The path to a goal: Understanding soccer possessions via path signatures

The path to a goal: Understanding soccer possessions via path signatures | 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 The path to a goal: Understanding soccer possessions via path signatures David Hirnschall, Robert Bajons This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7538719/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract We present a novel framework for predicting next actions in soccer possessions by leveraging path signatures to encode their complex spatio-temporal structure. Unlike existing approaches, we do not rely on fixed historical windows and handcrafted features, but rather encode the entire recent possession, thereby avoiding the inclusion of potentially irrelevant or misleading historical information. Path signatures naturally capture the order and interaction of events, providing a mathematically grounded feature encoding for variable-length time series of irregular sampling frequencies without the necessity for manual feature engineering. Our proposed approach outperforms a transformer-based benchmark across various loss metrics and considerably reduces computational cost. Building on these results, we introduce a new possession evaluation metric based on well-established frameworks in soccer analytics, incorporating both predicted action type probabilities and action location. Our metric shows greater reliability than existing metrics in domain-specific comparisons. Finally, we validate our approach through a detailed analysis of the 2017/18 Premier League season and discuss further applications and future extensions. Path signatures sports analytics possession value deep learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Mar, 2026 Reviews received at journal 27 Mar, 2026 Reviewers agreed at journal 27 Feb, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers invited by journal 11 Dec, 2025 Editor assigned by journal 27 Sep, 2025 Submission checks completed at journal 05 Sep, 2025 First submitted to journal 04 Sep, 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. 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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-7538719","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559095123,"identity":"aed6889e-8367-4e58-b734-8f0234d2a683","order_by":0,"name":"David Hirnschall","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDCCA0DEA0JAwMzAYCHDT6oWCR7JBqgMDx4tQMkDDHAtBgcIaOE7fsbwwBuGOzLms9sffi6okeAxvpFjwPCjhkHeHocWyTM5BgfnMDzjkblzxlh6xjEJHjOgFsaeYwyGPTi0GBxISzjMw3CYR0Iih42Zhw2o5TbQFt4GBkacWs4/g2lJf8bM8w/osNlAW/42MNjj1HIj+QBUS4IZM28b0PvSOQbMQFsScWmRvPH4wME5BmCHGUvz9knwSNx/VnBY5phEcs8BHCF2PrH5w5uKw/ZAhz38zPPNRo6/5/DGh29qbGzbG3BYA3EeGh9ovgQ+9aNgFIyCUTAKCAAAy8lU03cBEPQAAAAASUVORK5CYII=","orcid":"","institution":"Vienna University of Economics and Business","correspondingAuthor":true,"prefix":"","firstName":"David","middleName":"","lastName":"Hirnschall","suffix":""},{"id":559095128,"identity":"893c1389-5759-4e4c-86cc-44247631da53","order_by":1,"name":"Robert Bajons","email":"","orcid":"","institution":"Vienna University of Economics and Business","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Bajons","suffix":""}],"badges":[],"createdAt":"2025-09-04 19:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7538719/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7538719/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98339703,"identity":"043f525e-68fb-40a0-a475-5e194ff09017","added_by":"auto","created_at":"2025-12-16 17:06:26","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4021,"visible":true,"origin":"","legend":"","description":"","filename":"46d4827da932482094de2b798ad09200.json","url":"https://assets-eu.researchsquare.com/files/rs-7538719/v1/803b6fcc2a27255df861c808.json"},{"id":98438299,"identity":"14ac35c1-df06-4b84-943c-e7e1b775142b","added_by":"auto","created_at":"2025-12-17 16:58:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":664074,"visible":true,"origin":"","legend":"","description":"","filename":"SigModel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7538719/v1_covered_69fed7cd-66d0-438c-984c-439339af50bc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The path to a goal: Understanding soccer possessions via path signatures","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"international-journal-of-data-science-and-analytics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jdsa","sideBox":"Learn more about [International Journal of Data Science and Analytics](http://link.springer.com/journal/41060)","snPcode":"41060","submissionUrl":"https://submission.nature.com/new-submission/41060/3","title":"International Journal of Data Science and Analytics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Path signatures, sports analytics, possession value, deep learning","lastPublishedDoi":"10.21203/rs.3.rs-7538719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7538719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We present a novel framework for predicting next actions in soccer possessions by leveraging path signatures to encode their complex spatio-temporal structure. 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