fpl Tool · predicted Points · 2026/27 launch preview

FPL predicted points — free for every player, with the receipts published

Predicted points (expected points, xPts) estimate what a player will score in a gameweek by pricing every scoring event — appearing, goals, assists, clean sheets, saves, defensive contribution, bonus, cards — from his minutes, his underlying numbers and the fixture. Bawler publishes the full table free, and unlike the paid tools we also publish how the model actually performed, gameweek by gameweek, so you can check it before you trust it.

The 2026/27 launch preview: projected points per gameweek

#	Player	Club	Pos	Price	Owned	xPts	Big haul	Bad / good week	Attack	Defence	Involve
1	Haalandnew mgr	MCI	FWD	£15.5m	73.5%	5.26	16%	2 – 13	2.61	0.00	2.68
2	Gabriel	ARS	DEF	£8.0m	26.9%	5.06	12%	1 – 10	0.68	2.00	2.40
3	B.Fernandesnew mgr	MUN	MID	£12.0m	48.1%	5.04	16%	2 – 12	1.91	0.62	2.55
4	Palmernew mgr	CHE	MID	£9.5m	11.2%	4.60	14%	2 – 12	2.09	0.39	2.16
5	Mbeumonew mgr	MUN	MID	£8.0m	25.3%	4.21	12%	1 – 11	1.95	0.25	2.01
6	Isaknew mgr	LIV	FWD	£9.0m	13.5%	4.05	10%	2 – 9	1.63	0.00	2.44
7	Wirtznew mgr	LIV	MID	£7.5m	14.4%	3.92	10%	2 – 9	1.35	0.35	2.20
8	Thiago	BRE	FWD	£8.0m	16.1%	3.90	11%	1 – 11	1.75	0.08	2.10
9	Gibbs-Whitenew mgr	NFO	MID	£8.0m	11.6%	3.88	10%	2 – 10	1.53	0.27	2.09
10	Watkins70%England 3rd 18 Jul; not back as of 6 Aug, due 8 Aug, only ~2 weeks to GW1	AVL	FWD	£8.0m	12.7%	3.85	10%	1 – 11	1.74	0.00	2.13
11	Virgilnew mgr	LIV	DEF	£6.5m	16.8%	3.82	7%	1 – 8	0.65	1.29	1.90
12	Raya	ARS	GKP	£6.0m	31.0%	3.80	2%	1 – 8	0.00	1.71	2.02
13	João Pedronew mgr	CHE	FWD	£7.5m	55.9%	3.71	9%	1 – 9	1.48	0.02	2.21
14	O.Dango	BRE	MID	£6.5m	1.4%	3.63	9%	1 – 9	1.45	0.35	1.83
15	Thiawnew mgr	NEW	DEF	£5.0m	2.0%	3.62	7%	1 – 8	0.72	1.04	1.85
16	Rice40%England 3rd-place 18 Jul, back ~9 Aug; hamstring workload managed by Arteta	ARS	MID	£7.5m	21.8%	3.60	8%	1 – 8	0.73	0.88	2.00
17	Tarkowski	EVE	DEF	£6.0m	9.7%	3.60	6%	1 – 8	0.51	1.27	1.82
18	Collins	BRE	DEF	£5.5m	2.1%	3.58	5%	1 – 8	0.49	1.23	1.87
19	Enzonew mgr	CHE	MID	£7.0m	5.5%	3.56	10%	1 – 9	1.41	0.38	1.78
20	Richardsnew mgr	CRY	DEF	£5.0m	0.8%	3.56	6%	1 – 8	0.64	1.20	1.72

Attack = goals + assists points · Defence = clean sheets, goals conceded, saves and defensive-contribution points · Involve = appearance, bonus and card points. The three columns sum to xPts — check the arithmetic any time.
Big haul is the chance he scores 10 or more. Bad / good week is the sort of score he lands on when it goes badly and when it goes well — one week in five is at or under the first number, one in ten beats the second. Two players on the same xPts can have very different ranges: a defender is mostly clean sheet or nothing, while a forward can blank one week and score fifteen the next.

