Analysis and Prediction of Winning Team and Player Performance in Franchise Cricket using Machine Learning

Abstract

Sports, especially cricket, produces an enormous amount of statistical data that has led to the evolution of the game. Unlike other sports, cricket involves numerous variables ranging from pitch conditions, weather, boundary length, and several other factors that make each game unique. These variables make every match have its own significance, making cricket an ever-evolving sport that continues to captivate its audience. Over the years, cricket has witnessed a massive transformation with the integration of analytics. The use of data analytics has changed the way players and coaches approach the game. It has brought a new dimension to the game and has opened several new avenues for analyzing and interpreting the game. The Indian Premier League (IPL) has acted as a catalyst in showcasing the potential of cricket to the world and has bridged the gap between different cultures and audiences. In the present-day IPL, every statistic available is being used due to the high level of competitiveness of the tournament. Teams use data analytics to understand their strengths and weaknesses, analyze their opponents' gameplay, and devise strategies to win matches. These analytics provide valuable insights into various aspects of the game, such as player performance, team strategies, and game trends, among others. This paper is a sincere effort to uncover hidden insights in IPL by utilizing data from previous seasons. The vast amount of data available offers a treasure of formation that can help us to understand the nuances of the game and make informed decisions. By analyzing this data, we can identify patterns and trends, assess player performance, and make informed decisions that can impact the outcome of a match.