Development of Sequential Winning Percentage Prediction Model for Badminton Competitions: Applying the Expert System Sequential Probability Ratio Test

Development of Sequential Winning Percentage Prediction Model for Badminton Competitions: Applying the Expert System Sequential Probability Ratio Test | 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 Development of Sequential Winning Percentage Prediction Model for Badminton Competitions: Applying the Expert System Sequential Probability Ratio Test Eunhye Jo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4616347/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Mar, 2025 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted 4 You are reading this latest preprint version Abstract Background This study developed a sequential winning-percentage prediction model for badminton competitions using the expert system sequential probability ratio test (EXSPRT), aiming to calculate the difficulty of each event within a match and establish the initial prior probability. Methods We utilized data from 100 men's singles matches (222 games) held by the Badminton World Federation (BWF) in 2018 to evaluate event difficulty across six models for each determining factor. For setting the initial prior probability calculation method, 30 men's singles matches (74 games) organized by the BWF in 2019 were randomly selected. The odds for these matches were obtained from www.oddsportal.com. Results The efficacy of the six models was assessed based on application rates (15%, 20%, 25%, and 30%) of the collected odds, with the initial prior probability reflecting 25% of the odds chosen owing to its superior validity. Conclusions This research yielded six sequential winning percentage prediction models capable of offering real-time predictions during matches in badminton competitions by leveraging EXSPRT. These models enhance spectator engagement and provide foundational data for developing similar prediction models for other sports. Future research should focus on developing a program to identify the most effective model among the six and implement it practically. Badminton Real-time prediction EXSPRT Difficulty of event Single match Figures Figure 1 Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile.docx Cite Share Download PDF Status: Published Journal Publication published 13 Mar, 2025 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted Editorial decision: Revision requested 24 Jun, 2024 Editor assigned by journal 23 Jun, 2024 Submission checks completed at journal 23 Jun, 2024 First submitted to journal 21 Jun, 2024 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-4616347","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":318172127,"identity":"cc1c979a-f301-44b5-9762-a54a2168e750","order_by":0,"name":"Eunhye 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Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Badminton, Real-time prediction, EXSPRT, Difficulty of event, Single match","lastPublishedDoi":"10.21203/rs.3.rs-4616347/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4616347/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study developed a sequential winning-percentage prediction model for badminton competitions using the expert system sequential probability ratio test (EXSPRT), aiming to calculate the difficulty of each event within a match and establish the initial prior probability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized data from 100 men's singles matches (222 games) held by the Badminton World Federation (BWF) in 2018 to evaluate event difficulty across six models for each determining factor. For setting the initial prior probability calculation method, 30 men's singles matches (74 games) organized by the BWF in 2019 were randomly selected. The odds for these matches were obtained from www.oddsportal.com.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe efficacy of the six models was assessed based on application rates (15%, 20%, 25%, and 30%) of the collected odds, with the initial prior probability reflecting 25% of the odds chosen owing to its superior validity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research yielded six sequential winning percentage prediction models capable of offering real-time predictions during matches in badminton competitions by leveraging EXSPRT. These models enhance spectator engagement and provide foundational data for developing similar prediction models for other sports. Future research should focus on developing a program to identify the most effective model among the six and implement it practically.\u003c/p\u003e","manuscriptTitle":"Development of Sequential Winning Percentage Prediction Model for Badminton Competitions: Applying the Expert System Sequential Probability Ratio Test","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 18:14:35","doi":"10.21203/rs.3.rs-4616347/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-24T08:28:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-24T01:17:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-24T01:16:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2024-06-21T09:16:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"14240c2f-4292-44ab-86ea-886de99e4698","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-17T16:06:21+00:00","versionOfRecord":{"articleIdentity":"rs-4616347","link":"https://doi.org/10.1186/s13102-025-01078-6","journal":{"identity":"bmc-sports-science-medicine-and-rehabilitation","isVorOnly":false,"title":"BMC Sports Science, Medicine and Rehabilitation"},"publishedOn":"2025-03-13 15:58:52","publishedOnDateReadable":"March 13th, 2025"},"versionCreatedAt":"2024-07-15 18:14:35","video":"","vorDoi":"10.1186/s13102-025-01078-6","vorDoiUrl":"https://doi.org/10.1186/s13102-025-01078-6","workflowStages":[]},"version":"v1","identity":"rs-4616347","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4616347","identity":"rs-4616347","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}