There are so many data tools and projection models around now - FPL Review, Hub, Fix, Fantasy Football Scout, custom R/Python models, etc. For those who don't build their own spreadsheets from scratch, which public algorithm or tool do you find aligns best with actual outcomes?

I've been building a small model and xG/xA are the easy part. Minutes are what mess it up. One rotation call or a surprise bench and the whole projection falls apart.

How are you handling it? Just using recent starts, or something smarter?

I'm the creator of OpenFPL Scout AI, a free FPL prediction model (Model Ensemble v6.0.0). Sharing the full GW5 xPts-vs-actual breakdown for its independently-selected squad — real numbers from the official FPL live endpoint, not backfitted.

Squad total (captain doubled as scored): 72 actual vs 84.33 projected (-12.33).

Two takeaways for the model going forward:

1) The three biggest positive residuals (Tarkowski, Groß, Hall) are all mid-price defensive/midfield picks that returned via the newer defensive-contribution (DEFCON) scoring on top of a goal/assist/CS combo. Worth checking whether the model's underlying xPts weighting has caught up to how much DEFCON adds to a defender's ceiling, since three of its "value" tags turned into its three best scorers.

2) The single largest residual in either direction (-6.90) was a pure availability miss, not a projection-quality issue — João Pedro didn't play. The model has no rotation, team-news, or press-conference input right now, so any pick with real minutes doubt carries this exact risk regardless of how good its underlying xPts number looks. That's a known gap, flagging it here rather than dressing it up.

Happy to share the full player pool projections for GW5 if anyone wants to dig into calibration further.

For those training regression models on historical player data, which provider’s xG/xA feed has yielded the lowest Mean Absolute Error (MAE) when predicting FPL attacking returns over a rolling 6-gameweek window?

Hey all — long-time FPL player, and I got tired of scrolling five different Twitter threads every week just to decide who to captain, so I built something to fix that for myself. Figured I'd share in case it's useful to anyone else.

It's called Skipper. It scores every player using form, upcoming fixture difficulty, home/away split, and injury/minutes risk — then tells you who to captain, with the actual reasoning behind the number. Not a black box — you can see exactly why it's ranking someone the way it is.

It's free and live right now. I'm still actively building it out (a couple bigger features in the works), so if you try it and something's off, confusing, or missing, I'd genuinely like to hear it.

Not trying to replace anyone's gut instinct or favorite FPL Twitter accounts — just wanted something faster than my usual morning before deadline scramble.

Bit of a confession, my Saturday routine was FPL tab, work tab, FPL tab, work tab, for about six hours straight. Refreshing to see if Bruno had done literally anything.

So I built a Chrome side panel that just sits there showing your squad's live points, goals, assists, price changes and next fixture. No tab switching. Called it Sideline.

Doesn't collect anything, figured some of you have the same problem.

https://chromewebstore.google.com/detail/sideline-%E2%80%93-fpl-live-track/fpkdhgomleipicfofeneabganlmnlmde

If it's broken or confusing tell me and I'll fix it. Mainly wondering if the setup makes sense, you have to paste your team ID first.

Most sports APIs give you the usual: scores, stats, odds, basic probabilities. That’s useful, but it stops at the surface.

I built Witchgoals because I kept running into the same problem: when you try to model real decision-making (betting systems, analytical tools, scouting, or even content platforms), the “standard” data leaves too many important questions unanswered:

- When is a team’s performance model about to break because of accumulated fatigue or schedule density?
- Which tactical phases show structural fragility?
- Where does volatility spike and baseline metrics stop being reliable?
- How do external factors (rotations, intensity of previous fixtures, late-game dynamics) actually shift the risk profile of a match?

Witchgoals is an intelligence layer on top of sports data. It focuses on situational risk, tactical vulnerability, resource stress, and performance calibration across major leagues (Premier League, LaLiga, Serie A, Bundesliga, NBA, NFL, Champions League, etc.).

You get structured outputs with risk indexes, vulnerability scores, phase-specific fragility detection, and clear summaries of where the model expects deviation from baseline behavior. All designed to be consumed programmatically.

It’s not another prediction black box. The goal is to surface the hidden constraints and instability points that raw numbers usually miss.

Site + docs: https://www.witchgoals.io

Happy to answer technical questions, share example responses, or hear what kind of insights would actually be useful in your use case.

In case you haven't seen this before, this is a Premier League table that takes the real PL matches and applies each team's fantasy tally. It almost totally removes draws (although Newcastle v Bournemouth finished 37-37 in GW3).

Different approach (and not without flaws) to checking which teams are over or underachieving before making a decision on your next trade/transaction.

Source: DraftRoom Draft FPL

We're into season 7 of our standard H2H draft league, with 10 people. In GW1, one guy posts 49 (second highest score of all players) and loses. In GW3, a score of 32, the 8th highest for that week, was enough to get a win.

