How to Create Stunning Radar Charts with FBref Data | Visualize Football Stats

Welcome to today’s tutorial! In this video, we transform scraped FBref data into dynamic radar charts using Python. Learn step-by-step how to visualize football stats for individual players and analyze their performance with the mplsoccer package.

What You’ll Learn:

Setting up Python libraries for data visualization.
Cleaning and filtering FBref data for fullback analysis.
Creating radar charts using mplsoccer and pyPizza.
Adjusting percentiles for accurate comparisons.
Customizing visuals to highlight player strengths.
Key Highlights:

📊 Focus: Alex Moreno's performance stats.
🛠 Tools: Python, Pandas, mplsoccer, pyPizza.
🎨 Visuals: Tackles, assists, dribblers tackled, progressive carries, and more.
⚽ Context: Compare fullbacks' performances across Europe’s top leagues.
Resources Mentioned:

FEBref Scraping video    • FBref Soccer Python Scraping Tutorial  
FBref Data Pipeline Tutorial:    • How to Clean FBref Data with Python |...  
mplsoccer Library: https://mplsoccer.readthedocs.io/en/latest...
XAccount: https://x.com/Booteful_Game

Next Video: Stay tuned as we expand this project to compare multiple players’ stats side by side for deeper insights into team-building and player evaluation.

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