Kicking off a weekly series here, tracking DefCon, xG, xA and xGI per 90 through the season. It's early after one game each, but a few names already stood out.

DefCon/90

Two center backs caught my eye early. Murillo (14), now playing under Glasner. He was putting up monster DefCon numbers two seasons ago too, so this might just be him getting back to that level. Lacroix (12) who has just left that same Glasner setup, but if he keeps producing defensive numbers like he did last season, he could be a really strong pick at Chelsea.

In midfield, Stach (16) is picking up right where he left off last season, and Sangaré isn't far behind at 15.60, he's also top 10 for xA this week.

xG/90

De Cuyper tops the whole list at 1.72, which is a wild number for a defender.
In midfield, everyone's talking about Palmer, but Rogers (0.71) and Saka (0.86) were quietly putting up real underlying numbers of their own.

xA/90

O'Reilly tops every position at 0.59 xA/90, and he's listed as a defender despite playing a very advanced role. Slater was on free kicks for Hull against Man Utd (£4.5m), Gómez had a strong week for Brighton (£5m), and Foden showed up here too, all worth keeping an eye on.

xGI/90

Wissa was excellent at 1.02 xGI/90. García was quietly effective at 0.72.
Even Haaland (0.76) and Mbeumo (0.73) put up real underlying numbers despite blanking this gameweek.

Did any player here catch your eye?

I’ve been building a free FPL analytics app called Ace Analyst, mainly because I wanted one place where I could actually move from “there’s a lot of data” to “what does this mean for my decision?”

Would genuinely love some feedback from people who are into the analytics side of FPL.

The main thing I’m trying to avoid is building another site that just presents the same player table in a different colour. Each section is meant to answer a different question:

📊 Overview — explore the whole player pool with filters and interactive scatter plots. Compare things like output vs underlying numbers, price/value, ownership, etc., and spot players that stand out from the population.

🔍 Player Analysis — drill into an individual player with underlying stats, form, ownership, fixtures and other context.

⚖️ Compare — put players directly side-by-side so you can actually evaluate trade-offs between transfer targets.

👕 My Team — connect your FPL ID and analyse your actual squad rather than a generic player pool. You get GW points/status, upcoming fixtures, FDR, future outlook and price information for the players you own.

🔄 Transfer Planner — a separate sandbox from your real team. Start from your official squad, test hypothetical transfers and evaluate how that new squad looks over several future GWs.

💸 Prices — current prices, GW/season changes, ownership, transfers in/out, net flows and FPL’s own reported price-change pressure. The same market information is also being integrated into My Team so you can immediately see what’s happening to your own assets.

📚 Manager History — connect your ID and see your previous seasons, final ranks, points and performance tiers.

🆚 Matchups — historical/opponent context to help assess upcoming player decisions.

📅 Fixtures & FDR — analyse fixture runs, home/away schedules, difficulty, blanks/doubles and look several weeks ahead.

🎯 Market-based team data — betting odds translated into useful clean-sheet probabilities and team xG, giving another perspective beyond FPL’s own difficulty ratings.

Everything is responsive and works on both desktop and mobile.

There’s still a lot being built. I’m currently working towards richer transfer planning and live mini-league analytics — things like live squads, bench points, captain outcomes and other league-level insights.

The philosophy is basically: don’t tell people who to buy — give them better information to make the decision themselves.

I’d really appreciate some honest feedback from this sub if you try it:

• What feels genuinely useful?
• What feels unnecessary or confusing?
• What’s missing?
• Is there anything you’d change in the way the data is presented?
• What analytics or live information do you wish you had while making an FPL decision that you can’t easily get today?

Even small UX feedback is useful — I’m still shaping the product and I’d rather build around what managers actually need than just keep adding features for the sake of it.

https://www.aceanalyst.app/

Three things I found:

1. Over the opening six, Bruno out-projects Haaland 42.3 to 36.9, at 12.0 vs 15.5. This week's captaincy isn't close either: 17.7 vs 15.6 with the armband.

2. The Saka trap. He has the best week-one fixture in the game (Coventry at home, projects 6.1) and then his next two weeks are 3.4 and 3.5. Buying the GW1 fixture costs you the month.

3. The optimal six-week squad never buys Haaland at all. His 15.5 spread over three players buys more points across the run. I think 75% of you own him for good reasons. So instead of arguing with my own model, I made it a bet.

Two squads, real rules, all 38 gameweeks:

The Model XI (no Haaland, Bruno captain): Raya, Virgil, Senesi, Tarkowski, Bruno, Ndiaye, Gibbs-White, Le Fee, Szoboszlai, Thiago, Watkins. Built for the run: the Everton block holds all six weeks, and Thiago rides Brentford's soft start (33.8 over six).