Top goalkeepers by projected points

#	Player	Club	Pos	Price	Owned	xPts	Big haul	Bad / good week	Attack	Defence	Involve
1	Raya	ARS	GKP	£6.0m	31.0%	3.80	2%	1 – 8	0.00	1.71	2.02
2	Kelleher	BRE	GKP	£5.0m	5.7%	3.26	4%	1 – 8	0.01	1.14	2.04
3	Lammensnew mgr	MUN	GKP	£5.0m	19.6%	3.19	3%	1 – 8	0.00	1.12	2.01
4	Donnarummanew mgr	MCI	GKP	£5.5m	11.0%	3.16	2%	1 – 7	0.02	1.34	1.74
5	Roefs	SUN	GKP	£5.0m	4.9%	3.14	3%	1 – 8	0.00	1.12	1.95
6	Verbruggen	BHA	GKP	£4.5m	18.1%	3.09	2%	1 – 7	0.00	1.18	1.84
7	Selsnew mgr	NFO	GKP	£5.0m	1.5%	3.03	2%	1 – 7	0.00	1.11	1.85
8	Lenonew mgr	FUL	GKP	£4.5m	3.3%	3.00	2%	1 – 7	0.00	1.05	1.88
9	Traffordnew club	LEE	GKP	£5.0m	2.4%	2.96	3%	1 – 7	0.00	1.07	1.82
10	A.Beckernew mgr	LIV	GKP	£5.5m	4.0%	2.91	3%	0 – 7	0.00	0.97	1.85

Top defenders by projected points

#	Player	Club	Pos	Price	Owned	xPts	Big haul	Bad / good week	Attack	Defence	Involve
1	Gabriel	ARS	DEF	£8.0m	26.9%	5.06	12%	1 – 10	0.68	2.00	2.40
2	Virgilnew mgr	LIV	DEF	£6.5m	16.8%	3.82	7%	1 – 8	0.65	1.29	1.90
3	Thiawnew mgr	NEW	DEF	£5.0m	2.0%	3.62	7%	1 – 8	0.72	1.04	1.85
4	Tarkowski	EVE	DEF	£6.0m	9.7%	3.60	6%	1 – 8	0.51	1.27	1.82
5	Collins	BRE	DEF	£5.5m	2.1%	3.58	5%	1 – 8	0.49	1.23	1.87
6	Richardsnew mgr	CRY	DEF	£5.0m	0.8%	3.56	6%	1 – 8	0.64	1.20	1.72
7	Vuskovicnew club	BHA	DEF	£5.0m	2.7%	3.41	6%	0 – 8	0.70	1.00	1.72
8	Canvotnew mgr	CRY	DEF	£5.0m	0.6%	3.41	6%	1 – 8	0.40	1.14	1.88
9	Ballard	SUN	DEF	£5.0m	4.4%	3.20	5%	1 – 8	0.42	1.02	1.76
10	Truffertnew mgr	BOU	DEF	£5.5m	4.9%	3.15	6%	0 – 8	0.47	0.85	1.83

