C9 Scout With AI Assistance/Summary

Inspiration

I'm always excited about esports and software!

What it does

AI-powered esports intelligence that analyzes professional Valorant teams' performance data to provide strategic insights, counter-pick recommendations, map pick/bans, and matchup preparation reports.

How we built it

The stack is Ariadne for GraphQL and Streamlit for the frontend!

The full-stack is: Python, Streamlit, OpenAI GPT-4 (for chatting about the data and summary), GRID

Challenges we ran into

The Grid gg docs are a bit sparse and difficult to navigate but the graphql playground on their website made this possible!

Accomplishments that we're proud of

I'm proud of digging deep into the graphql playground and finding valuable series statistics that give us a good idea of how Valorant teams play.

What we learned

I learned that finding a graphql stack is a bit difficult, but once the engine is running, uncovering insights in the data is tons of fun.

What's next for C9 Scout With AI Assistance/Summary

I'd like to understand the map coordinate system to show player setups on a per-map basis, analyze eco rounds, and get more granular in uncovering each teams strategy in their numbers!

Built With

• grid
• openai-gpt-4-(for-chatting-about-the-data-and-summary)
• python
• streamlit