C9 Assistant Coach

Overview Page
Team Stats Page
Player Comparison Page
Match History
What if? Page
Performance Trends Page
Macro Review Page
Player Analysis Page
Landing Page
LOL Landing Screen

Inspiration

Esports coaches spend countless hours manually reviewing VODs, tracking player statistics across spreadsheets, and trying to identify patterns in team performance. We saw an opportunity to leverage the GRID API's rich match data to automate this analysis and give coaches back their time for what matters most—actually coaching their players.

What it does

Cloud9 Assistant Coach provides:

Overview Dashboard - Team health metrics, recent form, and KPIs at a glance
Player Profiles - Individual stats, champion/agent pools, and improvement areas
Player Comparison - Side-by-side statistical comparison for roster decisions
Match Timeline - Round-by-round breakdowns and game event analysis
Performance Trends - Historical charts showing improvement over time
Macro Review - AI-generated structured agenda for VOD review sessions
What-If Analysis - Explore hypothetical scenarios like "What if we played more aggressively?"

How we built it

Frontend: React 18 + TypeScript with Vite, styled with TailwindCSS
Backend: FastAPI (Python) with async support for high-performance APIs
Data: GRID Central and Live Data APIs via GraphQL
AI: Groq LLM integration for intelligent what-if analysis and macro review generation
Animations: Framer Motion for polished loading states and transitions

Challenges we ran into

GRID API Schema: Understanding the complex nested structure of match data required extensive exploration
LLM Response Parsing: Getting consistent, parseable outputs from the LLM for structured analysis
Real-time Updates: Balancing data freshness with API rate limits
Multi-game Support: Building abstractions that work for both LoL and VALORANT's different data structures

Accomplishments that we're proud of

Seamless GRID Integration: Built a robust GraphQL client that handles both LoL and VALORANT data
AI-Powered What-If: Created a unique feature that lets coaches explore hypothetical scenarios with LLM analysis
Professional UI: Designed a polished, premium interface with smooth animations and intuitive navigation
Pattern Detection: Implemented intelligent analysis that surfaces actionable insights from raw match data

What we learned

Deep familiarity with the GRID API ecosystem and esports data structures
Best practices for LLM prompt engineering to get consistent, structured outputs
How to build responsive, data-heavy dashboards that remain performant
The specific needs and workflows of professional esports coaching staff

What's next for C9 Assistant Coach

Real-time Live Match Analysis - Push notifications and insights during live matches
Custom Alert System - Let coaches set thresholds for automatic performance alerts
Team Collaboration - Multi-user workspaces with shared notes and annotations
Video Integration - Sync insights with VOD timestamps for instant replay review
Mobile App - Quick access to key metrics on-the-go