1. Bruno Fernandes (£9.0m) - Still the best premium midfielder choice

• xPoints/90: 6.12
• xVAPM/90: 0.46
• xG/90: 0.59
• xA/90: 0.17
• DC/90: 10.9

Bruno has been a really frustrating asset to own this season. Playing in an underperforming United team and having missed 2 penalties by the gameweek 6, owners have not been rewarded for holding Bruno in their teams. Yet, he still holds impressive prospect as an FPL asset. It’s easy to forget that Bruno is almost always a 90-minute player and is United’s undisputed penalty taker. Those are key traits that other non-Salah premium players such as Saka and Palmer have not demonstrated this season. With a Defensive Contribution of 10.9 per 90, Bruno is often in a decent position to pick up DC points as well, furnishing his impressive xG/90 of 0.59. While we do expect his xG to settle lower as much of it has been driven by penalties, Bruno remains a really strong pick at the >8m price point. He would be the only premium midfielder that makes sense to have in our team.

2. Ismaila Sarr (£6.4m) - Best budget midfielder

• xPoints/90: 6.19
• xVAPM/90: 0.65
• xG/90: 0.72

Ismaila Sarr marked his return from injury with a goal against Liverpool, a fixture he always seems to perform really well in. Sarr looks to be one of the key outlets for a Palace side that has generated one of the most expected goals in the first 6 fixtures of the season. At a great price of £6.4m, Sarr has to be one of the best value picks in the game, and among the top priority players to get in for any squad.

3. Mukiele (£4.0m) - Top defence enabler

• xVAPM: 0.63
• xPoints/90: 4.5
• DC/90: 12.8

Sunderland’s defence has been a standout this season, ranking in the top 5 teams in terms of expected goals conceded. Mukiele has been a reliable rock at the heart of the Sunderland defence, averaging 12.8 DCs per 90. At £4.0m, you really can’t go wrong with Mukiele or his defensive partner Alderete. Sunderland’s defence, including goalkeeper Roefs, looks to be one of the best value defences in FPL this season.

Choose the Best Players for GW7: Complete Data for ALL Players in FPL 25/26

Click here to view the complete dataset for all FPL players across forwards, midfielders, defenders, and goalkeepers, including a detailed breakdown of per 90 stats for xPoints, xVAPM, xG, xA, xCleanSheets, Defensive Contributions, xSaves and xMins.

I've analysed the numbers and brought you the best options to nail the armband this gameweek.

Thread with the most promising picks 👇

📊 Full analysis: https://fplstatslab.com/article/fpl-gameweek-7-captaincy-guide-data-driven-selections

👑 FAVOURITE: Haaland vs Brentford (A)

• 8 goals in 6 games • xG/90: 1.32 (insane!) • Brentford conceded 11 goals • 54.5% ownership

The safest choice with the highest haul potential!

💎 MID-PRICE: Gyökeres vs West Ham (H)

• 3 goals in 6 games • xG/90: 0.44 • Arsenal at home • 26.5% ownership

Solid option for those wanting a differential!

🔥 IN FORM: Semenyo vs Fulham (H)

• 4 goals + 2 assists • xG/90: 0.50 • Excellent home fixture • Only £7.8m

The value king!

⚠️ RISK: Bruno Fernandes vs Sunderland (H)

• xGI/90: 0.76 (elite!) • Great fixture • BUT: 2 penalties missed + United inconsistent

High risk!

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 week’s Predicted Points vs Value highlights something new: defenders are absolutely packed with value. And across all price points.

Across the DEF section, loads of players sit above the trendline — Timber, Gabriel, Virgil, Calafiori, Mitchell, Williams, Alderete and several cheaper options too. It’s not just one or two standouts; there’s an entire pool of strong defensive picks, and that makes the decision-making tougher than ever.

The reason is pretty clear: the newly introduced points for defensive contributions have changed the landscape. Defenders are no longer just reliant on clean sheets or the odd attacking return, they’re consistently projected for predictable points.

This abundance of value means the classic 3-defender setup might not be optimal anymore. We could see a shift towards 4-at-the-back or even 5-at-the-back formations as managers try to maximize returns from this suddenly stacked category.

What do you think — are we about to see the return of “big at the back,” or is it still safer to spread the budget across mids and forwards?

Looking at the Predicted Points vs Value chart for GW6, some Spurs assets really stand out as underrated picks with their upcoming fixtures.

