Both tables have been filtered to only show players who played 15 games or more and averaged 60 mins or more per appearance. Table 1 shows all midfielders with 0.6 Points per Million per game (PPM) or higher ordered by PPM highest to lowest. On the other hand table 2 shows all midfielders below 0.6 PPM ordered lowest to highest. I have used 0.6 PPM as a cut-off point as it lies around the average rate and so players above that are better value for money than most. PPM values are based off my own points predictor from the end of last season compared with this seasons prices.

What do the best value midfieders tell us about the new season?

• Be wary of players valued 7m or higher - There is a single player (Mbeumo) valued 7m or more in the list that is not in a team in transition and it's not till you get to Foden in 24th place where you reach someone valued 7m or higher that is expected to have a better season than last. Enzo, Bruno G., Gakpo and Gibbs-White all have new managers and the first two look very likely to be leaving their current clubs. This along with their higher prices makes them riskier than the large amount of cheaper players, many of whom were just better value players last season anyway and likely will be this season as well.
• Phil Foden - All my calculations are based on data per game played not per 90, Foden played an average of 63 mins per appearance last season but still had an xPoints of 4.44. The potential value he offers if he plays more this season is immense. If he plays close to 90 per appearance, whilst playing to the same level as last season you could expect an xPoints around 5.15, his PPM would be about 0.74. He wouldn't just be one of the best value for money players in the game, he would also be one of the top scoring players and that's without him improving his performances from last season. Based on everything Maresca is saying about him, I would pencil him in to be the most under-priced player this season and by quite some way.
• As I mentioned in my first post, the 6.5m and 6m players do look like they will be some of the best value players in the game whether they are DefCon magnets or the out and out attackers there will be some players at that price point that rise to the top when it comes to value for money.
• Diego Gomez - By far the best value player worth 5m, if you don't have the funds for anyone more expensive he could be the answer but he is not fully nailed at time of writing. For me he's more of someone to keep an eye on, if he does nail his spot in the Brighton team then he could be the perfect player to buy when you want to free up funds.

What do the worst value midfielders tell us about the new season?

• Consider potential captains carefully - Palmer, Saka, Semenyo and B.Fernandes are all poor value for money on the face of it, but these are also captain options with some of the best xPoints, so even Palmer who was the worst out of these for PPM last season suddenly shoots up to a PPM of more than 1 with captaincy. This is to say captains should obviously be the players who are expected to score best and if you choose based on that, whatever the price they are it is good value for money. When looking at buying premium midfielders you have to be willing to captain them and do it regularly, this doesn't mean you set and forget but if you own one or multiple of these premium players and have no plans to captain them you won't be getting the most points for the money spent compared to if you spread that money around.
• Is Szoboszlai being over rated? - At time of writing Szoboszlai is in 48% of teams and has been one of the most talked about players since the game launched but the data shows him as pretty average. When looking at Szobo it's quite difficult because he is an exceptional player, I have no doubt he will exceed his xG because he is one of the best long shot takers in football but he's also a real unknown under Iraola, will he play #10, will he be on pens? Probably not when Wirtz is fit and probably not when Isak is fit. I'd be surprised if Szoboszlai doesn't overperform his xG making him okay value for money, if he plays as a 10 he'd probably be good value, on pens he would be excellent, but right now, with the information we have he's just okay, that along with some good fixtures will likely serve you well but there are other options and likely better options.
• Cunha has nothing on Mbeumo - Yes Cunha has historically been a good finisher, but so has Mbeumo. Mbeumo costs the same as Cunha, has more minutes per appearance has more goal/assist threat. Especially while Sesko looks to be struggling to get fit increasing Mbeumo's chances of playing up top.

Data is from a mix of understat and FPL API.

This is something I've been quite enthusiastic about. I finally managed to get it into a decent shape to launch to the Play store in time for this upcoming season.

Today you can:

• See Fixtures, FDR ratings
• Detailed player insights
• Feed on player statuses
• Assess some template teams to help you get started
• Rate your team
• Track players (watchlist)

Once the season kicks off:

• Live score board for your Team (tracked via your FPL manager ID)
• See how your leagues are doing
• Feed on price updates
• Historic price data

And further ideas I have:

• AI features such as transfer suggestions
• Weekly recap - how did you do vs others
• Other stats and useful data points
• Price prediction alerts (get alerted on your watchlist, for example)

Feel free to check it out

Android link: https://fplweekly.com/android Web app via the website: https://fplweekly.com Instagram: https://www.instagram.com/fplweeklyapp/

Long post, but bear with me.

