Forecasting bowler performance in One-Day International cricket using Machine learning

Abstract

In cricket’s dynamic environment, bowlers’ performances are a key factor in determining success. In this dynamic sport, our study explores the field of bowler performance predictive analytics to enable decision-makers and strategists. Using advanced machine learning techniques, we provide a comprehensive analysis targeted at predicting and analyzing the complex factors impacting bowlers’ performance on the field. This research methodology is based on the sophisticated use of machine learning algorithms to develop a reliable prediction model. Our study reveals an abundance of information about the complex interactions between these diverse factors and how they affect bowler performance. Specifically, we highlight the important impact that opposing dynamics and contextual factors like venue-specific performance trends play, emphasizing the necessity of flexible tactics that depend on contextual circumstances. Prediction methods have significant ramifications not only in cricket but also in other fields. They provide actionable insights for player selection, strategic planning, and ongoing performance evaluation, making them indispensable tools for cricketing companies. Moreover, our study broadens the scope of predictive analytics and holds potential for use in a variety of sports and sectors that depend on complex strategic decision-making. This research demonstrates the critical role that predictive analytics plays in cricket. It offers a rigorous model for predicting and understanding the complex dynamics of bowler performance, greatly enhancing strategic decision-making within the game and expanding its potential into other areas.