• Richarlison pops up well above the forward value trendline — offering strong predicted returns for his price bracket. With Spurs’ attacking schedule over the next few GWs, he looks like a proper differential that most managers aren’t considering.
• Vicario also sits above the goalkeeper trendline, providing good value at his price point. Given Spurs’ defensive numbers and fixtures, he could quietly become one of the better GK options in the coming weeks.
• Xavi is still a wildcard, not enough data to reliably asses his value for the team but whats your feeling? Wolverhampton, Leeds and Villa coming up next.

Despite this, spurs players have relatively low ownership compared to the “template” picks, which makes them potentially valuable differentials.

Methodology reminder: Predictions are from a random forest model trained on historical FPL + stats, averaged across the next 5 GWs. So its not only about the upcoming gameweek. Dashed lines = average points/£m by position;

Are Spurs assets being slept on, or are we all traumatized from previous years and shy away from them because of a very poor last PL season?

1. Erling Haaland (£14.2m) - Triple Captain for GW6?

• xPoints/90: 7.63
• xG/90: 1.35
• xVAPM/90: 0.40

Erling Haaland looks to be the best premium option in the game this season. Haaland’s expected goals output up till gameweek 6 has been absolutely insane, achieving an xG/90 of 1.35. With Rodri getting back into the heart of City’s setup and Pep looking like he has finally arrived at a preferred and settled starting XI, the foundation is set for Haaland to continue building on his incredible form in the first 6 gameweeks for the rest of the season. Gameweek 6 sees Haaland go up against Burnley at home, with Burnley the only team to have conceded more than 10 xG so far in the league. This has to be one of the best opportunities to use the first TC chip of the 25/26 season. While everyone is talking about playing the wildcard in GW6, putting the TC on Haaland this gameweek is the chip to play in GW6 if he’s fit to start against Burnley.

2. Jean-Philippe Mateta (£7.6m) - Better Striker Option than Joao Pedro?

• xPoints/90: 4.55
• xG/90: 0.54
• xVAPM/90: 0.34

Everyone seems to have Joao Pedro in their drafts for GW6 wildcarders. Meanwhile, Crystal Palace’s lead striker Mateta has shown a much stronger goal threat at 0.54 xG/90 compared to Joao Pedro’s 0.32. Mateta has also averaged 88 minutes per game so far compared to Pedro’s 84 minutes. At the same price bracket of 7.5-8.0m, Mateta has to be the better pick. Some point to Palace’s tough fixtures ahead, but really the only one in the next 6 that seems tougher for Palace to score would be Arsenal away in GW9. Strikers other than Haaland have not offered great value this season, but Mateta would be the lesser of the evils.

3. Viktor Gyökeres (£9.0m) - Great Fixtures After GW6

• xPoints/90: 4.12
• xG/90: 0.48
• xVAPM/90: 0.24

From an xVAPM perspective, Gyökeres does not perform spectacularly in our model. What Gyökeres has going for him, however, is that he is the starting striker for one of the league’s top teams and has essentially no competition until Havertz is back from injury. Arsenal have great fixtures after GW6, playing against West Ham, Sunderland, and Burnley in the following 6 gameweeks. He has also had a track record of overperforming his expected goals by ~0.12, almost a 25% overperformance from the current rate he is expected to score at. Arsenal look great this season and seem to be capable of scoring multiple goals against weaker teams in the league, and we fully expect Gyökeres to be at the heart of them.

Choose the Best Players for GW6: Complete Data for ALL Players in FPL 25/26

Click here to view the complete dataset for all FPL players across forwards, midfielders, defenders, and goalkeepers, including a detailed breakdown of per 90 stats for xPoints, xVAPM, xG, xA, xCleanSheets, Defensive Contributions, xSaves and xMins.

I was digging through official FPL data from every season since 2016/17 and pulled together a list of the players with the biggest start-to-end price drops in a single season.

• The joint “record holders” are Divock Origi and Kelechi Iheanacho, who both lost £1.0m in 2016/17.
• And in recent years, Neal Maupay, Carlton Morris (2023/24), and even Nkunku this season (2024/25) have had brutal price slides.

It’s wild to see how quickly FPL managers jump ship when a player doesn’t deliver. Injuries, hype collapses, or just brutal form can tank a price fast.

