Context
Cricket has no shortage of data, but most of it is fragmented across score feeds, commentary, video clips, and historical records. The hard part is not collecting signals. The hard part is turning those signals into decisions that are useful before and during a match.
This project is my attempt to bridge that gap for IPL-style use cases.
What I Am Building
I am building a research pipeline that combines:
- structured historical match data
- live and archived audio/video signals
- model outputs that surface match-up and game-state patterns
The current system focus is:
- reliable ingestion and synchronization across data sources
- feature extraction that captures context, not just raw events
- model experiments that are interpretable for strategy workflows
Current Direction
Right now, this is still a research build. The goal is to first make the foundation robust, then iterate on higher-quality cricket intelligence outputs that can hold up in real match conditions.