This project aims to analyze F1 drivers' performance across different circuits during the 2021-2024 seasons. Through exploratory data analysis and statistical techniques, we seek to answer two key questions:

1. Does track qualities influence lap time across drivers?
2. What are some circuits that each driver performs best in?

An in-depth overview of the data analysis pipeline and results is available here.

📋 Overview

The workflow consists of several integrated components:

1. Data Collection - FastF1
   • FastF1 is an open-source resource providing: lap timing data, car telemetry and position, tire data, weather data.
2. Data Preprocessing - Removing outliers
3. Statistical Analysis - Simple statistical techniques (mean, std, var) and mixed-effects modeling
4. Developing Visualizations - Scatter plots and box plots

📋 Driver Selection Criteria

To ensure accurate comparisons, the project focuses on a roster of recent drivers who have:

• Remained with the same team for the past 3-4 years
• Consistent performance across multiple seasons

📋 Repository Structure

• fastf1.ipynb: Comprehensive Jupyter Notebook containing all code and analysis
• Data.csv and New_data.csv: Data retrieved using FastF1 API
• Lap Performance Across*.png: Visualization for across drivers comparisons
• LapTimePerKm*.csv: Statistical analysis summaries

📋 Getting Started

1. Clone the repository
2. Install required dependencies: Copy pip install fastf1 pandas matplotlib seaborn plotly statsmodels
3. Open fastf1.ipynb
4. Run the notebook to reproduce the analysis and visualizations