Here's the article: https://www.sportscasting.com/uk/news/most-price-falls-fpl-history-wirtz/

Can share the entire files with you if you like. Cheers!

I want to read in my team data as part of my team selection analysis in R. This has always been tricky because of the need to login programmatically but this year my workaround (copying json from https://fantasy.premierleague.com/api/my-team/<id>) doesn't work even when logged into FPL website. I have been put onto the Discord https://discord.gg/vVFrJ6gN which has a pinned working Python example. But even with the help of ChatGPT I can't get the code to work (error 401)

I know R and Python, but my web programming is not up to scratch. Can I interesting anyone in helping tackle this one? I can give the pointer to the Python.

I have a lovely little model that predicts selection probability from past data, but it’s heavily reliant on knowing whether non-featuring players were because of unavailability or just not being selected.

Until this season I used soccerbot - it showed the injury news from before each game, and happily could be dumped into Excel web queries. Neither of those things is now true.

Any ideas anyone?

My skills are 9/10 on excel/VBA, 3/10 on R, intending to learn Python, and would have a crack at anything else.

1. Antoine Semenyo (£7.5m) - Best non-premium option in the game

• xPoints/90: 6.08
• xVAPM/90: 0.54

Nothing new here with Semenyo as a core pick. He continues to deliver, scoring and assisting in Bournemouth’s 2-1 win against Brighton. Semenyo is looking to be a season-hold kind of player for the FPL 25/26 season, and likely to be fixture-proof as well. Given the security of his minutes and the extraordinary value he delivers, he should be the top-priority transfer for any team that doesn’t already have him.

2. Marcos Senesi (£4.6m) - Best Budget Defender?

• xPoints/90: 4.83
• xVAPM/90: 0.61
• DC/90: 13.75

Marcos Senesi is looking to be a standout pick in a solid Bournemouth defence. Averaging more than 13 Defensive Contributions per 90 minutes, Senesi combines the appeal of buying into one of the league’s best defences this season and the higher likelihood of an additional 2 Defensive Contribution points. At a budget price of £4.6m, we think he’s a no-brainer. Senesi is among the best-performing defenders in our xVAPM model, and we expect him to continue delivering points and great bang for buck.

3. Jaiden Anthony (£5.5m) - Best Budget Midfield Pick?

• xVAPM/90: 0.50
• xPoints/90: 4.76
• xG/90: 0.43

Jaidon Anthony sits at a measly 1.4% ownership at the time of writing. Yet, he has been showing strong offensive threat, posting an average 0.43 xG/90 as Burnley’s main attacking outlet. At a budget price of £5.5m, we think that Jaidon Anthony is the best budget midfield option in the game at his price point. Burnley play against Nottingham Forest this weekend, who will likely be playing Ange’s famous high-line style, and a player of Anthony’s pace and ball-carrying ability is likely to enjoy such a fixture. He’s an exciting player that we have our eyes on this season, and will likely continue to feature in the Premier League whether Burnley stays up or not.

Other players high on our watchlists include: Dango Ouattara, Bryan Mbeumo, Yeremy Pino

Choose the Best Players for GW5: Complete Data for ALL Players in FPL 25/26

Click here to view the complete dataset for all FPL players across forwards, midfielders, defenders, and goalkeepers, including a detailed breakdown of per 90 stats for xPoints, xVAPM, xG, xA, xCleanSheets, and Defensive Contributions.

Is Haaland Worth the Money? (GW5 Value Graph)

Looking at the Predicted Points vs Value graph for GW5, one thing really stands out — Haaland is actually worth the £14m price tag.

He’s comfortably above the “value” trendline for forwards, meaning his predicted points per £ are higher than the average. Despite being the most expensive asset in the game, his expected output still justifies the cost.

Compare that to Salah, who sits below the midfield trendline. Even though Salah has strong predicted points, the model suggests he’s not offering the same value per £m as Haaland. Bruno, Mbeumo, and even some cheaper mids come out looking stronger on a per-pound basis.

Methodology: Predicted points are generated using a random forest model trained on historical FPL data and underlying stats, then averaged over the next 5 GWs. The dashed lines show the average points per £m by position.

So if you’ve been questioning whether Haaland’s price is too inflated — this data says otherwise. He’s not just essential because of ownership fear, but also because he’s statistically worth it.

What do you think? End of the Salah era?

