{"id":"https://openalex.org/W4412103496","doi":"https://doi.org/10.1186/s40537-025-01216-4","title":"Predicting tennis match outcomes mid-game using machine learning on psychological and physical data","display_name":"Predicting tennis match outcomes mid-game using machine learning on psychological and physical data","publication_year":2025,"publication_date":"2025-07-08","ids":{"openalex":"https://openalex.org/W4412103496","doi":"https://doi.org/10.1186/s40537-025-01216-4"},"language":"en","primary_location":{"id":"doi:10.1186/s40537-025-01216-4","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01216-4","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01216-4","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01216-4","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5105064339","display_name":"Boyuan Li","orcid":"https://orcid.org/0009-0006-7159-5066"},"institutions":[{"id":"https://openalex.org/I4210153668","display_name":"Wenzhou-Kean University","ror":"https://ror.org/05609xa16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153668"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Boyuan Li","raw_affiliation_strings":["College of Science, Mathematics and Technology, Wenzhou-Kean University, Daxue Road, Wenzhou, Zhejiang, 305006, China"],"affiliations":[{"raw_affiliation_string":"College of Science, Mathematics and Technology, Wenzhou-Kean University, Daxue Road, Wenzhou, Zhejiang, 305006, China","institution_ids":["https://openalex.org/I4210153668"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105048827","display_name":"Zihui Deng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153668","display_name":"Wenzhou-Kean University","ror":"https://ror.org/05609xa16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153668"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zihui Deng","raw_affiliation_strings":["College of Science, Mathematics and Technology, Wenzhou-Kean University, Daxue Road, Wenzhou, Zhejiang, 305006, China"],"affiliations":[{"raw_affiliation_string":"College of Science, Mathematics and Technology, Wenzhou-Kean University, Daxue Road, Wenzhou, Zhejiang, 305006, China","institution_ids":["https://openalex.org/I4210153668"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084474994","display_name":"Gaurav Gupta","orcid":"https://orcid.org/0000-0002-9442-6007"},"institutions":[{"id":"https://openalex.org/I4210153668","display_name":"Wenzhou-Kean University","ror":"https://ror.org/05609xa16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153668"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gaurav Gupta","raw_affiliation_strings":["College of Science, Mathematics and Technology, Wenzhou-Kean University, Daxue Road, Wenzhou, Zhejiang, 305006, China"],"affiliations":[{"raw_affiliation_string":"College of Science, Mathematics and Technology, Wenzhou-Kean University, Daxue Road, Wenzhou, Zhejiang, 305006, China","institution_ids":["https://openalex.org/I4210153668"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105048828","display_name":"Jinger Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153668","display_name":"Wenzhou-Kean University","ror":"https://ror.org/05609xa16","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153668"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinger Li","raw_affiliation_strings":["College of Science, Mathematics and Technology, Wenzhou-Kean University, Daxue Road, Wenzhou, Zhejiang, 305006, China"],"affiliations":[{"raw_affiliation_string":"College of Science, Mathematics and Technology, Wenzhou-Kean University, Daxue Road, Wenzhou, Zhejiang, 305006, China","institution_ids":["https://openalex.org/I4210153668"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yixuan Miao","orcid":null},"institutions":[{"id":"https://openalex.org/I163151501","display_name":"Hangzhou Normal University","ror":"https://ror.org/014v1mr15","country_code":"CN","type":"education","lineage":["https://openalex.org/I163151501"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yixuan Miao","raw_affiliation_strings":["Alibaba Business College, Hangzhou Normal University, Xuelin Street, Hangzhou, Zhejiang, 311121, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Business College, Hangzhou Normal University, Xuelin Street, Hangzhou, Zhejiang, 311121, China","institution_ids":["https://openalex.org/I163151501"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5105064339"],"corresponding_institution_ids":["https://openalex.org/I4210153668"],"apc_list":{"value":1060,"currency":"GBP","value_usd":1300},"apc_paid":{"value":1060,"currency":"GBP","value_usd":1300},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22965775,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.9994999766349792,"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":0.9994999766349792,"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":0.995199978351593,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9847000241279602,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.8032487034797668},{"id":"https://openalex.org/keywords/computational-science-and-engineering","display_name":"Computational Science and Engineering","score":0.5781511664390564},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5047897100448608},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4747332036495209},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3278559744358063}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8032487034797668},{"id":"https://openalex.org/C68597687","wikidata":"https://www.wikidata.org/wiki/Q362601","display_name":"Computational