{"id":"https://openalex.org/W4410139730","doi":"https://doi.org/10.1007/s10791-025-09560-y","title":"ChampionNet: a transformer-enhanced neural architecture search framework for athletic performance prediction and training optimization","display_name":"ChampionNet: a transformer-enhanced neural architecture search framework for athletic performance prediction and training optimization","publication_year":2025,"publication_date":"2025-05-07","ids":{"openalex":"https://openalex.org/W4410139730","doi":"https://doi.org/10.1007/s10791-025-09560-y"},"language":"en","primary_location":{"id":"doi:10.1007/s10791-025-09560-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-025-09560-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09560-y.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09560-y.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083374325","display_name":"Lei Chang","orcid":"https://orcid.org/0000-0002-9824-4016"},"institutions":[{"id":"https://openalex.org/I117421001","display_name":"China Women's University","ror":"https://ror.org/0569s5x60","country_code":"CN","type":"education","lineage":["https://openalex.org/I117421001"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lei Chang","raw_affiliation_strings":["China Women\u2019s University, Beijing, 100101, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Women\u2019s University, Beijing, 100101, China","institution_ids":["https://openalex.org/I117421001"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004251248","display_name":"Shalli Rani","orcid":"https://orcid.org/0000-0002-8474-9435"},"institutions":[{"id":"https://openalex.org/I74319210","display_name":"Chitkara University","ror":"https://ror.org/057d6z539","country_code":"IN","type":"education","lineage":["https://openalex.org/I74319210"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shalli Rani","raw_affiliation_strings":["Chitkara University, Punjab, 14040, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chitkara University, Punjab, 14040, India","institution_ids":["https://openalex.org/I74319210"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101426168","display_name":"Muhammad Azeem Akbar","orcid":"https://orcid.org/0000-0002-4906-6495"},"institutions":[{"id":"https://openalex.org/I63548447","display_name":"Lappeenranta-Lahti University of Technology","ror":"https://ror.org/0208vgz68","country_code":"FI","type":"education","lineage":["https://openalex.org/I63548447"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Muhammad Azeem Akbar","raw_affiliation_strings":["LUT University, 53851, Lappeenranta, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LUT University, 53851, Lappeenranta, Finland","institution_ids":["https://openalex.org/I63548447"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5083374325"],"corresponding_institution_ids":["https://openalex.org/I117421001"],"apc_list":null,"apc_paid":null,"fwci":9.4393,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.97218862,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"28","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":0.9933000206947327,"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":0.9933000206947327,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9889000058174133,"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"}},{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9803000092506409,"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/transformer","display_name":"Transformer","score":0.6421287655830383},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6340723037719727},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.5718499422073364},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.519143283367157},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4871237277984619},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48448845744132996},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4777359366416931},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.3523743748664856},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18288537859916687},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.08113718032836914}],"concepts":[{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6421287655830383},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6340723037719727},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.5718499422073364},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.519143283367157},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4871237277984619},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48448845744132996},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4777359366416931},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.3523743748664856},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18288537859916687},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.08113718032836914},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s10791-025-09560-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-025-09560-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09560-y.