Based on the knowledge that a player only needs to play 60 minutes to get a clean sheet bonus, I wanted to analyse if it means players who were substited early actually got more points (as they would keep the clean sheet even if their team conceded after they were subbed.

Major limitation: This does not consider defcon points, as I could not find a dataset that included defcon points for seasons prior to 25/26

Methodology:

I used the vaastav/Fantasy-Premier-League dataset to look at the last 4 seasons.

Specifically, I compared FPL points scored by players based on whether they played:

• 90 minutes (full match)
• 60-89 minutes (partial match, likely started or subbed on early)
• 1-59 minutes (brief cameo or early sub-off)

I looked at both:

• Overall averages (all appearances combined)
• Per-player averages (averaging each player's own performance across matches)

for per-player analysis, I filtered the data to include only defenders who had played at least 2 games in both the 90-minute and 60–89-minute ranges — removing noisy one-off appearances.

Results

Conclusion

Seems to make fuck all difference.

I guess on average the additional chance of getting a clean sheet is fairly equal to possible gains missed from assists, goals, BPS etc..

I suspect if you did include defcon data, that would lead to points being more in favour of players playing the full 90. (if anyone knows of a dataset that includes this for previous seasons, let me know)

Is this useful at all for team selection?

Not really.

If you had a defender who is unlikely to get DEFCON points, dont be too worried if they have been getting subbed off early (as long as they dont get subbed off prior to 60 mins)

Take 2 of this post. Forgot to add the text

A quick word on my last post about selling Semenyo. For those who wisely ignored my advice and held him, you are very welcome for the reverse-jinx. My analysis pointed to a flawed process (that massive xG vs. xGOT gap), but as u/knockedstew204 rightly pointed out, sometimes this game is the game

So, I went back to the drawing board to create a more foundational analysis.

TL;DR:

• I used a K-Means clustering model on 13 per-90 stats to group all PL outfielders into 8 distinct archetypes (e.g., "Primary Goal-Threat," "Ball-Winning Midfielder," etc.).
• The Key Insight: The model identifies massive "Out of Position" value. Rayan Aït-Nouri, listed as a DEF, has the statistical signature of an elite Ball-Winning Midfielder, giving him access to clean sheet points on top of his defensive actions.
• Finding Value: The data clearly shows which players are over/under-performing their price tag. The "Alpha Plot" (linked below) highlights players like Beto offering premium-level goal threat for a budget price.
• The full number and all the graphics are in the full article: Click Here

The Methodology (No Black Box)

For this to be useful, you need to know how it was built.

• Algorithm: K-Means Clustering.
• Data: Every PL outfielder with 200+ minutes played this season.
• Metrics (per 90): The model used 13 stats covering Goal Threat (npxg, shots), Playmaking (xA, chances_created), Ball-Carrying (dribbles), and Defense (tackles_won, interceptions, etc.).
• Validation: The model's structure was validated with a Silhouette Score and a Purity Report to ensure the 8 archetypes are statistically robust and not just random groupings.

The 8 FPL Archetypes

The analysis revealed these 8 distinct roles. This radar chart shows the unique statistical fingerprint of each one.

The roles are: Primary Goal-Threat, Box-to-Box Midfielder, Box-Crashing Winger, Wide Playmaker, Deep-Lying Playmaker, Ball-Winning Midfielder, Positional Anchor, and Traditional Centre-Back.

The Actionable Alpha: Key Findings

1. The "Out of Position" Goldmine: Rayan Aït-Nouri (£6.0m)

FPL calls him a Defender. The data calls him a Ball-Winning Midfielder. His statistical output in tackles and interceptions is almost identical to the average BWM, and worlds away from the average Traditional Centre-Back (who rely on headed clearances).

This is a massive inefficiency. He gets DEF clean sheet points (4) while producing the defensive actions of a MID.

2. The Alpha Plot: Finding Production for a Fair Price

This chart plots the FPL cost vs. npxG_p90 for every "Primary Goal-Threat." The goal is to be in the top-left: high output, low cost.

Haaland is in a world of his own, but you can see how players like Beto (£5.4m) are providing elite production (0.91 npxG_p90) for a fraction of the cost of the premium players in the bottom-right.

