{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:52:51Z","timestamp":1743115971665,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031538322"},{"type":"electronic","value":"9783031538339"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-53833-9_12","type":"book-chapter","created":{"date-parts":[[2024,2,25]],"date-time":"2024-02-25T06:02:15Z","timestamp":1708840935000},"page":"144-154","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Elite Rugby League Players\u2019 Signature Movement Patterns and\u00a0Position Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8398-3609","authenticated-orcid":false,"given":"Victor Elijah","family":"Adeyemo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6196-9582","authenticated-orcid":false,"given":"Anna","family":"Palczewska","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4274-6236","authenticated-orcid":false,"given":"Ben","family":"Jones","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4348-9681","authenticated-orcid":false,"given":"Dan","family":"Weaving","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1007\/978-981-33-6835-4_41","volume-title":"Advances in Cyber Security","author":"VE Adeyemo","year":"2020","unstructured":"Adeyemo, V.E., Balogun, A.O., Mojeed, H.A., Akande, N.O., Adewole, K.S.: Ensemble-based logistic model trees for website phishing detection. In: Anbar, M., Abdullah, N., Manickam, S. (eds.) ACeS 2020. CCIS, vol. 1347, pp. 627\u2013641. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-33-6835-4_41"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Adeyemo, V.E., Palczewska, A., Jones, B.: LCCspm: l-length closed contiguous sequential patterns mining algorithm to find frequent athlete movement patterns from GPS. In: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 455\u2013460. IEEE (2021)","DOI":"10.1109\/ICMLA52953.2021.00077"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Adeyemo, V.E., Palczewska, A., Jones, B., Weaving, D.: Identification of pattern mining algorithm for rugby league players positional groups separation based on movement patterns. arXiv preprint arXiv:2302.14058 (2023)","DOI":"10.1371\/journal.pone.0301608"},{"issue":"7","key":"12_CR4","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1007\/s40279-015-0332-9","volume":"45","author":"R Chambers","year":"2015","unstructured":"Chambers, R., Gabbett, T.J., Cole, M.H., Beard, A.: The use of wearable microsensors to quantify sport-specific movements. Sports Med. 45(7), 1065\u20131081 (2015). https:\/\/doi.org\/10.1007\/s40279-015-0332-9","journal-title":"Sports Med."},{"issue":"2","key":"12_CR5","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1080\/17461391.2022.2027527","volume":"23","author":"N Collins","year":"2023","unstructured":"Collins, N., White, R., Palczewska, A., Weaving, D., Dalton-Barron, N., Jones, B.: Moving beyond velocity derivatives; using global positioning system data to extract sequential movement patterns at different levels of rugby league match-play. Eur. J. Sport Sci. 23(2), 201\u2013209 (2023)","journal-title":"Eur. J. Sport Sci."},{"issue":"1","key":"12_CR6","first-page":"559","volume":"18","author":"G Lema\u00eetre","year":"2017","unstructured":"Lema\u00eetre, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(1), 559\u2013563 (2017)","journal-title":"J. Mach. Learn. Res."},{"key":"12_CR7","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"12_CR8","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 4765\u20134774. Curran Associates, Inc. (2017). http:\/\/papers.nips.cc\/paper\/7062-a-unified-approach-to-interpreting-model-predictions.pdf"},{"key":"12_CR9","doi-asserted-by":"publisher","DOI":"10.4324\/9781315816340","volume-title":"An Introduction to Performance Analysis of Sport","author":"P O\u2019Donoghue","year":"2014","unstructured":"O\u2019Donoghue, P.: An Introduction to Performance Analysis of Sport. Routledge, London (2014)"},{"key":"12_CR10","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"24","key":"12_CR11","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1080\/02640414.2016.1273536","volume":"35","author":"AJ Sweeting","year":"2017","unstructured":"Sweeting, A.J., Aughey, R.J., Cormack, S.J., Morgan, S.: Discovering frequently recurring movement sequences in team-sport athlete spatiotemporal data. J. Sports Sci. 35(24), 2439\u20132445 (2017). https:\/\/doi.org\/10.1080\/02640414.2016.1273536. pMID: 28282752","journal-title":"J. Sports Sci."},{"issue":"2","key":"12_CR12","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1080\/02640414.2021.1982484","volume":"40","author":"R White","year":"2022","unstructured":"White, R., Palczewska, A., Weaving, D., Collins, N., Jones, B.: Sequential movement pattern-mining (SMP) in field-based team-sport: a framework for quantifying spatiotemporal data and improve training specificity? J. Sports Sci. 40(2), 164\u2013174 (2022)","journal-title":"J. Sports Sci."},{"issue":"1","key":"12_CR13","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1080\/02640414.2017.1282621","volume":"3","author":"CT Woods","year":"2018","unstructured":"Woods, C.T., Veale, J., Fransen, J., Robertson, S., Collier, N.F.: Classification of playing position in elite junior Australian football using technical skill indicators. J. Sports Sci. 3(1), 97\u2013103 (2018)","journal-title":"J. Sports Sci."}],"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\/978-3-031-53833-9_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T19:42:06Z","timestamp":1731440526000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-53833-9_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031538322","9783031538339"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-53833-9_12","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"26 February 2024","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":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlsa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dtai.cs.kuleuven.be\/events\/MLSA23\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31","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":"16","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":"52% - 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)"}}]}}