{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T16:17:59Z","timestamp":1774887479572,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-026-04903-y","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T15:36:12Z","timestamp":1774884972000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["How Do Football Teams Play? An Extended DEC Analysis to Uncover Playing Styles"],"prefix":"10.1007","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1987-0709","authenticated-orcid":false,"given":"Ege","family":"Demir","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2660-2141","authenticated-orcid":false,"given":"Naz\u0131m Kemal","family":"\u00dcre","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4134-9423","authenticated-orcid":false,"given":"Yusuf H.","family":"\u015eahin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,30]]},"reference":[{"key":"4903_CR1","doi-asserted-by":"publisher","unstructured":"Demir E, \u015eahin YH, \u00dcre NK. How do football teams play? A deep embedded clustering approach to reveal playing styles. In: Dong, J.S., Sun, J., Xie, X., Jiang, K. (eds.) Sports Analytics. ISACE 2025. Lecture Notes in Computer Science, vol.\u00a015925. Springer, Cham (2026). https:\/\/doi.org\/10.1007\/978-3-032-06167-6_4","DOI":"10.1007\/978-3-032-06167-6_4"},{"key":"4903_CR2","doi-asserted-by":"publisher","unstructured":"Xie J, Girshick R, Farhadi A. Unsupervised deep embedding for clustering analysis. In: Proceedings of the international conference on machine learning (ICML), pp. 478\u2013487 (2016). https:\/\/doi.org\/10.48550\/arXiv.1511.06335","DOI":"10.48550\/arXiv.1511.06335"},{"key":"4903_CR3","doi-asserted-by":"publisher","unstructured":"Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. In: Advances in neural information processing systems (NeurIPS), vol.\u00a030 (2017). https:\/\/doi.org\/10.48550\/arXiv.1705.07874","DOI":"10.48550\/arXiv.1705.07874"},{"key":"4903_CR4","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20:53\u201365. https:\/\/doi.org\/10.1016\/0377-0427(87)90125-7.","journal-title":"J Comput Appl Math"},{"key":"4903_CR5","doi-asserted-by":"publisher","unstructured":"Moffatt SJ, Gupta R, Rakshit S, Keller BS. Identifying team playing styles across phases of play: a user-specific cluster framework. In: International Sports Analytics Conference and Exhibition, pp. 129\u2013136 (2024). https:\/\/doi.org\/10.1007\/978-3-031-69073-0_11","DOI":"10.1007\/978-3-031-69073-0_11"},{"key":"4903_CR6","doi-asserted-by":"publisher","unstructured":"Born Z. Tactical performance insights for Australian rules football using deep learning. Master\u2019s thesis, The University of Western Australia (2022). https:\/\/doi.org\/10.26182\/3sv1-h009","DOI":"10.26182\/3sv1-h009"},{"issue":"2","key":"4903_CR7","doi-asserted-by":"publisher","first-page":"39","DOI":"10.3390\/jfmk8020039","volume":"8","author":"S Plakias","year":"2023","unstructured":"Plakias S, Moustakidis S, Kokkotis C, Tsatalas T, Papalexi M, Plakias D, et al. Identifying soccer teams\u2019 styles of play: a scoping and critical review. J Funct Morphol Kinesiol. 2023;8(2):39. https:\/\/doi.org\/10.3390\/jfmk8020039.","journal-title":"J Funct Morphol Kinesiol"},{"issue":"1","key":"4903_CR8","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1177\/1471082X18808628","volume":"19","author":"J Diquigiovanni","year":"2019","unstructured":"Diquigiovanni J, Scarpa B. Analysis of association football playing styles: an innovative method to cluster networks. Stat Model. 