F1 Red Bull Dominance Analysis

A complete end-to-end Python data analytics project analyzing Red Bull Racing's historic 2023 dominance and the rise of multi-team competition in the 2024 Formula 1 season.

Core Question

How did Formula 1 transition from Red Bull's historic dominance in 2023 to a balanced multi-team championship fight in 2024?

Key Findings

Metric	2023	2024
Red Bull Win Rate	95.5% (21/22)	37.5% (9/24)
Red Bull Points	790	537 (3rd place)
Red Bull Points Share	35.2%	22.0%
Verstappen Avg Finish	1.27	3.63
Verstappen Points/Race	24.1	16.6
Race-Winning Teams	2	4
McLaren DNF Rate	9.1%	0%

Analytics Pipeline

Phase 1 — Data Collection

• Connected to the official F1 live timing API using FastF1
• Enabled local disk caching to avoid repeat API calls
• Collected race results for all 22 rounds of 2023 and 24 rounds of 2024
• Built resume-safe collection loop with per-race CSV backups
• Handled rate limits using time.sleep() delays
• Final dataset: 919 rows across 46 races

Phase 2 — Data Cleaning

• Dropped 8 fully null columns
• Renamed all columns to snake_case
• Fixed data types for numeric and integer fields
• Standardised team names across seasons (Alfa Romeo → Sauber, AlphaTauri → RB F1 Team)
• Fixed DNF detection — separated retirements from lapped finishers

Phase 3 — Feature Engineering

• is_redbull boolean flag
• positions_gained = grid minus finish position
• dnf, dns, finished boolean flags from status values
• points_finish flag for top 10 finishes
• Derived KPIs: win rate, podium rate, points per race, points share

Phase 4 — EDA

• Wins per team by season
• Constructor points comparison
• Red Bull dominance metrics (win % and points share)
• Cumulative constructor points race by race

Phase 5 — Advanced Analysis

• Cumulative driver points — Verstappen vs top rivals
• Points gap to championship leader round by round
• Verstappen finishing position heatmap
• Win distribution pie charts
• Grid vs finish position scatter
• Driver consistency boxplot

Visualizations

#	Chart	Key Insight
1	Race Wins by Team	21 Red Bull wins in 2023 vs 9 in 2024
2	Constructor Points	Red Bull 790 in 2023, 3rd with 537 in 2024
3	Dominance Metrics	Win rate dropped from 95.5% to 37.5%
4	Cumulative Constructor Points	Red Bull ran away in 2023, tight all season in 2024
5	Driver Championship Battle	Rivals stayed close to Verstappen in 2024
6	Points Gap to Leader	Gap collapsed from 2023 to 2024
7	Verstappen Heatmap	Near-perfect 2023, visible dips in 2024
8	Win Distribution Pie	From one-team show to four-team fight
9	Grid vs Finish Scatter	Red Bull isolated in top-left in 2023, merged with field in 2024
10	Driver Consistency Boxplot	Pérez collapse visible — key reason Red Bull lost constructors title

Tech Stack

Tool	Purpose
Python 3.10	Core language
FastF1	F1 API data collection
Pandas	Data cleaning and analysis
Matplotlib	Primary charting library
Seaborn	Statistical visualizations
NumPy	Numerical operations

How to Run

1. Clone the repo

git clone https://github.com/your-username/f1-redbull-analysis.git
cd f1-redbull-analysis

2. Install dependencies

pip install fastf1 pandas matplotlib seaborn numpy

3. Run the scripts in order

python notebooks/01_data_collection.py
python notebooks/02_data_cleaning.py
python notebooks/03_eda.py
python notebooks/04_advanced_analysis.py

Note: Data collection requires a stable internet connection. The cache/ folder will be created automatically and speeds up any subsequent runs significantly.

Data

The cleaned dataset (master_results_cleaned.csv) contains 919 rows and 23 columns covering:

• Driver and team information
• Finishing and grid positions
• Points scored
• Race status (Finished, Retired, Lapped, etc.)
• Engineered features (DNF flags, positions gained, points share)

Raw data sourced from the official F1 live timing API via FastF1.

Portfolio Page

A full interactive portfolio webpage for this project is included in the web/ folder. Open web/index.html in any browser to view it locally.

Author

Shounak Mukherjee Aspiring Data & Business Analyst — B.Tech CSE, IEM Kolkata

• LinkedIn: linkedin.com/in/check-out-shounak-mukherjee
• Email: mukherjeeshounak05@gmail.com
• GitHub: github.com/codenak