The Role of Convolutional Neural Networks in Cricket Performance Analysis

Significant insights have arisen from an extensive review of the current literature, highlighting the importance of Convolutional Neural Networks (CNNs) in cricket performance analysis and mapping new directions for future research. Despite difficulties such as limited availability of data, processing difficulty, and interpretability issues, incorporating CNNs into cricket statistics is a potential effort made possible by advances in machine learning and deep learning methods. Instructors, players, and data analysts can use CNNs to better comprehend the game, extract meaningful information from video data, and improve decision-making processes. Key findings show that CNNs are effective tools for a variety of cricket analysis tasks involving batting, bowling, fielding, and player tracking. The use of CNNs represents an advancement in cricket analysis, promising to open up new aspects of performance and usher in a data-driven era of cricket genius. Augmenting data, the use of parallelization, explainable AI, and concerns about ethics, provide opportunities to address current challenges can be identified as future advances in sports analysis with CNNs. Embracing technological advancements and mapping out future research directions are critical steps towards realizing this revolutionary potential.