From Prior Beliefs to Lineup Truths: Bayesian Inference for Lineup Performance

From Prior Beliefs to Lineup Truths: Bayesian Inference for Lineup 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 From Prior Beliefs to Lineup Truths: Bayesian Inference for Lineup Performance Christos Petridis, Konstantinos Pelechrinis, Zoran Obradovic This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8714352/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 One of the key responsibilities of a team's coach is to identify the lineups that provide the best chance of winning a game. Traditional metrics such as offensive and defensive ratings summarize past performance, but they are inherently noisy and not predictive. To address this limitation, we adopt a fully Bayesian approach to estimate the posterior predictive distribution of each lineup's offensive and defensive rating. Specifically, we assume a normal prior for these ratings, while the observed points scored and allowed per possession for each lineup serve as our evidence. Given the normal likelihood and the conjugacy of the normal model, the posterior predictive distribution is also normal, with updated mean and variance reflecting both prior beliefs and observed data. Our out-of-sample evaluations show that forecasts based on the posterior predictive distribution outperform the baseline model considering only the past lineup performance observed. The performance gap also increases as the observation lineup samples get smaller. In addition to the prediction improvements, the proposed Bayesian framework naturally quantifies uncertainty in lineup performance, enabling the generation of different rankings, including probabilistic comparisons that better reflect the inherent variability in basketball outcomes. Physical sciences/Mathematics and computing Biological sciences/Psychology Social science/Psychology Full Text Additional Declarations No competing interests reported. Supplementary Files SIBLR.pdf 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-8714352","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":584281701,"identity":"0826ccb5-c7f7-4e72-99b2-455800b21023","order_by":0,"name":"Christos Petridis","email":"","orcid":"","institution":"Temple University","correspondingAuthor":false,"prefix":"","firstName":"Christos","middleName":"","lastName":"Petridis","suffix":""},{"id":584281702,"identity":"cbdbc528-bdb2-4304-a07f-3cf150f84c0a","order_by":1,"name":"Konstantinos Pelechrinis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACCQYGxgMMFQw8YB4PkVoYDjCcYeDhYSNJC2MbUDXRWiRnNx848HHeHRl7+QbGB2/biNAiLXMs4eDMbc9ADmM2nEuMFjmJHIPDvNsOg7SwSfMSpyX/w+G/c8Ba2H8TpUVaIofhMGMDxBZmorRIzjlmcLDnGNAvxxKbJeecI0KLxO3mhw9+1NyxZ28+fPDDmzIitIAjBhSbwFTQQIx6FC2jYBSMglEwCnAAAMelNOEPmwV1AAAAAElFTkSuQmCC","orcid":"","institution":"University of Pittsburgh","correspondingAuthor":true,"prefix":"","firstName":"Konstantinos","middleName":"","lastName":"Pelechrinis","suffix":""},{"id":584281703,"identity":"08ef52fd-fb0c-417b-82d6-e868dc60d8a1","order_by":2,"name":"Zoran Obradovic","email":"","orcid":"","institution":"Temple University","correspondingAuthor":false,"prefix":"","firstName":"Zoran","middleName":"","lastName":"Obradovic","suffix":""}],"badges":[],"createdAt":"2026-01-27 21:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8714352/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8714352/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109541653,"identity":"4b3794af-c310-498a-809a-044f76ec8fb6","added_by":"auto","created_at":"2026-05-19 10:11:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1980795,"visible":true,"origin":"","legend":"","description":"","filename":"BayesianLineupsScientificReports27Jan.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8714352/v1_covered_eea28d18-5dbb-4762-b289-c4ae10e04d77.pdf"},{"id":101732692,"identity":"17b17804-e293-47f9-86d2-ae016b5423b7","added_by":"auto","created_at":"2026-02-03 06:32:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":5411505,"visible":true,"origin":"","legend":"","description":"","filename":"SIBLR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8714352/v1/66bfde32af96cc8c4e1c38ba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Prior Beliefs to Lineup Truths: Bayesian Inference for Lineup Performance","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":"","lastPublishedDoi":"10.21203/rs.3.rs-8714352/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8714352/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"One of the key responsibilities of a team's coach is to identify the lineups that provide the best chance of winning a game. 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