Python for Football Analytics: Getting Started

Python is the most popular programming language in football analytics. This guide will help you set up your environment, find data, and complete your first football data analysis project.

Python dominates football analytics for several reasons: its readable syntax makes it accessible to non-programmers, its data science ecosystem (pandas, numpy, scikit-learn) is unmatched, and football-specific libraries like mplsoccer and statsbombpy make pitch visualizations straightforward. Over 80% of professional football analysts use Python as their primary tool.

A great starter project is creating a shot map for a specific team or player. Using statsbombpy, you can load free match data, filter for shot events, and plot them on a pitch visualization using mplsoccer. Color-coding by xG value and sizing by outcome creates an informative visualization that demonstrates core data analysis skills.

After mastering basic visualizations, progress to passing network analysis, defensive coverage models, and player similarity algorithms. Each project builds on fundamental pandas and matplotlib skills while introducing new analytical concepts. Publishing your work on GitHub and sharing visualizations on social media helps build the portfolio needed for professional opportunities in football analytics.