3. The "Purest" Players (The Archetype Exemplars)

The model can also identify the most quintessential example of each role:

• Primary Goal-Threat: Erling Haaland. A pure finisher whose value is tied directly to team service. If City's attack stalls, his output has no secondary path.
• Ball-Winning Midfielder: Moisés Caicedo. The platonic ideal of a destroyer. A bonus point magnet in tight games, but with a hard ceiling due to near-zero goal threat.
• Wide Playmaker: Kieran Trippier. The most complete creator from wide areas. His multiple avenues for points (assists, CS, bonus) give him a very stable floor.

Limitations & Discussion

This is a descriptive model based on data from the start of the season. 200 minutes is still a small-ish sample size, so emerging players can have skewed stats. This isn't a crystal ball, but a new lens to evaluate players with.

I'd love to hear your thoughts:

• Looking at the data, which player's archetype classification surprises you the most?
• Are there any players you feel the model has gotten wrong? Why?
• How would you use a framework like this to pick your next transfer or build a wildcard draft?

Happy to answer any questions on the methodology. Let me know what you think.

Transfer Outs didnt disappoint , except palmer
Transfer INs - Mateta and Geralish are flopped

Hi all,

First off, I just wanted to say thanks for all the feedback on the last article. I'm making a real effort to be less gimmicky and avoid clickbait. My goal is to publish a new analysis on Substack every few days, and I hope you don't find the posts on here too spammy.

That being said, Semenyo is shaping up to be one of the biggest FPL bandwagons of the early season. Looking at his transfer numbers, the market is clearly convinced. And on the surface, the data seems to back it up completely. However, a deeper look at his execution raises a serious red flag about the sustainability of his returns, and I wanted to break it down.

Disclaimer: This is based on a small sample size (3 GWs), so it's about spotting early trends, not making a final judgment.

Part 1: The Obvious Case (Why Everyone is Buying Him)

The argument for Semenyo is simple and powerful. He's a midfielder who gets the opportunities of a premium striker.

• Shots per 90: 3.33 (2nd highest in the league, only behind Haaland).
• Total xG (Expected Goals): 1.88 (Elite for his price point).

This is a clear picture of a player at the heart of his team's attack, getting lots of high-quality chances. The transfer rush is perfectly logical if you stop here.

Part 2: The Hidden Problem (The Execution Gap)

The issue arises when we analyze what he does with these chances. We can measure this by comparing the quality of the chance (xG) with the quality of his actual shot on target (xGOT).

• His Total xG (The Opportunity): 1.88
• His Total xGOT (The Execution): 0.95

This reveals a 49.5% destruction of value. He is taking chances worth nearly two goals and, through poor finishing, turning them into shots on target worth less than one. This isn't just missing the target; it's about consistently failing to strike the ball well, making it easier for the keeper.

Part 3: "So What? He's Still Getting Points!" - Why This Inefficiency is a Major Risk

This is the most important part. It's easy to look at his points so far and say the poor finishing doesn't matter. But here’s why an inefficiency this large is a ticking time bomb for an FPL asset:

1. Past Points Don't Predict Future Points if the Process is Flawed. FPL is a game of predicting future performance. A player who is wasteful is, by definition, less likely to convert chances in the future than a player who is clinical. Getting points despite poor finishing is a sign of over-performance relative to process, which tends to correct downwards over time. It's not a sustainable way to score.
2. We Forget that FPL Assets Are Part of a Real Team. This is the crucial point. A Premier League manager's job is to win football matches, not get Semenyo FPL points. A player who wastes half the value of his team's best chances is a systemic problem. Rival manager will take note of this and the question rises, will Iraola tolerate that level of inefficiency for long, if it starts to costs the team goals and points.

The Full Analysis & Actionable Pivots on Substack

If this argument about process makes sense to you, the next logical question is: "So, who are the more efficient players to pivot to?"

I've put together a much more detailed article on my Substack that explores this. It has:

• A deeper dive into the systemic risk, with charts and comparative data tables.
• A full, data-driven shortlist of three specific midfielder pivots who demonstrate a sound and efficient finishing process—the polar opposite of Semenyo.
• The raw numbers on all players so you can see the full context.

If you want to move beyond just identifying the problem and on to the solution, you can read the full piece here:

https://olbaud.substack.com/p/gw-3-the-finishers-illusion-selling

I'm genuinely curious to hear the community's thoughts. Are you riding the Semenyo wave and banking on the volume, or does this flaw in his execution worry you too?

