This project analyzes Formula 1 performance data from 2021-2025, focusing on the relationship between qualifying positions and race results, and tracking championship progress throughout seasons.
- How important is pole position? Analysis of grid position vs final race result
- Which drivers gain the most positions on race day? Identification of best racers
- How do championship battles evolve? Round-by-round points tracking
- Which tracks promote overtaking? Circuit analysis across seasons
- Best Qualifier: [Driver] (Avg Grid: [X])
- Best Racer: [Driver] (Avg Finish: [Y])
- Most Improved Race Day Driver: [Driver] (+[Z] positions avg)
- Championship Margin: [Champion] won by [points] points
- FastF1 Library: Primary data source for F1 timing data
- Pandas/NumPy: Data manipulation and analysis
- Matplotlib/Seaborn: Static visualizations
- Plotly: Interactive dashboards
- Jupyter Notebook: Development environment
- ✅ Grid vs finish position comparison for any race
- ✅ Multi-season performance analysis
- ✅ Championship progress tracking
- ✅ Driver consistency metrics
- ✅ Interactive dashboards
- ✅ Circuit overtaking analysis
f1-performance-analysis/
│
├── notebooks/
│ └── f1_analysis.ipynb # Main analysis notebook
│
├── data/
│ ├── cache/ # FastF1 cache directory
│ └── exports/ # Exported CSV files
│
├── visualizations/
│ ├── grid_vs_finish.png
│ ├── championship_progress.html
│ └── dashboard.html
│
├── reports/
│ └── insights_summary.md
│
└── README.md
- Working with specialized sports data APIs
- Time-series analysis in sports context
- Creating narrative-driven visualizations
- Building interactive dashboards for non-technical audiences
- Add telemetry analysis (speed traces, throttle/brake)
- Include weather impact analysis
- Pit stop strategy analysis
- Machine learning for race outcome prediction