Miami Grand Prix 2025 - F1 Race Prediction Model

Overview

This predictive model forecasts the likely outcomes of the 2025 Miami Grand Prix using machine learning and statistical simulations. The system analyzes:

• Current season performance (2025 races)
• Historical Miami GP data (2022-2024)
• Qualifying results
• Team and driver characteristics
• Environmental factors

Model Architecture

Data Pipeline

1. Input Sources:

   • drivers_2025.csv: Current driver roster with team assignments
   • 2025_previous_races.csv: Results from all 2025 races prior to Miami
   • 2025_miami_qualifiers.csv: Official qualifying results
   • FastF1 API: Historical Miami GP data (2012-2024)

2. Feature Engineering:

   • Recent form (average position/points)
   • Position change trends
   • Miami-specific performance history
   • Qualifying position
   • Team performance factors
   • Weather conditions (when available)

3. Prediction Engine:

   • Weighted performance model combining:
     • 60% recent form
     • 30% track-specific history
     • 10% qualifying position
   • Monte Carlo simulation (1000 iterations)
   • Random Forest feature importance analysis

Key Visualizations

The model generates several insightful visualizations:

1. Performance Trends - Season-long driver performance trajectories
2. Top 10 Prediction - Probabilistic finishing positions with confidence ranges
3. Weather Impact - Temperature/humidity effects on historical performance
4. Feature Importance - Relative weight of predictive factors
5. Position Distribution - Heatmap of simulated finishing probabilities

Requirements

• Python 3.8+
• Required packages:

  pip install fastf1 pandas numpy scikit-learn matplotlib seaborn

Usage

• Install dependencies:

pip install -r requirements.txt

• Run the prediction model:

python miami_f1.py

• Output Files:

  • miami_gp_predictions.csv: Complete prediction results

  • miami_gp_top10_prediction.png: Top 10 visualization

  • miami_gp_performance_trends.png: Season performance chart

  • miami_gp_weather_impact.png: Weather analysis (if data available)

  • miami_gp_feature_importance.png: Model factors breakdown

  • miami_gp_position_heatmap.png: Full position probability matrix

Customization

Adjustable parameters in the script:

• Simulation parameters sim_count = 1000 # Number of race simulations

• Prediction weights form_weight = 0.6 # Recent performance importance track_weight = 0.3 # Track history importance grid_weight = 0.1 # Qualifying position importance

License

This project is provided for educational purposes under the MIT License.