Predicting Accuracy of Players in Cricket using Machine Learning

Cricket is one of the most widely played and analyzed sports globally, with player performanceevaluation being a crucial aspect of team selection, match strategy, and talent identification.Traditional statistical models have long been used to assess a player's potential; however, with theadvent of machine learning and data-driven analytics, performance prediction has become moresophisticated and accurate. This project focuses on predicting the accuracy of a cricket player basedon historical data and background information using advanced machine learning techniques. Thestudy explores various independent variables, including batting and bowling averages, strike rates,match experience, fitness levels, age, weather conditions, and opposition strength, to determine theirimpact on a player's overall accuracy. The dependent variable in this analysis is the player accuracyscore, which serves as a measure of performance consistency and reliability. Despite advancementsin predictive analytics, challenges such as variability in playing conditions, limited historical data foremerging players, and non-linear performance trends remain key obstacles. However, by integratingstatistical, machine learning, and deep learning approaches, this study aims to bridge the gap betweentraditional cricket analytics and modern AI-driven performance evaluation. The results of thisresearch can be beneficial for team management, player development, and fantasy sports, enablingdata-driven decision-making in the cricketing world.