Reacon is an AI-powered esports scouting platform designed for professional coaches and analysts. It integrates verified GRID match data with Google Gemini's structured intelligence engine to deliver comprehensive, four-page scouting briefings in minutes.

Reacon transforms raw match data into actionable intelligence that coaches can immediately implement. The platform links insights to evidence IDs, enforces strict formatting standards, and maintains a professional, coach-ready tone across all workflows.

Opponent Profile

• Roster role analysis
• Composition trend identification
• Team momentum assessment

Map Tendencies

• Site hit frequency analysis
• Execute timing patterns
• Defensive rotation mappings

Player Intelligence

• Utility usage patterns
• Clutch performance metrics
• Individual matchup analysis

Draft Strategy

• Counter-pick recommendations
• Ban priority suggestions
• Strategic alignment with team objectives

• Real-time tactical consultation with AI scout
• Win condition analysis and counter-strategy development
• Player-specific tendency breakdowns
• PDF transcript export for team briefings

Head-to-Head Analysis

• Direct team comparisons across performance metrics
• Player-versus-player statistical breakdown
• Strategic approach comparison

Battle Simulator

• Monte Carlo simulation for match outcome prediction
• Tactical narrative generation
• Probability-based scenario analysis

Deterministic Diff Engine

• Automatic change detection between reports (Added/Removed/Changed)
• Order-independent comparison algorithms

Evidence Linking System

• Every insight mapped to specific Match ID (seriesId)
• Full traceability from conclusion to source data

Resilient API Client

• Exponential backoff implementation (delay_n = 500 × 2^n ms)
• Rate limit compliance with GRID API specifications

Framework: Next.js 15 (App Router, Turbopack)

AI Engine: Google Gemini 2.0 Flash (Structured JSON outputs, conversational tactical analysis)

Data Source: GRID API (Official Esports Central Data)

Database: Supabase (Optional report persistence layer)

Styling: Tailwind CSS, Framer Motion

Visualization: Recharts

Export: jsPDF, html2canvas

Create .env.local from .env.example:

# GRID API (Required)
GRID_API_URL=https://api-op.grid.gg
GRID_API_KEY=your_grid_api_key

# Google Gemini (Required)
GEMINI_API_KEY=your_gemini_api_key

# Supabase (Optional)
NEXT_PUBLIC_SUPABASE_URL=your_supabase_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key
SUPABASE_SERVICE_ROLE_KEY=your_supabase_service_role_key

# Install dependencies
bun install

# Start development server
npm run dev

Access the application at http://localhost:3000

reacon/
├── app/                    # Next.js App Router
│   ├── api/               # API routes
│   ├── dashboard/         # Main application pages
│   └── layout.tsx         # Root layout
├── components/            # React components
│   ├── scout/            # Scouting-specific components
│   ├── ui/               # Reusable UI components
│   └── visualizations/   # Data visualization components
├── lib/                   # Core utilities
│   ├── grid/             # GRID API client
│   ├── gemini/           # Gemini AI integration
│   └── utils/            # Helper functions
├── public/               # Static assets
└── styles/               # Global styles

Category: Automated Scouting Report Generator

Data Source: GRID GraphQL API for official esports match statistics

Intelligence Scope: Generated reports include team strategic analysis, individual player tendency mapping, and actionable counter-strategy recommendations

Data Handling:

• Built for non-commercial hackathon use
• Stores only derived analytical summaries
• Does not redistribute raw GRID data payloads
• Full compliance documentation available in DATA_COMPLIANCE.md

Development Tools:

• JetBrains WebStorm with Junie AI assistant
• Complete tool usage documentation in JETBRAINS_USAGE.md

Technological Implementation:

• Production-grade Next.js architecture
• Structured AI output with type safety
• Resilient API integration with exponential backoff
• Evidence-linked intelligence system

Design Excellence:

• Professional coach-focused UI/UX
• Modular page-based intelligence delivery
• Export functionality for team distribution

Potential Impact:

• Reduces scouting preparation time from hours to minutes
• Democratizes professional-grade analysis for all competitive levels
• Provides actionable, evidence-backed intelligence

Quality of Idea:

• Bridges gap between raw data and tactical application
• Unique evidence-linking approach ensures traceability
• Simulator and comparison tools extend beyond basic reporting

MIT License - See LICENSE for full text

Built by Manoj for the Cloud9 x JetBrains Hackathon

• DATA_COMPLIANCE.md - Data usage and compliance details
• JETBRAINS_USAGE.md - Development tool usage documentation
• ARCHITECTURE.md - System architecture and design decisions
• API.md - API reference and integration guide

Version: 1.0.0
Status: Hackathon Submission