[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126109-en":3,"doc-seo-126109-105":31,"detail-sidebar-cat-0-en-105":97},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126109,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning application in soccer - a systematic review - overview","Due to the chaotic nature of soccer, predictive statistical models are central to evidence-based decision-making. This systematic review identifies original studies applying machine learning (ML) to soccer data, outlining current capabilities and future uses. PubMed, SPORTDiscus, and FECYT databases were searched following PRISMA guidelines. From 145 initial studies, 32 were fully reviewed, extracting and analyzing outcome measures clustered into injury (n=7), performance (n=21), and talent forecasting (n=5).","Original Paper DOI: [https://doi.org/10.5114/biolsport.2023.112970](https://doi.org/10.5114/biolsport.2023.112970)  \nMachine learning application in soccer: a systematic review  \nAUTHORS: Markel Rico-González 1, José Pino-Ortega2,3, Amaia Méndez4, Filipe Manuel Clemente5,6, Arnold Baca7  \n1 Department of Didactics of Musical, Plastic and Corporal Expression, University of the Basque Country, UPV-EHU. Leioa, Spain  \n2 BIOVETMED & SPORTSCI Research group. University of Murcia, San Javier. España  \n3 Faculty of Sports Sciences. University of Murcia, San Javier. Spain  \n4 Department of mechanics, design and industrial management, Faculty of engineering, University of Deusto, Bilbao, Spain  \n5 Escola Superior Desporto e Lazer, Instituto Politécnico de Viana do Castelo, Rua Escola Industrial e Comercial de Nun’Álvares, 4900-347 Viana do Castelo, Portugal  \n6 Instituto de Telecomunicações, Delegação da Covilhã, Lisboa 1049-001, Portugal  \n7 Centre for Sport Science and University Sports, University of Vienna, Austria  \nABSTRACT: Due to the chaotic nature of soccer, the predictive statistical models have become in a current challenge to decision-making based on scientific evidence. The aim of the present study was to systematically identify original studies that applied machine learning (ML) to soccer data, highlighting current possibilities in ML and future applications. A systematic review of PubMed, SPORTDiscus, and FECYT (Web of Sciences, CCC, DIIDW, KJD, MEDLINE, RSCI, and SCIELO) was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 145 studies initially identified, 32 were fully reviewed, and their outcome measures were extracted and analyzed. In summary, all articles were clustered into three groups: injury (n = 7); performance (n = 21), which was classified in match/league outcomes forecasting, physical/physiological forecasting, and technical/tactical forecasting; and the last group was about talent forecasting (n = 5) . The development of technology, and subsequently the large amount of data available, has become ML in an important strategy to help team staff members in decision-making predicting dose-response relationship reducing the chaotic nature of this team sport. However, since ML models depend upon the amount of dataset, further studies should analyze the amount of data input needed make to a relevant predictive attempt which makes accurate predicting available.  \nCITATION: Rico-González M, Pino-Ortega J, Méndez A et al. Machine learning application in soccer: a systematic review. Biol Sport. 2023;40(1):249–263 .  \nReceived: 2021-09-03; Reviewed: 2021-12-21; Re-submitted: 2021-12-23; Accepted: 2022-01-03; Published: 2022-03-16.  \nCorresponding author:  \nMarkel Rico-González  \nDepartment of Didacticsof Musical, Plastic and Corporal Expression, University of the Basque Country, UPV-EHU  \nE-mail: [markeluniv@gmail.com](markeluniv@gmail.com)  \nORCID:  \nMarkel Rico-González 0000-0002-9849-0444  \nJosé Pino-Ortega  \n0000-0002-9091-0897  \nAmaia Méndez  \n0000-0002-0539-4753  \nFilipe Manuel Clemente 0000-0002-0539-4753  \nArnold Baça  \n0000-0002-1704-0290  \nKey words:  \nTeam sports Prediction Algorithm Computer science Big data  \nINTRODUCTION ~~ ~~  \nMachine learning (ML) is the science that allows computers to act as humans and learn, improving their knowledge from data feed overtime in an autonomous way in any area of life [1]; where sport isnot an exception [2–4] . The use of ML allows coaches to continually gain new knowledge, continuously adding data which leads toa constant solution update. Therefore, as long as the most appropriate and constantly changing data sources are used, the sport scientist may predict the future indicating some pieces of advice. These highlights may provide information about training design to minimize the occurrence of injuries [4], inducing both players´ and teams´ performance improvements [5–7], and even predicting athl","cbCaiqyozOU9WrGO","https://ap.wps.com/l/cbCaiqyozOU9WrGO","pdf",1069807,7,1,15,"English","en",105,"# Introduction\n## Machine learning in soccer\n## Prediction models and dataset features\n# Methods\n## Systematic search and PRISMA framework\n# Results\n## Injury prediction\n## Performance forecasting\n## Talent forecasting\n# Discussion and future perspectives","[{\"question\":\"What is the main goal of this systematic review?\",\"answer\":\"To systematically identify original studies that applied machine learning to soccer data, and to highlight current possibilities and future applications.\"},{\"question\":\"How were the included studies selected and analyzed?\",\"answer\":\"The review searched PubMed, SPORTDiscus, and FECYT using PRISMA guidelines, then fully reviewed 32 studies and extracted their outcome measures.\"},{\"question\":\"Which outcome areas are covered by the reviewed ML research?\",\"answer\":\"The studies were clustered into three groups: injury (n=7), performance forecasting (n=21), and talent forecasting (n=5).\"},{\"question\":\"Why does the review emphasize data amount and dataset quality?\",\"answer\":\"Because ML models depend on the quantity and quality of the dataset features used, and further work is needed to determine how much input data is required for accurate prediction.\"}]","Machine learning application in soccer - 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