Data Pipelines: Getting and Cleaning Match Data

How to collect, clean and store football data responsibly: public datasets, APIs, scraping rules and simple storage.

Models are only as good as their data. Here we cover where football data comes from, how to respect each source's terms, and how to clean and store results so your analysis can be repeated.

Tutorials in this module

Data Pipelines

Turn Odds Into Probabilities in Python

Proportional, power and Shin margin removal in Python, tested on 2,660 Premier League matches. On average market odds, the method barely matters.

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Data Pipelines

Load and Clean Football Data With pandas

A tested pipeline that downloads eleven Premier League seasons of CSV results, fixes the encoding and date pitfalls and builds one clean table.

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Poisson match predictorEnter expected goals for both teams and get every scoreline, 1X2, over/under 2.5 and BTTS.