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How to Access the Fantasy Premier League API, Build a Dataframe, and Analyze Using Jupyter, Python, and Pandas

Documentation for building a Pandas Dataframe from the FPL API, and running a value analysis on 19/20 season

As of the writing of this tutorial, before match week 30+, I’m ranked #3,919 in the world in Fantasy Premier League Soccer (team: Yin Aubameyang), which equates to the top 0.05% in the world.

It didn’t happen by accident, and it wasn’t all luck.

It’s taken several years of playing this game to learn the patience, skill, and strategy required to succeed. From #1,181,262 in 2011/12 to #39,804 in 2018/19, and now #3,919 in 2019/20 with 8 GWs left:

A screenshot of the author’s previous season performance on fantasy.premierleague.com

Over these several years, I’ve learned 2 skills really help:

1. Identifying your biases
2. Understanding how to read data

The first will help you stop making stupid mistakes. The second will assist you in uncovering value that other fantasy managers are missing.