{"id":"https://openalex.org/W4392472645","doi":"https://doi.org/10.3390/make6010025","title":"Refereeing the Sport of Squash with a Machine Learning System","display_name":"Refereeing the Sport of Squash with a Machine Learning System","publication_year":2024,"publication_date":"2024-03-05","ids":{"openalex":"https://openalex.org/W4392472645","doi":"https://doi.org/10.3390/make6010025"},"language":"en","primary_location":{"id":"doi:10.3390/make6010025","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make6010025","pdf_url":"https://www.mdpi.com/2504-4990/6/1/25/pdf?version=1709619029","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"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":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/6/1/25/pdf?version=1709619029","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103210803","display_name":"Enqi Ma","orcid":"https://orcid.org/0009-0007-8977-6620"},"institutions":[{"id":"https://openalex.org/I139025015","display_name":"Pioneer (United States)","ror":"https://ror.org/05jke7315","country_code":"US","type":"company","lineage":["https://openalex.org/I139025015","https://openalex.org/I4210104019"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Enqi Ma","raw_affiliation_strings":["Pioneer Academics, Philadelphia, PA 19102, USA"],"raw_orcid":"https://orcid.org/0009-0007-8977-6620","affiliations":[{"raw_affiliation_string":"Pioneer Academics, Philadelphia, PA 19102, USA","institution_ids":["https://openalex.org/I139025015"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034716112","display_name":"Z. J. Kabala","orcid":"https://orcid.org/0000-0002-3502-7081"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zbigniew J. Kabala","raw_affiliation_strings":["Department of Civil & Environmental Engineering, Duke University, Durham, NC 27708, USA"],"raw_orcid":"https://orcid.org/0000-0002-3502-7081","affiliations":[{"raw_affiliation_string":"Department of Civil & Environmental Engineering, Duke University, Durham, NC 27708, USA","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103210803"],"corresponding_institution_ids":["https://openalex.org/I139025015"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":9.7239,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.97318371,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"6","issue":"1","first_page":"506","last_page":"553"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.9962999820709229,"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":0.9962999820709229,"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":0.9896000027656555,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9598000049591064,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/squash","display_name":"Squash","score":0.9145046472549438},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.43667155504226685},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34293031692504883},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.17757508158683777}],"concepts":[{"id":"https://openalex.org/C190114075","wikidata":"https://www.wikidata.org/wiki/Q133201","display_name":"Squash","level":2,"score":0.9145046472549438},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43667155504226685},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34293031692504883},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.17757508158683777},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make6010025","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make6010025","pdf_url":"https://www.mdpi.com/2504-4990/6/1/25/pdf?version=1709619029","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"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":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c828b433f491404c8a95f87a0c88adae","is_oa":true,"landing_page_url":"https://doaj.org/article/c828b433f491404c8a95f87a0c88adae","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":"Machine Learning and Knowledge Extraction, Vol 6, Iss 1, Pp 506-553 