PREDICTING ACCURACY OF PLAYERS IN THE CRICKET USING MACHINE LEARNING

Abstract

Delivery in cricket is the sole action of bowling a cricket ball towards the batsman. The outcome of the ball is immensely pivoted on the grip of the bowler. An instance when whether the ball is going to take a sharp turn or keeps straight through with the arm depends entirely upon the grip. And to the batsmen, the grip of the cricket bowl is one of the biggest enigmas. Without acknowledging the grip of the bowl and having any clue of the behavior of the ball, the mis-hit of a ball is the most likely outcome due to the variety in bowling present in modern-day cricket. The paper proposed a novel strategy to identify the type of delivery from the finger grip of a bowler while the bowler makes a delivery. The main purpose of this research is to utilize the preliminary CNN architecture and the transfer learning models to perfectly classify the grips of bowlers. A new dataset of 5573 images from Real-Time videos in offline mode were prepared for this research, named GRIP DATASET, consisted of grip images of 13 different classes. Hence the preliminary CNN model and the pre-trained transfer learning models - VGG16, VGG19, ResNet101, ResNet152, DenseNet, MobileNet, AlexNet, Inception V3, and NasNet were used to train with GRIP DATASET and analyze the outcome of grips. The training and validation accuracies of the models are noteworthy with the maximum validation accuracy of the preliminary model reaching 98.75%. This study is expected to be yet another steppingstone in the use of deep learning for the game of cricket.