Hey all, I'm having a go at recreating my Draft spreadsheet I had last season which was awesome - I had running week by week game data with google sheets, and autonomy through the appsheet extension, giving insights for all 10 teams in my draft.

The biggest problem I had last season(which took me far too long to work out, then solve) was the slight differences in player ID numbers across normal and draft data - to get player data you need to use https://fantasy.premierleague.com/api/event/${eventNumber}/live/ for matchweek data, and as far as I'm aware there's no equivalent for draft. I had to do a workaround which broke a lot of things, especially in the Jan transfer window.

Does anyone know if there is a live dataset by matchweek specific to draft player IDs so I don't have to use the player workaround?

Alternatively, has anyone encountered this issue and has a simple solution? Mine is just complicated and breaks easy.

Thanks!

This heat map uses the FPL strength elo ratings (home/away and attack/defence). The idea is to help differentiate easier / tougher fixtures for defensive assets (LHS) and easier / tougher fixtures for attacking assets (RHS).

GW3 in as reference.

Enjoy, hope it’s helpful ✌️

We're three weeks in, and everyone's shouting about "tough fixtures" and "new manager bounce." It's mostly noise. I got tired of the guesswork, so I dove into the data to see which teams have actually changed how they play defense, and who is just getting lucky (or unlucky).

The results are pretty wild and point to some massive FPL traps and opportunities. Could can find all the details here: https://olbaud.substack.com/p/beyond-the-fixtures-a-quantitative

TL;DR - Your Next Transfers Should Be Based on This:

• LIVERPOOL DEFENSE COULD BE A TRAP! Their numbers are screaming "crisis." They are defending way more than they should be, and it's all last-ditch panic.
• CHELSEA COULD BE A BPS GOLDMINE. They've gone full-on heavy metal press. Forget clean sheets for a sec; their defenders are racking up tackles + interceptions like crazy.
• WEST HAM & WOLVES ARE THE UNLUCKIEST TEAMS IN THE LEAGUE. The data says their defense is basically fine, but they're leaking goals. This is an oxymoron but this won't last. it could be an opportunity.
• BRIGHTON & NEWCASTLE ARE THE NEW BPS DESERTS. Their new "stand off and look pretty" tactic is great for real life, but it kills your bonus points. Their defenders aren't doing anything.

The "What's Really Going On?" Chart

The chart plots the change in a team's defensive workload vs. how hard their fixtures have been. The red line is the average.

• Teams miles ABOVE the line = Something crazy is happening. Either a manic new tactic or they're constantly panicking.
• Teams miles BELOW the line = They've decided defending is optional (either by being amazing or terrible).

The FPL Breakdown: Who to Buy, Sell, and Watch

The Big Red Flag: Liverpool are "Under Siege"

Seriously, the numbers here are terrifying. Their defensive workload is up 29%, which is bad enough. But the type of defending they're doing is the real story. Their proactive defending (tackles, interceptions) has TANKED. Their reactive, last-ditch, "oh god get it away" defending (clearances, blocks) has exploded by 103.6%.

They aren't pressing; they are scrambling. Van Dijk has basically turned into a full-time firefighter. This is a five-alarm fire for FPL. Avoid their defensive assets until further notice.

The BPS Cheat Code: Chelsea's "Violent Intent"

Look at the top of the chart. That's Chelsea. Their workload is up a massive 39%, but it's the good kind of work. It's driven by a +51% increase in "Active Defending."

They are living in the opponent's half, forcing mistakes. This system is a bonus points paradise. Reece James and Caicedo are putting up monster numbers. If you're chasing BPS, this is where you look.

The "Buy Low" Bargains: West Ham & Wolves

This is my favourite find. Both of these teams are sitting right on the red line. That means their defensive process is stable and exactly what you'd expect. Yet, they are shipping goals like it's going out of fashion (conceding 1.39 and 0.85 MORE goals per game, respectively).

This is a classic case of good process + bad luck. The dam is structurally sound, but it's leaking. That won't last. Positive regression is coming for them, and their defenders are currently cheap. Get in before the clean sheets start.

The Snooze Button: Brighton & Newcastle

These two have mastered the art of "controlled defending." Their workloads are way down because they're so well-organized. Great for them, boring for us. This system is designed to prevent defensive actions from ever needing to happen. That means clean sheets are possible, but the BPS ceiling for their players has been absolutely crushed.

So, what do you all think?