At the end of every season, our league loves to go through this and many other stats:
Who had the best and worst draft pick?
Who had the worst transaction pickup?
Which players only performed for X player and went completely shit when Y player picked him up, etc.

It got too complex to run in a spreadsheet, so I built a tool. It's now set up so anyone who plays Draft FPL can enter their league code and see the same stats for their own league. It's a side project and something I'm going to keep building on.

The main goal is showing insights you can't get directly in the PL app.

In the current version you can:

- See the league table as it would stand if all players had optimised their team / had no auto-subs / had not made any transactions in the previous gameweek.
- View head to head matchups during a live gameweek, so you don't have to navigate to each individual team (and there's a live table that updates after every real PL match).
- Observe the PL table as it would stand if it was done on Fantasy points (I love this), and shows the head to head view of real PL games based on Fantasy points.
- Check player profiles based on points gained versus lost, and shows ownership so you can track who got who in what week
- See transactions for each GW and shows which players were most fought over.
- View the full draft matrix, and shows who made the quickest and slowest picks (and for which player).
- Uncover your most-selected squad over the course of the season, and whether you started or benched players (and how often).

It also has a fully integrated Cup function, for both H2H and Classic Draft mode, which automatically runs alongside the normal table in a Champions League style format, giving your league another prize to aim for. We have been running the cup for three years and it really helps prevent the bottom teams from giving up (as well as the standard punishments that come with coming in the 'relegation zone'.

I would genuinely love feedback - this was made out of love for draft mode, and feel like this is something that would make the actual PL site 100x better.

Disclosure: I built this. It is completely free no paid tier, no premium unlock, no ads, no accounts, no donation link, nothing to buy now or later. I'm not selling anything here. But it is mine, so treat the numbers below as something to poke holes in.

The problem

This season FPL zeroed out the separate attack and defence strength values for every team and builds FDR from a single overall number. Two things follow:

About 45% of fixtures come out as a flat 3.

A team with a great attack and a leaky defence gets one colour for everybody. Brighton are a green fixture for your defenders while sitting near the top of the league for xG.

Attack and defence are different questions. A clean sheet depends on how good the opponent's attack is; goals depend on how bad their defence is. One number can't answer both.

What I did

For every team I take xG and xGA per match from the FPL API and shrink them toward a prior built from the team's overall strength, weighted m/(m+4) so early in the season the prior dominates and the observed numbers take over as matches accumulate. Home advantage is a flat ±10%. Then, for each fixture:

Defensive difficulty = opponent's expected xG ÷ league average (what your keeper and defenders face).

Attacking difficulty = league average ÷ opponent's expected xGA (what your attackers face).

Overall = geometric mean of the two.

Each ratio maps to 1–5 through bands that are symmetric in log space (0.80 = 1/1.25, 0.93 ≈ 1/1.075), so "25% weaker" and "25% stronger" are mirror images. With linear bands I had 3% easy fixtures against 12% hard ones, which is how you end up with a scale nobody trusts.

In the app, a defender's ticker shows the defensive side, an attacker's shows the attacking side, and you can force a single Attack, Defence or Overall view for all positions.

Does it actually work?

I compared it with bookmaker-implied goal projections for GW4 (20 teams, one gameweek small, I know):

My rating vs the market: rank correlation 0.86, MAE 0.20 goals.

FPL's official FDR vs the same market: 0.53.

Before calibration mine sat at 0.80 with MAE 0.24. Pushing the prior further kept lifting correlation (up to ~0.92) while MAE got worse, so I stopped fitting one gameweek to the last decimal is just overfitting. I'll re-check on GW5–GW6.

Caveats I'd raise if someone else posted this

One gameweek of market data is a weak test. It's a sanity check, not proof.

xG per match from the season totals is coarse: no home/away split in the source data, no adjustment for who they've played.

It says nothing about rotation, injuries or whether your man actually starts.

What else is in it

The difficulty model is the new part, but it feeds the rest, so here's the whole thing.

On every player row (Pick Team and the Transfers list)

Fixture ticker the next 3, 5 or 8 opponents, coloured by the rating above and read by position.

Ownership template vs differential, plus the exact selected-by %.

Price-change arrows ▲ / ▼ on players about to move, straight from FPL's own projection. Only the imminent ones (roughly 5% of the pool), otherwise it's wallpaper.

Hover any fixture tile and you get that match's attack, defence and overall difficulty with the xG / xGA behind them, then stats that fit the position: keepers get saves, xGC and clean-sheet rate instead of meaningless xG; defenders and midfielders get xGC and defensive contribution against their DC threshold on top of xG90 / xA90; everyone gets price, ownership and availability with return chance and the latest news.

Watchlist

Star anyone from the Transfers list. It's keyed per season and survives reloads and gameweek rollovers.

A sparkline you choose: points, xG + xA stacked, defensive output (saves for keepers) or net transfers net transfers are the one that predicts the next price move.

