This systematic review examines recent statistical methodologies for assessing cricket player performance across batting, bowling, and fielding. Traditional metrics such as batting averages, runs scored, and wickets taken often fail to capture a player's comprehensive contribution to the game. Recent approaches, however, employ advanced statistical techniques including Data Envelopment Analysis (DEA), Generalized Geometric Distributions (GGD), Bayesian methods, copula functions, and machine learning algorithms, to develop novel performance indices. These methods incorporate contextual factors like opposition quality, pitch conditions, and match situations, offering a better evaluation, particularly in fast-paced formats like T20 cricket. The review emphasizes the integration of metrics across all skill areas to achieve an overall assessment, highlighting the significance of opening partnerships, high-pressure bowling performances, and fielding efficiency in determining match outcomes. It also discusses the growing utilization of real-time data and sophisticated analytics in strategic decision-making and player selection. However, challenges remain such as the difficulty in collecting detailed performance data, particularly for fielding actions, and considering psychological factors like how different players handle pressure. The review concludes by underscoring the need for future research to improve evaluation methods, include more detailed data, and integrate psychological dimensions. As cricket analytics continue to evolve, embracing these new approaches will help teams make better use of data, leading to improved strategies, player performance, and a deeper understanding of the game.
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