Cricket Match

Cricket research increasingly leverages data-driven approaches to analyze player performance and predict match outcomes. Current studies utilize machine learning algorithms, including regression models, support vector machines, and deep learning architectures like MobileNet and YOLO, to extract insights from diverse data sources such as match commentary, player statistics, and video footage. This work aims to improve strategic decision-making in areas like team selection, player profiling, and fantasy sports, with applications ranging from coaching strategies to enhanced fan engagement. The development of novel performance metrics, such as context-aware measures, demonstrates a shift towards more nuanced and comprehensive evaluations of player contributions.