Player Performance Prediction in Football Game

Player Performance Prediction in Football Game | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Player Performance Prediction in Football Game Rajiv Jha, Richa Rani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3183901/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the game of football (soccer), evaluation of players for transfers, scouting, team building, and strategic planning are essential. Due to the huge pool of grassroots players, brief career span, varying performance over the course of a person's career, various play circumstances, positions, and variable club resources, it is difficult to determine a player's total performance worth. In order to approach this difficult topic analytically, our Player Performance Prediction System draws knowledge from a range of football player abilities and traits. It takes into account the football player's skill set values and forecasts a performance value that shows the player's potential for progress as well as his or her capacity. The objective of this system is to help team managers and coaches both at the amateur and professional levels find the football players who have the best chance of succeeding in the future without being influenced by arbitrary variables like club finances, league competition, or player importance Artificial Intelligence and Machine Learning football soccer machine learning root mean squared value regression model Full Text Additional Declarations Competing interests: The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3183901","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":219796527,"identity":"556b8fd3-b368-40d9-bd50-8557f8851bed","order_by":0,"name":"Rajiv Jha","email":"","orcid":"","institution":"Vivekanand Education Society's Institute Of technology","correspondingAuthor":false,"prefix":"","firstName":"Rajiv","middleName":"","lastName":"Jha","suffix":""},{"id":219796528,"identity":"ff8ea978-ac3d-402e-bc0e-8a4ee9bce8f2","order_by":1,"name":"Richa Rani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIie2QMUsDMRTH3/Egt6S9NUWoXyGQpYJcv0rCgaMIXQSlngjnUux6fheHB4G6FLsedHFyPhCkIIgPa8ElV0fB/CAJj/f/kbwARCJ/kAyRiE8UvBmA411DBZXBbeV+Kif7Fb1cGtoVCOB/8bLGakoe8rSvLEzOLldunlHSbmB0GjKS2lqClwIFK6ZerN19bXEwAzUJKagsj0+IQhIYKdZGNwAHPIsrA4rgFitX38rHkxmvCN+7FCk9sOJRpCWYXkVDDVZ03qLSCsjSIyvXpendFUPVuOpopsPK2GevbUsXxeENLox8y2U2977ZnE+DyhcWoODPq/S2TDisu/Jbcl74vD8XiUQi/5FPAaJPECgq5LMAAAAASUVORK5CYII=","orcid":"","institution":"Vivekanand Education Society's Institute Of technology","correspondingAuthor":true,"prefix":"","firstName":"Richa","middleName":"","lastName":"Rani","suffix":""}],"badges":[],"createdAt":"2023-07-19 07:25:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3183901/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3183901/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":40503453,"identity":"764d7e10-13c9-42d2-bee5-8b0ac7f2294e","added_by":"auto","created_at":"2023-07-25 02:50:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":416959,"visible":true,"origin":"","legend":"","description":"","filename":"research.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3183901/v1_covered_f00a2e08-a1ac-44ff-80df-021705d0f2d8.pdf"}],"financialInterests":"\u003cp\u003eCompeting interests: The authors declare no competing interests.\u003c/p\u003e","formattedTitle":"\u003cp\u003ePlayer Performance Prediction in Football Game\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"football, soccer, machine learning, root mean squared value, regression, model","lastPublishedDoi":"10.21203/rs.3.rs-3183901/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3183901/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the game of football (soccer), evaluation of players for transfers, scouting, team building, and strategic planning are essential. Due to the huge pool of grassroots players, brief career span, varying performance over the course of a person's career, various play circumstances, positions, and variable club resources, it is difficult to determine a player's total performance worth. In order to approach this difficult topic analytically, our Player Performance Prediction System draws knowledge from a range of football player abilities and traits. It takes into account the football player's skill set values and forecasts a performance value that shows the player's potential for progress as well as his or her capacity. 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