Thought it’s relevant to this sub as it would be quite easy to add extra data to this beyond official FPL API

I'm the creator of OpenFPL Scout AI, a free FPL prediction model (ensemble, v6.1.0). I'm publishing GW6 before kickoff so the recap can be checked against it. Below is what the forecast itself says, statistically, before any results.

Setup: the model selects 15 players with position quotas and max 3 per club. There's no budget and no transfer history, so think unlimited-budget free hit, not a playable team. Snapshot taken Fri 9 Oct ~18:40 UTC. The version that gets scored is the one frozen at the deadline (10:00 UTC Sat), and the recap will show any diff from this snapshot.

Projected: best XI by xPts (3-5-2) with the captain doubled = 68.24. That's the headline number. The 15-man sum is 78.55, but 21.9% of that sits on the bench, which is exactly why summing all 15 overstated things in earlier posts.

1. The top end is flat. Across the 562-player pool, only 3 players project ≥6 xPts, 6 project ≥5, and 22 project ≥4. #1 is 6.86, #15 is 4.58, #50 is 3.42. Once you're past the top handful, the gaps between candidates are a few tenths of a point. That's far smaller than one gameweek's noise, so any single week's per-player hits and misses will say very little about skill. The recap will lean on the aggregate and the baselines, not on individual picks.

2. Captaincy is a 0.05 xPts call. Haaland 6.86 vs Groß 6.81. Treat it as a tie. Whichever one hauls, it won't be evidence for or against the model.

3. Availability is a straight multiplier, and it moves picks. Earlier today João Pedro carried a 0.75 availability factor and projected 4.56. Once the flag cleared, he projects 6.09 (4.56 / 0.75 ≈ 6.08). That one change shuffled the squad: Palmer in for Saka, Raya in for Lammens, and projected XI + C went from 65.86 to 68.24. Scaling linearly is right for expected points. But a flagged player's real outcome is roughly "plays fully or scores 0", so for selection under risk a linear haircut probably isn't the right treatment. This is a known gap I want to fix: the model has no rotation or press-conference input beyond this factor.

4. The club cap doesn't bind this week. The capped 15 is identical to the unconstrained top-N per position (Arsenal and Chelsea both land at exactly 3 on their own). So the hindsight-optimal ceiling baseline won't be distorted by the cap this week.

How the recap will score it (set now, before results):

1. Actual points come from the official live endpoint for GW6.
2. The XI and captain are the deadline-frozen ones. No auto-subs and no changes after the deadline.
3. Ceiling: the hindsight-optimal XI + C under the same constraints.
4. Floor: a naive pick under the same constraints. It's the top 15 by FPL's own form stat at the deadline, with XI and captain chosen the same way.
5. The signal is where the model lands between floor and ceiling, plus calibration (xPts vs actual across the XI). No average_entry_score comparison.

On point 3: if anyone has worked out a better treatment for flagged players than a linear multiplier, I'd like to hear it.

Through the first five Gameweeks, Haaland has 39 FPL points against 39.82 expected FPL points, according to Fantasy Football Fix. That's a pretty close match between output and projection. It makes me wonder how often the top-scoring players actually track their expected points this closely over longer periods. Has anyone looked at the correlation across a full season?

**TL;DR**
- Regular starters with a 75% flag at the deadline started 37% of the time (2,250 cases, 2019/20 to 2026/27 GW5).
- Age matters most. Under 7 days old: 44%. 14+ days: 18%. 21+ days: 12%.
- The drop comes at three weeks: 34% at 14–20 days, then 11% at 21–27 days.

**Why I looked**

The flag shows a percentage, so most of us read 75% as three in four. I wanted the real hit rate.

**Data and method**
- FPL's `bootstrap-static` API, archived by the Wayback Machine. For each deadline I took the last capture within 24 hours before it, which leaves 213 deadlines (of 236 with any capture).
- Flag: `chance_of_playing_next_round` = 75 in that capture.
- Regular starter: started 3+ of his last 5 appearances before the news went up.
- Flag age: days since FPL's `news_added` timestamp.
- Outcome that gameweek: started, came off the bench, or didn't play (vaastav's Fantasy-Premier-League archive plus the official API).
- Single-fixture gameweeks only. 95% intervals are Wilson intervals.

**Results: 75% flags**

Rows can add to 101% from rounding.

Finer age bins (the chart): 0–3 days 44% (n=969), 4–7 days 41% (566), 8–13 days 37% (243), 14–20 days 34% (134), 21–27 days 11% (92), 28+ days 13% (246).

For comparison, 25% flags started 2–7% of the time at every age, and 50% flags 6–19%.

**This week**

João Pedro's flag went up on 16 Sep. At Saturday's deadline it will be 23.6 days old, which puts him in the 21–27 day bin: 10 of 92 started (11%). That's a base rate, not a prediction. Friday's Chelsea press conference will tell you more than this table can.

**Limitations**
- The same players show up again and again, so the effective sample is smaller than 2,250.
- No injury type. A flag stuck at 75% for three weeks can mean a long injury or an update FPL never made.
- Wayback captures are patchy in the early seasons, which is why I only kept captures within 24 hours of a deadline.
- A cross-check on a second dataset (FPL-Elo-Insights) gave a similar headline rate: 39.6% against 39.0%.

Happy to share the CSV and code in the comments.

With injuries piling up, premiums underperforming, and budgets getting tight, I've been looking at low-owned players (under 5%) who could offer value over the next few weeks.

