A Large-Scale Mixed Reality Stadium for Training Combination Tactics in Basketball

A Large-Scale Mixed Reality Stadium for Training Combination Tactics in Basketball | 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 A Large-Scale Mixed Reality Stadium for Training Combination Tactics in Basketball Hayato Saiki, Masakazu Hirokawa, Modar Hassan, Kenji Suzuki This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4111207/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 paper proposes a mixed reality (MR)-based large-scale environment for training combination tactics in basketball. This MR environment enables human players to practice various combination tactics such as ball passing and screens with virtual players overlaid in the real environment using a see-through head-mounted display (HMD). Positions of human players and the ball were tracked using an optical motion capture system and transmitted to a unity-based control system, facilitating quantitative and real-time evaluation of gameplay. The evaluation was conducted through coaching experiments using the MR system involving novice players, along with validation evaluation experiments between the developed environment and real space through skilled basketball players. The coaching experiment with novice learners demonstrated that the proposed MR environment was more effective than conventional methods for training combination tactics.Additionally, the validation experiment with skilled players showed no significant differences in player behavior between the proposed environment and the real space, suggesting that the observed coaching results could be transferrable to real courts. These results demonstrate the potential of MR technology in skill training involving multiplayer coordination. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology 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. 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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-4111207","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":286204543,"identity":"ef8379b9-f0b8-4e31-83fb-26dbeb954885","order_by":0,"name":"Hayato Saiki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie2QsWoCMRiA/xC4LgeuEdr6ChGhOMj1VRIO7KKdC5XrlYAuUteI4mM4Xwl4iw9wo0f3clO5qZizRDpcpN1E8g0hIfnIlwA4HGcJEsCewAeM4sOa6PnPRnxK2R6U1xjYnxTAgMbmzG/FRmuiBM1XwTVc4ckOlRGfy+F7AaMA8KL+GrrlgvF1aMIUX5DHkMAmBLRM6hXgIuFrbJSEr8iAEvASQJLVh81yrSxfjBJVSqeEb7sCWRUWK6NgHTa4I2hsV2iWC8o2qe9pRbK+6synn/0ufwt921tas4ePZjl6vm800l1R9KIbmQ5VVnwFt23Ljx3xquFYoid+W5426gLIvxWHw+G4TPbcSVqsXIbkKQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Tsukuba","correspondingAuthor":true,"prefix":"","firstName":"Hayato","middleName":"","lastName":"Saiki","suffix":""},{"id":286204544,"identity":"5911ade2-8e56-41c1-8a0d-169b0123f439","order_by":1,"name":"Masakazu Hirokawa","email":"","orcid":"","institution":"NEC (Japan)","correspondingAuthor":false,"prefix":"","firstName":"Masakazu","middleName":"","lastName":"Hirokawa","suffix":""},{"id":286204545,"identity":"77b47e49-9f32-45ab-9eb8-4ddb4d8abe12","order_by":2,"name":"Modar Hassan","email":"","orcid":"","institution":"University of Tsukuba","correspondingAuthor":false,"prefix":"","firstName":"Modar","middleName":"","lastName":"Hassan","suffix":""},{"id":286204546,"identity":"1b5f2bb8-ae01-4a57-b3b4-0eb5ebd2fe07","order_by":3,"name":"Kenji Suzuki","email":"","orcid":"","institution":"University of Tsukuba","correspondingAuthor":false,"prefix":"","firstName":"Kenji","middleName":"","lastName":"Suzuki","suffix":""}],"badges":[],"createdAt":"2024-03-16 04:29:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4111207/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4111207/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62835098,"identity":"0cefcab7-c185-41b2-b996-b44fae5dafb1","added_by":"auto","created_at":"2024-08-20 04:55:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1574897,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReportsSaiki.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4111207/v1_covered_ba38ad10-a66d-4c0a-ba76-a44cab6a731c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Large-Scale Mixed Reality Stadium for Training Combination Tactics in Basketball","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4111207/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4111207/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper proposes a mixed reality (MR)-based large-scale environment for training combination tactics in basketball. 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