Top midfielders by projected points

#	Player	Club	Pos	Price	Owned	xPts	Big haul	Bad / good week	Attack	Defence	Involve
1	B.Fernandesnew mgr	MUN	MID	£12.0m	48.1%	5.04	16%	2 – 12	1.91	0.62	2.55
2	Palmernew mgr	CHE	MID	£9.5m	11.2%	4.60	14%	2 – 12	2.09	0.39	2.16
3	Mbeumonew mgr	MUN	MID	£8.0m	25.3%	4.21	12%	1 – 11	1.95	0.25	2.01
4	Wirtznew mgr	LIV	MID	£7.5m	14.4%	3.92	10%	2 – 9	1.35	0.35	2.20
5	Gibbs-Whitenew mgr	NFO	MID	£8.0m	11.6%	3.88	10%	2 – 10	1.53	0.27	2.09
6	O.Dango	BRE	MID	£6.5m	1.4%	3.63	9%	1 – 9	1.45	0.35	1.83
7	Rice40%England 3rd-place 18 Jul, back ~9 Aug; hamstring workload managed by Arteta	ARS	MID	£7.5m	21.8%	3.60	8%	1 – 8	0.73	0.88	2.00
8	Enzonew mgr	CHE	MID	£7.0m	5.5%	3.56	10%	1 – 9	1.41	0.38	1.78
9	E.Le Fée	SUN	MID	£6.0m	12.8%	3.54	8%	1 – 8	1.05	0.64	1.87
10	Sarrnew mgr	CRY	MID	£6.5m	7.9%	3.51	9%	0 – 9	1.64	0.24	1.64

Top forwards by projected points

#	Player	Club	Pos	Price	Owned	xPts	Big haul	Bad / good week	Attack	Defence	Involve
1	Haalandnew mgr	MCI	FWD	£15.5m	73.5%	5.26	16%	2 – 13	2.61	0.00	2.68
2	Isaknew mgr	LIV	FWD	£9.0m	13.5%	4.05	10%	2 – 9	1.63	0.00	2.44
3	Thiago	BRE	FWD	£8.0m	16.1%	3.90	11%	1 – 11	1.75	0.08	2.10
4	Watkins70%England 3rd 18 Jul; not back as of 6 Aug, due 8 Aug, only ~2 weeks to GW1	AVL	FWD	£8.0m	12.7%	3.85	10%	1 – 11	1.74	0.00	2.13
5	João Pedronew mgr	CHE	FWD	£7.5m	55.9%	3.71	9%	1 – 9	1.48	0.02	2.21
6	Calvert-Lewin	LEE	FWD	£6.0m	25.2%	3.44	9%	1 – 9	1.50	0.00	1.96
7	Gyökeres	ARS	FWD	£7.5m	13.1%	2.85	7%	1 – 8	1.28	0.00	1.59
8	Osulanew mgrrotation	NEW	FWD	£6.0m	1.3%	2.85	6%	1 – 7	1.06	0.00	1.79
9	Evanilsonnew mgr	BOU	FWD	£6.0m	2.1%	2.72	6%	1 – 6	1.12	0.01	1.59
10	Wright	COV	FWD	£5.5m	1.7%	2.71	4%	1 – 6	0.90	0.01	1.82

The full table — every player, free

The complete sortable table — the surface other sites charge £50–96 a year for. Search any player, filter by position and price, and click any column to sort. The Mins column is the quiet one to watch: a projection is minutes first, and a nailed £5m starter beats a rotating £7m name most weeks.

Best projected value: xPts per £m

Divide each player's projection by his price and the budget enablers surface — this is the column that builds a squad, since every pound saved on a defender who scores anyway is a pound spent upgrading a captain. The full squad this points at is on best FPL team.

#	Player	Club	Pos	Price	Owned	xPts	xPts / £m
1	Thiawnew mgr	NEW	DEF	£5.0m	2.0%	3.62	0.72
2	Richardsnew mgr	CRY	DEF	£5.0m	0.8%	3.56	0.71
3	Verbruggen	BHA	GKP	£4.5m	18.1%	3.09	0.69
4	Vuskovicnew club	BHA	DEF	£5.0m	2.7%	3.41	0.68
5	Canvotnew mgr	CRY	DEF	£5.0m	0.6%	3.41	0.68
6	Maatsen	AVL	DEF	£4.5m	0.7%	3.03	0.67
7	Lenonew mgr	FUL	GKP	£4.5m	3.3%	3.00	0.67
8	Collins	BRE	DEF	£5.5m	2.1%	3.58	0.65
9	Kelleher	BRE	GKP	£5.0m	5.7%	3.26	0.65
10	Rodon	LEE	DEF	£4.5m	2.0%	2.93	0.65

The backtest: how the model actually did

Before publishing a single projection we replayed the whole 2025/26 season the hard way: for every gameweek from 8 to 38, the model predicted every likely starter using only information available before that gameweek's deadline, and was scored against what actually happened — 6,493 predictions in total. Paid tools tell you their model is accurate; this table is what accurate looks like, including the misses.

