Performance Prediction and Evaluation of Wind Effects in Ski Jumping using Machine Learning

Performance Prediction and Evaluation of Wind Effects in Ski Jumping using Machine Learning | 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 Performance Prediction and Evaluation of Wind Effects in Ski Jumping using Machine Learning Toren Hawk, Travis Hockin, Ola Elfmark, Gavin Taylor This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8742401/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 demonstrates a data-driven approach to modeling ski-jump performance using machine learning algorithms trained on publicly available International Ski and Snowboard Federation (FIS) competition data. By modeling jump distance as a function of in-run speed, wind conditions, and hill size, we evaluate the capabilities of four distinct model classes to predict performance, explain the impact of wind, and critique wind point compensation in order to demonstrate a machine-learning-based approach to analyzing the sport. The analysis delineates the strengths of this approach in predicting jump distances and confirming the empirical influence of wind on flight distance. The study also identifies significant drawbacks inherent to using only public data, specifically determining how the absence of athlete-specific properties and high-resolution environmental data constrains the precision of these predictive models. ski jumping machine learning human performance sports science 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8742401","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588482229,"identity":"90f4a94b-1b26-43a6-8cd2-e1ea96f5b738","order_by":0,"name":"Toren Hawk","email":"","orcid":"","institution":"United States Naval Academy","correspondingAuthor":false,"prefix":"","firstName":"Toren","middleName":"","lastName":"Hawk","suffix":""},{"id":588482231,"identity":"885103c5-c554-48e4-b061-aa02a707bd93","order_by":1,"name":"Travis Hockin","email":"","orcid":"","institution":"United States Naval Academy","correspondingAuthor":false,"prefix":"","firstName":"Travis","middleName":"","lastName":"Hockin","suffix":""},{"id":588482232,"identity":"e117e141-d47a-4053-b96d-a8cd2afa548a","order_by":2,"name":"Ola Elfmark","email":"","orcid":"","institution":"Norwegian University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ola","middleName":"","lastName":"Elfmark","suffix":""},{"id":588482234,"identity":"72ed5c1c-15af-4f5a-bc15-254516fae3db","order_by":3,"name":"Gavin Taylor","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYHACxgMMDDZQNhsR6nmAGKgljXQth0nQYs9+xuDAxz3nE+fPbn7A8KHsMGEtPDw5BgdnPLuduOHOMQPGGeeI0cKQY3CY5wBQi0SCATNvGzFa+N+AtJxLnD8j/QPzX6K0SIBtOZDYcCPHgJmRKC03nhUcnHEg2XjDjZyCgz3n0glrYe9P3vjgwwE7WaDDNj74UWZNWAsDA4cBnHmAGPUgex4QqXAUjIJRMApGLAAA3fxBDQvghMsAAAAASUVORK5CYII=","orcid":"","institution":"United States Naval Academy","correspondingAuthor":true,"prefix":"","firstName":"Gavin","middleName":"","lastName":"Taylor","suffix":""}],"badges":[],"createdAt":"2026-01-30 14:54:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8742401/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8742401/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107481429,"identity":"1cee92ca-4dfc-4a41-a5f6-390500b307cc","added_by":"auto","created_at":"2026-04-22 02:17:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7425274,"visible":true,"origin":"","legend":"","description":"","filename":"MLinskijumping.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8742401/v1_covered_7d76e8bb-231b-4e45-b85b-50785a5047ec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance Prediction and Evaluation of Wind Effects in Ski Jumping using Machine Learning","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":"ski jumping, machine learning, human performance, sports science","lastPublishedDoi":"10.21203/rs.3.rs-8742401/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8742401/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study demonstrates a data-driven approach to modeling ski-jump performance using machine learning algorithms trained on publicly available International Ski and Snowboard Federation (FIS) competition data. 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