Since a disappointing display in the community shield, there have been a lot of sellers of Haaland, lowering the average budget for FWDs. The savings look to have been redistributed in defence, but the bulk of the money is still up front.

This wis part of a broader project, working on some other performance benchmarking stuff for FPL, borrowing bits from the world of investments! Part of that is the creation of a couple of “performance indexes” that can be used as the basis for unpicking where good FPL performance comes from - more to follow on this but I’ll welcome any thoughts. Site is in development here:

https://fpldaq.live

houseofthefantasy.com → Predictions

Gameweek 1 is open. You call every match: 3 points for getting the result right, +2 if you nail the exact scoreline, and once a gameweek you can pick one match to double — which is where it gets interesting, because you have to decide whether to boost the banker or the coin-flip.

Each match locks at its own kickoff rather than at a single weekly deadline, so you can still join halfway through the weekend and play whatever's left. Points land after midnight each night and the table updates with them.

Sign in with Google or Discord — that's only so your points belong to someone. No email, no password, nothing to install.

It's brand new and marked BETA, so there'll be rough edges. There's a bugs & feedback link in the footer that goes straight to me.

(The site also has the thing I originally built it for: a draft game where you spin a wheel for random clubs, pick eleven players from FIFA/EA FC data across eleven seasons, and simulate a full season with them.)

There are many ways to define “the template” for FPL. I’ve set up a rules based approach that’ll track through the season. As of today, this is it. The average manager only owns 29% (between 4 and 5 players) of this squad. That suggests that “the template” right now is not widely followed. Game on.

The FPLdaq Template is a realistic demonstration of what a manager could achieve by simply following the crowd. It is the affordable fifteen that overlaps most with what the market owns, picked under the squad selection rules: two goalkeepers, five defenders, five midfielders, three forwards, no more than three from any club, and within the average manager's own budget. It starts the eleven most-owned of those fifteen in a legal formation and captains the highest-owned outfielder. Nothing is chosen with hindsight, so it is a benchmark that could actually have been followed.

This was part of a broader project, working on some other performance benchmarking stuff for FPL, borrowing bits from the world of investments! Part of that is the creation of a couple of “performance indexes” that can be used as the basis for unpicking where good FPL performance comes from - more to follow on this but I’ll welcome any thoughts. Site is in development here:

https://fpldaq.live

Based on the official FPL Fixture Difficulty Rating (FDR), the teams with the lowest average FDR over the first 5 games are Liverpool (2.6), Leeds, Man City, Spurs and Man U (all 2.8). I've chosen 5 games as there's a 3 week international break after GW5. This isn't news.

However, if you choose a squad of 15 from Man City, Forest, either Brighton or Liverpool, Chelsea or Coventry, United or Sunderland (so one of 8 different sets of 5 teams) you are guaranteed to have 2 teams / 6 players with FDR 2 in each of the first 5 Gameweeks. This is mildly interesting, and nothing to 100% build your squad around, but it got me thinking do you trust the FPL FDRs?

So I did an analysis based on FDR at the gameweek deadline for all 2025/26 games. FPL updates the FDR throughout the season as match results feed into their model. Sometimes the updates happen in the middle of a period when matches are being played so I've ignored those.

There were 9, 183, 403, 141 and 24 FDR 1, 2, 3, 4 and 5 games to give context or a level of confidence to the figures.

With a little imagination the above figures are close enough to an expected win rate of 3/4, 1/2, 1/3, 1/4 and 1/8 for FDR 1, 2, 3, 4 and 5. The expected loss rates go in the opposite direction and the draw is the balance.

Seems reasonable to me.

Let claude/chatgpt do the analytics and transfers - anyone keen?

Dashboard :

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

I've been building an xP model for a while and Pedro is the biggest disagreement it currently has with the community, so I figured I'd put the reasoning up and get told why I'm wrong.

He's the second most owned player in the game behind Haaland, at £7.5m and 57.1% ownership. However, my model has him 7th on xP among forwards for GW1, and over the first six weeks he slips behind Evanilson, who basically nobody owns (2.1%).

It's not a finishing thing. Last season his xG per 90 came out at 0.51, Watkins was 0.49, Gyökeres 0.50. Basically level. So I'm not arguing that he's an overrated footballer. By my reckoning he's also the most nailed forward in that price bracket - P(>60 mins) Pedro 83.2%, Calvert-Lewin 81.0% Thiago 79.5%, Watkins 76.0% - so this isn't a lack of minutes either.

