🥊 Virtual Fight Lab

The Ultimate AI-Powered UFC Fighting Analysis & Counter-Strategy Platform

🎯 Overview

Virtual Fight Lab is a revolutionary AI-powered platform designed to provide fighters, coaches, and MMA enthusiasts with elite-level fight analysis, counter-strategies, and personalized training feedback. This system combines cutting-edge artificial intelligence with world-class coaching methodologies to democratize access to professional-grade fight preparation tools.

🌟 Key Features

🥋 Fighting Style Database - Comprehensive analysis of 9 major MMA fighting styles
🎯 Counter-Strategy Engine - Detailed blueprints for defeating any fighting style
👤 Elite Fighter Profiles - In-depth scouting reports on 12+ top UFC fighters
⚔️ Interactive Matchup Simulator - AI-powered fight outcome predictions
📹 AI Video Analysis - Upload fight footage for technical feedback
📊 Combat Domains Framework - Professional rating system across 8 domains
💰 Monetization Ready - Complete subscription and gym partnership model

✨ Features

1. Fighting Style Database

Browse and study comprehensive AI-generated analyses of all major MMA fighting styles:

Boxing
Kickboxing
Muay Thai
Sanda (Sanshou)
Brazilian Jiu-Jitsu (BJJ)
Wrestling
Judo
Karate
Taekwondo

Each style includes:

Core characteristics and techniques
Primary strengths
Critical weaknesses
Tactical vulnerabilities
Physical requirements

2. Counter-Strategy Engine

Select an opponent's fighting style and receive a complete game plan including:

Pre-fight preparation checklist
Striking defense and counters
Grappling defense and counters
Offensive strategies
Range and position control
Round-by-round strategy
Training camp recommendations

3. Elite Fighter Scouting Database

Access detailed profiles of top UFC fighters with ratings across all combat domains:

Israel Adesanya
Alex Pereira
Conor McGregor
Max Holloway
Khabib Nurmagomedov
Islam Makhachev
Khamzat Chimaev
Jon Jones
Charles Oliveira
And more...

4. Interactive Matchup Simulator

The game-changing feature that lets you:

Pit any two fighters or styles against each other
Get AI-predicted outcomes with win probabilities
Analyze problems and advantages for each fighter
Identify critical pain points
Receive style adaptation recommendations

5. AI Video Analysis

Upload sparring or fight footage and receive:

Technical breakdown of positives and negatives
Specific corrections needed
Recommended drills
Personalized improvement plan
Performance ratings

6. Combat Domains Categorization

Professional framework rating fighters across:

Striking: Boxing, Kickboxing, Muay Thai, Karate/TKD
Grappling: Wrestling, BJJ, Judo
Clinch Work: Offensive and defensive
Cage Craft: Positioning and control
Transitions: Between positions and ranges
Fight IQ: Strategic decision-making
Physical Attributes: Speed, power, cardio
Mental Game: Composure and resilience

🛠 Tech Stack

Backend

Python 3.11+ - Core application logic
Supabase - Database, authentication, and storage
PostgreSQL - Relational database
OpenAI API - AI-powered analysis generation

AI & Machine Learning

GPT-4 / Gemini 2.5 Flash - Natural language generation
Computer Vision (planned) - Video analysis
Pattern Recognition - Fighter tendency analysis

Infrastructure

Row Level Security (RLS) - Data protection
Real-time Subscriptions - Live updates
CDN Storage - Video and image delivery
RESTful API - Client integrations

📦 Installation

Prerequisites

Python 3.11 or higher
Supabase account (sign up here)
OpenAI API key or compatible endpoint

1. Clone the Repository

git clone https://github.com/YOUR_USERNAME/virtual-fight-lab.git
cd virtual-fight-lab

2. Install Dependencies

pip install -r requirements.txt

3. Set Up Environment Variables

Create a .env file in the root directory:

SUPABASE_URL=your_supabase_project_url
SUPABASE_KEY=your_supabase_anon_key
OPENAI_API_KEY=your_openai_api_key

4. Deploy Database Schema

Open your Supabase project dashboard
Go to SQL Editor
Copy the contents of database/supabase_schema.sql
Run the SQL script

5. Seed the Database

python scripts/seed_supabase_data.py

🚀 Quick Start

Running the Backend API

from backend.virtual_fight_lab_app import *

# Initialize managers
user_manager = UserManager()
fighter_manager = FighterManager()
matchup_simulator = MatchupSimulator()

# Get all fighters
fighters = fighter_manager.get_all_fighters()
print(f"Found {len(fighters)} fighters")

# Run a matchup simulation
matchup = matchup_simulator.generate_matchup_analysis(
    fighter_a_id="uuid-here",
    fighter_b_id="uuid-here",
    user_id="user-uuid"
)
print(matchup)

