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:
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.
Link to spreadsheet does not work.
Hope you can access the link now. If not I’ve uploaded a version on the slack in the channel called spreadsheets
Hi Carl,
Wanted to ask about players moving clubs – specifically using Maintland Niles to Wolves as the example provided on twitter. What would his prediction be and how is the algorithm impacted by the moves? Doherty would be another. Thanks!
The link to the article is
https://fantasyfootballhub.co.uk/the-algorithm-fpl-sky-prediction-planning-toolkit/#comments
this does give some detail on the workings.
When a player joins a club, the algorithm takes their stats as the average for their position in terms of scoring and assisting a team goal. So when at Wolves the algorithm assumes he scores 11.7% of Wolves goals and 4.6% of assists. When he joins a new club he will be assumed to contribute like an average defender (2.9% and 3.7%). The algorithm then looks at the expected goals a team scores and works out points accordingly. As the season progresses the algorithm will monitor players performances and alter the stats accordingly.
Below is a link to an unlocked version that I will delete sometime next weekend. To change a players club go to the “Sky” page and change the club for a player and everything should update.
https://1drv.ms/x/s!AkYcP_eLvYMVsAXOH4scSV26MOxl?e=4C3s0I
You need to be very careful with players new to a club at the beginning of the season and also for players like Greenwood who may have lower stats from last season due to several cameo appearances.
Hope this helps and happy to discuss in more detail.