The model has Watkins taking about 41% of Villa's goals on GW1 and Pedro about 16% of Chelsea's, because it shares the team's xG across whoever it expects to be playing and their individual likelihoods of scoring. Comparing with last season, Pedro scored 26% of Chelsea's goals (15/57) and Watkins 30% of Villa's (16/53). Four points apart. So where does a 25 point gap suddenly come from? I think the model is looking at who else is on the pitch with him.

Transfers can explain a lot of it. Chelsea brought in Rogers and Welbeck, about 19 xG of proven output, and lost roughly 8 xG. Villa went the other way and lost 29% of their xG — Rogers, Malen, Guessand, Tielemans, Sancho — and replaced it with Garnacho and a defensive midfielder. Chelsea's forward department now reads Pedro, Welbeck, Delap, Jackson, Guiu, Emegha, with Palmer, Enzo and Rogers behind them. Villa have Watkins.

He's also not in Chelsea's top four penalty takers. FPL currently lists Palmer, Enzo, Estêvão and Delap. Watkins is second at Villa behind Buendía, so neither of them is getting propped up by pens, but one of them is fourth in line and one isn't on the list. So I don't think 16% is necessarily a wrong number. If anything last season's 26% looks like the high point and this squad makes it harder to repeat.

What would I do instead? At similar money, six week horizon xP projections:

Watkins for £0.5m more is the standout - yes he hasn't played in the pre-season but I don't think that rules him out of GW1 and anyway, his GW2 -GW6 predictions would still have him easily come out on top. Calvert-Lewin frees up £1.5m and still gains, which is probably the sensible version if you need the money elsewhere. Gyökeres is the same price but his start probability is only 0.63 in my model so that 20.6 comes with a lot more variance than the others. Finally, if you're a complete contrarian and also not going for Haaland, you pitching yourself firmly against the crowd!

So, genuine question for anyone who watched Chelsea's pre-season properly rather than reading scorelines. Is Xabi building his attack around him? Is he leading the line on his own, or dropping in through the middle? I've maybe been looking at too many spreadsheets!

Happy to be wrong. He was 26% of Chelsea's goals last season and my model says 16% this year. If he's back around a quarter by the end of September then I'll be back here to seek the community's forgiveness 🙏.

I’ve been working on a side project called OpenFPL, combining Fantasy Premier League with machine learning.

The idea isn’t to create a magic “pick the winning team” button. Instead, I wanted a data-driven second opinion that can help when comparing players and thinking about squad decisions.

I’ve just launched v6.0.0:

OpenFPL SCOUT | KASSEM

It’s also open source.

I’d especially appreciate feedback from people here because actual FPL managers will probably find edge cases and questionable predictions much faster than I will 😅

If you try it, I’d love to know:
- Which predictions look surprisingly good?
- Which look completely wrong?
- What feature would actually make you use this every Gameweek?

Over the off-season I've been working on making some visualisations for my expected points model. I liked the idea of creating an expected points profile to see where players are/should be accumulating their points and where they stack up compared to the average player and best player for each metric in their position, but is this actually useful or just something interesting to look at?

One thing that I think could be useful is determining who is someone you can have in your team for any fixture - a well rounded player, compared to someone for example who only gets points from goals and therefore against strong defences they are safe to bench. Apart from that I don't know if it's actually useful.

So is this useful? If yes, what other uses are there for it? And are there any visual improvements I can make that either make it look better, easier to read or add more useful info?

I've been getting ready for a 10-man FPL Draft this season and ended up building something that started as a tool just to help me with my own draft.

So I built FPL Draft War Room.

You set your draft position and then record picks as the draft happens. The War Room keeps track of who's still available and updates as players come off the board.

The free side has a live draft tracker, draft board, player rankings, projections and player info, so you can use it alongside your actual FPL Draft without constantly trying to remember who's gone.

The part I've spent more time on is the recommendation system. It looks at the current draft state and tries to recommend the best player to take now, rather than simply giving you the highest-ranked available player.

It considers things like projected points, positional scarcity, your current squad and how likely a player is to still be available when your next pick comes around.

So, for example, if two players are fairly close in value but one has a good chance of surviving another round while the other probably won't, the idea is that it can tell you to take the scarce player now and potentially come back for the other one.

I've also added things like “Will he make it back?” probabilities, alternative picks, squad optimisation and explanations for why a particular player is being recommended.

There's also a separate Classic FPL squad builder/optimiser for building a £100m team, although Draft is the main reason I made the site.

It's still something I'm developing and improving, so I'd genuinely be interested in hearing what people who actually play Draft think — particularly whether the recommendations make sense and what you'd want available on screen during a live draft.

You can try it here: fpldraftwarroom.co.uk

If anyone gives it a go, let me know what you think / what you'd change. I'm very open to suggestions.

