Resident Hub statistician Carl Weeks takes us through some high-level data points we should be considering when picking our FPL teams for Gameweek 1 and beyond.

The Premier League has recently just launched their fantasy football game for the 2020/2021 season. Though I found the restart very difficult to keep up with the fast pace and was glad when the season ended, the launch of the new game has got me excited for the new season ahead.

For each fantasy game, I like to have the data in a spreadsheet so I can quickly analysis players value and spot possible players of value. I try and keep this updated after each set of games. The main purpose of this article is to provide the link to the spreadsheet and how I use the data to assess players.

Data Summary for FPL

The first step for the new season is to gather information on players, positions and points scored from the previous season and use this to understand how I should be spending the budget. I have needed to match the data with another file, so it is possible that there are a few errors in relation to how many games a player may have played.

The summary data for the game is in the following link:

FPL summary spreadsheet

The structure of the spreadsheet is:

Summary - contains all the players, their position, price and points scored.

Season top 20 – list the top 20 by position based on total points, points per £m and points per game per £m

Team – Select a team from cell C3 and it lists that players teams.

Value For Money – Shows a scatter plot for each position of points per game by cost of player. A line of best fit is shown, and the top eight players compared to the line are labelled.


Metrics and initial view

It is quite difficult to know the best metric to use to compare players, but my preference is the number of points per game per value of player.  The points per game, makes adjustments for players that have missed a game through injury or rested etc.

It is worth noting that the metrics only give a limited view of players performance and can be miss leading for a few. For example, some emerging players such as Greenwood and Foden had several games where they had a cameo few minutes at the end so their points per £m per game would be lower.

The page I view initially is “VFM” where there is a plot of points per game by cost for each position with a best line of fit. The best lines of fit are in the chart around cell BS4 as follows:

Value for Money by Position

The gradient of the lines indicates additional value for extra money invested. Most lines have a similar gradient apart from defence which is steeper and indicates best value for money.

Individual position charts

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