When the model says 6+, what actually happens?

A projection is only useful if its numbers mean what they say. Group every backtest prediction by what the model projected, and the actual averages climb in step — big projections really did deliver big scores:

Read it honestly: the 5–6 band over-promised slightly (5.37 projected, 4.85 delivered) — that is what a real calibration table looks like, and we publish it anyway. Overall bias: -0.06 points per prediction.

And when it says “32.1% chance of a big haul”?

Same test, different claim. Every projection also carries a chance of a big haul — a 10+ point gameweek. Group the 6,493 backtest predictions by that percentage and compare it with how often a big score actually landed. If the number means what it says, the two columns should track each other:

They climb together, which is the point — but read the size of it honestly. Across the whole set the model called 4.7% and 5.33% happened, so it is still a little shy on big scores, and it beats simply reading the haul rate off the projected points by about 0.77%. That is a real improvement and a small one. Use the percentage to separate two players on the same projection, not as a promise about any single week.

How the model works, in plain English

• Minutes first — no minutes, no points. Recent starts, sub patterns and official availability flags set how much of the game a player is likely to play, and everything else is scaled by it.
• Attacking returns — the player's underlying goal threat and chance creation (his xG and xA per 90, steadied over small samples), scaled up or down by how leaky the opponent is and home advantage.
• Clean sheets and saves — how likely the team is to shut out this opponent, plus keeper save volume and the goals-conceded deduction. This one does not come from FPL history: it comes from Bawler's match forecasting engine, the same one that prices every fixture on the rest of the site. Tested across a full season it predicted clean sheets measurably better than a fantasy-only estimate, and the gain was biggest in the opening weeks when there is barely any current-season form to go on.
• A range, not just an average — every projection also carries the chance of a big haul and what a bad and a good week look like, built from the same pieces. It matters most for defenders and keepers, whose week is really clean sheet or no clean sheet, so an average lands in a gap they rarely actually score.
• Defensive contribution — the newest scoring rule (2 pts for high tackle/block/interception counts) is modelled from how often each player actually hits the threshold — the quiet reason cheap centre-backs rank so high now.
• Bonus and cards — each player's real bonus and booking rates, not guesses.

Every input is from the Official FPL API; nothing is scraped from other tools. A projection is a fair average, not a promise — a 6-point projection can blank on Saturday. What the backtest above shows is that over a season, trusting these averages beat trusting form or last year's totals. One caveat we'd rather state than have found: the model's internal settings were tuned on the same season the backtest covers. We tested how much that flatters the numbers by perturbing every setting and re-running — the accuracy figure moves by at most ±0.02, so the margin over the baselines stands either way. Full method notes live on methodology; the match model behind the fixture inputs keeps its own track record.

Use the projections

• Player stats — the numbers underneath the projection: minutes, goals against the chances behind them, form windows, per player.
• Captain picks — the projections doubled, ranked, with the season-long armband proof.
• Differentials — high projections the crowd doesn't own yet, with the backtested hit-rate.
• Best FPL team — the highest-projected legal XI the model can build.
• Fixture difficulty — the model's own run ratings that drive every fixture term above.
• Team builder — build a legal £100m squad with these projections live on every pick.
• My team — import your real squad and see its projected points, decisions and transfer plan.
• Fixture planner — the projections stretched over the coming weeks: best runs and rotation pairs.
• The FPL hub — deadlines, prices and the daily rebuild.