Science and Engineering","level":2,"score":0.5781511664390564},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5047897100448608},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4747332036495209},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3278559744358063}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1186/s40537-025-01216-4","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01216-4","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01216-4","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:87eac7cfa83747a992423fd13b44e566","is_oa":true,"landing_page_url":"https://doaj.org/article/87eac7cfa83747a992423fd13b44e566","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of Big Data, Vol 12, Iss 1, Pp 1-16 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s40537-025-01216-4","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s40537-025-01216-4","pdf_url":"https://journalofbigdata.springeropen.com/counter/pdf/10.1186/s40537-025-01216-4","source":{"id":"https://openalex.org/S2737955091","display_name":"Journal Of Big Data","issn_l":"2196-1115","issn":["2196-1115"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Big Data","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4412103496.pdf","grobid_xml":"https://content.openalex.org/works/W4412103496.grobid-xml"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W964460774","https://openalex.org/W1862394037","https://openalex.org/W1995875735","https://openalex.org/W2005779862","https://openalex.org/W2034582451","https://openalex.org/W2049348684","https://openalex.org/W2082566952","https://openalex.org/W2096776776","https://openalex.org/W2112630042","https://openalex.org/W2143742995","https://openalex.org/W2195337249","https://openalex.org/W2314829355","https://openalex.org/W2328359983","https://openalex.org/W2336677943","https://openalex.org/W2396294226","https://openalex.org/W2569114383","https://openalex.org/W2576917530","https://openalex.org/W2587520514","https://openalex.org/W2786657691","https://openalex.org/W2949687910","https://openalex.org/W2959521506","https://openalex.org/W2980162198","https://openalex.org/W3007052498","https://openalex.org/W3008299293","https://openalex.org/W3013734981","https://openalex.org/W3020626653","https://openalex.org/W3029151951","https://openalex.org/W3112614053","https://openalex.org/W3123779506","https://openalex.org/W3130219998","https://openalex.org/W3130910473","https://openalex.org/W3168222379","https://openalex.org/W3168319761","https://openalex.org/W4220989092","https://openalex.org/W4226177585","https://openalex.org/W4283272896","https://openalex.org/W4285219217","https://openalex.org/W4318683023","https://openalex.org/W4361271148","https://openalex.org/W4361803873","https://openalex.org/W4386441478","https://openalex.org/W4388730026","https://openalex.org/W4391436932","https://openalex.org/W4392029839","https://openalex.org/W4392120324","https://openalex.org/W4392126235","https://openalex.org/W6601657081"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Predicting":[0],"game":[1,45],"outcomes":[2],"has":[3],"significantly":[4],"garnered":[5],"the":[6,23,28,55,70,129,140,156,161,176],"interest":[7],"of":[8,15,27,57,89,96,180],"researchers":[9],"in":[10],"recent":[11],"years.":[12],"The":[13,73],"role":[14],"player":[16],"performance":[17],"is":[18,76],"integral":[19],"in-game":[20],"analytics,":[21],"impacting":[22],"interpretation":[24],"and":[25,53,60,79,92,110,114,122,143,153],"results":[26],"analysis.":[29],"Our":[30],"work":[31],"presents":[32],"an":[33],"AI":[34],"for":[35,68],"Science":[36],"(AI4Sci)":[37],"method":[38,138],"to":[39,47,85,98,127],"use":[40],"real-time":[41],"data":[42,67,74,179],"from":[43,77,83],"each":[44],"point":[46],"determine":[48],"essential":[49],"feature":[50],"values,":[51],"formulate":[52],"assess":[54,128],"impact":[56],"psychological":[58,112],"momentum,":[59,113],"employ":[61],"machine":[62],"learning":[63],"methodology":[64],"on":[65],"mid-match":[66],"predicting":[69],"game\u2019s":[71],"victor.":[72],"source":[75],"Wimbledon":[78],"US":[80],"Open":[81],"games":[82,95],"2017":[84],"2022,":[86],"a":[87,134,181],"total":[88],"1592":[90],"games,":[91],"utilize":[93],"363":[94],"2023":[97],"evaluate":[99],"their":[100],"forecasting":[101],"ability.":[102],"We":[103],"first":[104,177],"obtained":[105],"weights":[106],"through":[107],"information":[108],"entropy":[109],"defined":[111],"then":[115],"3":[116],"best":[117,162],"classifiers,":[118],"random":[119],"forest,":[120],"CatBoost,":[121],"Logistic":[123],"Regression,":[124],"were":[125],"detected":[126],"features.":[130],"Additionally,":[131],"we":[132],"implemented":[133],"soft":[135,157],"voting":[136,158],"ensemble":[137],"integrating":[139],"Random":[141],"Forest":[142],"CatBoost":[144],"classifiers.":[145],"All":[146],"four":[147],"models":[148,169],"achieve":[149,170],"over":[150],"90%":[151],"accuracy":[152],"F1-score,":[154],"with":[155],"classifier":[159],"performing":[160],"(accuracy:":[163],"97.5%,":[164],"F1":[165],"score:":[166],"97.4%).":[167],"These":[168],"predictive":[171],"accuracies":[172],"above":[173],"70%":[174],"using":[175],"25%":[178],"game.":[182]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
