Cricket shot analysis using machine learning

Abstract

Cricket, being one of the most popular sports globally, relies heavily on the skills and techniques of players. With the advent of technology, there is a growing interest in leveraging machine learning techniques to analyze and enhance players' performance. This research focuses on the development of a Cricket Shot Analysis system using advanced machine learning algorithms. The proposed system utilizes computer vision and machine learning models to automatically recognize and classify cricket shots batsmen play. Highspeed cameras capture the footage of the cricket match, and the frames are processed to extract relevant features such as bat speed, shot angle, and player stance. My dataset contains 8000 images of different shot. Here sweep image data 2000, pullshot images data 2000, legglance-flick images data 2000, drive images data 2000. These features are then fed into a machine learning model, trained on a diverse dataset of cricket shots, to classify each shot into predefined categories like cover drive, square cut, pull shot, etc. The system aims to provide valuable insights into a player's shot selection, technique, and overall performance. Coaches and players can benefit from the detailed shot analysis, identifying strengths and weaknesses to tailor training regimens accordingly. In this research I use Xception, InceptionV3, and DensNet. Anlysis shows that the highest accuracy achieved by DenseNet121 is 98.26% and the lowest accuracy is 96.38% which is achieved by the InceptionV3 model. The development of this Cricket Shot Analysis system not only addresses the growing demand for data-driven insights in sports but also showcases the potential of machine learning in refining and revolutionizing traditional coaching methodologies. Through continuous refinement and validation, this technology has the potential to become an indispensable tool for cricket professionals, contributing to the evolution of player training and game strategy in the modern era of cricket.