3 Reasons Why Football Fans Are Best Placed to Learn Python for Analytics
And why you can do it too
Hi friend,
Welcome to The Python Football Review #015.
When it comes to learning Python for analytics, football fans are the best-suited group out there.
Yes — that’s the claim we’re exploring today.
Let’s not waste time and jump straight into it.
Reason #1: Football is the perfect sandbox for the entire data science workflow.
After 10 years in consulting, I can safely say that most business problems only cover two or three parts of the data science workflow (collect → wrangle → visualise → model → deploy). But only rarely, the full spectrum.
Football covers all of them.
It’s rich enough to challenge you, familiar enough to stay fun, and concrete enough to show progress fast.
Every key stage of the workflow appears naturally:
Collection — If you stick around long enough, you’ll eventually touch all the main methods — using community-built wrappers (where most of us start), web scraping, and calling APIs from data providers. It’s a natural progression: from hobby projects to more serious analytical work. Football lets you go all the way — from analyst to data engineer.
Wrangling — Football data is gloriously messy — and that’s a good thing. Even early on, you’ll run into challenges like merging sources where “Man Utd” in one dataset needs to match “Manchester United” in another. This is the unglamorous middle of every real project — and football gives you plenty of practice.
Visualisation — Few domains offer better opportunities to visualise data. From simple scatter plots and line charts to more advanced visuals — xG flow charts, player radars, passing networks, pass sonars — football has it all. In analytics, visuals are often the final product — and football gives you endless ways to master the craft.
Modelling — When it comes to modelling, we can safely say that machine learning is the name of the game in football analytics. Think of the buzzword expected goals — behind it often sits a simple but powerful model: logistic regression. How do teams find replacements for departing players? They could turn to clustering and dimensionality reduction — algorithms that combine dozens of metrics into just a few, then group players by similarity. How do platforms project a team’s chances of winning the title — or even the next game? That’s regression analysis at work.
My point is this: the four main pillars of machine learning — prediction, classification, clustering, and dimensionality reduction — are the same questions fans, scouts, and journalists ask every week.
Deployment — Finally, football pushes you to share your insights — from hobby dashboards to professional tools. Whether it’s tracking trends, explaining tactics, or previewing matches, football gives you a reason to build and publish your work.
When you learn Python through football analytics, you naturally learn to wear all three hats:
Engineer → collecting and structuring data
Analyst → finding and visualising insights
Scientist → building and testing models
That’s what makes football the perfect playground for learning analytics end to end.
Reason #2: The easiest way to learn something? Start with what you love.
I learned that the hard way.
I improved my French through Football Manager. Back when I was a junior economics consultant trying to break into the Paris consulting world, that game became my tutor.
You might think knowing the difference between a Meneur de jeu en retrait (Deep-Lying Playmaker) and a Milieu récupérateur (Ball-Winning Midfielder) wouldn’t help me professionally — and you’d be right.
But beyond the tactics and jargon, I was absorbing grammar, vocabulary, and sentence structures without even realising it. The learning stuck because it felt like play.
The same pattern repeated in my career.
I perfected my R skills not through formal training, but by combining client projects with late-night forecasting experiments. There’s no better way to master dplyr than trying to fuzzy-match messy data sources full of special characters. Sure, I used R at work — but the real progress came from side projects that didn’t feel like work.
Now, I sharpen my Python through The Python Football Review. At work, it’s only the third tool I use (not my choice!). But I knew one truth: if you don’t use a language — any language — you lose it. The newsletter gave me a reason to keep learning: exploring football questions that genuinely interested me, while staying sharp with new libraries and trends.
Same lesson every time: When you learn through something you already love, the learning sticks.
That’s why football fans have a real head start when learning Python for analytics. You already understand the context and the meaning behind the numbers.
Picking your favourite team as your “dataset” is what most data learners dream of.
Imagine collecting Arsenal’s data from this season. Modelling Arsenal’s title chances using their average xG difference — that’s linear regression in action. Predicting their next match result based on their 6-game rolling form — that’s logistic regression. Grouping Ødegaard, Rice, and Nørgaard by playing style — that’s K-Means clustering.
Suddenly, abstract algorithms become intuitive. When you start with football, the learning never feels forced. It’s relevant, motivating, and — most importantly — sustainable.
As a fan, you already have the perfect excuse to get into Python. The subject you love is the best teacher you’ll ever have.
Reason #3: The Feedback loop is fast (and addictive)
The best kind of learning is the kind that gives you instant feedback.
In football, that feedback comes naturally. The data changes every week — new matches, new patterns, new stories. You can test your code, tweak it, rerun it after the next round, and instantly see how things evolve.
That sense of progress keeps you motivated far longer than any textbook ever could.
And then there’s the community.
Football has one of the most active, passionate data communities anywhere online — fans and analysts sharing visuals, models, and insights for every league, every team.
Imagine posting your own shot map after the weekend’s game and joining the discussion around what it means. You’ll get instant feedback — on your visual style, your code, your interpretation — from people who genuinely care about the same questions you do.
That kind of feedback is priceless. It’s fast, constructive, and it helps you improve without feeling like work.
With quick feedback loops and a supportive community, learning Python through football starts to feel like play. You don’t think of it as “practicing coding” — you’re just exploring, visualising, modelling, sharing. And without noticing, you get better every single week.
Boom — those were the 3 reasons football fans are perfectly suited to learn Python for analytics.
If you’ve been meaning to learn Python for a while, stop waiting for the “perfect” course or moment. Start with football.
And if you’re ready to get started fast, grab my Fast-Track Guide to Football Analytics with Python.
It’s built around helping you get started with Python for football analytics without falling into the common beginner traps that cause so many people to give up.
Thanks for reading,
Martin