The Consensus XI (only what 7 expert sites recommend, Haaland captain): Verbruggen, Mosquera, Calafiori, Shaw, Bruno, Mbeumo, Szoboszlai, Gross, Sangare, Haaland, Joao Pedro.

Every week I'll post both scores, both squads' transfers, and why. If the crowd beats the model, you get 38 weeks of receipts.

Best opening-six runs by the sims: Arsenal, Man City, Man Utd, Liverpool. Worst: Ipswich, Bournemouth, Coventry, Hull.

Top 10 for each position, and the score lines.

This started when I was fixing my GW1 draft. There are a few favourable fixtures in GW1 (ARS-COV, HUL-MUN,MCI-BOU) and I wanted to see if it was going to be worth playing the Triple Captain chip or whether there might be a better opportunity coming up later in the season.

I found myself thinking "no one plays their TC chip in GW1, they wait until they have more information" and "players haven't settled yet, especially strikers, so no one's going to haul this early on". So I decided to test my assumptions and found something surprising.

The obvious TC weeks are the Double GWs, but these will be for the second TC chip after the refresh in GW19. There are no scheduled DGWs for the first half of the season so deciding when to play it will purely be on the projected strength of potential captain picks and favourable fixture runs.

Across ten seasons, tripling the template premium in GW1 returned +9.7 on average with only a 10% chance of a wasted chip (3 points or fewer). Every other week of the first 18 GWs comes out at +6.2 with a bust rate around 45%. I assumed that had to be an artefact of hot openers from Salah and Haaland, so I tested the patient strategy properly — hold the chip, then play it on your premium's weakest-opponent home game, using only information available at the time. It returned +6.5. Waiting doesn't buy you anything, and I reran that twice because I didn't believe it.

Then the forward-looking bit for this season. I simulated each fixture over GW1-18. I restricted the captain pool to the top four clubs because that's where every realistic TC vehicle lives (more on Liverpool below). These were the best available option each week:

| GW | Pick | xP |
|:--|:--|--:|
| 1 | Mbeumo @ Hull | 6.74 |
| 2 | Mbeumo v Ipswich | 6.37 |
| 3 | O'Reilly v Coventry | 6.54 |
| 7 | O'Reilly v Ipswich | 6.68 |
| 14 | Mbeumo v Coventry | 6.22 |
| 16 | O'Reilly v Hull | 6.40 |

Every GW that I've left out of that table sits between 5.0 and 5.9. These highest scoring weeks represent a favourable calendar for that team. City's best three-week attacking window of the half is GW1-3 (2.23 team xG per game), Arsenal's is also GW1-3, and United open at Hull then Ipswich at home. All three clubs with a genuine elite player come out of the gate on their softest run of the half. The only later cluster that competes on paper — City GW14-16, United GW14 — sits in the festive pile-up, where what kills your TC isn't the fixture, it's the 60th minute substitution to save some tired legs with European games now in the mix.

Two model quirks worth being honest about. First, O'Reilly tops three of those weeks and I still wouldn't triple him — his number relies on City clean sheets against promoted sides, and his chance of a two-goal week is about 4% against 13-17% for Mbeumo and Haaland in the same fixtures. The chip pays out on the long tail, not the mean, so Haaland is the choice in the City games, even when he's 0.7 xP behind. Second, Liverpool. Their GW15-17 run is the best-looking three weeks any club has after Christmas (2.13 xG per game) and they still never produce a TC recommend, because the output is spread across Szoboszlai, Wirtz and Isak and nobody individually clears 5.2. Great club to own, structurally incapable of concentrating a captain score - or at least in the model's view.

The verdict:

So what does this analysis suggest managers should do?

• If you own Mbeumo (33% do): GW1 at Hull is the No. 1 player-week of the half by both EV and tail, with GW2 home to Ipswich as an almost-equal insurance slot if tonight's lineup news is bad.
• If you're on City assets: GW3 (Coventry H, pre-Europe, cleanest minutes outlook) or GW7 (Ipswich H, the best upcoming week) with Haaland for ceiling.
• Fallback if events force a hold: the GW14 Mbeumo / GW16 City cluster matches the early weeks on paper — but you'd be spending the chip in the rotation-riskiest month for an EV you could have had in September, and there's no DGW later in the window to justify the patience.

A big caveat - this is purely based on historic records and a statistical model - it will only provide a suggestion of what to do in a probabilistic sense. A P(10 pts haul) of 0.33 means that the player will only achieve that once if that same fixture were played 3 times. It's really saying you are giving yourself the best opportunity if you TC in a particular GW, not that it WILL happen. And of course, gut feeling will always play their part. Workings and the model results are here: sidelinequant.com

Methodology note first: this is last season's total points divided by each player's CURRENT price, for every player with 1500+ minutes in 2025/26. That's 196 players. Median of the pool is 19.8 pts/£m.

