Performance Evaluation using IPL Performance Impact Model - Ghayaz, Z. Mohammed; V. V., Aswin; Rahman, J. Abdul; M., Magesh Manickam; P., Vinothiyalakshmi (Journal of Information Systems Research and Practice, 2024)
In the context of the Indian Premier League (IPL), assessing player performance is crucial for team success and strategic planning, as the tournament demands players who can balance high-scoring rates with consistency and reliability. Player performance evaluations help teams identify top-performing individuals who contribute significantly to both offense and defense, supporting optimal team compositions. This study delves into evaluating IPL 2024 player performances through a machine learning-driven model, designed to calculate impact scores that reveal each player’s contribution across batting, bowling, and all-rounder roles. By integrating Euclidean and perpendicular distances from origin-referenced metrics, the model identifies players with a balanced performance profile across key indicators like strike rate, batting and bowling averages, and consistency. This data-driven analysis helps identify potential retention candidates for the IPL 2025 Mega Auction, offering IPL teams objective insights into players who bring strategic value and performance reliability, thus optimizing team compositions for future tournaments.