We have taken data from kaggle - Chinese Formula One Grand Prix 2025. Link- https://www.kaggle.com/datasets/umerhaddii/formula-1-chinese-grand-prix-2025
This project focuses on analyzing Formula One race data using SQL queries to evaluate driver performance, lap consistency, tyre management, race pace trends, and strategy effectiveness.
The project demonstrates how structured data analysis can be used to generate insights that support performance evaluation and decision-making in motorsport environments.
I built this project to strengthen my understanding of motorsport analytics and explore how race data can be transformed into actionable insights using SQL and analytical thinking.
Modern Formula One teams rely heavily on data-driven engineering and operational strategies to optimize race performance, improve efficiency, and support rapid decision-making during competitive race conditions.
This project applies SQL-based analysis to simulate how performance data can help:
- identify race trends
- evaluate driver consistency
- improve operational decision-making
- support competitive strategy analysis
- SQL
- Docker(furn running SQL server)
- VS Code
- Relational Databases
- Data Analysis Techniques
- Pitstop impact on laptimes
- Average lap time analysis
- Consistency Analysis
- Race strategy trends
- Performance ranking
- Comparative query analysis
- SQL Query Writing
- Data Aggregation
- Analytical Thinking
- Performance Analysis
- Relational Database Concepts
- Motorsport Data Interpretation
This project serves as the foundational data analysis layer for my Python/Pandas-based Formula One Race Strategy Analysis project.
The SQL queries were used to extract and structure race performance metrics, which were later analyzed further using Python, statistical methods, and visualization techniques to generate deeper strategic insights.