{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T15:39:04Z","timestamp":1743003544420,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031211300"},{"type":"electronic","value":"9783031211317"}],"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\/978-3-031-21131-7_45","type":"book-chapter","created":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T18:04:15Z","timestamp":1674669855000},"page":"579-591","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving the\u00a0Characterization and\u00a0Comparison of\u00a0Football Players with\u00a0Spatial Flow Motifs"],"prefix":"10.1007","author":[{"given":"Alberto","family":"Barbosa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro","family":"Ribeiro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"In\u00eas","family":"Dutra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,26]]},"reference":[{"key":"45_CR1","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/978-3-031-02044-5_5","volume-title":"Machine Learning and Data Mining for Sports Analytics","author":"A Barbosa","year":"2022","unstructured":"Barbosa, A., Ribeiro, P., Dutra, I.: Similarity of football players using passing sequences. In: Brefeld, U., Davis, J., Van Haaren, J., Zimmermann, A. (eds.) Machine Learning and Data Mining for Sports Analytics, pp. 51\u201361. Springer International Publishing, Cham (2022)"},{"issue":"4","key":"45_CR2","doi-asserted-by":"publisher","first-page":"299","DOI":"10.3233\/JSA-190290","volume":"5","author":"J Bekkers","year":"2019","unstructured":"Bekkers, J., Dabadghao, S.: Flow motifs in soccer: what can passing behavior tell us? J. Sports Anal. 5(4), 299\u2013311 (2019)","journal-title":"J. Sports Anal."},{"issue":"20","key":"45_CR3","doi-asserted-by":"publisher","first-page":"1983","DOI":"10.1080\/02640414.2016.1149602","volume":"34","author":"JS Fenner","year":"2016","unstructured":"Fenner, J.S., Iga, J., Unnithan, V.: The evaluation of small-sided games as a talent identification tool in highly trained prepubertal soccer players. J. Sports Sci. 34(20), 1983\u20131990 (2016)","journal-title":"J. Sports Sci."},{"key":"45_CR4","unstructured":"Gyarmati, L., Kwak, H., Rodriguez, P.: Searching for a unique style in soccer (2014). arXiv:1409.0308"},{"key":"45_CR5","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1515\/jqas-2019-0097","volume":"16","author":"EM H\u00e5land","year":"2020","unstructured":"H\u00e5land, E.M., Wiig, A.S., Hvattum, L.M., St\u00e5lhane, M.: Evaluating the effectiveness of different network flow motifs in association football. J. Quant. Anal. Sports 16, 311\u2013323 (2020)","journal-title":"J. Quant. Anal. Sports"},{"key":"45_CR6","doi-asserted-by":"crossref","unstructured":"Matesanz, D., Holzmayer, F., Torgler, B., Schmidt, S.L., Ortega, G.J.: Transfer market activities and sportive performance in European first football leagues: a dynamic network approach. PLoS ONE 13 (2018)","DOI":"10.1371\/journal.pone.0209362"},{"issue":"5594","key":"45_CR7","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1126\/science.298.5594.824","volume":"298","author":"R Milo","year":"2002","unstructured":"Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824\u2013827 (2002)","journal-title":"Science"},{"key":"45_CR8","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.physa.2014.06.037","volume":"412","author":"T Narizuka","year":"2014","unstructured":"Narizuka, T., Yamamoto, K., Yamazaki, Y.: Statistical properties of position-dependent ball-passing networks in football games. Phys. A Stat. Mech. Appl. 412, 157\u2013168 (2014)","journal-title":"Phys. A Stat. Mech. Appl."},{"issue":"1","key":"45_CR9","doi-asserted-by":"publisher","first-page":"1","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., Giannotti, F.: A public data set of spatio-temporal match events in soccer competitions. Sci. Data 6(1), 1\u201315 (2019)","journal-title":"Sci. Data"},{"key":"45_CR10","unstructured":"Pe\u00f1a, J.L., Navarro, R.S.: Who can replace xavi? a passing motif analysis of football players (2015). arXiv:1506.07768"},{"key":"45_CR11","doi-asserted-by":"crossref","unstructured":"Razali, N., Mustapha, A., Utama, S., Din, R.: A review on football match outcome prediction using Bayesian networks. J. Phys. Conf. Seri. 1020, 012004. IOP Publishing (2018)","DOI":"10.1088\/1742-6596\/1020\/1\/012004"},{"key":"45_CR12","doi-asserted-by":"crossref","unstructured":"Rossi, A., Pappalardo, L., Cintia, P., Iaia, F.M., Fern\u00e1ndez, J., Medina, D.: Effective injury forecasting in soccer with gps training data and machine learning. PLoS ONE 13 (2018)","DOI":"10.1371\/journal.pone.0201264"},{"issue":"4","key":"45_CR13","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1007\/s40745-021-00323-2","volume":"8","author":"P Thakkar","year":"2021","unstructured":"Thakkar, P., Shah, M.: An assessment of football through the lens of data science. Annals Data Sci. 8(4), 823\u2013836 (2021)","journal-title":"Annals Data Sci."},{"key":"45_CR14","first-page":"41","volume":"71","author":"K Tuyls","year":"2021","unstructured":"Tuyls, K., Omidshafiei, S., Muller, P., et al.: Game plan: what AI can do for football, and what football can do for AI. J. AI Res. 71, 41\u201388 (2021)","journal-title":"J. AI Res."},{"key":"45_CR15","doi-asserted-by":"publisher","first-page":"44","DOI":"10.2478\/ijcss-2019-0017","volume":"18","author":"AS Wiig","year":"2019","unstructured":"Wiig, A.S., H\u00e5land, E.M., St\u00e5lhane, M., Hvattum, L.M.: Analyzing passing networks in association football based on the difficulty, risk, and potential of passes. Int. J. Comput. Sci. Sport 18, 44\u201368 (2019)","journal-title":"Int. J. Comput. Sci. Sport"},{"key":"45_CR16","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1109\/TVCG.2018.2865041","volume":"25","author":"Y Wu","year":"2019","unstructured":"Wu, Y., Xie, X., Wang, J., Deng, D., Liang, H., Zhang, H., Cheng, S., Chen, W.: Forvizor: visualizing spatio-temporal team formations in soccer. IEEE Trans. Visual. Comput. Graph. 25, 65\u201375 (2019)","journal-title":"IEEE Trans. Visual. Comput. Graph."}],"container-title":["Studies in Computational Intelligence","Complex Networks and Their Applications XI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21131-7_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T18:09:28Z","timestamp":1674670168000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21131-7_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031211300","9783031211317"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21131-7_45","relation":{},"ISSN":["1860-949X","1860-9503"],"issn-type":[{"type":"print","value":"1860-949X"},{"type":"electronic","value":"1860-9503"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"26 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"COMPLEX NETWORKS 2016","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Complex Networks and Their Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Palermo","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwcna2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.complexnetworks.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}