Python and R for football analysis: the order to learn

Football analytics is done in spreadsheets until it is not. The moment a question requires joining two datasets, repeating the same calculation across a season or rebuilding a number months later, code stops being optional and becomes the working tool.

This guide explains why analysts write code, what Python and R are each good at, and the order in which the skills are worth learning if the goal is football analysis rather than software engineering.

Why code at all

A spreadsheet is a fine instrument for one table and a poor one for a season. Code makes every step explicit: which rows were dropped, how a metric was defined, which fixtures were excluded. That record is what allows a number to be defended in a meeting.

Code also makes repetition cheap. Once a routine exists, updating it for a new match is a command rather than an afternoon, and that difference decides whether an analyst can answer a question while it still matters to the coaching staff.

What each language is good at

Both languages handle data, and most football work can be done in either. The choice usually comes down to the surrounding ecosystem and to what colleagues already use.

Python is the more general tool. It sits comfortably next to web scraping, APIs, machine learning libraries and production code, which is why clubs building internal tools tend to standardise on it.

R was built for statistics and remains stronger for exploratory analysis, modelling and publication-quality graphics. Analysts who spend the week testing hypotheses rather than shipping software often prefer it.

The concepts matter more than the syntax. Grouping, joining, reshaping and plotting exist in both languages under different names, so somebody who understands the operations can move between them in weeks rather than years.

The order to learn things

Learn data handling first, because most of the job is cleaning. Reading files, selecting columns, filtering rows, grouping and joining are the operations that appear in every project, and fluency there removes most of the frustration beginners blame on analysis.

Then learn visualisation. A chart that communicates is worth more than a model that impresses, and the skill transfers directly to the reports on which the job is judged.

Modelling comes third. Regression, classification and validation are useful, but they sit on top of clean data and clear questions. Learning them first produces candidates who can fit a model to a problem nobody asked about.

Data handling before anything else

Event data arrives as nested structures: a match contains events, an event contains a location, a location contains a pair of coordinates. Flattening that into a table without losing information is the single most useful skill of the first months.

Learn to check your own work as you go. Row counts before and after a join, the number of unique matches, the range of coordinates. Small checks catch most errors before they reach a report and embarrass somebody in a meeting.

Visualisation and communication

Pitch plots are a language of their own. Coordinates need orienting, the attacking direction needs fixing, and density needs a scale that a reader can interpret without a lecture on the legend.

The rules are the same as for any chart: one message, a labelled scale and a comparison. A shot map without a baseline tells a coach where the shots came from, while the same map beside the league average tells them whether that pattern is unusual.

Modelling and reproducibility

Once data handling is comfortable, a simple model becomes approachable: define the target, choose features, split the sample, and check the result against a baseline that assumes nothing.

Reproducibility is the habit that separates a professional from an enthusiastic beginner.

• Keep projects in version control from the first commit.
• Write scripts that run end to end without manual steps.
• Fix random seeds so results can be rebuilt.
• Record the source and version of every dataset.
• Name files so a colleague can guess their contents.
• Document the definition of every derived metric.

What not to learn first

Deep learning, cloud infrastructure and interactive dashboards are attractive and rarely needed at the start. They add surface area without improving the answer to a football question.

Equally, do not learn a language for its own sake. The goal is to answer questions about football, and every hour spent on syntax is an hour not spent understanding what a metric actually measures.

How to practise on real questions

Pick a question you genuinely want answered and rebuild it from raw data: how often a team progresses through the left half-space, how its pressing height changes with the scoreline, which pass types precede shots.

Publish the result with the code and a short write-up. The exercise is not the certificate. It is the ability to take a vague football question and turn it into a reproducible answer, which is exactly what the job asks for every week.