Comparative Analysis of Players in T-20 International Cricket Using Survival Analysis

This research conducts a comparative examination of exponential and Weibulldistributions for assessing batting performance in T20 international cricket, utilizing survival analysis techniques to measure player reliability. The investigation analyzes innings data from 10 top-tier batsmen (featuring prominent players like Babar Azam, David Warner, and Virat Kohli) obtained from ESPNcricinfo, applying maximum likelihood estimation (MLE) to calibrate both distributions. Model comparison is performed using Akaike (AIC) and Bayesian (BIC) information criteria along with chi-square goodness-of-fit tests.Findings indicate that the exponential distribution, with its constant hazard function, offers better modeling accuracy for most batsmen, as evidenced by superior AIC/BIC scores and non-significant chi-square results (p > 0.05). Analysisof survival probabilities at critical run intervals (10, 30, 50, 80, and 100 runs) identifies Babar Azam as demonstrating exceptional consistency, showing a 75.6% likelihood of exceeding 10 runs and a 24.7% probability of achieving a half-century. Conditional probability analysis further reveals his superior conversion rate (57.1% chance of advancing from 30 to 50 runs) compared to more aggressive batsmen like Glenn Maxwell, who display greater performance fluctuation.While the Weibull distribution provides modeling flexibility, the study finds it overly complex for T20 batting performance data. The research highlights practical implementations for team management, including batting order optimization, in-game decision-making, and player skill development programs. These results emphasize the effectiveness of the exponential model in cricket performance analytics and recommend its incorporation into strategic planning for cricket teams.