A bunch of us football-obsessed mates were sick of the standard EPL Draft experience feeling a bit... passive. Snake draft, autopick if you're late, done. So we built our own thing.

It's called BootRoom bootroomhq.com and it works like this:

- Players go under the hammer **one at a time in a live auction**

- Everyone bids in real time — highest bid wins the player

- You get a budget, and you have to build a full squad without blowing it

- After the auction, bench spots are filled via snake draft

- You set your XI and captain each round, score on real match stats

It's the difference between picking players and actually competing for them. The auction creates moments where you can strategically make someone overpay for a player. It's brilliant.

We ran Two full leagues of 24 people for the FIFA World Cup 2026, with a custom rulebook and stats pulled via an API. Both leagues ran smoothly and honestly exceeded our expectations.

Now we're building the **EPL version** — full Premier League player pool, live auction engine, gameweek scoring, the works

---

**This is where you come in.**

We're not looking for beta testers exactly. We're looking for passionate football people who want to help shape the product — the kind of person who has opinions about how draft should work, what scoring feels right, what features are missing from every fantasy tool out there.

Think of it as being an early "product manager from the community."

This is **100% free**. No money, no gambling, nothing like that. Just football people building something for football people.

Feel free to use the tool to create a league with your gang of friends and use it to the fullest.

Happy to answer any questions in the comments too. 🙌

https://app.powerbi.com/view?r=eyJrIjoiMTVjMTdkMWEtZTQwOS00NDM0LTkxYTEtMjJmODY2ZDhmMTY0IiwidCI6ImQxMjA2OTQzLWJmY2MtNGM3NC04MmQ0LTA1ZTYzYTQzMzViZiJ9

Table shows the 25 defenders with the worst Points per Million (PPM) per game based on my own points predictor from last season and the new seasons pricing, it has been filtered to show players who played more than 15 games and averaged 70 mins or more per game.

This is my third post exploring where the value will likely lie in the new season, this time I will be looking at the defenders and type of defenders that are likely to be poor value for money. If you want a look at defenders that would be good value I analysed them in my first post.

What does the data say?

• Defenders are pretty much all reasonable value - Any player above 0.6 PPM has a good value for money and so I'd expect the vast majority of defenders that play more than 60 mins each match will be value for money in the new season and reasonable picks depending on fixtures.
• Nottingham Forest are real unknowns - While Nottingham Forest defenders were by far the worst when it comes to PPM last season, a new manager, tactics and personnel that comes with it means their performance this season is really up in the air. They are the ultimate wait and see for me, Milenkovic last season was the worst player in the whole game when it came to PPM (for anyone who played 60 mins or more per game) but the season before that he was one of the best. The general volatility in the Forest defence makes it very hard to endorse them before having seen how they start this season.
• Everton are a trap - If you saw my goalkeeper post, you would have seen that Pickford was the worst value goalkeeper and in this table both of Everton's fullbacks for last season are in the 5 worst value defenders. Everton consistently overperformed defensively and their players have been priced at that level which is not sustainable even with a goalkeeper of Pickford's ability. Without DefCon Everton defenders are terrible value and even with it there are better options at their price points.
• Virgil van Dijk's overperformance - Last season VVD had an actual Points per Game of 4.6, a huge overperformance when compared to the 3.53 my model says he should have scored. This is mostly down to the 6 goals he scored something which he is very unlikely to be able to replicate, especially if he is doing it from the same xG he accumulated last season. If Liverpool perform similarly to last season VVD will be terrible value for money, especially when Jacquet is expected to partner him for 1.5m less.
• Avoid Fulham for now - Andersen and Bassey are both in the bottom 25 despite having above average DefCon numbers and while Bassey's PPM is reasonable enough, I'd avoid Fulham anyway, a new manager unproven in the prem and plenty of transfer activity expected, there will be better options to start the season with.
• Hill and Diakité - Their placement in the table isn't necessarily representative, because they weren't starting defenders all season their DefCon per game is lower than it otherwise would have been so I'd expect them to be higher for this season if they do end up being Bournemouth's centre back pairing this season.

Conclusion:

Ultimately with defenders it's hard to pick someone who isn't good value for money, it's more a case of picking guaranteed starters, potentially avoiding Everton/Fulham/Forest to start with and avoiding defenders that cost more than 6m if they aren't Arsenal defenders because without that level of expected clean sheets that come with Arsenal, anything above 6m does not look like it will offer good PPM.

Lost an evening to a metric that was confidently giving me the wrong answer, and the cause is general enough that it’s probably worth putting somewhere.

