Optimized Dream11 Cricket T20I Team Selection Using Machine Learning and Integer Programming

Optimized Dream11 Cricket T20I Team Selection Using Machine Learning and Integer Programming | 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 Optimized Dream11 Cricket T20I Team Selection Using Machine Learning and Integer Programming Yashwanth Ajjilapuram This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7713554/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 research proposes an optimized fantasy cricket team selection system for the Dream11 platform using machine learning and Integer Linear Programming (ILP). A novel contextual selection score is developed, incorporating eight match-specific performance factors including recent form, venue history, opposition matchups, pitch conditions, environmental variables, and innings-specific effectiveness. The system demonstrates strong predictive performance using Ridge Regression (R2 = 0.88) and generates teams that show meaningful player overlap with Dream11’s publicly presented lineups in 25 T20I matches. The approach is implemented via a user-friendly Google Colab interface, enabling scenario-specific team generation aligned with Dream11’s constraint requirements. This work contributes to the growing field of sports ana- lytics by offering a reproducible, interpretable, and constraint-valid framework for fantasy team optimization that bridges machine learning prediction with operations research methodologies. This research was conducted as part of the MSc Data Science dissertation at the University of Sussex, UK. Artificial Intelligence and Machine Learning Operations Research Fantasy Sports Analytics Cricket Performance Prediction Machine Learning In- teger Linear Programming Sports Optimization Full Text Additional Declarations The authors declare no competing interests. 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. 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