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make6010025","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make6010025","pdf_url":"https://www.mdpi.com/2504-4990/6/1/25/pdf?version=1709619029","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"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":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5099999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4392472645.pdf"},"referenced_works_count":21,"referenced_works":["https://openalex.org/W39472335","https://openalex.org/W127101348","https://openalex.org/W817574810","https://openalex.org/W2093794514","https://openalex.org/W2330049972","https://openalex.org/W2761346084","https://openalex.org/W2791602634","https://openalex.org/W2906396877","https://openalex.org/W2922295854","https://openalex.org/W2953359281","https://openalex.org/W3159324493","https://openalex.org/W3173432365","https://openalex.org/W3204732847","https://openalex.org/W4200210953","https://openalex.org/W4200402723","https://openalex.org/W4241166441","https://openalex.org/W4243928561","https://openalex.org/W4289543855","https://openalex.org/W6605162215","https://openalex.org/W6760484505","https://openalex.org/W6802806775"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2565964027","https://openalex.org/W2089615140","https://openalex.org/W2378225086","https://openalex.org/W3024848823","https://openalex.org/W2417607431","https://openalex.org/W2557340366","https://openalex.org/W4233654904","https://openalex.org/W2802660191","https://openalex.org/W2323615694"],"abstract_inverted_index":{"Squash":[0],"is":[1,182,206,239,279],"a":[2,34,214,225],"sport":[3],"where":[4],"referee":[5,32,44,62,265],"decisions":[6,14,57,63,234,266],"are":[7,15],"essential":[8],"to":[9,54,130,210,272,281],"the":[10,23,26,31,43,78,82,86,90,92,97,103,117,122,125,131,151,237,242,244,262,270,274],"game.":[11],"However,":[12],"these":[13,143],"very":[16],"subjective":[17],"in":[18,89,241,267],"nature.":[19],"Disputes,":[20],"both":[21,255],"from":[22,60,124],"players":[24,120],"and":[25,70,102,121,140,150,161,191,199,232,257,269],"audience,":[27],"regularly":[28],"occur":[29],"because":[30],"made":[33],"controversial":[35],"call.":[36],"In":[37],"this":[38,282],"study,":[39],"we":[40],"propose":[41],"automating":[42],"decision":[45],"process":[46,275],"through":[47,65],"machine":[48,221,277],"learning.":[49],"We":[50,109],"trained":[51,136,184],"neural":[52],"networks":[53],"predict":[55],"such":[56,115,196],"using":[58,142,276],"data":[59,188,194],"400":[61],"acquired":[64],"extensive":[66],"video":[67],"footage":[68],"reviewing":[69],"labeling.":[71],"Six":[72],"positional":[73],"values":[74],"were":[75,135],"extracted,":[76],"including":[77],"attacking":[79,104,126],"player\u2019s":[80,84,105,127],"position,":[81,85],"retreating":[83],"ball\u2019s":[87,93,98,132],"position":[88],"frame,":[91],"projected":[94,99],"first":[95],"bounce,":[96,101],"second":[100],"racket":[106,128],"head":[107,129],"position.":[108],"calculated":[110],"nine":[111],"additional":[112],"distance":[113,118,123],"values,":[114],"as":[116,180,197,249],"between":[119],"path.":[133],"Models":[134],"on":[137],"Wolfram":[138,147],"Mathematica":[139,148],"Python":[141,153],"values.":[144],"The":[145,201],"best":[146,152],"model":[149,154,174,205,245],"achieved":[155],"accuracies":[156,167],"of":[157,203,264],"86%":[158],"\u00b1":[159,163],"3.03%":[160],"85.2%":[162],"5.1%,":[164],"respectively.":[165],"These":[166],"surpass":[168],"85%,":[169],"demonstrating":[170],"near-human":[171],"performance.":[172],"Our":[173],"has":[175],"great":[176],"potential":[177],"for":[178,254],"improvement":[179],"it":[181],"currently":[183],"with":[185,213],"limited,":[186],"unbalanced":[187],"(400":[189],"decisions)":[190],"lacks":[192],"crucial":[193],"points":[195],"time":[198],"speed.":[200],"performance":[202],"our":[204],"almost":[207],"surely":[208],"going":[209],"improve":[211],"significantly":[212],"larger":[215],"training":[216],"dataset.":[217],"Unlike":[218],"human":[219],"referees,":[220],"learning":[222,278],"models":[223],"follow":[224],"consistent":[226],"standard,":[227],"have":[228],"unlimited":[229],"attention":[230],"spans,":[231],"make":[233],"instantly.":[235],"If":[236],"accuracy":[238],"improved":[240],"future,":[243],"can":[246],"potentially":[247],"serve":[248],"an":[250],"extra":[251],"refereeing":[252],"official":[253],"professional":[256],"amateur":[258],"squash":[259,268],"matches.":[260],"Both":[261],"analysis":[263],"proposal":[271],"automate":[273],"unique":[280],"study.":[283]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}