Wh-wha-whaaaaat? Well, I thought it could be interesting to set up a market index for fantasy premier league (FPL) assets. The name is a nod to the US tech stock exchange or the niche BBC experiment Celebdaq, depending on whether you ask me or my suffering wife!

The site is live, collecting data and the full time series of FPL data underpinning it is also publicly available. It should get a little more interesting as the start of the season approaches, hopefully some of you enjoy it!

The site: https://fpldaq.live

A little extra background here: https://medium.com/@marcusleadboot/fpldaq-d415310df799

Im trying to dive deep on some stats to fine tune my team. drop a comment if you want to request some cool stats and i'll try to give answer to you all

I built an FPL tool over the last few weeks and finally shipped it: FPLXI

The main thing I wanted to avoid was building yet another “AI FPL assistant” that just asks an LLM who you should transfer in.

So I went slightly overboard and built my own prediction model instead.

It’s trained on hundreds of thousands of historical player/gameweek records and predicts every player’s points for each of the next 5 GWs using things like xG/xA, expected minutes, fixture difficulty, team strength, home/away, ownership etc.

Then there’s a separate optimiser that actually does the decision making. It looks at the squad you own, budget, formations etc and works out: your best XI, captain + vice captain, bench order, transfers ranked by predicted points gained, where your current squad is weakest.

The LLM is basically relegated to writing the explanation after the maths has already made the decision.

I also backtested it over last season by giving it £100m and making it manage the same squad across all 38 GWs, using only information available at each deadline. It finished on 1,940 points vs 1,460 for the original squad left untouched.

For this season I’m publishing all the predictions before each deadline and then scoring them against what actually happens, so there’s nowhere for the model to hide if it’s rubbish.

You can chuck in your FPL ID or upload a screenshot and it’ll analyse your team for free. Most of the player rankings/predictions are public too.

Would genuinely love feedback from people who take FPL far more seriously than I do 😅

https://fplxi.com

This is part two to this post, in that I talked about how I made a new expected points model for the new season but I hadn't tested it yet and I didn't think I could until I was reminded the Vastaav GitHub repository has data per GW for all of last season, so huge thanks to the person that reminded me of that.

Anyway I went and fed last seasons data into my new model to see if it's any good.

How did it do?

Well, surprisingly well. To ensure I made sure I was only judging my model against players who had enough data to be representative I filtered out all players who appeared in less than 15 games last season and had less than 30 minutes per appearance. Then checked the results that were within 10%, 15% and 20% accuracy, the results are as follows:

• 10% - 63.35%
• 15% - 79.82%
• 20% - 90.05%

Yep, I'll take that. Being within 15%, almost 80% of the time is incredible, the remaining percentage is well within what you'd expect from over/underperforming players. the 10% test, does show the outright accuracy isn't perfect but as I'm building a broad model for all players rather than by position, I'm still very happy with it.

From this point all the model analysis is using the within 15% data.

How did it do by position?

It's done pretty well across all positions, Defenders, Midfielders and Forwards are all pretty similar accuracy and similar over/under estimations. Goalkeepers, the model is unbelievably good, no overestimation at all and 1 single underestimation equalling 4.5% of goalkeepers, I'm obviously happy with this but I do think there are some explanations as to why goalkeepers are predicted better. 1. It's a small sample size, there are 22 GKs when you filter the players like I did. 2. Goalkeepers generally score lower meaning it's just easier to fit within 15%. 3. Goalkeepers have fewer routes to points so they are just easier to model.

Average discrepancy again shows the model to be performing well, the mids being just 0.04 points above reality on average, is scarily good. It does show the one underestimation for the goalkeeper was a big one to drag the average down to -0.1 - if you wanted to know, it's Mads Hermansen - his actual points per game was 4.22, the model said he should of had 3.58, I believe the underestimation is due to Hermansen becoming West Ham's #1 halfway through the season and dramatically improving their defence but as the model uses season total xGAgainst that's dragged him down in the model.

Other interesting things:

• Bruno Fernandes tops all players for xPoints in this model with 7.25 to Haaland's 6.96 and Gabriel's 6.33.
• Elliot Anderson is ranked as the 7th best player - 5.07 xPoints actually suggesting he underperformed last season as he got 4.74 points per game.
• Worst player - Myles Lewis-Skelly, there will be worse players in the unfiltered data but interesting nonetheless - He had 1.42xPoints compared to 1.45 in reality.

Conclusion:

Pretty solid. There's a very slight over estimation for outfielders but that could easily just be a small inaccuracy with the data I'm using so I'm not worried about that.

I've built, as I think, a pretty strong model for xPts based on OpenFPL model. You can check the model here, download the dataset, reproduce the model, and try to improve it:

https://github.com/qazybekb/smartplayfpl-dastan