A comprehensive Formula 1 analytics project built using Python, Pandas, Power BI, and DAX.
The project analyzes over 26,000 race records across 75 Formula 1 seasons, uncovering insights into driver performance, constructor dominance, racecraft, qualifying effectiveness, and championship evolution.
This project follows a complete analytics pipeline:
Raw F1 Data
↓
Data Cleaning
↓
Feature Engineering
↓
KPI Generation
↓
Power BI Dashboard
↓
Business Insights
The goal was to transform historical Formula 1 race data into an interactive analytics solution suitable for executive reporting and performance analysis.
- Python
- Pandas
- NumPy
- Power BI
- DAX
- Git
- GitHub
Source:
Ergast Formula One Database
The project combines multiple Formula 1 datasets including:
- Race Results
- Drivers
- Constructors
- Qualifying Results
- Circuits
- Seasons
Performed:
- Missing value handling
- Data type conversion
- Dataset merging
- Duplicate removal
- Date formatting
Created custom analytics metrics:
Grid Position - Finish Position
Measures a driver's ability to gain positions during a race.
1 if driver qualified P1
0 otherwise
1 if driver started P1 and won
0 otherwise
Finish Position - Qualifying Position
Measures performance relative to qualifying.
Generated:
- Driver KPIs
- Constructor KPIs
- Seasonal Driver KPIs
- Seasonal Constructor KPIs
Provides a high-level summary of Formula 1 performance metrics.
- Total Drivers
- Total Constructors
- Total Races
- Total Points
- Average Consistency Score
- Top Drivers
- Top Constructors
- Historical Trends
Analyzes career performance of Formula 1 drivers.
- Career Points
- Wins
- Podiums
- Consistency Score
- Performance Trends
- Driver Comparison
Evaluates Formula 1 teams across all seasons.
- Team Points
- Wins
- Podiums
- Team Rankings
- Constructor Dominance
- Seasonal Trends
Focuses on race execution and qualifying effectiveness.
- Average Position Gain
- Pole Conversion Rate
- Qualifying vs Race Performance
- Strategy Metrics
- Driver Racecraft Analysis
Explores Formula 1 evolution over 75 seasons.
- Championship Evolution
- Driver Dominance by Era
- Constructor Dominance by Era
- Seasonal Performance Trends
Formula1-Strategy-Analytics
│
├── data
│ ├── raw
│ └── processed
│
├── scripts
│ ├── data_cleaning.py
│ ├── feature_engineering.py
│ └── kpi_generation.py
│
├── dashboard
│ └── F1_Strategy_Analytics.pbix
│
├── screenshots
│ ├── page1_overview.png
│ ├── page2_driver_analysis.png
│ ├── page3_constructor_analysis.png
│ ├── page4_strategy_analysis.png
│ └── page5_season_trends.png
│
├── requirements.txt
├── .gitignore
└── README.md
- Identified drivers with the highest career points and consistency scores.
- Compared race performance against qualifying performance.
- Evaluated long-term team dominance.
- Measured constructor success across multiple eras.
- Quantified position gains achieved during races.
- Evaluated pole position conversion effectiveness.
- Analyzed how Formula 1 performance trends evolved across 75 seasons.
- Identified dominant drivers and constructors by era.
- Data Cleaning
- Data Transformation
- Feature Engineering
- Exploratory Data Analysis
- KPI Development
- Power BI Dashboard Design
- DAX Measures
- Data Visualization
- Business Intelligence
- Git Version Control
Vyom Mangtani