Machine learning application in soccer: a systematic review

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dc.contributor.authorRico González, Markel
dc.contributor.authorPino Ortega, José
dc.contributor.authorMéndez Zorrilla, Amaia
dc.contributor.authorClemente, Filipe Manuel
dc.contributor.authorBaca, Arnold
dc.date.accessioned2025-11-10T15:53:29Z
dc.date.available2025-11-10T15:53:29Z
dc.date.issued2023
dc.date.updated2025-11-10T15:53:29Z
dc.description.abstractDue 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.en
dc.description.sponsorshipMRG gratefully acknowledge the support of a Spanish government subproject Integration ways between qualitative and quantitative data, multiple case development, and synthesis review as main axis for an innovative future in physical activity and sports research [PGC2018-098742-B-C31] (Ministerio de Ciencia, Innovación y Universidades, Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema I + D + i), that is part of the coordinated project New approach of research in physical activity and sport from mixed methods perspective (NARPAS_MM) [SPGC201800X098742CV0]. FMC: This work is funded by Fundação para a Ciência e Tecnologia/ Ministério da Ciência, Tecnologia e Ensino Superior through national funds and when applicable co-funded EU funds under the project UIDB/50008/2020. No other specific sources of funding were used to assist in the preparation of this articleen
dc.identifier.citationRico-González, M., Pino-Ortega, J., Méndez, A., Clemente, F. M., & Baca, A. (2023). Machine learning application in soccer: a systematic review. Biology of Sport, 40(1), 249-263. https://doi.org/10.5114/BIOLSPORT.2023.112970
dc.identifier.doi10.5114/BIOLSPORT.2023.112970
dc.identifier.eissn2083-1862
dc.identifier.issn0860-021X
dc.identifier.urihttps://hdl.handle.net/20.500.14454/4337
dc.language.isoeng
dc.publisherInstitute of Sport
dc.subject.otherAlgorithm
dc.subject.otherBig data
dc.subject.otherComputer science
dc.subject.otherPrediction
dc.subject.otherTeam sports
dc.titleMachine learning application in soccer: a systematic reviewen
dc.typereview article
dcterms.accessRightsopen access
oaire.citation.endPage263
oaire.citation.issue1
oaire.citation.startPage249
oaire.citation.titleBiology of Sport
oaire.citation.volume40
oaire.versionVoR
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