[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124622-en":3,"doc-seo-124622-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124622,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Tactically Maximize Game Advantage by Predicting Football Substitutions Using Machine Learning","Football’s massive global audience highlights the strategic importance of player substitutions, yet teams often rely on instinct because accurate prediction methods or software are lacking. This research evaluates whether machine learning can forecast effective substitution decisions and their likely impact on match outcomes. Models including logistic regression, decision trees, k-nearest neighbors, support vector machines, multinomial naive Bayes, and random forests are trained on Kaggle data covering 51,738 substitutions from 9074 matches across five European leagues and six seasons. The random forest model achieves the best accuracy over 70% and F1-score around 0.65, supporting data-driven match advantage for managers and coaches.","Mohandas, Alex, Ahsan, Mominul and Haider, Julfikar ORCID logoORCID: [https://orcid.org/0000-0001-7010-8285](https://orcid.org/0000-0001-7010-8285) (2023) Tactically Maximize Game Advantage by Predicting Football Substitutions Using Machine Learning. Big Data and Cognitive Computing, 7 (2) . p. 117. ISSN 2504-2289  \nDownloaded from: [https://e-space.mmu.ac.uk/632127/](https://e-space.mmu.ac.uk/632127/)  \nVersion: Published Version  \nPublisher: MDPI AG  \nDOI: [https://doi.org/10.3390/bdcc70201](https://doi.org/10.3390/bdcc70201) 17  \nUsage rights: Creative Commons: Attribution 4.0 Please cite the published version  \n[https://e-space.mmu.ac.uk](https://e-space.mmu.ac.uk)  \n4.9  \nArticle  \nTactically Maximize Game Advantage by Predicting Football Substitutions Using Machine Learning  \nAlex Mohandas, Mominul Ahsan and Julfikar Haider  \n[https://doi.org/10.3390/bdcc70201](https://doi.org/10.3390/bdcc70201) 17  \nArticle  \nTactically Maximize Game Advantage by Predicting Football Substitutions Using Machine Learning  \nAlex Mohandas 1, Mominul Ahsan 2, * and Jul􀀂kar Haider 3  \nCitation: Mohandas, A.; Ahsan, M.; Haider, J. Tactically Maximize Game Advantage by Predicting Football Substitutions Using Machine Learning. Big Data Cogn. Comput. 2023, 7, 117. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/bdcc7020117](10.3390/bdcc7020117)  \nAcademic Editor: Salvador García López  \nReceived: 18 April 2023  \nRevised: 7 June 2023  \nAccepted: 8 June 2023  \nPublished: 12 June 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Enterprise SSD Division, Micron Technology, Bengaluru 560103, India; [alexm@micron.com](alexm@micron.com)  \n2 Department of Computer Science, University of York, Deramore Lane, York YO10 5GH, UK  \n3 Department of Engineering, Manchester Metropolitan University, Chester Street, Manchester M1 5GD, UK; [j.haider@mmu.ac.uk](j.haider@mmu.ac.uk)  \n* [Correspondence: md.ahsan2@mail.dcu.ie](Correspondence: md.ahsan2@mail.dcu.ie)  \nAbstract: Football (also known as Soccer), boasts a staggering fan base of 3.5 billion individuals spread across 200 countries, making it the world’s most beloved sport. The widespread adoption of advanced technology in sports has become increasingly prominent, empowering players, coaches, and team management to enhance their performance and re􀀂ne team strategies. Among these advancements, player substitution plays a crucial role in altering the dynamics of a match. However, due to the absence of proven methods or software capable of accurately predicting substitutions, these decisions are often based on instinct rather than concrete data. The purpose of this research is to explore the potential of employing machine learning algorithms to predict substitutions in Football, and how it could in􀀃uence the outcome of a match. This study investigates the effect of timely and tactical substitutions in football matches and their in􀀃uence on the match results. Machine learning techniques such as Logistic Regression (LR), Decision tree (DT), K-nearest Neighbor (KNN), Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), Random Forest (RF) classi􀀂ers were implemented and tested to develop models and to predict player substitutions. Relevant data was collected from the Kaggle dataset, which contains data of 51,738 substitutions from 9074 European league football matches in 5 leagues spanning 6 seasons. Machine learning models were trained and tested using an 80-20 data split and it was observed that RF model provided the best accuracy of over 70% and the best F1-score of 0.65 on the test set across all football leagues. SVM model achieved the best Precision of almost 0.8 . However","cbCaivVdFG4sE0Z8","https://ap.wps.com/l/cbCaivVdFG4sE0Z8","pdf",3480801,1,29,"English","en",105,"# Introduction\n## Player substitution and game advantage\n## Machine learning for predicting substitutions\n## Data-driven approach and expected outcomes","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper targets the lack of proven methods to accurately predict football substitutions, which currently leads teams to make decisions based largely on instinct.\"},{\"question\":\"Which machine learning models were used to predict substitutions?\",\"answer\":\"Logistic Regression, Decision Tree, K-nearest Neighbor, Support Vector Machine, Multinomial Naive Bayes, and Random Forest classifiers were implemented and tested.\"},{\"question\":\"What were the best-performing results?\",\"answer\":\"The Random Forest model achieved the best overall accuracy (over 70%) and the best F1-score (about 0.65). The SVM model delivered the best precision (around 0.8).\"}]","Tactically Maximize Game Advantage by Predicting Football Substitutions Using Machine Learning | PDF",1785893366,73,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"tactically-maximize-game-advantage-by-predicting-football-substitutions-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/tactically-maximize-game-advantage-by-predicting-football-substitutions-using-machine-learning/124622/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address?","Question",{"text":75,"@type":76},"The paper targets the lack of proven methods to accurately predict football substitutions, which currently leads teams to make decisions based largely on instinct.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models were used to predict substitutions?",{"text":80,"@type":76},"Logistic Regression, Decision Tree, K-nearest Neighbor, Support Vector Machine, Multinomial Naive Bayes, and Random Forest classifiers were implemented and tested.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the best-performing results?",{"text":84,"@type":76},"The Random Forest model achieved the best overall accuracy (over 70%) and the best F1-score (about 0.65). 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