2019;19(1):28\u201354. https:\/\/doi.org\/10.1177\/1471082X18808628.","journal-title":"Stat Model"},{"key":"4903_CR9","doi-asserted-by":"publisher","unstructured":"Pe\u00f1a JL, Touchette H. A network theory analysis of football strategies. arXiv preprint arXiv:1206.6904 (2012). https:\/\/doi.org\/10.48550\/arXiv.1206.6904","DOI":"10.48550\/arXiv.1206.6904"},{"key":"4903_CR10","doi-asserted-by":"publisher","unstructured":"Gyarmati L, Kwak H, Rodriguez P. Searching for a unique style in soccer. arXiv preprint arXiv:1409.0308 (2014). https:\/\/doi.org\/10.48550\/arXiv.1409.0308","DOI":"10.48550\/arXiv.1409.0308"},{"key":"4903_CR11","doi-asserted-by":"publisher","unstructured":"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 workshops, pp. 9\u201314 (2014). https:\/\/doi.org\/10.1109\/ICDMW.2014.167","DOI":"10.1109\/ICDMW.2014.167"},{"issue":"1","key":"4903_CR12","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1080\/02640414.2021.1976488","volume":"40","author":"A Lopez-Valenciano","year":"2022","unstructured":"Lopez-Valenciano A, Garcia-G\u00f3mez JA, L\u00f3pez-Del Campo R, Resta R, Moreno-Perez V, Blanco-Pita H, et al. Association between offensive and defensive playing style variables and ranking position in a national football league. J Sports Sci. 2022;40(1):50\u20138. https:\/\/doi.org\/10.1080\/02640414.2021.1976488.","journal-title":"J Sports Sci"},{"key":"4903_CR13","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2022.899199","volume":"13","author":"L Ruan","year":"2022","unstructured":"Ruan L, Ge H, Shen Y, Pu Z, Zong S, Cui Y. Quantifying the effectiveness of defensive playing styles in the Chinese Football Super League. Front Psychol. 2022;13:899199. https:\/\/doi.org\/10.3389\/fpsyg.2022.899199.","journal-title":"Front Psychol"},{"issue":"3","key":"4903_CR14","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1006\/brcg.1995.1024","volume":"27","author":"RM Chapman","year":"1995","unstructured":"Chapman RM, McCrary JW. EP component identification and measurement by principal components analysis. Brain Cognit. 1995;27(3):288\u2013310. https:\/\/doi.org\/10.1006\/brcg.1995.1024.","journal-title":"Brain Cognit"},{"issue":"1","key":"4903_CR15","doi-asserted-by":"publisher","first-page":"201","DOI":"10.48550\/arXiv.2102.03645","volume":"16","author":"C Hennig","year":"2022","unstructured":"Hennig C. An empirical comparison and characterisation of nine popular clustering methods. Adv Data Anal Classif. 2022;16(1):201\u201329. https:\/\/doi.org\/10.48550\/arXiv.2102.03645.","journal-title":"Adv Data Anal Classif"},{"issue":"5","key":"4903_CR16","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.48550\/arXiv.2002.01822","volume":"30","author":"SE Akhanli","year":"2020","unstructured":"Akhanli SE, Hennig C. Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes. Stat Comput. 2020;30(5):1523\u201344. https:\/\/doi.org\/10.48550\/arXiv.2002.01822.","journal-title":"Stat Comput"},{"key":"4903_CR17","doi-asserted-by":"publisher","unstructured":"Agarwal S. Data mining: data mining concepts and techniques. In: International conference on machine intelligence and research advancement, pp. 203\u2013207 (2013). https:\/\/doi.org\/10.1109\/icmira.2013.45","DOI":"10.1109\/icmira.2013.45"},{"key":"4903_CR18","doi-asserted-by":"publisher","unstructured":"Hundal RS, Liu Z, Wadhwa B, Hou Z, Jiang K, Dong JS. Soccer strategy analytics using probabilistic model checkers. In: International sports analytics conference and exhibition, pp. 249\u2013264 (2024). https:\/\/doi.org\/10.1007\/978-3-031-69073-0_22","DOI":"10.1007\/978-3-031-69073-0_22"},{"key":"4903_CR19","doi-asserted-by":"publisher","unstructured":"Bandara I, Shelyag S, Rajasegarar S, Dwyer DB, Kim E, Angelova M. Time-series analysis of ball carrier open-space in association football. In: International sports analytics conference and exhibition, pp. 1\u201317 (2024). https:\/\/doi.org\/10.1007\/s42979-025-03815-7","DOI":"10.1007\/s42979-025-03815-7"},{"key":"4903_CR20","doi-asserted-by":"publisher","unstructured":"Schilling A, Anurathan J, M\u00fchlberger J, Gerschner F, R\u00f6\u00dfle M, Theissler A, Klaiber M. Querying football matches for event data: towards using large language models. In: International sports analytics conference and exhibition, pp. 216\u2013227 (2024). https:\/\/doi.org\/10.1007\/978-3-031-69073-0_19","DOI":"10.1007\/978-3-031-69073-0_19"},{"issue":"1","key":"4903_CR21","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1038\/s41597-019-0247-7","volume":"6","author":"L Pappalardo","year":"2019","unstructured":"Pappalardo L, Cintia P, Rossi A, Massucco E, Ferragina P, Pedreschi D, et al. A public data set of spatio-temporal match events in soccer competitions. Sci Data. 2019;6(1):236. https:\/\/doi.org\/10.1038\/s41597-019-0247-7.","journal-title":"Sci Data"},{"key":"4903_CR22","unstructured":"van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res, 9(11) (2008)."