{"id":"https://openalex.org/W3005757432","doi":"https://doi.org/10.1109/uemcon47517.2019.8993072","title":"What Makes a National Football League Team Successful? an Analysis of Play by Play Data","display_name":"What Makes a National Football League Team Successful? an Analysis of Play by Play Data","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3005757432","doi":"https://doi.org/10.1109/uemcon47517.2019.8993072","mag":"3005757432"},"language":"en","primary_location":{"id":"doi:10.1109/uemcon47517.2019.8993072","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon47517.2019.8993072","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics &amp; Mobile Communication Conference (UEMCON)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041944251","display_name":"Josef Ur","orcid":null},"institutions":[{"id":"https://openalex.org/I924318406","display_name":"MacEwan University","ror":"https://ror.org/003s89n44","country_code":"CA","type":"education","lineage":["https://openalex.org/I924318406"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Josef Ur","raw_affiliation_strings":["Department of Computer Science, MacEwan University, Edmonton, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, MacEwan University, Edmonton, Canada","institution_ids":["https://openalex.org/I924318406"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112191471","display_name":"Mathew Craner","orcid":null},"institutions":[{"id":"https://openalex.org/I924318406","display_name":"MacEwan University","ror":"https://ror.org/003s89n44","country_code":"CA","type":"education","lineage":["https://openalex.org/I924318406"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mathew Craner","raw_affiliation_strings":["Department of Computer Science, MacEwan University, Edmonton, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, MacEwan University, Edmonton, Canada","institution_ids":["https://openalex.org/I924318406"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064426688","display_name":"Rehab El-Hajj","orcid":null},"institutions":[{"id":"https://openalex.org/I154425047","display_name":"University of Alberta","ror":"https://ror.org/0160cpw27","country_code":"CA","type":"education","lineage":["https://openalex.org/I154425047"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Rehab El-Hajj","raw_affiliation_strings":["Department of Computer Science, University of Alberta, Edmonton, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Alberta, Edmonton, Canada","institution_ids":["https://openalex.org/I154425047"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21705669,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"0419","last_page":"0425"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.9524000287055969,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/league","display_name":"League","score":0.9310437440872192},{"id":"https://openalex.org/keywords/football","display_name":"Football","score":0.7985103130340576},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.6185829043388367},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4642302393913269},{"id":"https://openalex.org/keywords/football-team","display_name":"Football team","score":0.44412752985954285},{"id":"https://openalex.org/keywords/team-sport","display_name":"Team sport","score":0.4421577453613281},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.44183504581451416},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.43728888034820557},{"id":"https://openalex.org/keywords/athletes","display_name":"Athletes","score":0.4183163046836853},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4183039963245392},{"id":"https://openalex.org/keywords/operations-research","display_name":"Operations research","score":0.40158146619796753},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.37389546632766724},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.36793053150177},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.3372235894203186},{"id":"https://openalex.org/keywords/public-relations","display_name":"Public relations","score":0.3334118723869324},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.32180166244506836},{"id":"https://openalex.org/keywords/political-science","display_name":"Political science","score":0.22808530926704407},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.19053465127944946}],"concepts":[{"id":"https://openalex.org/C207456731","wikidata":"https://www.wikidata.org/wiki/Q660818","display_name":"League","level":2,"score":0.9310437440872192},{"id":"https://openalex.org/C2778444522","wikidata":"https://www.wikidata.org/wiki/Q1081491","display_name":"Football","level":2,"score":0.7985103130340576},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.6185829043388367},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4642302393913269},{"id":"https://openalex.org/C2993400877","wikidata":"https://www.wikidata.org/wiki/Q28083137","display_name":"Football