Biology of Sport
eISSN: 2083-1862
ISSN: 0860-021X
Biology of Sport
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abstract:
Review paper

Machine learning application in soccer: A systematic review

Markel Rico-González
1
,
José Pino-Ortega
2, 3
,
Amaia Méndez
4
,
Filipe Manuel Clemente
5, 6
,
Arnold Baca
7

1.
Department of Didactics of Musical, Plastic and Corporal Expression, University of the Basque Country, UPV-EHU. Leioa, Spain
2.
BIOVETMED & SPORTSCI Research group. University of Murcia, San Javier. España
3.
Faculty of Sports Sciences. University of Murcia, San Javier. Spain
4.
Department of mechanics, design and industrial management, Faculty of engineering, University of Deusto, Bilbao, Spain
5.
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
6.
Instituto de Telecomunicações, Delegação da Covilhã, Lisboa 1049-001, Portugal
7.
Centre for Sport Science and University Sports, University of Vienna, Austria
Biol Sport. 2023;40(1):249–263
Online publish date: 2022/03/18
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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 Itemsfor 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.
keywords:

Team sports, Prediction, Algorithm, Computer science, Big data

 
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