},{"key":"4903_CR23","unstructured":"Nair V, Hinton GE. Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the international conference on machine learning, pp. 807\u2013814 (2010)."},{"key":"4903_CR24","unstructured":"Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980."},{"issue":"1","key":"4903_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B. 1977;39(1):1\u201338.","journal-title":"J R Stat Soc B"},{"key":"4903_CR26","unstructured":"Ng AY, Jordan MI, Weiss Y. On spectral clustering: analysis and an algorithm. In: Advances in neural information processing systems, pp. 849\u2013856 (2002)."},{"issue":"4","key":"4903_CR27","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U von Luxburg","year":"2007","unstructured":"von Luxburg U. A tutorial on spectral clustering. Stat Comput. 2007;17(4):395\u2013416.","journal-title":"Stat Comput"},{"key":"4903_CR28","first-page":"583","volume":"3","author":"A Strehl","year":"2002","unstructured":"Strehl A, Ghosh J. Cluster ensembles: a knowledge reuse framework for combining multiple partitions. J Mach Learn Res. 2002;3:583\u2013617.","journal-title":"J Mach Learn Res"},{"issue":"1","key":"4903_CR29","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert L, Arabie P. Comparing partitions. J Classif. 1985;2(1):193\u2013218. https:\/\/doi.org\/10.1007\/BF01908075.","journal-title":"J Classif"},{"key":"4903_CR30","doi-asserted-by":"publisher","unstructured":"Hastie T, Friedman J, Tibshirani R. The elements of statistical learning: data mining, inference, and prediction, 2nd ed. Springer, New York (2009). https:\/\/doi.org\/10.1007\/978-0-387-84858-7","DOI":"10.1007\/978-0-387-84858-7"},{"key":"4903_CR31","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp. 785\u2013794 (2016). https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"4903_CR32","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y. LightGBM: a highly efficient gradient boosting decision tree. In: Advances in neural information processing systems (2017)."},{"issue":"1","key":"4903_CR33","doi-asserted-by":"publisher","first-page":"72","DOI":"10.2307\/1412159","volume":"15","author":"C Spearman","year":"1904","unstructured":"Spearman C. The proof and measurement of association between two things. Am J Psychol. 1904;15(1):72\u2013101. https:\/\/doi.org\/10.2307\/1412159.","journal-title":"Am J Psychol"},{"issue":"1","key":"4903_CR34","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1214\/aoms\/1177730491","volume":"18","author":"HB Mann","year":"1947","unstructured":"Mann HB, Whitney DR. On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat. 1947;18(1):50\u201360.","journal-title":"Ann Math Stat"},{"key":"4903_CR35","doi-asserted-by":"publisher","unstructured":"Efron B, Tibshirani RJ. An introduction to the bootstrap. Chapman and Hall\/CRC, New York (1994). https:\/\/doi.org\/10.1201\/9780429246593","DOI":"10.1201\/9780429246593"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-026-04903-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-026-04903-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-026-04903-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T15:36:15Z","timestamp":1774884975000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-026-04903-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,30]]},"references-count":35,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["4903"],"URL":"https:\/\/doi.org\/10.1007\/s42979-026-04903-y","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,30]]},"assertion":[{"value":"22 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable. This study uses publicly available, anonymized data and does not involve human participants or animals.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}],"article-number":"324"}}