{"id":"https://openalex.org/W3027038927","doi":"https://doi.org/10.3390/sym12050835","title":"Regression Tree Model for Predicting Game Scores for the Golden State Warriors in the National Basketball Association","display_name":"Regression Tree Model for Predicting Game Scores for the Golden State Warriors in the National Basketball Association","publication_year":2020,"publication_date":"2020-05-19","ids":{"openalex":"https://openalex.org/W3027038927","doi":"https://doi.org/10.3390/sym12050835","mag":"3027038927"},"language":"en","primary_location":{"id":"doi:10.3390/sym12050835","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym12050835","pdf_url":"https://www.mdpi.com/2073-8994/12/5/835/pdf?version=1590655048","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/12/5/835/pdf?version=1590655048","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031967712","display_name":"Mei\u2010Ling Huang","orcid":"https://orcid.org/0000-0002-0187-6130"},"institutions":[{"id":"https://openalex.org/I65446980","display_name":"National Chin-Yi University of Technology","ror":"https://ror.org/040bs6h16","country_code":"TW","type":"education","lineage":["https://openalex.org/I65446980"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Mei-Ling Huang","raw_affiliation_strings":["Department of Industrial Engineering and Management National Chin-Yi University of Technology, Taichung 411, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-0187-6130","affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering and Management National Chin-Yi University of Technology, Taichung 411, Taiwan","institution_ids":["https://openalex.org/I65446980"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110532637","display_name":"Yi-Jung Lin","orcid":null},"institutions":[{"id":"https://openalex.org/I65446980","display_name":"National Chin-Yi University of Technology","ror":"https://ror.org/040bs6h16","country_code":"TW","type":"education","lineage":["https://openalex.org/I65446980"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yi-Jung Lin","raw_affiliation_strings":["Department of Industrial Engineering and Management National Chin-Yi University of Technology, Taichung 411, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial Engineering and Management National Chin-Yi University of Technology, Taichung 411, Taiwan","institution_ids":["https://openalex.org/I65446980"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5031967712"],"corresponding_institution_ids":["https://openalex.org/I65446980"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":10.2476,"has_fulltext":true,"cited_by_count":39,"citation_normalized_percentile":{"value":0.98076523,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"12","issue":"5","first_page":"835","last_page":"835"},"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/T10157","display_name":"Sports Performance and Training","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/2732","display_name":"Orthopedics and Sports Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12677","display_name":"Sports Dynamics and Biomechanics","score":0.9782000184059143,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/basketball","display_name":"Basketball","score":0.8917931914329529},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5570945739746094},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.5290350317955017},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5161855816841125},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.49124616384506226},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.48665887117385864},{"id":"https://openalex.org/keywords/association","display_name":"Association (psychology)","score":0.4749301075935364},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.47449517250061035},{"id":"https://openalex.org/keywords/multilevel-model","display_name":"Multilevel model","score":0.4596938192844391},{"id":"https://openalex.org/keywords/team-sport","display_name":"Team sport","score":0.4168761670589447},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.3441489338874817},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3324117660522461},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3022953271865845},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2511962652206421},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16561591625213623},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11893394589424133},{"id":"https://openalex.org/keywords/athletes","display_name":"Athletes","score":0.09994009137153625},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.09444960951805115},{"id":"https://openalex.org/keywords/physical-therapy","display_name":"Physical therapy","score":0.08346149325370789}],"concepts":[{"id":"https://openalex.org/C103189561","wikidata":"https://www.wikidata.org/wiki/Q5372","display_name":"Basketball","level":2,"score":0.8917931914329529},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5570945739746094},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.5290350317955017},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5161855816841125},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.49124616384506226},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.48665887117385864},{"id":"https://openalex.org/C142853389","wikidata":"https://www.wikidata.org/wiki/Q744778","display_name":"Association (psychology)","level":2,"score":0.4749301075935364},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.47449517250061035},{"id":"https://openalex.org/C53059260","wikidata":"https://www.wikidata.org/wiki/Q374758","display_name":"Multilevel model","level":2,"score":0.4596938192844391},{"id":"https://openalex.org/C2780082397","wikidata":"https://www.wikidata.org/wiki/Q216048","display_name":"Team