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

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.