Probabilistic Approach for Studying the Effects of Name Alphabets on Player Performance

Abstract

This study investigates the impact of the first letter of players' names on the performance and characteristics of T-20 international cricket players, focusing on data spanning from 2005 to 2023. Utilizing secondary data obtained from reputable sources such as Cricinfo and Cricbuzz, the research encompasses various player types, including batsmen, bowlers, and wicketkeepers, across notable cricketing nations. The analysis employs a tree diagram to categorize players based on the initial letter of their names, facilitating an examination of performance trends within these groups. Additionally, the study implements Bayesian probability to develop a predictive model for assessing the performance outcomes of left-handed and right-handed batsmen. This probabilistic approach allows for continuous refinement of predictions as new data is collected, enabling a comprehensive understanding of how a player's handedness and the alphabetical positioning of their names might influence their performance metrics. The findings contribute valuable insights into cricket analytics and player performance dynamics.