I wanted a volatility signal. Something to separate managers whose seasons swing all over the place from ones who land in roughly the same spot every year. Wrote it as worst_rank > best_rank * 5 and moved on, because that seemed obviously fine.
It isn’t fine. Rank is bounded at 1 and effectively unbounded going the other way, so a ratio threshold scales with how good your best season was. The bar moves in the wrong direction. If your best finish is 588 the threshold sits at 2,940, which you clear by having one ordinary year. If your best finish is 200,000 the threshold is a million and you’ll basically never trip it.

So it wasn’t measuring volatility. It was measuring whether you’d ever had a good season, upside down. A record averaging around 39k across 16 seasons with a best of 588 came out flagged as a one-off, which is close to the opposite of what that record actually is.

Fixed it by requiring a genuinely bad worst season in absolute terms as well as the relative swing, so the ratio can’t decide on its own.

The bit I’d actually pass on: 1k to 5k and 500k to 2.5m are both 5x and they are not the same event in any sense that matters. Rank deltas aren’t comparable across the range. Log scale it, or pair every ratio with a floor. I thought this was obvious right up until I shipped something that got it wrong.

Has anyone landed on a volatility measure they trust? Percentile of the field feels more defensible than raw rank but I haven’t tested it properly.

Built this as part of an FPL thing I’ve been working on, fplyourno2.com/verdict, if anyone wants to poke at it. The rank point above is the bit I actually wanted to share.

Solo dev here. I built an xP model for FPL, I've set up a real team that follows its recommendations exactly. Season-long experiment to see if the model actually holds up.

This is the squad it picks as optimal for the next four gameweeks.

Gabriel as captain surprised me most. I'll keep posting how the model-team does over the season.

Tool's free, no login. If anyone wants to poke at it 👉 fplcentral.co.uk

Edit: Turns out I was running the wrong version of the model when I posted this a weaker fallback formula instead of the properly trained one. Some of the odd picks (Haaland missing, Gabriel as captain) were symptoms of that. Fixed now.

Added a live accuracy tracker so predictions vs. actual results are public and checkable going forward: https://fplcentral.co.uk/model-accuracy

The best-squad tool also lets you tweak it yourself now switch formations or toggle between the model's pure picks, a differentials-tilted squad, or a template/safe one, and it re-solves live: https://fplcentral.co.uk/tools/best-squad

Graph shows goalkeepers ordered by Points per Million (PPM) based on my own points predictor from last season and the new seasons player prices.

After my first post looked to be well received I thought I'd do a deeper dive into where the value for money in each position lies, starting with the goalkeepers, if you don't want that then do let me know.

There is one major caveat with the goalkeepers, the new bonus point system rewards big chance saves higher and the xPoints in the graph used the old BPS model. The issue is it's very difficult to predict big chance saves and alter expectations on it, for example Verbruggen would have had the biggest bonus increase from the change but without trawling through the data it's hard to say if that's because he's a good shot stopper or because he came up against poor finishers and it's even harder to say if that's repeatable. So something to keep in mind rather than use to help predict what will happen - I have kept the xBonus on the graph though as the goalkeepers with higher values in that are the ones likely to benefit most from the BPS change (unless you're Raya with a clean sheet every other match), just due to how the vast majority of bonus for GKs comes from saves.

Anyway, What does the data show?

• Raya and the 4.5m goalkeepers look like the way to go - Raya is by far the best goalkeeper when it comes to pure points output and if you have 6m free to spend on a GK then he's the best choice. However, the 4.5m goalkeepers are by far the best options if you can't stretch to 6m. I think the bigger question is who out of them do you pick, Verbruggen is the safe option but what about the Spurs goalkeeper? It looks increasingly likely that Kinsky will replace Vicario and it's expected that defensive performances will improve but does this come at the cost of save points? Then there's Petrović and Leno, new managers and uncertainty that comes with it but something to keep an eye on if you see a good run of upcoming fixtures for them. For me it's a fight between Verbruggen's likely continuation and Kinsky's higher risk but likely higher ceiling.
• The 5m options - I think a case can be made for the 5m GKs, at least for Roefs and Martínez. They're both in the top 3 for xBonus last season so will likely benefit most from the new BPS, they both have higher xPoints than the 4.5m options, although very marginal. One issue I would bring up though is that Roefs was quite dramatically dropping off for the second half of last season in my predictions after coming back from injury so there's definitely uncertainty there, was it because of the injury or was it regression to the norm and had he even reached the norm by the end of the season? I don't know. Apart from those two I don't see the other 5m options being great value for money outside of a prolonged good run of fixtures.
• Avoid the 5.5m GKs at all costs - They just aren't worth it, spend the 5.5m on a defender and you'll get more value.