team","level":3,"score":0.44412752985954285},{"id":"https://openalex.org/C2780082397","wikidata":"https://www.wikidata.org/wiki/Q216048","display_name":"Team sport","level":3,"score":0.4421577453613281},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.44183504581451416},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.43728888034820557},{"id":"https://openalex.org/C2781054738","wikidata":"https://www.wikidata.org/wiki/Q4813730","display_name":"Athletes","level":2,"score":0.4183163046836853},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4183039963245392},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.40158146619796753},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.37389546632766724},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.36793053150177},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.3372235894203186},{"id":"https://openalex.org/C39549134","wikidata":"https://www.wikidata.org/wiki/Q133080","display_name":"Public relations","level":1,"score":0.3334118723869324},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.32180166244506836},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.22808530926704407},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19053465127944946},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1862650","wikidata":"https://www.wikidata.org/wiki/Q186005","display_name":"Physical therapy","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/uemcon47517.2019.8993072","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uemcon47517.2019.8993072","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE 10th Annual Ubiquitous Computing, Electronics &amp; Mobile Communication Conference (UEMCON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.800000011920929}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1484413656","https://openalex.org/W1990852058","https://openalex.org/W2009337558","https://openalex.org/W2104761564","https://openalex.org/W2140190241","https://openalex.org/W2140785063","https://openalex.org/W2495484804","https://openalex.org/W4240562676","https://openalex.org/W6628750762"],"related_works":["https://openalex.org/W4389954502","https://openalex.org/W2771255398","https://openalex.org/W2930428186","https://openalex.org/W3200027047","https://openalex.org/W4385770464","https://openalex.org/W3125536479","https://openalex.org/W4224262160","https://openalex.org/W3120363735","https://openalex.org/W4214820172","https://openalex.org/W2887955671"],"abstract_inverted_index":{"The":[0,15],"National":[1],"Football":[2],"League":[3],"(NFL)":[4],"is":[5,23,35,41],"one":[6],"of":[7,105,199],"the":[8,42,69,73,91,97,103,124,127,131,141,147,159,172,196,213,221],"most":[9,178],"popular":[10],"sports":[11],"in":[12,51,72,130,168,171],"North":[13],"America.":[14],"league":[16,132],"showcases":[17],"many":[18,88],"strong":[19],"athletes":[20],"and":[21,39,66,94,117,135,150,183,207],"winning":[22,32,137],"very":[24],"important":[25],"to":[26,45,49,53,122,157,220],"all":[27],"teams;":[28],"however,":[29],"for":[30,162],"every":[31],"team,":[33,38],"there":[34,190],"a":[36],"losing":[37],"it":[40],"coaches'":[43],"responsibility":[44],"decide":[46],"what":[47,166],"plays":[48,84,92],"call":[50],"order":[52],"help":[54,63,104],"their":[55],"teams":[56,71,129,142,149,152,179,185,219],"win.":[57],"Data":[58],"mining":[59,107],"play":[60],"data":[61,106],"can":[62],"show":[64],"trends":[65],"areas":[67,125],"where":[68,95,126],"top":[70,148,182],"NFL":[74,222],"excel":[75],"by":[76],"looking":[77],"at":[78],"questions":[79],"like":[80],"how":[81,87],"often":[82],"certain":[83],"are":[85,99],"run,":[86],"yards":[89],"do":[90],"get":[93],"on":[96],"field":[98],"touchdown":[100],"scored.":[101],"With":[102],"intelligent":[108],"tools":[109],"such":[110],"as":[111],"Naive":[112],"Bayes,":[113],"decision":[114],"tree":[115],"algorithms":[116],"association":[118],"rules,":[119],"we":[120,154,175],"worked":[121],"isolate":[123],"best":[128],"separate":[133],"themselves":[134],"produce":[136],"franchises.":[138],"By":[139],"classifying":[140],"into":[143],"two":[144,160,197],"categories":[145],"-":[146,153,180,186],"bottom":[151,184],"were":[155,191],"able":[156],"compare":[158],"classes":[161],"differences":[163],"which":[164],"explain":[165],"results":[167],"more":[169,214],"success":[170],"league.":[173],"Although":[174],"found":[176,211],"that":[177,194],"both":[181],"use":[187],"similar":[188],"plays,":[189],"also":[192],"factors":[193,206],"distinguished":[195],"types":[198],"teams.":[200],"This":[201],"research":[202],"highlights":[203],"these":[204],"specific":[205],"some":[208],"overall":[209],"distinctions":[210],"between":[212],"successful":[215,218],"versus":[216],"less":[217],"community.":[223]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
