{"id":614,"date":"2016-03-22T02:37:42","date_gmt":"2016-03-22T02:37:42","guid":{"rendered":"http:\/\/course.oeru.org\/sia\/?page_id=614"},"modified":"2016-03-22T02:37:42","modified_gmt":"2016-03-22T02:37:42","slug":"pattern-recognition","status":"publish","type":"page","link":"https:\/\/course.oeru.org\/sia\/course-content\/informatics-and-analytics\/pattern-recognition\/","title":{"rendered":"Pattern recognition"},"content":{"rendered":"<div id=\"content\" class=\"mw-body container\" role=\"main\">\n<div class=\"row\">\n<div class=\"col-md-12\">\n<div class=\"panel\">\n<div class=\"panel-body\">\n<div id=\"bodyContent\">\n<div id=\"mw-content-text\" lang=\"en\" dir=\"ltr\" class=\"mw-content-ltr\">\n<h2><span class=\"mw-headline\" id=\"Pattern_recognition\">Pattern recognition<\/span><\/h2>\n<p>In the <a href=\"\/Sport_Informatics_and_Analytics\/Pattern_Recognition\" title=\"Sport Informatics and Analytics\/Pattern Recognition\">Pattern Recognition theme (T2)<\/a>, we considered:\n<\/p>\n<ul>\n<li> Systematic observation of performance.\n<\/li>\n<li> Supervised learning approaches to data analysis.\n<\/li>\n<li> Connections between performance trends and athlete actions.\n<\/li>\n<li> Use of open source tools such as <a rel=\"nofollow\" class=\"external text\" href=\"http:\/\/wikieducator.org\/Sport_Informatics_and_Analytics\/Pattern_Recognition\/Using_R\">R<\/a> to analyse performance and visualise data.\n<\/li>\n<\/ul>\n<p>We shared GPS data from <a rel=\"nofollow\" class=\"external text\" href=\"http:\/\/wikieducator.org\/Sport_Informatics_and_Analytics\/Pattern_Recognition#Analysing_AFL_GPS_Data\">an Australian Rules Football team&#8217;s performance<\/a> in a competitive game in order to explore the potential of such data to provide interesting and actionable insights. We presented Mladen Jovanovi\u0107&#8217;s <sup id=\"cite_ref-1\" class=\"reference\"><a href=\"#cite_note-1\">[1]<\/a><\/sup> analysis of the data set as a case study.\n<\/p>\n<p>This theme included a topic on <a rel=\"nofollow\" class=\"external text\" href=\"http:\/\/wikieducator.org\/Sport_Informatics_and_Analytics\/Pattern_Recognition\/Knowledge_Discovery\">knowledge discovery in databases<\/a> (KDD) and used two examples from the sport literature to explore the practice of KDD.\n<\/p>\n<h2><span class=\"mw-headline\" id=\"Reference\">Reference<\/span><\/h2>\n<ol class=\"references\">\n<li id=\"cite_note-1\"><span class=\"mw-cite-backlink\"><a href=\"#cite_ref-1\">\u2191<\/a><\/span> <span class=\"reference-text\"><span class=\"citation web\">Jovanovi\u0107, Mladen (13 March 2015). <a rel=\"nofollow\" class=\"external text\" href=\"http:\/\/complementarytraining.net\/wp-content\/uploads\/2015\/03\/AFL_Analysis.html\">&#8220;AFL Data Analysis Report&#8221;<\/a><span class=\"printonly\">. <a rel=\"nofollow\" class=\"external free\" href=\"http:\/\/complementarytraining.net\/wp-content\/uploads\/2015\/03\/AFL_Analysis.html\">http:\/\/complementarytraining.net\/wp-content\/uploads\/2015\/03\/AFL_Analysis.html<\/a><\/span><span class=\"reference-accessdate\">. Retrieved 26 March 2016<\/span>.<\/span><\/span>\n<\/li>\n<\/ol>\n<p><!-- \nNewPP limit report\nCPU time usage: 0.039 seconds\nReal time usage: 0.042 seconds\nPreprocessor visited node count: 505\/1000000\nPreprocessor generated node count: 10406\/1000000\nPost\u2010expand include size: 1839\/2097152 bytes\nTemplate argument size: 950\/2097152 bytes\nHighest expansion depth: 10\/40\nExpensive parser function count: 0\/100\n--><\/p>\n<p><!-- Saved in parser cache with key wikiedu-mw_:pcache:idhash:174948-0!*!0!!*!*!* and timestamp 20160322012419 and revision id 991730\n -->\n<\/div>\n<div class=\"visualClear\"><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"row\">\n<div class=\"col-md-12\">\n<ul class=\"pager\">\n<li class=\"previous\">\n            <a href=\"\/sia\/course-content\/informatics-and-analytics\/connecting-learners-with-open-resources\">\u2190 Previous<\/a>\n          <\/li>\n<li class=\"next\">\n            <a href=\"\/sia\/course-content\/informatics-and-analytics\/monitoring-performance\">Next \u2192<\/a>\n          <\/li>\n<\/ul><\/div>\n<\/p><\/div>\n<\/div>\n<footer>\n<br \/>\n<\/footer>\n","protected":false},"excerpt":{"rendered":"<p>Pattern recognition In the Pattern Recognition theme (T2), we considered: Systematic observation of performance. Supervised learning approaches to data analysis. Connections between performance trends and athlete actions. Use of open source tools such as R to analyse performance and visualise data. We shared GPS data from an Australian Rules Football team&#8217;s performance in a competitive [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":608,"menu_order":6400,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-614","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/course.oeru.org\/sia\/wp-json\/wp\/v2\/pages\/614","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/course.oeru.org\/sia\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/course.oeru.org\/sia\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/course.oeru.org\/sia\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/course.oeru.org\/sia\/wp-json\/wp\/v2\/comments?post=614"}],"version-history":[{"count":1,"href":"https:\/\/course.oeru.org\/sia\/wp-json\/wp\/v2\/pages\/614\/revisions"}],"predecessor-version":[{"id":615,"href":"https:\/\/course.oeru.org\/sia\/wp-json\/wp\/v2\/pages\/614\/revisions\/615"}],"up":[{"embeddable":true,"href":"https:\/\/course.oeru.org\/sia\/wp-json\/wp\/v2\/pages\/608"}],"wp:attachment":[{"href":"https:\/\/course.oeru.org\/sia\/wp-json\/wp\/v2\/media?parent=614"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}