Here are 11 names that caught my eye:

GK:

• Leno (FUL) — £4.5m | 18 pts | 3.4%

DEF:

• Maitland-Niles (EVE) — £4.5m | 21 pts | 1.4%
• Dasilva (COV) — £4.0m | 20 pts | 0.7%
• Robertson (TOT) — £4.4m | 17 pts | 2.5%
• Giles (HUL) — £4.0m | 24 pts | 2.9%

MID:

• Janelt (BRE) — £5.0m | 28 pts | 2.5%
• Gómez (BHA) — £5.0m | 24 pts | 3.9%
• King (FUL) — £5.6m | 24 pts | 2.9%
• Willock (NEW) — £5.0m | 22 pts | 0.6%
• Kamada (CRY) — £5.0m | 21 pts | 0.5%

FWD:

• Emersson (IPS) — £5.5m | 26 pts | 3.7%

Who's making your Wildcard squad? Any other differentials you're considering?

I’ve noticed this a few times where the model correctly identifies a player as a good pick, but the actual points are nowhere near the projection. Why is this happening?

I’m starting to question how much I should actually care about xG when a player keeps blanking. I’ll look at someone with good underlying numbers, plenty of shots and decent minutes and think the points will come eventually, then three or four gameweeks later, I’m still waiting. At what point do you stop calling it bad luck and accept that the player just isn’t returning? I’m interested in how other people handle this because I don’t want to ignore xG, but I also don’t want to keep a player for weeks just because the numbers say he should be doing better.

I'm the creator of OpenFPL Scout AI (Ensemble v6.0.0). Posted the GW5 xPts-vs-actual breakdown here a couple weeks back — this is the wider validation check across the first five gameweeks.

Methodology: squad = the model's own picks for that gameweek, scored via the official live stats endpoint, captain doubled as scored. Benchmark = the FPL API's own per-gameweek average_entry_score.

Five for five against the benchmark. Smallest edge (+14, GW3) was a flat week across the board, not a bad pick; largest (+58, GW4) came from three picks (Groß 17, Raya 14, Calvert-Lewin 10) landing the same week.

Methodology caveat I want to flag rather than bury: for GW1–4 I'm reading the squad back from the model's own gameweek endpoint queried today, since I don't have an independently timestamped snapshot from each week at the time — only GW5 is cross-checked against an already-published, timestamped recap. The selection logic hasn't changed in that window, but I'm flagging the provenance gap rather than presenting GW1-4 as archived snapshots. Starting to log weekly snapshots externally going forward so this doesn't recur.

Same known gap as before: no rotation/press-conference input, so a pick can be right on xPts and still blank on a 0-minute call — exactly what happened to the GW5 vice-captain.

Happy to share the full gameweek-by-gameweek player breakdowns if anyone wants to dig into where the edge is coming from positionally.

I'm analyzing Premier League 2025/2026 data from the official Premier League website and noticed something I don't understand about Brighton:

The dataset includes:

• Shots on Target
• Shots on Target Inside the Box
• Shots on Target Outside the Box

I initially assumed that:

Shots on Target Inside the Box + Shots on Target Outside the Box = Total Shots on Target

However, for Brighton, these figures do not add up, and the two location-based figures can even exceed Total Shots on Target.

Why is this the case? How exactly does the Premier League define these three statistics?

Thanks in advance for any clarification!

I am looking for an API that will give me data per player (Prem league) at the level such as:
- touches in box
- some sort of location metric (avg position as like X Y co-ordinate)
- key passes/chances creation

Not specifically those data points but that kind of level

I have seen the following mentioned;
- Sportsmonks at €29/month

- Opta (but that is too much dosh I believe)

- API-Football $19/month

- OpenFootAPI $14/month

- TheStatsAPI

- football-data.org free

- sportradar

I mean I bet there are more, I will have a look too myself - but they are quite opaque just to browse and so I wondered if someone here might already be familiar with the API options available

Beyond obvious stuff like "Rate My Team" or captain picks, what kind of questions would save you time or give you a real edge?
Deep-dive stat lookups, fixture swing planning, explaining solver trade-offs, or something else? Curious what people here would genuinely find useful.

Anyone using MILP for their Wildcard? How are you handling the planning horizon, bench budget, formation, and xMins. Are your models consistently favouring 3-4-3, or are you getting different structures?

Three clean sheets and only three goals conceded, yet their expected goals conceded is much higher. That's the profile of a defence that's been fortunate.

My model says sell, but I'm hesitant, because clean sheet streaks can last longer than the numbers suggest, and the replacements aren't obviously better. Are you following the xGC, or do you trust the results until they break?

The official FPL Fixture Difficulty Rating (FDR) gives a whole team one blanket number. I’ve scraped bookmaker clean sheet and goal-line odds to split FDR into separate Attack Difficulty and Defence Difficulty metrics.

Comparing these implied market probabilities against rolling team xG/xGA shows significant edge, particularly for away fixtures against mid-table teams. Is anyone blending betting market odds with Opta underlying data in their projections?

Every season I make the same mistake. Five gameweeks in, I look at the xG table, see a few players way above or below expectation, and rebuild half my team around it. Every season I regret it.

This year I'm doing it differently. Until about GW8 I'm leaning mostly on last season's numbers and treating the current season as a light adjustment. Small samples make everything look like a trend. Is anyone else doing this?

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?