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s10791-025-09560-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10791-025-09560-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10791-025-09560-y.pdf","source":{"id":"https://openalex.org/S5407036663","display_name":"Discover Computing","issn_l":"2948-2992","issn":["2948-2992"],"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Computing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410139730.pdf","grobid_xml":"https://content.openalex.org/works/W4410139730.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W2953097038","https://openalex.org/W3178906774","https://openalex.org/W4210768189","https://openalex.org/W4226191277","https://openalex.org/W4285109585","https://openalex.org/W4285146129","https://openalex.org/W4292582017","https://openalex.org/W4312184469","https://openalex.org/W4313598807","https://openalex.org/W4313639719","https://openalex.org/W4367360708","https://openalex.org/W4372306494","https://openalex.org/W4381198968","https://openalex.org/W4381331155","https://openalex.org/W4386295342","https://openalex.org/W4386424182","https://openalex.org/W4388714281","https://openalex.org/W4391470278","https://openalex.org/W4391853495","https://openalex.org/W4391892621","https://openalex.org/W4392973599","https://openalex.org/W4395475466","https://openalex.org/W4399671334","https://openalex.org/W4400010421","https://openalex.org/W4400721625","https://openalex.org/W4401027312","https://openalex.org/W4401068025","https://openalex.org/W4401671778","https://openalex.org/W4402125975","https://openalex.org/W4402724274","https://openalex.org/W4402916065","https://openalex.org/W4404031727","https://openalex.org/W4406457853"],"related_works":["https://openalex.org/W230091440","https://openalex.org/W2233261550","https://openalex.org/W2810751659","https://openalex.org/W258997015","https://openalex.org/W2997094352","https://openalex.org/W3216976533","https://openalex.org/W100620283","https://openalex.org/W2495260952","https://openalex.org/W4366179611","https://openalex.org/W2038503502"],"abstract_inverted_index":{"Abstract":[0],"Neural":[1],"architecture":[2,193],"search":[3,194],"(NAS)":[4],"has":[5],"emerged":[6],"as":[7,181],"a":[8,66,105],"promising":[9],"approach":[10,86],"for":[11,170,191],"automating":[12],"deep":[13],"learning":[14],"model":[15],"design.":[16],"However,":[17],"its":[18],"application":[19],"in":[20,77,115,149,154],"sports":[21,140,171],"analytics":[22],"faces":[23],"unique":[24],"challenges":[25],"due":[26],"to":[27,44,55,74,94,110,196],"the":[28,47,119,123,160,189],"complex":[29,164],"interplay":[30],"between":[31],"biomechanical":[32,97,174],"patterns,":[33],"physiological":[34,100,133],"adaptations,":[35],"and":[36,70,81,99,118,131,151,166],"coaching":[37,58],"expertise.":[38],"Traditional":[39],"NAS":[40,69],"methods":[41],"need":[42,190],"help":[43],"effectively":[45],"capture":[46,95],"multifaceted":[48],"nature":[49],"of":[50,122],"athletic":[51,79,116,184],"performance,":[52],"often":[53],"failing":[54],"integrate":[56],"qualitative":[57],"insights":[59,158],"with":[60,102,163,172],"quantitative":[61],"measurements.":[62],"We":[63],"introduce":[64],"ChampionNet,":[65],"framework":[67],"incorporating":[68],"large":[71],"language":[72],"models":[73,127,146],"enhance":[75],"accuracy":[76,150],"predicting":[78],"performance":[80,129,162,185],"tailoring":[82],"training":[83,124],"regimens.":[84],"Our":[85,136],"offers":[87],"three":[88],"primary":[89],"contributions:":[90],"integrating":[91],"hyperdimensional":[92],"embedding":[93],"fine-grained":[96],"features":[98],"parameters":[101],"exceptional":[103],"detail,":[104],"structure-preserving":[106],"graph":[107,125],"encoding":[108],"leverages":[109],"maintain":[111],"crucial":[112],"spatiotemporal":[113],"relationships":[114],"movements,":[117],"novel":[120],"comprehensiveness":[121],"that":[126,142],"forward":[128],"prediction":[130],"backward":[132],"adaptation":[134],"pathways.":[135],"experiments":[137],"on":[138],"various":[139],"demonstrate":[141],"ChampionNet":[143],"outperforms":[144],"other":[145],"by":[147],"2.5%":[148],"over":[152],"61.9%":[153],"computational":[155],"cost.":[156],"Further":[157],"illustrate":[159],"framework's":[161],"patterns":[165],"multi-modal":[167],"data,":[168],"especially":[169],"advanced":[173],"needs.":[175],"These":[176],"findings":[177],"support":[178],"ChampionNet's":[179],"effectiveness":[180],"an":[182],"integrative":[183],"optimization":[186],"solution,":[187],"highlighting":[188],"automated":[192],"tailored":[195],"sports.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":2}],"updated_date":"2026-06-13T06:13:01.061226","created_date":"2025-10-10T00:00:00"}