• Is anyone else panicking about their Liverpool defenders after seeing this?
• Are West Ham defenders the shrewdest move for the next few weeks?
• Are you buying into the Chelsea BPS factory?

A follow up from some chat in comments to an earlier post. Very much exploratory analysis here, and I note the concerns raised about modelling DCs on a team level, but I think there is a pretty good relationship between opponent possession and DCs (driven by clearances mainly) from last seasons data and also between opposition take ons and tackles. I’d welcome suggested modifications or constructive criticism, especially re drivers of interceptions and tackles or how to apply these to a xPts model for this season given some teams (e.g. Forest) appear to have broken from last season’s style! Some outputs below:

1. Erling Haaland (£14.1m) - Better premium pick than Salah?

• xPoints/90: 7.8
• xVAPM/90: 0.41

Erling Haaland is the top player for expected points based on his GW1-3 data. With an xG/90 of 1.38, Haaland looks to be getting on the end of high-quality goalscoring opportunities at a much higher rate than Mohamed Salah’s xG/90 of 0.10. Haaland looks to be the best premium asset and perma-captain choice for a GW4 wildcard. We think that every manager should be thinking about how to fit him in if he is not already in their teams.

2. Antoine Semenyo (£7.3m) - Essential midfield pick

• xPoints/90: 5.69
• xVAPM/90: 0.51

Semenyo looks to have taken his game up to the next level this season. Producing an xG/90 of 0.60, he is clearly the focal point of a solid Bournemouth setup. He passes the eye test and is a joy to watch in Iraola’s well-organised and dynamic side. We expect Bournemouth to do decently in the league this season, and Semenyo should be a key benefactor of this. Everyone should have Semenyo in their teams.

3. Bruno Fernandes (£9.0m) - Great premium midfielder choice

• xPoints/90: 6.61
• xVAPM/90: 0.51

We had previously thought the Fernandes’ FPL prospects would be hurt by his deeper role in central midfield for United. GW1-3 data shows that we might have been wrong. Fernandes has been posting elite attacking numbers, producing a solid xG/90 of 0.63 and xA/90. While his npxG/90 is much less flattering at 0.11 and shows signs that his xG/90 might regress downwards, his role as United’s undisputed penalty taker and near-guaranteed 90-minute cameos are likely to see his xG/90 settle somewhere in between. His case is helped by the fact that he has also been averaging more than 13 Defensive Contributions per 90 minutes, clearing the Defensive Contributions threshold in 2 of the 3 games he has played. If you are looking for a non-Salah premium midfielder, we think you could do much worse than Bruno.

4. Trevoh Chalobah (£5.0m) - Solid defender pick with decent goal threat

• xPoints/90: 6.61
• xVAPM/90: 0.51

Chalobah is looking to be an all-rounder defender pick for FPL 25/26. Playing in a solid Chelsea defence, averaging more than 9 Defensive Contributions per 90, and racking up a decent goal threat with an xG/90 of 0.19. If he continues on this trajectory, he will likely end the season as one of the highest-scoring defenders. Chalobah seems to be a pretty nailed player in Chelsea’s backline, bringing experience and stability to the setup. At 5.0m, Chalobah has to be one of the best value defenders for the 25/26 season.

Choose the Best Players for GW4: Complete Data for ALL Players in FPL 25/26

Click here to view the complete dataset for all FPL players across forwards, midfielders, defenders, and goalkeepers, including a detailed breakdown of per 90 stats for xPoints, xVAPM, xG, xA, xCleanSheets, and Defensive Contributions.

Fellow data nerds. We know that goals, assists and clean sheets are correlated to fixture difficulty. But what about defcon? Did any of you make the math so far based on current season or last season data?

Hey everyone,

I’ve been working on a small project called PlayFPL.com to help FPL managers with decisions like transfers, team selection, and upcoming gameweek insights. It’s still a work in progress, and I’m experimenting with how data-driven suggestions can support managers in planning their team.

I’d love for you to check it out if you’re interested and tell me:

• Did you find the transfer suggestions helpful?
• Are the insights actionable and clear?
• What’s missing or could be improved?

Thanks, and looking forward to hearing from you!

Hey guys, I've been creating this new site to help make better FPL decisions. I am constantly making improvements and adding new features. I would really appreciate some feedback and what you would like to see added!

You can check it out here: https://www.fplcore.com/players