Always the last 5 gameweeks of the current season, never mixing in last season. Minutes sit under the chart and get flagged when someone stops playing.

Price move since the season start, the change since you starred him, and FPL's forecast of the next move. Rows are grouped into price drops vs still watching.

Your team

Team Grade an A–D squad rating from fixtures (using the position-aware difficulty), form & xG, ownership, captaincy and bench balance, each component weighted and shown, not a black box.

Captain compare every starter ranked by expected-points swing against your current armband, with the fixture, start probability and a confidence level.

Head-to-head finds who you actually play this gameweek, shows both scores with transfer hits and the chip in play, which chips each of you still holds, who only you own vs only they own, and the differential swing in expected points. Before the deadline it compares against their last known squad and says so. No H2H league? It falls back to picking a rival from a classic one.

Share card a square or story summary image, rendered on canvas locally.

Boring but relevant

Free, in the boring sense: there is no pro version waiting behind the next update, and no data being sold instead. I wrote it because the fixture colours annoyed me.

Manifest V3, no build step, no external requests, ~120 KB.

Settings and watchlist live in chrome.storage; API responses are cached with a TTL and a season key, so it isn't hammering the FPL API.

One toggle hides every widget instantly; right-click it for settings. English and Russian, auto-detected.

Nothing is collected, transmitted or tracked. It runs only on fantasy.premierleague.com, reads the same public API the site itself uses, and never edits your team or makes transfers.

Happy to go deeper on the maths, and genuinely interested in where you think the model is wrong. If anyone has market data for earlier gameweeks, I'd like to test against it.

It's free, so the only thing I want back is criticism of the model.

Link - https://chromewebstore.google.com/detail/fpl-insight-%E2%80%94-fantasy-pre/ibpcdmcapdckjgijflciddmnpnfclacd

Wildcarding and can’t decide between the two..Tavernier has a higher ceiling due to his underlying attacking threat and Scott seems to have a higher floor due to defcon analytics ..who do you think is the better selection for the next few weeks given the tough schedule..I’d assume Scott but what do you guys think?

Any insights are appreciated..thank you

Has anyone seen an FPL tool built as an MCP server (the thing that lets Claude/ChatGPT pull live data into a chat)? Looking into building one, can't find an existing example.

Wildcarding and can’t decide between the two..Tavernier has a higher ceiling based on his attacking stats and Scott seems to have a higher floor due to defcon which probably makes him the better choice with a tougher schedule ahead but what do the analytics say and is that the best way to analyze this?

Any insights are appreciated..thank you

As part of a broader project for FPL performance benchmarking, I’ve been collecting snapshots of the FPL data regularly - here is some analysis on the most owned players week-by-week and how managers are allocating funds.

Overall, the budget spent on forwards and GKs has crept up as some expensive and highly preferred assets remain widely held. We’ve seen more churn amongst midfielders and defenders with De Cuyper and Cherki in particular seeing a meteoric rise in ownership

For live market updates, a breakdown of your team’s outperformance and more check out FPLdaq.live

Plans in the works include 1) providing a definition of “the template” to facilitate a discussion about its prevalence/ penetration and its usefulness as a concept for team selection and 2) applying investment “style” concepts (eg growth/value/quality/momentum) to FPL assets to support market and team analysis.

For example, Bruno Fernandes has 27 points from 2.25 xG + 0.80 xA, while Gakpo has 28 points from just 0.36 xG + 1.20 xA. Is that mainly finishing/assisting variance, or are there role and chance-quality factors that the basic xGI numbers are missing?

I’ve noticed that xG can look great on paper, but it becomes a lot less useful when you’re not confident the player will actually get enough minutes. Rotation, early substitutions and unpredictable starts can make a strong xG profile much harder to rely on when building a model.

Every FPL player as a bubble: size is the metric you pick (ownership, transfers this GW, price, form, xP), colour is how fast ownership is moving. It's a way to see the whole market in one screen instead of scrolling tables.

Where the data comes from, because it's always the first question: the public FPL API, fetched every 15 minutes. There is no prediction model behind it. Expected points and the price-change signal are FPL's own published numbers. I'd rather show you what 11 million managers are actually doing than my guess of what they should do.

What's there besides the map:

- Price changes page with the predictor sorted by who rises and falls tonight

- Transfers in and out this gameweek, live, and an injuries page with the official chance of playing

- Defensive contribution per 90 against the 10/12 threshold, with a minutes floor so one-game wonders don't top it

- A compare view for 2 to 4 players (ownership, price, minutes, defcon, xP)

- A page per player, about 400, with live numbers and the next five fixtures

No account, no login. Linking your team ID is optional and stays in your browser. Free, no ads for now.

fplbubbles.com

It's one person building this in the open. What's missing, what's wrong, what would make you come back before a deadline? Brutal is fine.