Piyush Ojha

Cricvision--AI

Modern cricket dashboards mostly focus on static scorecards and basic statistics, which fail to explain the actual momentum, pressure shifts, tactical collapses, and turning points of a match. Fans, analysts, and learners often struggle to understand how a game evolved ball-by-ball and which moments truly changed the outcome. CRICVISION AI was built to transform raw IPL ball-by-ball data into an interactive intelligence platform capable of visualizing match momentum, detecting collapses, identifying pressure phases, and generating AI-powered tactical summaries. The goal was to make cricket a Process The project began with cleaning IPL ball-by-ball datasets and building a modular analytics pipeline for momentum tracking, pressure analysis, wicket impact detection, and phase-wise match evaluation. Multiple visualization approaches were tested before finalizing an interactive Plotly momentum engine capable of representing tactical shifts throughout the innings. The dashboard was developed using Streamlit with dynamic filters for seasons, teams, and matches. Additional AI-style tactical summaries and collapse detection systems were integrated to convert raw statistics into readable match intelligence. Finally, the UI and graph layouts were refined to create a premium sports analytics experience with better readability and interactivity. Results The final platform successfully transformed raw IPL datasets into an interactive cricket intelligence dashboard capable of visualizing tactical momentum shifts, pressure phases, wicket impacts, and AI-generated match narratives. The application delivers a significantly more engaging experience compared to traditional static scorecards by allowing users to explore matches dynamically through filters, momentum graphs, and tactical insights. The deployment of the project on Streamlit and GitHub also demonstrated end-to-end product development capability including data engineering, analytics, Reflection If given more development time, I would expand the platform by integrating predictive machine learning models for win probability forecasting and player performance prediction. I would also optimize the dashboard further for mobile responsiveness and improve scalability for real-time match streaming. Another improvement area would be adding advanced AI-generated commentary and comparative team analytics to make the platform feel even closer to a professional sports intelligence product.