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
Short DOI	https://doi.org/g9hsc4

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