sport","level":3,"score":0.4168761670589447},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3441489338874817},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3324117660522461},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3022953271865845},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2511962652206421},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16561591625213623},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11893394589424133},{"id":"https://openalex.org/C2781054738","wikidata":"https://www.wikidata.org/wiki/Q4813730","display_name":"Athletes","level":2,"score":0.09994009137153625},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.09444960951805115},{"id":"https://openalex.org/C1862650","wikidata":"https://www.wikidata.org/wiki/Q186005","display_name":"Physical therapy","level":1,"score":0.08346149325370789},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/sym12050835","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym12050835","pdf_url":"https://www.mdpi.com/2073-8994/12/5/835/pdf?version=1590655048","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:3368de68d0cf49e095a12b789145ef93","is_oa":true,"landing_page_url":"https://doaj.org/article/3368de68d0cf49e095a12b789145ef93","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 12, Iss 5, p 835 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2073-8994/12/5/835/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/sym12050835","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/sym12050835","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym12050835","pdf_url":"https://www.mdpi.com/2073-8994/12/5/835/pdf?version=1590655048","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3027038927.pdf","grobid_xml":"https://content.openalex.org/works/W3027038927.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W12684916","https://openalex.org/W1572978214","https://openalex.org/W1955325228","https://openalex.org/W1970122754","https://openalex.org/W2016023958","https://openalex.org/W2057997183","https://openalex.org/W2074152770","https://openalex.org/W2128812068","https://openalex.org/W2172229041","https://openalex.org/W2270330859","https://openalex.org/W2338178179","https://openalex.org/W2410221421","https://openalex.org/W2443849356","https://openalex.org/W2533151390","https://openalex.org/W2567628451","https://openalex.org/W2577362171","https://openalex.org/W2588587315","https://openalex.org/W2594967417","https://openalex.org/W2726406610","https://openalex.org/W2742964204","https://openalex.org/W2749023953","https://openalex.org/W2753300659","https://openalex.org/W2805789393","https://openalex.org/W2883583580","https://openalex.org/W2887661471","https://openalex.org/W2891869937","https://openalex.org/W2894948714","https://openalex.org/W2907013527","https://openalex.org/W2908448204","https://openalex.org/W2935846866","https://openalex.org/W2967204512","https://openalex.org/W2997901049","https://openalex.org/W3123740670","https://openalex.org/W3124990352","https://openalex.org/W4213113494","https://openalex.org/W6664827710","https://openalex.org/W6685378934","https://openalex.org/W6751730271"],"related_works":["https://openalex.org/W2369330680","https://openalex.org/W2387007275","https://openalex.org/W2389047293","https://openalex.org/W2348951541","https://openalex.org/W2363923555","https://openalex.org/W2379277877","https://openalex.org/W2359223739","https://openalex.org/W2377467184","https://openalex.org/W2375049945","https://openalex.org/W4225966085"],"abstract_inverted_index":{"Data":[0],"mining":[1],"is":[2,24],"becoming":[3],"increasingly":[4],"used":[5,74],"in":[6,113],"sports.":[7],"Sport":[8],"data":[9,104],"analyses":[10,23],"help":[11,33],"fans":[12,34],"to":[13,35,48,58,78,89,94,160],"understand":[14],"games":[15],"and":[16,61,110,124,154,192],"teams\u2019":[17],"results.":[18],"Information":[19],"provided":[20],"by":[21],"such":[22],"useful":[25],"for":[26,130,139],"game":[27,103],"lovers.":[28],"Specifically,":[29],"the":[30,50,80,99,106,114,118,144,156,162,169,180,187,193,197],"information":[31],"can":[32,184],"predict":[36,79,95,186],"which":[37],"team":[38,66,170],"will":[39],"win":[40],"a":[41,135,202],"game.":[42],"Many":[43],"scholars":[44,63],"have":[45,64,73,87],"devoted":[46],"attention":[47],"predicting":[49,59,143],"results":[51,164,174],"of":[52,82,105,117,146,168,171,175,189,196,205],"various":[53],"sporting":[54],"events.":[55],"In":[56],"addition":[57],"wins":[60],"losses,":[62],"explored":[65],"scores.":[67,97],"Most":[68],"studies":[69,86],"on":[70,149],"score":[71,140,188,195],"prediction":[72],"linear":[75],"regression":[76,91,136,181],"models":[77,93],"scores":[81,145,159],"ball":[83],"games;":[84],"nevertheless,":[85],"yet":[88],"use":[90],"tree":[92,137,182],"basketball":[96],"Therefore,":[98],"present":[100],"study":[101,177],"analyzed":[102],"Golden":[107],"State":[108],"Warriors":[109],"their":[111],"opponents":[112],"2017\u20132018":[115],"season":[116],"National":[119],"Basketball":[120],"Association":[121],"(NBA).":[122],"Strong":[123],"weak":[125],"symmetry":[126],"requirements":[127],"were":[128],"identified":[129],"each":[131,147,190],"team.":[132,198],"We":[133],"developed":[134],"model":[138,183,200],"prediction.":[141],"After":[142],"player":[148,191],"two":[150],"teams,":[151],"we":[152],"summed":[153],"compared":[155],"predicted":[157,163],"total":[158,194],"obtain":[161],"(lose":[165],"or":[166],"win)":[167],"interest.":[172],"The":[173,199],"this":[176],"revealed":[178],"that":[179],"effectively":[185],"achieved":[201],"predictive":[203],"accuracy":[204],"87.5%.":[206]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":24},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}