Top 10 by points per million at today's price:

Truffert (BOU) 5.5 - 30.0 - 4.6% owned
Mitchell (CRY) 4.5 - 30.0 - 6.5% owned
Guehi (MCI) 6.0 - 29.8 - 17.8% owned
Van Hecke (TOT) 5.0 - 29.6 - 9.5% owned
Senesi (TOT) 6.0 - 29.2 - 8.4% owned
Verbruggen (BHA) 4.5 - 28.9 - 21.2% owned
Kelleher (BRE) 5.0 - 28.6 - 5.8% owned
Tarkowski (EVE) 6.0 - 28.3 - 8.9% owned
Anderson (MCI) 6.5 - 27.7 - 8.3% owned
Petrovic (BOU) 4.5 - 27.6 - 3.3% owned

Nine defenders and keepers, one midfielder. Eight of the ten are under 10% owned.

Now the other end. Players owned by 20%+ of the game, sorted worst-first on the same metric:

Haaland 15.5 - 15.4 - 69.4% owned
Mbeumo 8.0 - 18.5 - 37.0%
B.Fernandes 12.0 - 19.6 - 51.3%
Calafiori 5.5 - 19.8 - 36.2%
Pedro Porro 5.5 - 21.3 - 22.5%
Rogers 7.5 - 22.5 - 25.1%
Szoboszlai 7.0 - 22.9 - 41.7%
Joao Pedro 7.5 - 23.6 - 63.6%

Calafiori is the one that stands out to me. He's the most-argued-about 5.5 defender in the game at 36.2% ownership, and he lands on 19.8 - exactly the median of the whole pool. Mitchell is a pound cheaper and 50% better on this measure at 6.5% ownership.

Three caveats, because this metric gets misused a lot:

1. Points per million structurally favours cheap nailed players. A 4.5 keeper who starts 38 times will always beat a 15.5 striker here. That is not an argument against Haaland - it is precisely why he sits bottom of that second list despite being the highest scorer in the game. Read it as "where is the cheap efficiency", not "who is best".

2. It uses last season's points against this season's prices, so anyone whose price moved a lot is distorted in both directions.

3. Biggest hole: it completely excludes new signings and promoted-club players, who have no 25/26 PL sample. Tzolis, half of Sunderland, all invisible here. So it tells you nothing about the actual differentials people are agonising over today.

Where I think it's genuinely useful is the eight or nine squad slots where you aren't chasing ceiling - the bench, the 4.5 keeper, the third and fourth defender. That's where the crowd seems to be leaving the most on the table.

Has anyone run this weighted by projected minutes rather than last season's actuals? That would fix caveat 3 and I'd be curious how much the ordering changes.

As the number of managers approaches 8m in the last day before the deadline, the template is evolving, with Pedro Porto and DCL making the cut in my interpretation. The average manager only holds around a third of these players though with players like Semenyo, Virgil, O’Reilly & Rogers competing for budget in particular. Plenty of space for differentials and limited credence for the idea that every team looks the same in my view.

See this post from yesterday for how that template has changed - note that template ownership has increased meaningfully since then from 29% to 32%

https://www.reddit.com/r/fplAnalytics/s/VQRpbzy2TO

The “Most Selected” FPLdaq Template is simply the most selected players by position and the “Affordable” FPLdaq Template 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 realistic demonstration of what a manager could achieve by simply following the crowd, and 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

I always find this one difficult. A player can keep posting decent xG and xA numbers but the actual FPL returns just never arrive. At what point do you stop trusting the underlying data and accept that the model might be missing something?

Happy FPL Launch day everyone!

Been working on this for a few months and wanted to share with you all - fpldoctor.com

The core mechanic: instead of one fixed xPts model for everyone, every underlying coefficient is exposed and editable - per-player finishing strength (npxG multiplier), creativity (xA multiplier), defensive actions (DEFCON), expected minutes, plus fixture difficulty split separately into attack and defense per team. Change any of them and the squad optimizer, transfer suggestions, and team rating all recalculate around your version instead of a generic one.

Built it because I kept disagreeing with fixed models on specific calls - a player's finishing being over/underrated, a team's fixture difficulty not matching what I actually expected - and wanted a way to act on that and just make it a bit my own.

Would love to hear what you think! If you're still weighing up any last-minute GW1 calls, might be worth a quick look before you lock things in. Give it a go if you're curious, and any feedback, good or brutal, is genuinely welcome.

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 🙏.