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dc.contributor.authorArbués Sangüesa, Adrià
dc.contributor.authorB. Moeslund, Thomas
dc.contributor.authorH. Bahnsen, Chris
dc.contributor.authorBenítez Iglesias, Raúl
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2019-04-10T16:06:05Z
dc.date.issued2017
dc.identifier.citationArbués, A. [et al.]. Identifying basketball plays from sensor data; towards a low-cost automatic extraction of advanced statistics. A: 2017 IEEE International Conference on Data Mining Workshops. "2017 IEEE International Conference on Data Mining Workshops (ICDMW)". Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 894-901.
dc.identifier.isbn978-1-5386-3800-2
dc.identifier.urihttp://hdl.handle.net/2117/131599
dc.description.abstractAdvanced statistics have proved to be a crucial tool for basketball coaches in order to improve training skills. Indeed, the performance of the team can be further optimized by studying the behaviour of players under certain conditions. In the United States of America, companies such as STATS or Second Spectrum use a complex multi-camera setup to deliver advanced statistics to all NBA teams, but the price of this service is far beyond the budget of the vast majority of European teams. For this reason, a first prototype based on positioning sensors is presented. An experimental dataset has been created and meaningful basketball features have been extracted. 97.9% accuracy is obtained using Support Vector Machines when identifying 5 different classic plays: floppy offense, pick and roll, press break, post-up situation and fast breaks. After recognizing these plays in video sequences, advanced statistics could be extracted with ease.
dc.format.extent8 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject.lcshData mining
dc.titleIdentifying basketball plays from sensor data; towards a low-cost automatic extraction of advanced statistics
dc.typeConference report
dc.subject.lemacMineria de dades
dc.contributor.groupUniversitat Politècnica de Catalunya. ANCORA - Anàlisi i control del ritme cardíac
dc.identifier.doi10.1109/ICDMW.2017.123
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8215757
dc.rights.accessRestricted access - publisher's policy
drac.iddocument24013524
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorArbués, A.; B. Moeslund; H. Bahnsen; Benitez, R.
upcommons.citation.contributor2017 IEEE International Conference on Data Mining Workshops
upcommons.citation.publishedtrue
upcommons.citation.publicationName2017 IEEE International Conference on Data Mining Workshops (ICDMW)
upcommons.citation.startingPage894
upcommons.citation.endingPage901


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