Automated Cricket Analytics for Player Classification and Commentary Generation
Year: 2025
Type: article
Abstract: 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.
Source: IEEE Access
Authors: Rithvik Pabbati, Bijjula Sai Srujan Reddy, K. Karthik
Institution: Vellore Institute of Technology University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4413155658