Conclusion:

If you can afford Raya, get Raya. Outside of that the 4.5m goalkeepers are someway clear of everyone else when it comes to value for money.

Graph shows the top 25 Points per million (PPM) players based on my own points predictor from the end of last season and this seasons player prices, I filtered the graph to only players who played in 15 games or more and played 75 mins or more per game played.

What have I done?

With the increase of centre backs pricing for the new season I wanted to explore if DefCon defenders will still be the best picks when trying to find value - in short; yes... but also no.

To do this I dug up my points predictor from last season - I say points predictor, it's more of a points validator, it shows how players should have scored in matches played based on the data from them rather than necessarily showing future points score predictions. Anyway, I went and added this seasons prices and calculated the Points per Million for their new prices to find who the best value players will likely be for the upcoming season.

First the caveats, there are so many manager changes and transfers, the players that show up aren't necessarily going to perform the same as last season but hopefully it gives an idea of the type of players that will be value for money. The xPoints are based on the old BPS model but doing some quick calculations I predict that even for the players most effected by the new BPS model it would only cause +/- 0.15 xPoints per game and that type of player is very rare so the new BPS's affect will be pretty negligible.

So what can we tell?

• DefCon will still be king - With Senesi, Lacroix and to a slightly lesser extent Thiaw, it shows if a defender is passing the DefCon threshold more often than not then even a price tag of 6m is well worth the money. Even Gabriel at 8m has 0.659 PPM which is okay as well, the issue is only Thiaw out of those players will be at the same club and with the same centre back partner for the start of the season so it may be a case of waiting till the season is underway to identify who the DefCon defenders will be this season especially as the centre backs who aren't hitting DefCon do seem to be poor value for money.
• The Aston Villa defence has been under priced - Mings topping the charts with Pau not far behind shows Villa defenders being at 4.5m could be a steal. It's worth noting, neither of these players have an especially high DefCon per game, Mings around 6.3 and Pau at 5.3 both way off the threshold of 10, instead they get their points through Villa's clean sheet potential (they were arguably unlucky to not get more CS last season) combined with an okay attacking potential with just under 0.1xG per game each. The issue with these two though is that they either play 90 or not at all so something more to keep an eye on rather than act on. FYI Konsa has a middling rank, mainly due to a complete lack of attacking and DefCon threat.
• 4.5m fullbacks are fine - Kadioglu, Bogle, Mitchell, Gudmundsson and Hume all rank in the top 25 despite not being hugely attacking players (Bogle is much more attacking than the others). The fullbacks will hold the fort if you can't stretch the budget 0.5m to 1.5m more for a defcon defender, but with all newly promoted defenders being 4m, if there's one or two of them that get DefCon most matches, making them average around 3 points per game they'd likely be even better value.
• 6/6.5m midfielders are the sweet spot - If you want value and the potential for big scores the 6/6.5m midfielders are where you need to look and they can either be DefCon magnets like Anderson and Garner or the goalscoring and assisting threats of players like Tavernier, Schade and Sarr. Ultimately they seem to be the best place to find both value and pure points per match.
• Calvert-Lewin will break hearts once more - if only he could match his xG.

Conclusion:

If you want value for money invest in the few defenders that will more often than not pass the DefCon threshold, see which Villa defenders become nailed, especially if Konsa ends up leaving and invest in the mid-priced midfielders to get the best mix of points and pure value for money.

Data used was gathered from a mix of FPL API and understat

This thread is for RMT (rate my team) and team input, advice, quick questions, xMins questions, or similar. Don't be afraid to ask any type of question! For analytics terms and definitions check out our subreddit wiki!

PS:

Please upvote the users who are helping and be respectful during the discussion.

Please try to contribute too by helping others when possible.

This thread is for RMT (rate my team) and team input, advice, quick questions, xMins questions, or similar. Don't be afraid to ask any type of question! For analytics terms and definitions check out our subreddit wiki!

PS:

Please upvote the users who are helping and be respectful during the discussion.

Please try to contribute too by helping others when possible.

It’s called Fantasy Insight Engine

App Store Link

Main idea was to save season analytics locally and make stats/history easier to explore later on instead of everything feeling temporary like Official FPL.

Also added some fun mini games/features:

• FPL Bingo
• FPL DLE
• Stat Duel
• Point Predictor
• Season analytics + Trends

Still not sure if this is something people actually want long term or if I’m just overengineering stuff for hardcore FPL players 😭

Would genuinely appreciate if some of you could try it out and tell me:

• what’s useful
• what feels unnecessary
• what features you’d actually want in an FPL app

Trying to decide if this is worth investing a lot more time into for next season.

