Bat.CG: Development of a Customizable Cricket Character Generator Web Application for Enhanced Broadcasting Experience

Abstract

Cricket broadcasting has evolved significantly with technological advancements, yet traditional systems remain fragmented and technically complex for broadcasters. This research presents Bat.CG (Cricket Character Generator), an innovative web-based application that integrates customizable broadcasting graphics with realtime scoring, neural network-driven predictions, and enhanced audience interaction. The system addresses critical gaps in existing broadcasting infrastructure by providing a unified platform that eliminates the dependency on separate Character Generator (CG) and ball-by-ball scoring systems. Through comprehensive market research involving 81 industry professionals, this study identified key requirements including customizable graphics (99% demand), emergency score updating capabilities (87.7% essential), and integrated CG-scoring systems (49.4% extremely valuable). The proposed solution utilizes the MERN stack (MongoDB, Express.js, React, Node.js) architecture with hybrid neural network and regression models for match predictions. Key innovations include drag-and-drop graphic customization without programming knowledge, real-time data synchronization with sub-second latency, role-based access control, and interactive viewer engagement features. The system's modular design ensures scalability, security, and accessibility while maintaining professional broadcast quality. This research contributes to democratizing cricket broadcasting technology and establishing a foundation for future sports media innovations in developing regions.