Real-Time Performance Evaluation of SDAT Men’s Volleyball Team Using NAC Sport Elite and Machine Learning Algorithms

Real-Time Performance Evaluation of SDAT Men’s Volleyball Team Using NAC Sport Elite and Machine Learning Algorithms | 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 Real-Time Performance Evaluation of SDAT Men’s Volleyball Team Using NAC Sport Elite and Machine Learning Algorithms Dr. Yokesh T.P, Dr. Viswanath S, Geethanjali R, Dr.Jayaraman S, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6859300/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 aims to identify key performance indicators (KPIs) influencing match outcomes for the SDAT Men’s Volleyball Team, governed by the Sports Development Authority of Tamil Nadu (SDAT), which competes in various state and national-level tournaments. Match data were collected over two competitive seasons (2022–2023 and 2023–2024), covering 47 matches involving 14 teams and comprising a total of 130 sets. Analysis was conducted using NAC Sport Elite, a high-performance sports analysis software, for which a lifetime license was acquired. This enabled in-depth tagging and extraction of 22 specific gameplay metrics. Notably, this study marks the first-time implementation of real-time performance analysis using NAC Sport Elite in India. The teams were categorized into high, middle, or low tiers based on their final tournament standings. A machine learning framework was developed in Python using Support Vector Machine (SVM) and Random Forest classifiers to assess the predictive value of various performance indicators such as attack success, block efficiency, service accuracy, reception attempts, and error patterns. The SVM model achieved an accuracy of 90%, with a precision of 91%, recall of 88%, and F1-score of 89%. The Random Forest model yielded an accuracy of 80%, precision of 79%, recall of 77%, and F1-score of 78%. Key findings revealed that metrics related to attack and block performance, as well as reception and service quality, were the most influential factors determining match success. The results also confirmed that the quality of the opposition significantly impacts match outcomes. The integration of NAC Sport Elite with machine learning techniques offers a powerful, data-driven approach to performance analysis, delivering precise and actionable insights. These findings can assist coaches and performance analysts in designing effective training programs and match strategies tailored to the specific strengths and weaknesses of their teams. Performance Analysis Key Performance Indicators (KPIs) Machine Learning NAC Sport Elite Volleyball Analytics 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-6859300","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501101880,"identity":"57b257e8-d67c-49a0-a0bc-8cea20a6681d","order_by":0,"name":"Dr. Yokesh T.P","email":"","orcid":"","institution":"Alagappa University","correspondingAuthor":false,"prefix":"Dr.","firstName":"Yokesh","middleName":"","lastName":"T.P","suffix":""},{"id":501101881,"identity":"1d49bdf3-cb0a-4569-b41d-bc642a6be661","order_by":1,"name":"Dr. Viswanath S","email":"","orcid":"","institution":"Visva-Bharati 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Match data were collected over two competitive seasons (2022\u0026ndash;2023 and 2023\u0026ndash;2024), covering 47 matches involving 14 teams and comprising a total of 130 sets. Analysis was conducted using NAC Sport Elite, a high-performance sports analysis software, for which a lifetime license was acquired. This enabled in-depth tagging and extraction of 22 specific gameplay metrics. Notably, this study marks the first-time implementation of real-time performance analysis using NAC Sport Elite in India. The teams were categorized into high, middle, or low tiers based on their final tournament standings. A machine learning framework was developed in Python using Support Vector Machine (SVM) and Random Forest classifiers to assess the predictive value of various performance indicators such as attack success, block efficiency, service accuracy, reception attempts, and error patterns. 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