Football Analytics with Python – The Fast-Track Guide for Beginners

This is a guide built around one simple idea: help you get started with Python for football analytics — fast.

Together, we’ll work through the four main stages of any football analytics project:

Collecting data → Wrangling data → Visualising data → Modelling data

At each stage, we’ll focus on the quickest, most efficient ways to get results without falling into the common beginner bottlenecks that cause so many people to give up.

Interested? Here's what's inside.

What you’ll learn (in a nutshell)

A clear, end-to-end workflow that mirrors how real analysts work:

• Collect football data in seconds with simple, reliable methods.
• Wrangle messy datasets with a library that reads like plain English.
• Visualise patterns and tell clear stories with charts.
• Model data with practical, foundational algorithms that actually matter.

What’s inside

• The guide: A 35,000+ word (155-page) PDF that explains everything you need to start doing football analytics with Python—in clear, friendly language, without heavy math.
• The code: 31 step-by-step Python notebooks covering fundamentals, reusable templates, and full examples for collecting, wrangling, visualising, and modelling—so you can replicate everything end-to-end.

Guide Chapters

1. Python Fundamentals — Set up Python, learn core syntax and building blocks—explained with football examples.
2. Data Collection — Fast paths to match, team, and event data, plus templates.
   • Python Toolkit: soccerdata, pandas
3. Data Wrangling — Clean, reshape, and join data.
   • Python Toolkit: polars
4. Data Visualisation — Core plots and football-specific visuals (xG flow, radars, shot maps, pass networks).
   • Python Toolkit: matplotlib and mplsoccer
5. Modelling — Learn Four fundamental models through practical case studies (using scikit-learn):
   • Linear Regression (predict league points from wage budgets)
   • Logistic Regression (build a simple xG model)
   • K-Means (cluster midfield profiles into attacking, defensive and box-to-box)
   • Principal Component Analysis (Reduce many attacking metrics into meaningful axes to separate creators from finishers)

You also get

• 31 Python scripts including ready-to-run templates to avoid setup stalls.
• Curated resource lists (data sources, references, third-party templates).
  

For a more detailed overview of what’s inside—plus why I wrote this guide, why football is the best entry point to learning Python, and why it’s worth it even with so much free content—check out this the link below.

The Bottlenecks Beginners Face (and how this guide fixes them)

Every beginner in football analytics hits the same walls.

You start excited — maybe you just replicated a great visualisation from a GitHub tutorial or followed a YouTube walkthrough line-by-line. But the moment you step outside that sandbox, things fall apart.

Anything related to wrangling requires a Google search… so you move at turtle speed. You try to analyse a different league … and suddenly you have no idea how to get the data.

Very quickly, the excitement turns into doubt — “Why does this feel harder than the tutorial?”

Hours pass, and you’re no closer to answering the football question you started with.

That’s normal.

It’s also the point where most people quit.

This guide is built around tackling the biggest obstacles that stop beginners at each stage of the data journey.

Here are the common bottlenecks—and how we help you move past them.

1. Python Fundamentals

• The bottleneck: Overwhelm, no practical roadmap, and impatience to skip basics.
• What happens: You drown in generic tutorials made for engineers, spend weeks on concepts you won’t use, or rush into xG models and visuals—then small Python mistakes break everything.
• How we fix it: A focused, football-first basics roadmap: the essential 50% of Python you’ll reuse constantly, taught through applied examples. Clear progression (9 short notebooks), quick wins before depth, and just enough theory to stay unblocked.
• You’ll do: Set up your environment, learn core syntax and data structures, write clean, readable code, and practice with football-flavoured mini-tasks—so when you hit the data workflow (Collect → Wrangle → Visualise → Model), you’re steady, not guessing.
  

2. Data Collection

• The bottleneck: Days lost building brittle scrapers or not knowing where to find analysis-ready data.
• What happens: You copy code, it breaks, and you can’t tell if the site changed, the wrapper changed, or your environment is off.
• How we fix it: Start with reliable shortcuts—soccerdata and pandas.read_html()—plus simple verification and fallback patterns so you can keep progressing even when something upstream changes.
• You’ll do: Load team, match, and event data in minutes using reusable templates across leagues.

