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      Machine learning application in soccer: a systematic review

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          Abstract

          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.

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          Most cited references51

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          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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            Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement

            To describe the processes used to update the PRISMA 2009 statement for reporting systematic reviews, present results of a survey conducted to inform the update, summarize decisions made at the PRISMA update meeting, and describe and justify changes made to the guideline.
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              PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews

              Background Literature searches underlie the foundations of systematic reviews and related review types. Yet, the literature searching component of systematic reviews and related review types is often poorly reported. Guidance for literature search reporting has been diverse, and, in many cases, does not offer enough detail to authors who need more specific information about reporting search methods and information sources in a clear, reproducible way. This document presents the PRISMA-S (Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension) checklist, and explanation and elaboration. Methods The checklist was developed using a 3-stage Delphi survey process, followed by a consensus conference and public review process. Results The final checklist includes 16 reporting items, each of which is detailed with exemplar reporting and rationale. Conclusions The intent of PRISMA-S is to complement the PRISMA Statement and its extensions by providing a checklist that could be used by interdisciplinary authors, editors, and peer reviewers to verify that each component of a search is completely reported and therefore reproducible. Supplementary Information The online version contains supplementary material available at 10.1186/s13643-020-01542-z.
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                Author and article information

                Journal
                Biol Sport
                Biol Sport
                JBS
                Biology of Sport
                Institute of Sport in Warsaw
                0860-021X
                2083-1862
                16 March 2022
                January 2023
                : 40
                : 1
                : 249-263
                Affiliations
                [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
                Author notes
                Corresponding author: Markel Rico-González, Department of Didactics of Musical, Plastic and Corporal Expression, University of the Basque Country, UPV-EHU. E-mail: markeluniv@ 123456gmail.com

                ORCID: Markel Rico-González 0000-0002-9849-0444, José Pino-Ortega 0000-0002-9091-0897, Amaia Méndez 0000-0002-0539-4753, Filipe Manuel Clemente 0000-0002-0539-4753, Arnold Baça 0000-0002-1704-0290

                Article
                112970
                10.5114/biolsport.2023.112970
                9806754
                36636183
                9b1b16e1-f3df-4428-b129-74f6cf7a2e18
                Copyright © Biology of Sport 2023

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Share Alike 4.0 License, allowing third parties to copy and redistribute the material in any medium or format and remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.

                History
                : 03 September 2021
                : 21 December 2021
                : 23 December 2021
                : 03 January 2022
                Categories
                Review Paper

                team sports,prediction,algorithm,computer science,big data
                team sports, prediction, algorithm, computer science, big data

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