A Formula 1 championship prediction and simulation engine powered by real-world data.
Maybe reality might be different xD
KYC (KnowYourChamps) is a Python-based analytics project that predicts:
- 🏆 World Drivers' Championship (WDC)
- 🏎️ World Constructors' Championship (WCC)
using:
- ✅ Real Formula 1 data
- ✅ Monte Carlo simulations
- ✅ Official FIA point systems
- ✅ Season-aware rules
- ✅ (Optional) live race updates
The goal is to model how championships evolve, not just display standings.
📖 New to F1? Check out our detailed guide: How F1 Scores
A driver competes in races and earns points based on finishing position. Drivers compete for the World Drivers' Championship (WDC).
👉 Example: Max Verstappen
A constructor (team) fields two drivers.
- Team points = sum of both drivers' points
- Competes for World Constructors' Championship (WCC)
👉 Example: Red Bull Racing
| Type | Description |
|---|---|
| Race | Main event (25–1 points) |
| Sprint | Short race (8–1 points) |
| Fastest Lap | +1 point (removed from 2025) |
- ✔ One race affects both WDC and WCC
- ✔ Sprint and Race handled separately
- ✔ Rules vary by season
- ✔ Simulation respects FIA scoring system
- Build season state from real F1 data
- Simulate remaining races using Monte Carlo
- Predict:
- WDC winner probability
- WCC winner probability
- Supports:
- Sprint weekends
- Rule changes by season
- Future-proof architecture
- Weighted randomness (based on current form)
- Thousands of season simulations
- Probabilistic outcome modeling
- No hardcoded results
- Python 3.10+
- Dataclasses
- OOP-based domain modeling
- FastF1 → Official Formula 1 timing & standings
- NumPy
- Monte Carlo Simulation
- Probabilistic ranking
- Modular design
- Clean separation of concerns
- No tight coupling
- Simulation-ready
F1KYC/
│
├── src/
│ ├── models.py # Core domain models
│ ├── simulation.py # Monte Carlo engine
│ ├── data_fetching.py # FastF1 integration
│ └── newwww.py # Runner / entry point
│
├── cache/ # FastF1 cache
├── howf1scores.md # F1 scoring system explained
├── README.md # This file
└── requirements.txt # Dependencies
season = build_season_state(2025)results = monte_carlo_championship(season, simulations=3000)WDC:
VER → 71.2%
NOR → 18.4%
LEC → 7.1%
WCC:
Red Bull → 65.9%
McLaren → 29.3%
- Python 3.10 or higher
- pip package manager
-
Clone the repository
git clone https://github.com/biv720/kyc.git cd kyc -
Install dependencies
pip install -r requirements.txt
-
Run the simulation
python src/newwww.py
from src.data_fetching import build_season_state
from src.simulation import monte_carlo_championship
# Load current season
season = build_season_state(2025)
# Run 5000 simulations
results = monte_carlo_championship(season, simulations=5000)
# View predictions
print(results)| Principle | Description |
|---|---|
| ✔ Domain-first modeling | Models reflect real F1 structure |
| ✔ No hardcoded logic | Rules driven by season data |
| ✔ Rules driven by season | Adapts to FIA regulation changes |
| ✔ Extendable for live data | Ready for real-time integration |
| ✔ Clean separation | Data & logic are independent |
This project was built to scale into:
- 📡 Live race analysis
- 🎯 Strategy simulations
- 📊 Data visualization dashboards
- 🕰️ Historical season replay
- 🟢 Live Race Mode (real-time WDC/WCC updates)
- 📊 Streamlit Dashboard (interactive visualization)
- 🧪 Unit test suite (comprehensive testing)
- 🌍 Multi-season comparison (historical analysis)
- 🧠 ML-based driver performance weighting (intelligent predictions)
- 📈 Strategy optimizer (pit stop & tire strategy)
- 🎮 "What-if" scenario simulator (alternative outcomes)
- 🌐 Web API (REST endpoints for predictions)
Coming soon: Dashboard previews, simulation outputs, and prediction visualizations
Contributions are welcome! Whether it's bug fixes, new features, or documentation improvements.
- Fork the project
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
This project is for educational and analytical purposes only.
Formula 1 is unpredictable — weather, strategy, penalties, and chaos always apply.
Maybe reality might be different xD 🏎️💨
The predictions are probabilistic models based on historical data and simulations, not guarantees of actual race outcomes.
This project is licensed under the MIT License - see the LICENSE file for details.
Bivraj — F1 Enthusiast | Data & Design
Built with 🏁 for F1 data nerds and championship dreamers