Success Score: A Deep Learning Framework for Predicting Football Match Outcomes and Evaluating Team Performance

Success Score: A Deep Learning Framework for Predicting Football Match Outcomes and Evaluating Team Performance | 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 Article Success Score: A Deep Learning Framework for Predicting Football Match Outcomes and Evaluating Team Performance Farzam Manafzadeh, Changiz Eslahchi, Hadi Nobari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7736577/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 This study presents a novel predictive framework for estimating football match outcomes and assessing team performance through a new metric called the Success Score. This metric integrates Expected Goals (xG) with actual scoring results to provide a comprehensive evaluation of both opportunity creation and offensive execution. A Deep Neural Network (DNN) was trained on three seasons (2020–2021, 2021–2022, 2022–2023) of match data from four major European leagues, incorporating features such as tactical play styles, rolling performance averages, and team quality indicators. The model demonstrated strong predictive performance, achieving a Mean Absolute Error (MAE) of 0.3142 and an R² score of 0.8592 in cross-validation. It also generalized effectively to out-of-sample matches from the 2024–2025 season. Furthermore, the Success Score allowed for the classification of outcomes, enabled outcome prediction with 73.30% accuracy for Win vs. Not Win and 75.13% for Lose vs. Not Lose. Beyond predictive accuracy, the framework offers interpretable insights into team dynamics, revealing patterns of overperformance and underperformance. Case studies of FC Barcelona and Manchester United illustrate their practical utility for tactical analysis and strategic planning. This scalable, data-driven approach advances modern football analytics by supporting continuous performance monitoring and informed decision-making. Health sciences/Health care Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx 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. 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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-7736577","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":531350203,"identity":"9da1ed9b-48c4-4c8b-9c7b-af7384b84826","order_by":0,"name":"Farzam Manafzadeh","email":"","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":false,"prefix":"","firstName":"Farzam","middleName":"","lastName":"Manafzadeh","suffix":""},{"id":531350204,"identity":"ac358f72-8c40-4eec-baed-88b2cc9f2513","order_by":1,"name":"Changiz Eslahchi","email":"","orcid":"","institution":"Shahid Beheshti 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