook Collection, Springer, Cham, 3rd ed. 2018 edn. (2018)","DOI":"10.1007\/978-3-662-53927-9"},{"key":"4_CR13","doi-asserted-by":"publisher","unstructured":"Hellstr\u00f6m, T., Dignum, V., Bensch, S.: Bias in machine learning \u2013 what is it good for? (2020). https:\/\/doi.org\/10.48550\/ARXIV.2004.00686","DOI":"10.48550\/ARXIV.2004.00686"},{"key":"4_CR14","unstructured":"Koshute, P., Zook, J., McCulloh, I.: Recommending training set sizes for classification. arXiv preprint arXiv:2102.09382 (2021)"},{"key":"4_CR15","doi-asserted-by":"publisher","unstructured":"Lamas, L., Senatore, J.V., Fellingham, G.: Two steps for scoring a point: creating and converting opportunities in invasion team sports. PLOS ONE 15(10), e0240419 (2020). https:\/\/doi.org\/10.1371\/journal.pone.0240419","DOI":"10.1371\/journal.pone.0240419"},{"key":"4_CR16","unstructured":"Llana, S., Madrero, P., Fern\u00e1ndez, J., Barcelona, F.: The right place at the right time: advanced off-ball metrics for exploiting an opponent\u2019s spatial weaknesses in soccer. In: Proceedings of the 14th MIT Sloan Sports Analytics Conference (2020). https:\/\/www.semanticscholar.org\/paper\/8fc0d83684301b6ba2aa459bbed548515e2462f2"},{"key":"4_CR17","unstructured":"von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton, NJ, 3 edn. (1953), third edition"},{"key":"4_CR18","doi-asserted-by":"publisher","unstructured":"Power, P., Ruiz, H., Wei, X., Lucey, P.: Not all passes are created equal. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2017). https:\/\/doi.org\/10.1145\/3097983.3098051","DOI":"10.1145\/3097983.3098051"},{"key":"4_CR19","doi-asserted-by":"publisher","unstructured":"Robberechts, P., Van\u00a0Roy, M., Davis, J.: un-xPass: measuring soccer player\u2019s creativity. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4768\u20134777. KDD \u201923, ACM (2023). https:\/\/doi.org\/10.1145\/3580305.3599924","DOI":"10.1145\/3580305.3599924"},{"key":"4_CR20","unstructured":"Singh, K.: Introducing expected threat (xT) (2019). https:\/\/karun.in\/blog\/expected-threat.html. Accessed 13 May 2025"},{"key":"4_CR21","doi-asserted-by":"publisher","unstructured":"Singh, R., Mangat, N.S.: Stratified sampling. In: Elements of Survey Sampling, Kluwer Texts in the Mathematical Sciences, vol.\u00a015, pp. 70\u201396. Springer, Dordrecht (1996). https:\/\/doi.org\/10.1007\/978-94-017-1404-4_5","DOI":"10.1007\/978-94-017-1404-4_5"},{"key":"4_CR22","unstructured":"StatsBomb: StatsBomb open data. https:\/\/github.com\/statsbomb\/open-data (2021). Accessed 19 May 2025"},{"key":"4_CR23","doi-asserted-by":"publisher","unstructured":"Van Calster, B., McLernon, D.J., van Smeden, M., Wynants, L., Steyerberg, E.W.: Calibration: the Achilles heel of predictive analytics. BMC Med. 17(1) (2019). https:\/\/doi.org\/10.1186\/s12916-019-1466-7","DOI":"10.1186\/s12916-019-1466-7"},{"key":"4_CR24","unstructured":"Van Haaren, J.: \u201cwhy would i trust your numbers?\u201d On the explainability of expected values in soccer. arXiv preprint arXiv:2105.13778 (2021). https:\/\/www.semanticscholar.org\/paper\/09ff80f6990632e75ac6cd5f5f481a1c43f6d06b"},{"key":"4_CR25","doi-asserted-by":"crossref","unstructured":"Wunderlich, F., Seck, A., Memmert, D.: The influence of randomness on goals in football decreases over time. An empirical analysis of randomness involved in goal scoring in the English Premier League. J. Sports Sci. 39, 2322-2337 (2021)","DOI":"10.1080\/02640414.2021.1930685"}],"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\/978-3-032-15165-0_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T14:22:32Z","timestamp":1769523752000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15165-0_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032151643","9783032151650"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15165-0_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"28 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlsa2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dtai.cs.kuleuven.be\/events\/MLSA25\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}