Deep Learning Model for Predicting Basket-Ball Player Injuries Using VGG16

This growing need for player safety in sports has spurred the creation of newer models for predictive analysis. The project involves predicting the injury risk of basketball players using foot images and image classification techniques. Since VGG16 is a popular Convolutional Neural Network (CNN) whose architecture has been shown to perform well in image recognition, this model is used in this study. The target is to pinpoint and categorize movements more susceptible to inducing injuries and further recommend tactical alterations in training and gameplay. The scheme achieves high accuracy for predicting potential injury risks by finetuning the VGG16 model. The project presents how deep learning can be applied in sports analytics with the objective to reduce injury instances and augment player safety. The discussed methodology predicts the incidence of injury and can be a useful resource to coach- and medial staff to help formulate prevention plans.