Data in Football

Football is full of numbers these days. Possession percentages flash across our screens, pundits debate expected goals after every match, and social media overflows with charts and heatmaps. We’re surrounded by data like never before — but here’s the catch: numbers don’t speak for themselves.

In football, as in life, data can be brilliantly insightful or wildly misleading, depending on how it’s interpreted.

Football is no longer just a game of instinct — it’s a game of information.
This course introduces the principles of interpreting data in football, guiding learners from raw match data to actionable tactical and performance insights and exploring common mistakes in interpretations.

Topics include for example event and tracking data, expected goals (xG) models, and interactive visualization techniques such as pitch control and possession flow mapping. We’ll explore what football data really measures, what it can tell you, and what it cannot capture. Because behind every number, there’s a story — the style of play, the tactics, the situations that shaped those stats.

This course includes access to two interactive tools for football analysis: Situational xG modeling and Pitch control.

By the end, you’ll see football stats differently — you will not just take them at face value and draw false conclusions as most people do, but you will consider them critically and draw meaningful insights from them.

So whether you’re a fan who loves post-match analysis, a coach trying to apply data insights, or someone aiming for a career in football analytics, this course will give you the foundation to interpret data intelligently.

In football, as in all data work, it’s not just about knowing the stats — it’s about knowing what they mean.