# Beta Distribution Example for Cricket Score Analysis This post represents a real-world example of Binomial and Beta probability distribution from the sports field. In this post, you will learn about how the run scored by a Cricket player could be modeled using Binomial and Beta distribution. Ever wanted to predict the probability of Virat Kohli scoring a half-century in a particular match. This post will present a perspective on the same by using beta distribution to model the probability of runs that can be scored in a match. If you are a data scientist trying to understand beta and binomial distribution with a real-world example, this post will turn out to be helpful.

First and foremost, let’s identify the random variable that we would like to study. In this post, lets set the random variable as the half-century (runs equal to or more than 50) scored by a player in a match.

X = Half-century (runs equal to or more than 50) scored by a player in a match

First and foremost, let’s identify the random variable that we would like to study. In this post, lets set the random variable as the half-century (runs equal to or more than 50) scored by a player in a match. If the player scores a half-century, the random variable takes the value of SUCCESS (X = 1). If the player does not score a half-century, the random variable takes the value of failure (X = 0).