FPL Trader is currently ranked 37th in our Hall of Fame and here outlines his method for calculating value in Fantasy Premier League (FPL), highlighting the players who could be underpriced in 2022/23.
Three years ago, I wrote an article on players’ value based on their average points per match. Hopefully, it contributed to how some FPL managers perceive value and helped them to make better-informed decisions.
I slightly tweaked the methodology and would like to present an updated approach together with the list of most underpriced players we can choose for the 2022/23 season.
Methodology
This analysis tries to find overpriced/underpriced players based on their last season’s performances and this year’s prices. I consider average points per match (PPM) rather than total points over the season to be a more valuable metric. Fantasy managers are usually able to transfer out injured or otherwise unavailable players, so a player should not be penalised for him being temporary unavailable (however, you should avoid injury-prone players as transfers are limited).
I utilised regression analysis of 2021/22 average points per match and 2022/23 prices. In regression analysis, I only used outfield players, as I consider goalkeepers to be a special case and I analysed them separately. To account for defenders having a different base price (they start at £4.0m, while forwards and midfielders start at £4.5m), I subtracted base costs from the players’ prices (essentially, a £6.5m defender and a £7.0m midfielder would be regarded as costing the same in my analysis).
For data reliability purposes, I excluded players who played 10 games or fewer. I also excluded players who are currently owned by less than 2.9% of managers, as I consider these players not to be “Fantasy material” (I arbitrarily decided Callum Wilson (£7.5m) with 2.9% ownership to be the first reasonable Fantasy pick).
For players that were reclassified from one position to another, I used their recalculated PPM (I did not recalculate their potential change in bonus points).
Results
I came up with the regression of PPM=3.01+0.49 x (Price – base cost). To put it in simple language, when buying a player for a base cost (i.e. £4.0m defender or £4.5m midfielder), you should get 3.01 PPM. Moreover, you should get additional 0.49 PPM for every additional million spent.
I then compared actual PPM of players with required PPM according to derived formula. For example, Mohamed Salah (£13.0m) had 7.6 PPM last season. According to derived formula, he should score PPM=3.01+0.49 x (13 – 4.5) = 7.2 for his price. Therefore, while selecting him you earn a Surplus PPM of 0.4.
Based on my methodology the most underpriced players in each position would be as follows:
The way to interpret the results is simple: the higher the Surplus PPM, the more underpriced the player is. If the Surplus PPM is negative, the player is overpriced.
We can see from the results that the hype about defenders is justified by my analysis. On the other hand, all forwards are overpriced.
Goalkeepers
I excluded goalkeepers from my regression analysis because this position is very specific. They do not compete against other positions in terms of formation (ie you will always play one goalkeeper, you can`t play two goalkeepers at the expense of forward or defender, etc). The way they compete with other positions is in terms of extra investment. The only question we need to answer is should we buy the cheapest playing goalkeeper for £4.5m, or should we spend £5m or £5.5m?
We saw from the regression analysis that for outfield players, every extra £1.0m should earn you 0.49 PPM. You should invest in goalkeepers more than £4.5m, if the additional investment can earn you more than the investment in outfield players. Last season, a £4.5m goalkeeper on average earned 3.5 PPM. Therefore, you should consider investing in a £5.5m goalkeeper if the extra £1.0m spent would earn you more than 0.49 PPM. To arrive at required PPM for goalkeepers I used the following formula: 3.5 + 0.49 x (Price – 4.5).
I then compared actual PPM of players with required PPM according to derived formula. For example, Alisson had 4.9 PPM last season. According to derived formula, he should earn PPM=3.5 +0.49 x (5.5 – 4.5) = 4.0. Therefore, while selecting him you earn Surplus PPM of 0.9.
Based on my methodology the most underpriced goalkeepers are as follows:
How to use my approach making daily decisions
The tables above indicate the underpriced/overpriced players solely based on their historic PPM. However, if you believe that a certain player may underperform/overperform his historic PPM, you may recalculate his value.
For example, if you believe that Andrew Robertson (£7.0m) this season will average 5.0 PPM (rather than 6.4 as last year, when he materially overperformed his expected goal involvement), you can recalculate his surplus PPM by using the same formula 5 – 3.01 – 0.49 x (Price – base cost) resulting to Surplus PPM of 0.52 (which is still impressive).
I have also built the inferred average PPM for each price point. If you believe that a certain player can overachieve the PPM for his price bracket – go for it!
There is no need to memorise this table as you can do mental calculations for any price point by yourself. You can use simplified formula of Price/2+1 to arrive at required PPM to make the transfer.
Let`s take Mason Mount (£8.0m) as an example. Using simplified formula, you will arrive at 8/2+1=5.0 PPM to justify the transfer. It is 0.3 more than in the table above, however this is not a big problem as you want to transfer in players who offer above-average value anyway.
Other implications – cost of making transfers late
Another widely discussed topic is whether we should make transfers early or incur price increase/decrease and wait as close to the deadline as possible. My regression can calculate the cost of waiting. The value of any additional 0.1 in team value diminishes from 1.8 points at the start of the season to 0 points at the end.
As an example, if there are 37 Gameweeks left, the cost of 0.1 increase in price can be calculated as 0.1 multiplied by incremental return of 1m (0.49) and multiplied by remaining Gameweeks (37). That would give us 0.1 x 0.49 x 37 = 1.8 points for every 0.1 increase in price. The cost of missing 0.2 price increase (3.6 points) is just below the cost of a hit (4 points).
Conclusions
The main takeaways:
- Defenders are underpriced, forwards are overpriced.
- Use the formula Price/2+1 to arrive at required PPM for any player that you want to transfer in. If you believe he can overachieve it, transfer him in!
- A £6.0m defender costs the same as a £6.5m midfielder/forward.
- Do not compare goalkeepers to defenders. The idea of comparing investment in Ederson (£5.5m) to investment in Ruben Dias (£6.0m) is flawed. Goalkeepers compete mainly among themselves.
- Traditional value metric would use a basic formula of Value=PPM/Price, which is biased towards cheaper players and is flawed (in statistical terms, this metric does not account for intercept in Y axis).
- The value of any additional 0.1 in team value diminishes from 1.8 points at the start of the season to 0 points at the end.
Fantasy Football is not only about data, of course. There are certainly more factors that Fantasy managers need to navigate, i.e. changes in forms of teams/players, injuries, captaincy, rotation risk and many more. However, I believe that my approach offers practical tools to assess players’ value even without using complex models offered by many tools online.
Me and my team
I am currently on a Bench Boost draft. I believe that such cheap options as Leon Bailey (£5.0m) and Pedro Neto (£5.5m) offer a nice opportunity to use Bench Boost in Gameweek 1 and get it out of the way. Nothing is decided yet, but I used Bench Boost in Gameweek 1 last season and it was refreshing not to worry about it later and instead to concentrate on high-value moves.
Good luck to all of you in the final contemplations! If you liked my thoughts, you can also find me on Twitter here.
I am looking for ways to be more involved in the FPL community. I am currently ranked #37 in the Fantasy Football Scout Hall of Fame and approaches that I shared here contributed to my improvement over the past seasons.
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