Predict the College Sports Scores using a Weighted BP-SVR Model

Predict the College Sports Scores using a Weighted BP-SVR Model | 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 Predict the College Sports Scores using a Weighted BP-SVR Model Quanying Zhu, Jiani Miao, JinRong Liu, Lina Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3996736/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 A key determinant of college students' physical health is their level of physical accomplishment. As a result, assessment and forecasting of physical accomplishment have grown in significance for both society and university lecturers. Research predicting collegiate athletes' success in sports is currently lacking, and the majority of forecasts were based on imprecise observations. Thus, our goal is to create a model that uses theoretical techniques to forecast the kids' academic success. The Central University of Finance and Economics (CUFE) student basketball performance data was utilized in this study to forecast performance using the back propagation (BP) neural network regression model and the support vector machine regression model (SVR). We thought about integrating the two machine learning prediction techniques to increase accuracy. We created a model using the gradient descent approach, with the real scores acting as dependent factors and the BP and SVR prediction results acting as independent variables, in order to determine the link between the predicted scores of the two models and the real scores. Our methodology, which we call the "BP-SVR weighted prediction model," may be used to predict performance for sports courses other than basketball and is more accurate than only using BP or SVR. Physical sciences/Mathematics and computing Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Statistics BP Neural network model gradient descent method physical education of college students sport scores prediction support vector machine regression Full Text Additional Declarations No competing interests reported. Supplementary Files scores.xlsx 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-3996736","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":280888122,"identity":"5d800972-06c5-4412-9d90-de317f7638cc","order_by":0,"name":"Quanying Zhu","email":"","orcid":"","institution":"Central University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Quanying","middleName":"","lastName":"Zhu","suffix":""},{"id":280888123,"identity":"df824321-df35-4183-beb4-a826d5928e51","order_by":1,"name":"Jiani Miao","email":"","orcid":"","institution":"Central University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Jiani","middleName":"","lastName":"Miao","suffix":""},{"id":280888124,"identity":"cf4bce39-29c7-4a8f-85bb-b08ae9a91fc9","order_by":2,"name":"JinRong Liu","email":"","orcid":"","institution":"Central University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"JinRong","middleName":"","lastName":"Liu","suffix":""},{"id":280888125,"identity":"111f7fa2-e58d-4b69-b658-90a845b592dc","order_by":3,"name":"Lina Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACAzBZIMHAwN7AwFAB4hwgSosBUAsPUOkZErQAsUQCkVrMJZKfPfxiYJHHP/PxM4kDFQxyfDcSGD8X4NFiOSPN3FjGQKJY4naamcSBMwzGkjcSmKVn4HPYjQQzaQkDicSG2zls0h/bGBI33EhgY+bBqyX9G1jL/Jtn2CQO/mOoJ0JLjpnkB6CWDTd4gFoaGBIMCGo586ZMGhjIiRvPpBlbHDgmYTjzzMNmabxajqdvk/xRUZc47/jhhzcO1NjI8x1PPvgZnxYQQHYGME4ZGBsIaAAq+UFQySgYBaNgFIxoAADMpE1aI+nWtwAAAABJRU5ErkJggg==","orcid":"","institution":"Central University of Finance and Economics","correspondingAuthor":true,"prefix":"","firstName":"Lina","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-02-28 13:14:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3996736/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3996736/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79467919,"identity":"47c3f608-56dc-4c57-925a-5c3244530710","added_by":"auto","created_at":"2025-03-28 20:16:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":577288,"visible":true,"origin":"","legend":"","description":"","filename":"PredicttheCollegeSportsScoresusingaWeightedBPSVRModel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3996736/v1_covered_3f4a273f-37eb-4b2b-aa5f-6b60378ab400.pdf"},{"id":52978301,"identity":"636577f0-cc6c-4e75-a3e4-eeee2a61abb6","added_by":"auto","created_at":"2024-03-19 09:40:52","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33907,"visible":true,"origin":"","legend":"","description":"","filename":"scores.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3996736/v1/3147ebcff83809c246a1d434.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predict the College Sports Scores using a Weighted BP-SVR Model","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":"BP Neural network model, gradient descent method, physical education of college students, sport scores prediction, support vector machine regression","lastPublishedDoi":"10.21203/rs.3.rs-3996736/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3996736/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA key determinant of college students' physical health is their level of physical accomplishment. As a result, assessment and forecasting of physical accomplishment have grown in significance for both society and university lecturers. Research predicting collegiate athletes' success in sports is currently lacking, and the majority of forecasts were based on imprecise observations. Thus, our goal is to create a model that uses theoretical techniques to forecast the kids' academic success. The Central University of Finance and Economics (CUFE) student basketball performance data was utilized in this study to forecast performance using the back propagation (BP) neural network regression model and the support vector machine regression model (SVR). We thought about integrating the two machine learning prediction techniques to increase accuracy. 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