Fantasy Sports Team Optimization Using Data Science: Predicting Fantasy 11 for Cricket

Author(s)	Mr. Rohit Unmesh Kshirsagar, Mr. Rishabh Manoj Kothari, Mr. Parth Tushar Lhase
Country	India
Abstract	Fantasy sports have surged into a $20 billion in- dustry, blending fan engagement with data analytics. This pa- per presents FantasyTeamOptimizer, a machine learning-driven framework to predict the optimal playing 11 for cricket fantasy teams. Leveraging historical data (e.g., career averages) and real- time metrics (e.g., recent form), the model employs weighted scoring and linear programming to maximize fantasy points under cricket-specific constraints (e.g., minimum batsmen). Im- plemented in Python with Pandas and PuLP, it achieves 87% accuracy and 943 points on 2023 IPL data, outperforming traditional methods by 12% in accuracy and 15% in points. We review related analytics advancements, detail our methodology, and address challenges like data privacy and computational scalability. This work enhances user outcomes, fills a cricket- specific research gap, and sets the stage for future innovations like real-time processing, offering a scalable solution for a global audience.
Keywords	Fantasy Sports, Machine Learning, Cricket, Team Selection, Predictive Analytics, Linear Programming
Field	Computer > Data / Information
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-04
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.41449

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research