Working with Fantasy Premier League Data on Google Sheets

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I love FPL for or two reasons — football and data. While I understood the former, getting access to the latter was always a frustrating process.

This motivated me to make a tool that extracts the data daily and downloads FPL data onto a Google Sheet, which provides a powerful platform and language (Google Apps Script) to create web apps. Furthermore, the official FPL website maintains great API endpoints that can be tapped into.

So without further ado, here’s the link: Google Sheets

The Google Sheet works in the background, and automatically fetches the latest FPL data every 12 hours. You can bookmark this link and come back to it whenever you need access — this Sheet will work in perpetuity.


I’ve taken the liberty to do some of my own analysis, which you will find in the same sheet. I’m going to quickly jump ahead and provide a brief explanation of each module. There are 4 in total:

  1. Raw FPL data
  2. Transfer recommendation for the next gameweek
  3. Transfers made by the global top 100 FPL managers in the last gameweek
  4. Team composition of the top 100 FPL managers as of the last gameweek

1. Raw FPL data

In the sheet called “Data”, you will find over 30 datapoints for all current Premier League players, such as Points, Goals, Cost, Form, Total Transfers among others.

How can you use it? This will depend entirely on you.


2. Transfer recommendation for the next gameweek

Transfers are recommended based on certain numerical calculations.

I realised that when making player transfers, the two most important variables for me were:

  1. Player form
  2. Upcoming fixtures difficulty

Player form is the average points earned by the player in the last one month (roughly 5 games), and Upcoming fixtures difficulty is the sum of difficulty of opposition teams for the next 5 games.


I created a third variable called FD Index;

FD Index = Player form / Upcoming fixtures difficulty

You’ll notice that the higher the value of FD Index, the more likely the player is to succeed in the near future (the next 5 games).

In the sheet called “Transfer Pick”, you will find the recommendations sorted in descending order of FD Index (best to worst).

3. Transfers made by the top 100 FPL managers in the last gameweek

The idea is to aggregate the transfer activity of the top 100 FPL managers from the last gameweek, and identify crucial players that I maybe missing out on (or players that I need to get rid of).


Note that usually a transfer is made keeping the player’s next 5 gameweeks in mind.

For example, you’ll see that on GW8, thirty of the top 100 managers transferred in Bruno. This sheet will give you the info before GW9, so you can make a similar transfer then, and still hope to receive 80% points overlap over the next 4 gameweeks.

Remember that you are aggregating the top 100 managers, instead of following 1 particular manager, hence maximizing your chances of success.


4. Team composition of the top 100 FPL managers as of the last gameweek

Similar to the last sheet, we tap into the aggregate brains of the best FPL managers, and find out what the current teams look like for the top 100 managers.

For example, as I write this article, a whopping 89 of the top 100 managers possess Bruno Fernandes, and 85 possess Mohamed Salah. If you’re missing out on these players, it should raise an immediate red flag.


Note: All of the above data is calculated as of the most recent gameweek

That’s about it for now.

I’m always looking for ideas on analyzing the data to achieve actionable insights, so do reach out and I will be happy to chat about feedback and feature requests.

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