Football prediction model based on the teams' Elo ratings and scoring indicators

Football prediction model based on the teams' Elo ratings and scoring indicators | 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 Football prediction model based on the teams' Elo ratings and scoring indicators Gor Saribekyan, Nikolay Yarovoy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3861295/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 The study focuses on proposing a solution for the 2023 Soccer Prediction Challenge organized in conjunction with the Machine Learning Journal's special issue on Machine Learning for Soccer. The challenge aimed to predict the outcomes of future matches from various leagues worldwide within a specific timeframe. In this paper, we examine the solution provided by our team "Friends of Elo" in detail. We experimented experimented with using Elo ratings and scoring indicators (goals) as arguments for the linear regression method. Poisson distribution was employed to predict the match results. The performance was evaluated based on the root mean squared error and the ranked probability score. Submitted predictions (714 matches) ranked 7th among 11 contestants in Task 1 and 5th among 13 contestants in Task 2, respectively. Futhermore, we conducted additional tests, where our model performed even greater on the expanded dataset of over 6800 matches to predict. The most significant advantage of our approach is that it does not require advanced match data, making it applicable worldwide. Overall, our study provides an in-depth analysis of the solution proposed by the "Friends of Elo" team, and we offer a superior alternative that performs well and is highly practical. Physical sciences/Mathematics and computing/Applied mathematics Physical sciences/Mathematics and computing/Statistics 2023 Soccer Prediction Challenge Soccermatics Poisson distribution Elo ranking Linear regression Soccer forecasting Full Text Additional Declarations No competing interests reported. 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-3861295","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":268414297,"identity":"e6f50cf6-2351-4eef-b7e9-8cd77f16328c","order_by":0,"name":"Gor Saribekyan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYJCCwwwMCTxQtg0QMzYeIEVLGkhLA0EtzEAtSNqBAK8W+fazDw8XVKTJ8M9uYH7xcc95u7Xth4G21NhE49JicCbd4PCMMzk8EncOsFnOeHY7eduZRKCWY2m5Dbi0AF1/mLetgofhRgKbMc+B28lmB4BaGBsO49Qi3/8MqOVfBY88RMu5ZLPzD/FrYbgBsqUhh8fgRgLzY54DB+zMbhCwxeAG0JYZx9J4DG8ktjHOOJCcYHYDaEsCHr/I96cxfy6oSbaXu5F8+MOHA3b2ZufTHz74UGOD22EIwNgmASQTwSoTCCsHA+YPQMKeSMWjYBSMglEwggAAbWpmdrwZ8JYAAAAASUVORK5CYII=","orcid":"","institution":"National Research University Higher School of Economics","correspondingAuthor":true,"prefix":"","firstName":"Gor","middleName":"","lastName":"Saribekyan","suffix":""},{"id":268414298,"identity":"c223555e-69e3-4594-8daa-27b860a4c9e8","order_by":1,"name":"Nikolay Yarovoy","email":"","orcid":"","institution":"National Research University Higher School of Economics","correspondingAuthor":false,"prefix":"","firstName":"Nikolay","middleName":"","lastName":"Yarovoy","suffix":""}],"badges":[],"createdAt":"2024-01-13 21:14:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3861295/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3861295/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58390655,"identity":"9f7569fd-4947-4c65-b9a7-4515d07194c7","added_by":"auto","created_at":"2024-06-14 20:17:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":200634,"visible":true,"origin":"","legend":"","description":"","filename":"SNScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3861295/v1_covered_d021a099-97fe-4c1b-bddc-d940074c8e90.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Football prediction model based on the teams' Elo ratings and scoring indicators","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"2023 Soccer Prediction Challenge, Soccermatics, Poisson distribution, Elo ranking, Linear regression, Soccer forecasting","lastPublishedDoi":"10.21203/rs.3.rs-3861295/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3861295/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The study focuses on proposing a solution for the 2023 Soccer Prediction Challenge organized in conjunction with the Machine Learning Journal's special issue on Machine Learning for Soccer. 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