Analyzing a Fighting Style

from backend.virtual_fight_lab_app import FightingStyleManager

# Get boxing analysis
boxing = FightingStyleManager.get_style_by_name("Boxing")
print(boxing['ai_analysis_full'])

# Get counter-strategy
counter = FightingStyleManager.get_counter_strategy(boxing['id'])
print(counter['ai_strategy_full'])

Video Upload and Analysis

from backend.virtual_fight_lab_app import VideoAnalyzer

# Upload video
video = VideoAnalyzer.upload_video(
    user_id="user-uuid",
    file_path="sparring_session.mp4",
    title="Training Session",
    description="Working on striking defense"
)

# Analyze video
analysis = VideoAnalyzer.analyze_video(
    video_id=video['id'],
    analysis_focus="striking"
)
print(analysis)

📁 Project Structure

virtual-fight-lab/
├── backend/
│   └── virtual_fight_lab_app.py      # Main backend API
├── database/
│   └── supabase_schema.sql           # Complete database schema
├── scripts/
│   ├── seed_supabase_data.py         # Database seeding
│   ├── analyze_fighting_styles_v2.py # Style analysis generator
│   ├── generate_counter_strategies.py # Counter-strategy generator
│   ├── generate_elite_fighter_profiles.py # Fighter profile generator
│   ├── virtual_fight_lab_simulator.py # Matchup simulator
│   └── virtual_fight_lab_video_analysis.py # Video analysis module
├── data/
│   ├── analysis_*.txt                # Fighting style analyses
│   ├── counter_*.txt                 # Counter-strategies
│   ├── fighter_*.txt                 # Fighter profiles
│   └── matchup_*.txt                 # Example matchups
├── docs/
│   ├── SUPABASE_IMPLEMENTATION_GUIDE.md
│   ├── VIRTUAL_FIGHT_LAB_README.md
│   ├── combat_domains_framework.md
│   ├── virtual_fight_lab_video_analysis_framework.md
│   └── virtual_fight_lab_master_plan.md
├── .gitignore
├── requirements.txt
├── LICENSE
└── README.md

📚 Documentation

Comprehensive documentation is available in the docs/ directory:

Supabase Implementation Guide - Complete setup instructions
Master Plan - Full platform overview
Combat Domains Framework - Fighter rating system
Video Analysis Framework - Video analysis specs

🔌 API Usage

User Management

from backend.virtual_fight_lab_app import UserManager

# Create user profile
user = UserManager.create_user_profile(
    user_id="uuid",
    email="fighter@example.com",
    full_name="John Fighter",
    user_type="fighter"
)

# Update subscription
UserManager.update_subscription("user-uuid", "fighter_pro")

Fighter Database

from backend.virtual_fight_lab_app import FighterManager

# Get fighter with full details
fighter = FighterManager.get_fighter_by_id("fighter-uuid")
print(fighter['fighter'])
print(fighter['ratings'])
print(fighter['techniques'])
print(fighter['weaknesses'])

Matchup Simulation

from backend.virtual_fight_lab_app import MatchupSimulator

# Generate matchup analysis
matchup = MatchupSimulator.generate_matchup_analysis(
    fighter_a_id="uuid-1",
    fighter_b_id="uuid-2",
    user_id="user-uuid"
)

# Get user's matchup history
history = MatchupSimulator.get_user_matchups("user-uuid")

🗄 Database Schema

The platform uses 16 interconnected tables:

Core Tables

user_profiles - User accounts and subscriptions
gyms - Gym partnerships
fighting_styles - Style database
counter_strategies - Counter-strategy guides
fighters - Fighter profiles
fighter_combat_ratings - Combat domain ratings
fighter_techniques - Signature moves
fighter_weaknesses - Known vulnerabilities

Feature Tables

matchup_simulations - Matchup analyses
video_uploads - Uploaded videos
video_analyses - AI analysis results
video_timestamps - Timestamped feedback

System Tables

user_activity_log - Activity tracking
user_favorites - Saved items
subscriptions - Billing management
usage_limits - Tier-based limits

💰 Monetization

Subscription Tiers

Tier	Price	Features
Free	$0/month	Basic style database, 2 video analyses/month
Fighter Pro	$29/month	Full access, 10 video analyses/month
Coach Elite	$99/month	Unlimited analyses, team management, API access
Gym Partnership	Custom	White-label, unlimited access for all members

Revenue Streams

Monthly Subscriptions - Recurring revenue from individual users
Gym Partnerships - B2B white-label solutions
Sponsorships - Fight gear, nutrition brands
Premium Content - Exclusive fighter breakdowns

🤝 Contributing

We welcome contributions! Please follow these steps:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Development Guidelines

Follow PEP 8 style guide for Python code
Add docstrings to all functions
Include unit tests for new features
Update documentation as needed

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
backend		backend
data		data
database		database
docs		docs
scripts		scripts
.env.example		.env.example
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
requirements.txt		requirements.txt

License

IndustriesAI/virtual-fight-lab

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