A detailed analysis of the complete IPL dataset and visualization of various features necessary for IPL evaluation is performed. Many machine learning algorithms have been used to compare and predict the winner between any two teams. Few models exist that try to rank players either based on simple formulae or based on few mathematical models. Efficiency was very low, in the absence of valuable data sets in large proportions. This is because enough data was not available when these models were suggested. T20 game has its own requirements which weren’t satisfied by current models. In this paper, we have portrayed the results of using a detailed ball-by-ball dataset of all the matches played in the history of IPL and doing a comprehensive analysis of various aspects regarding measures involved in the game along with pragmatic visualizations. We faced issues with ranking the players and we overcame that by modelling their strength and weakness against a particular opponent, their performance on a particular pitch, etc. details which can be of great benefit and can give the team a winning edge to a large extent. We have also ranked the players, based on the Player Ranking Index using machine learning techniques. The accuracy of predictions have increased upto 81% using the proposed system (Comprehensive data analysis on IPL (CDAI)) causing a hike of 12% compared to the existing system (Deep mayo predictor (DMP)). © 2020, Research Trend. All rights reserved.