How Data Analytics Has Transformed Player Selection in Franchise Cricket: Evidence from the IPL

This paper examines how data analytics has reshaped player selection in franchise cricket, using the Indian Premier League (IPL) as a primary setting from 2015 to the present. Drawing on secondary research, the study synthesizes evidence on how franchises have shifted from reputation- and intuition-led recruitment toward role-based, context-sensitive evaluation using ball-by-ball data, matchup profiling, phase-wise performance splits (powerplay/middle/death), and auction simulations. The paper frames IPL recruitment as decision-making under uncertainty and a constrained optimization problem, where analytics supports valuation discipline through projected impact and opportunity-cost awareness. Recent league policy features, such as expanded retention/RTM structures and the continued use of the Impact Player rule, are discussed as factors that further incentivize specialization and data-driven role allocation in squads (ESPNcricinfo, 2024; Olympics.com, 2025). Evidence from franchise practices indicates that analytics is now integrated into recruitment planning, shortlist construction, and matchups, increasingly shaping how teams identify undervalued talent and set auction price ceilings (Cricbuzz, 2025). The paper also evaluates limitations, including small-sample volatility, contextual transfer issues across leagues/venues, and auction-market distortions that can override model recommendations. Overall, the review concludes that analytics has not replaced cricketing judgment but has structured and standardized selection logic in the IPL, making recruitment more evidence-based, role-specific, and cost-aware.