  Cricket, "a Gentleman's Game", is a prominent sport rising worldwide. Due to
the rising competitiveness of the sport, players and team management have
become more professional with their approach. Prior studies predicted
individual performance or chose the best team but did not highlight the
batter's potential. On the other hand, our research aims to evaluate a player's
impact while considering his control in various circumstances. This paper seeks
to understand the conundrum behind this impactful performance by determining
how much control a player has over the circumstances and generating the
"Effective Runs",a new measure we propose. We first gathered the fundamental
cricket data from open-source datasets; however, variables like pitch, weather,
and control were not readily available for all matches. As a result, we
compiled our corpus data by analyzing the commentary of the match summaries.
This gave us an insight into the particular game's weather and pitch
conditions. Furthermore, ball-by-ball inspection from the commentary led us to
determine the control of the shots played by the batter. We collected data for
the entire One Day International career, up to February 2022, of 3 prominent
cricket players: Rohit G Sharma, David A Warner, and Kane S Williamson. Lastly,
to prepare the dataset, we encoded, scaled, and split the dataset to train and
test Machine Learning Algorithms. We used Multiple Linear Regression (MLR),
Polynomial Regression, Support Vector Regression (SVR), Decision Tree
Regression, and Random Forest Regression on each player's data individually to
train them and predict the Impact the player will have on the game. Multiple
Linear Regression and Random Forest give the best predictions accuracy of 90.16
percent and 87.12 percent, respectively.