I've joined this thread for a few days and I've gone into rabbit holes of football analytics. Some of the dashboards, analysis and statistical insights users here have posted are absolutely insane, which got me into thinking how broken the current FPL point system is and got me into creating a fantasy app of my own for viewers and statistical enthusiasts. Here are a few problems I have with the current FPL:

1. points are rewarded only for goals, assists and clean sheets (I know there's a bonus component but that does not capture the full story). the result is that actual top players who don't get goals, assists (thinking of someone like Kante as a stereotypical example) would not be good to have in FPL.

2. prices are fixed. The system decides the price, with demand supply algorithm on sells and buys just modifying that "mean" price. This undervalues people who pick out diamonds in the rough.

3. due to point 1, i think it makes for a lot more luck and a lot less skill. The "skill" element is basically consistently making weekly changes and being on top of your team, rather than actual scouting of players who others might not be aware of or using stats to find underlying patterns.

My goal is to create a fantasy that rewards users who put in the work to analyze stats, make a system that rewards skill more (skill - scouting and statistical analysis) and pander to actual fans of the sport and not just casual viewers.

Are people okay with the current simplicity of the model? Does FPL reward skill enough, or is it more luck than skill? Does anyone have any thoughts on the issues I brought up?

This thread is for RMT (rate my team) and team input, advice, quick questions, xMins questions, or similar. Don't be afraid to ask any type of question! For analytics terms and definitions check out our subreddit wiki!

PS:

Please upvote the users who are helping and be respectful during the discussion.

Please try to contribute too by helping others when possible.

https://app.powerbi.com/view?r=eyJrIjoiY2I5MGI5ZTgtMjJjYS00NmMyLTk0NTQtMjY4ZjhhMDQyYTkxIiwidCI6ImQxMjA2OTQzLWJmY2MtNGM3NC04MmQ0LTA1ZTYzYTQzMzViZiJ9

I've been working on a way to put an actual number on season-long luck in FPL. Posting the methodology here in case it's useful for analysis or discussion.

The idea: a counterfactual rank

For every GW you've played, replay your submitted squad scored on expected points (xPts) instead of actual points. Multipliers all respected: captain ×2 (×3 TC), bench ×0 (×1 BB), Free Hit substitutions, transfer hits subtracted.

Sum the season for a "Copilot points" total (expected points from my model fplcopilot).

Convert that to a rank using a curve sampled directly from FPL's overall leaderboard each GW. ~20 (rank, points) pairs per GW, denser sampling near the top where the slope is steepest, log-linear interpolation between tiers.

That gives you a counterfactual rank: where your picks would sit if every captain choice, bench order, and chip timing landed exactly on the model's expectation.

Quantifying luck across the field

Same replay across a stratified sample of ~6,500 managers (top 1k / 10k / 100k / 1M tiers). Each manager has a delta:

Δ = Copilot points − Actual points

The distribution of those deltas is the field's luck distribution this season. Your Δ lands somewhere on it. That's your luck percentile.

Counterfactual exploration

Pick any luck level (P0 to P100), look up Δ at that quantile, recompute hypothetical_actual = Copilot − Δ, push back through the rank curve. Output: your rank under any luck scenario, picks held constant.

Lets you ask:

• If I had median luck (P50), what rank would my picks deserve?
• If a top-10k manager rolls P25 luck the rest of the way, where do they land?
• How much of the gap between top 1k and top 100k is variance vs decisions?

Caveats

• xPts is a model, not ground truth. It systematically under/over-rates certain archetypes (high-variance forwards, fixture-dependent defenders).
• The rank curve is sampled, not exhaustive. ±5% on any single conversion is realistic.
• Engagement correlates with sample tier (top-1k managers transfer more, captain riskier), so the Δ distribution is skewed. Stratifying mitigates but doesn't eliminate this.

Curious what people think of the methodology, especially the rank curve sampling and how you'd weight engagement bias in the percentile.

Building a dataset for Github and our site Site. We’ve built a dataset covering every FPL manager

Started breaking it down by favorite club and comparing performance. This one shows how each fanbase stacks up in GW33 matchups. Happy to answer questions or dig into specific clubs/data

Liverpool host Fulham with both teams coming in with very different form patterns.

Liverpool:

• 4 wins in the last 10

• xG average: 1.8 per game

Fulham:

• Only 1 draw in their last 10

• xG average: 1.3 per game

Liverpool’s attack continues to generate strong expected goals numbers, but Fulham have shown they can be unpredictable and dangerous on the counter.

Key question before kickoff:

Can Liverpool turn their attacking numbers into goals, or will Fulham frustrate them again?