Football Analytics with Python: Build a Shot Map

Learn Football Analytics with Python by building a complete shot map using real StatsBomb match data.

In this hands-on beginner course, you'll transform football event data into a professional-looking visualization that shows where shots were taken, their outcomes, and their expected goals (xG).

Using Python, pandas, StatsBombPy, Matplotlib, and mplsoccer in Google Colab, you'll follow a practical workflow from preparing raw shot data to creating and exporting your finished shot map.

Rather than focusing on complex programming concepts, this course helps you develop a practical football data visualization skill you can apply to different matches and teams.

By the end of the course, you will be able to:

• Load real football event data with StatsBombPy
• Filter and prepare shot data with pandas
• Extract x and y coordinates from event locations
• Plot shots on a StatsBomb football pitch with mplsoccer
• Use xG to represent chance quality through marker size
• Distinguish goals from other shot outcomes
• Add titles, legends, and data attribution to your visualization
• Export your finished shot map as an image
• Interpret the visualization and apply the same workflow to another match or team

The course includes the complete Python notebook and a downloadable course guide so you can reproduce the project and continue experimenting after the lessons.

This course is ideal for beginners interested in football analytics, sports data, or football data visualization who want to move from event data to a practical visual analysis.