Automated Cricket Analytics for Player Classification and Commentary Generation

Cricket is one of the most popular sports around the world, and there is an increasing demand for intelligent commentary generation and real-time analytics. In this paper, we propose a system called Automated Cricket Analytics for Player Classification and Commentary Generation that uses computer vision and deep learning to analyze cricket match footage using a pre-trained VGG16 model to generate visual features from extracted frames of video input, which are then processed by a bidirectional LSTM-based neural network to generate descriptive commentary. In addition, a custom object detection model is deployed via the Roboflow API that detects which player is doing what (i.e. whether they are a bowler or a batsman) in each frame. The generated textual commentary is further refined using transformer-based NLP pipelines for summarization and grammatical correction prior to text-to-speech synthesis (gTTS). The experimental results show that the model can correctly recognize the role of each player and provide a suitable commentary with high BLEU scores (BLEU-1: 0.877; BLEU-2: 0.818; BLEU-3: 0.798; BLEU-4: 0.712). The average confidence score for the player classification and detection module was over 90%, showing that this system can recognize players correctly in all match frames. This system could be used to create highlights, automate sports broadcasting, or even allow fans to interact with a game.