3. Data Wrangling

• The bottleneck: Clunky, forgettable syntax (pandas “bracket soup”), confusing index resets, and too many ways to do the same thing.
• What happens: You’re stuck in the 80% nobody explained; even small tasks require searching; the dataset never feels “clean enough.”
• How we fix it: Use Polars for readable, chainable transforms—a fast, modern library with plain-English verbs that are easy to remember.
• You’ll do: Work through 9 bite-size notebooks to master the most common cleaning and transformation moves so you’re ready when you see them again.

4. Data Visualisation

• The bottleneck: Not knowing what to plot or why; jumping to fancy charts and getting lost in styling.
• What happens: You’re tweaking fonts instead of learning visualisation fundamentals.
• How we fix it: Separate exploration (basic plots to check quality and patterns) from storytelling (football-specific visuals once the data is trustworthy). Start with Matplotlib, then add mplsoccer.
• You’ll do: Core plots (scatter, line, bar, histogram, box) → then football charts (xG flow, radars, shot maps, pass networks) with clean, minimal styling.

5. Data Modelling

• The bottleneck: Over-complication and “black-box” thinking; jumping into advanced models before mastering the basics.
• What happens: You can’t explain results; you’re unsure when to model, how to approach it; metrics feel abstract.
• How we fix it: Focus on four foundational models with football use-cases that answer most beginner needs.
• You’ll do: In 4 Python notebooks, quantify relationships, classify outcomes, cluster players, and reduce dimensionality—then swap in your own data when you’re ready.

6. Motivation & Momentum

• The bottleneck: Projects feel generic; progress feels slow; you lose the “why.”
• What happens: You quit before the compounding kicks in.
• How we fix it: Every example is football-based (even Python fundamentals). Visualisation and modelling notebooks end with “make it yours” prompts—pick your league, position group, or metrics and rerun.
• You’ll do: Stack small, meaningful wins tied to your interests—and build a body of work.

Requirements

• Level: Beginner friendly. No prior Python required.
• Where to run: Google Colab (free) — you just need a Gmail account.

Who it’s for

• Football fans and non-technical experts (scouts, coaches, journalists) who want to learn Python by doing.
• Students and career-switchers building a portfolio of football analytics projects.
• Anyone who’s tried free tutorials and got stuck connecting the dots.

About the author

MartinOnData is a competition economist with 10+ years working on large, messy datasets. Martin writes the The Python Football Review—beginner-friendly newsletter covering anything Football x Data x Python related.

Guarantee (7 days)

Try it for a week. If it’s not for you, email me (martin@pythonfootball.com) within 7 days of purchase for a 100% refund—no questions asked.

FAQs

1. How is this different from free tutorials?

Free content is great but fragmented. This guide gives you one coherent path, realistic football projects, and notebooks that run together.

2. Do I need prior coding experience?

No. We start with the essentials, then move fast—but not carelessly.

3. What exactly do I get on purchase?

The full 155-page PDF guide (readable on any device) + all 31 Jupyter notebooks (.ipynb) ready to run.

4. Will I need paid data?

No. We use public sources and free community wrappers. You should always read and follow each source’s terms.

5. What if libraries or websites change?

It happens. Community wrappers may evolve; I can’t guarantee third-party maintenance, but the workflows remain useful.

6. License & sharing

Personal use only. Reuse and adapt the code for your own projects/portfolio.

7. Refunds?

Yes—7-day money-back guarantee, no questions asked.

Ready to learn Python the way football fans actually learn—by building?

Grab the guide and start your first project today.

Dislaimer:

Third-party tools & community wrappers. Examples in this guide may rely on open-source/community libraries, APIs, and data wrappers. These projects can change, break, or be discontinued at any time. The author of this guide does not control or maintain them and cannot guarantee their availability, accuracy, performance, or security. You are responsible for installing, updating, and configuring any third-party services or API usage.

Data sources & website terms. If you collect data from public websites or APIs, you must review and comply with their Terms of Service, rate limits, and any licensing restrictions. Obtain permissions where required. Do not scrape or redistribute data in ways that violate applicable laws, contracts, or site policies.

Accuracy & reproducibility. Datasets, APIs, and code dependencies evolve. Results, charts, and metrics shown here may not be exactly reproducible later. Environment differences (OS, library versions, random seeds) can also change outputs. Treat models as illustrative; no outcome or performance is guaranteed.