Decoding Defensive Performance: A MachineLearning Approach to Football Player Valuation

Decoding Defensive Performance: A MachineLearning Approach to Football Player Valuation | 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 Decoding Defensive Performance: A MachineLearning Approach to Football Player Valuation Michał Zaręba, Tomasz Piłka, Tomasz Górecki, Bartłomiej Grzelak, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7030573/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Dec, 2025 Read the published version in Journal of Big Data → Version 1 posted 12 You are reading this latest preprint version Abstract Analyzing defensive actions, which have traditionally received less attention thanoffensive metrics, is a significant challenge in football analytics. This researchpresents an innovative methodology that utilizes XGBoost and deep neuralnetworks to evaluate defensive performance using metrics such as On-Ball Value(OBV), Valuing Actions by Estimating Probabilities (VAEP), and eXpectedThreat (xT). The study proposes a machine learning-based framework forevaluating defensive player value. A case study using expert ratings and marketvalues from the Polish PKO BP Ekstraklasa demonstrates the method’seffectiveness. The results advance the field of sports analytics by addressing thepersistent problem of accurately valuing the defensive contributions of footballplayers. football player evaluation machine learning deep learning XGBoost Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Dec, 2025 Read the published version in Journal of Big Data → Version 1 posted Editorial decision: Revision requested 26 Aug, 2025 Reviews received at journal 26 Aug, 2025 Reviews received at journal 21 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviews received at journal 14 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers invited by journal 14 Aug, 2025 Editor assigned by journal 29 Jul, 2025 Submission checks completed at journal 03 Jul, 2025 First submitted to journal 02 Jul, 2025 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. 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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-7030573","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501339001,"identity":"3e430be3-0e51-43fc-bf82-2a1bc846fb76","order_by":0,"name":"Michał Zaręba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDACZgY2BoYCMBPIqIAISgCxDH4tBjAtZxBaePDYg6SFsY0ILbrtzM8e/DBgsOefffjYg5/z7KINDjAfvM3DcAenFrPDbOaGPQYMzBLn0tINe7cl5244wJZszcPwDI8WHjYJHgOgo87wmEnwbmMGauExk+ZhOIxXi+QfAwYe+TP83yT/zqkHauH/RlCLNNAWCYMzQAZvw2GQLWwEtLCZScsYSBgYnmEzN5Y5djx35mE2Y8s5Bnj8cv7wM8k3FTb2cmeYnz18U1Od23e8+eGNNxV35HBpgQIJJDYziDA4QEAHFkCGllEwCkbBKBiuAADBOksFrE1cyAAAAABJRU5ErkJggg==","orcid":"","institution":"Adam Mickiewicz University in Poznań","correspondingAuthor":true,"prefix":"","firstName":"Michał","middleName":"","lastName":"Zaręba","suffix":""},{"id":501339002,"identity":"67562f94-0428-431f-af03-21f0167ca526","order_by":1,"name":"Tomasz Piłka","email":"","orcid":"","institution":"Adam Mickiewicz University in Poznań","correspondingAuthor":false,"prefix":"","firstName":"Tomasz","middleName":"","lastName":"Piłka","suffix":""},{"id":501339003,"identity":"11eacfd0-6462-45be-8b87-1d2d4117eef9","order_by":2,"name":"Tomasz Górecki","email":"","orcid":"","institution":"Adam Mickiewicz University in Poznań","correspondingAuthor":false,"prefix":"","firstName":"Tomasz","middleName":"","lastName":"Górecki","suffix":""},{"id":501339004,"identity":"2003e4b1-18ae-44a5-9bb5-9fe606b56b9d","order_by":3,"name":"Bartłomiej Grzelak","email":"","orcid":"","institution":"Adam Mickiewicz University in Poznań","correspondingAuthor":false,"prefix":"","firstName":"Bartłomiej","middleName":"","lastName":"Grzelak","suffix":""},{"id":501339005,"identity":"3754ee6e-5721-4415-b841-d10d78ff4d35","order_by":4,"name":"Krzysztof Dyczkowski","email":"","orcid":"","institution":"Adam Mickiewicz University in Poznań","correspondingAuthor":false,"prefix":"","firstName":"Krzysztof","middleName":"","lastName":"Dyczkowski","suffix":""}],"badges":[],"createdAt":"2025-07-02 14:53:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7030573/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7030573/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40537-025-01302-7","type":"published","date":"2025-12-17T15:58:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":98815198,"identity":"95afcc92-a01e-4e5e-9847-8268c00a01e0","added_by":"auto","created_at":"2025-12-22 16:14:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1254885,"visible":true,"origin":"","legend":"","description":"","filename":"J.BigData2025DecodingDefensivePerformanceAMachineLearningApproachtoFootballPlayerValuation4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7030573/v1_covered_e226004d-bf32-4288-9512-4a75be08590d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decoding Defensive Performance: A MachineLearning Approach to Football Player Valuation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-big-data","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bigd","sideBox":"Learn more about [Journal of Big Data](http://journalofbigdata.springeropen.com)","snPcode":"40537","submissionUrl":"https://submission.nature.com/new-submission/40537/3","title":"Journal of Big Data","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"football, player evaluation, machine learning, deep learning, XGBoost","lastPublishedDoi":"10.21203/rs.3.rs-7030573/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7030573/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Analyzing defensive actions, which have traditionally received less attention thanoffensive metrics, is a significant challenge in football analytics. 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