Champions League Fantasy · league phase

European Nights

Matchday 4

Match predictions and expected points for every squad, built for your captain call.

First kickoff

3 Nov, 17:45

Tue, 3 Nov, 17:45 UTC

Model fit

matches · Elo + Dixon-Coles

Captain picks

P(start) blends minutes with UEFA's own availability flags; lineups can still surprise

K. Mbappé

RMA · FWD · €11.0m · v AEK (A)

1st

6.9xPtsP(start) 96%

G 3.8A 0.7REC 0.4

Picked by 59% of all managers· buying now ▲

L. Yamal

BAR · MID · €10.2m · v AVL (H)

2nd

6.8xPtsP(start) 96%

G 2.2A 1.6REC 0.7

Picked by 47% of all managers· buying now ▲

S. Brunstad Fet

BOD · MID · €5.0m · v LIL (H)

3rd

6.4xPtsP(start) 89%

A 2.1G 2.0REC 0.7

Picked by 1% of all managers· buying now ▲

The predicted XI

3-5-2 · 63.8 xPts combined

A. Østbø3.6

F. Sjøvold5.9

S. Dest5.6

G. Stensness5.2

L. Yamal6.8

S. Brunstad Fet6.4

Z. Tripić6.2

F. Wirtz6.0

N. Paz5.8

K. Mbappé6.9

S. Guirassy5.4

The captain call

K. Mbappé13.8xPts doubled

RMA v AEK (A) · P(start) 96%

How we compute this

The highest combined xPts across valid formations, with UCL Fantasy's maximum of three players per club. It ignores the budget on purpose: this is the XI we would field, not a purchasable squad.

Full methodology

Match predictions

1.6 – 1.0

Tue 17:45 UTC

most likely 1–0

SHK 52%draw 22%SCP 25%

Clean sheet: SHK 36% · SCP 20%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

1.6 – 2.0

Tue 17:45 UTC

most likely 1–2

GAL 31%draw 20%VFB 50%

Clean sheet: GAL 13% · VFB 21%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

1.5 – 1.1

Tue 20:00 UTC

most likely 1–0

FEY 48%draw 23%INT 29%

Clean sheet: FEY 32% · INT 22%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

2.8 – 1.5

Tue 20:00 UTC

most likely 2–1

BAR 64%draw 16%AVL 20%

Clean sheet: BAR 22% · AVL 6%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

1.9 – 1.1

Tue 20:00 UTC

most likely 1–0

ATM 57%draw 20%BAY 23%

Clean sheet: ATM 33% · BAY 15%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

2.2 – 2.6

Tue 20:00 UTC

most likely 2–2

VIL 33%draw 17%PSG 49%

Clean sheet: VIL 7% · PSG 11%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

2.2 – 1.3

Tue 20:00 UTC

most likely 2–1

MUN 58%draw 19%ROM 24%

Clean sheet: MUN 26% · ROM 11%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

3.2 – 1.2

Tue 20:00 UTC

most likely 3–1

BOD 77%draw 12%LIL 11%

Clean sheet: BOD 31% · LIL 4%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

3.2 – 0.8

Tue 20:00 UTC

most likely 3–0

LAS 85%draw 9%SVB 6%

Clean sheet: LAS 47% · SVB 4%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

1.2 – 3.0

Wed 17:45 UTC

most likely 1–3

AEK 13%draw 13%RMA 74%

Clean sheet: AEK 5% · RMA 30%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

1.3 – 4.2

Wed 17:45 UTC

most likely 1–4

FEN 7%draw 8%LIV 85%

Clean sheet: FEN 2% · LIV 27%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

1.6 – 1.8

Wed 20:00 UTC

most likely 1–2

RBL 34%draw 21%MCI 45%

Clean sheet: RBL 16% · MCI 21%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

0.6 – 2.4

Wed 20:00 UTC

most likely 0–2

SLP 8%draw 14%ARS 78%

Clean sheet: SLP 9% · ARS 54%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

1.5 – 1.3

Wed 20:00 UTC

most likely 1–1

PSV 42%draw 23%CLU 35%

Clean sheet: PSV 26% · CLU 23%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

2.1 – 1.0

Wed 20:00 UTC

most likely 1–0

BVB 62%draw 19%BET 19%

Clean sheet: BVB 35% · BET 13%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

1.5 – 1.6

Wed 20:00 UTC

most likely 1–1

POR 38%draw 22%NAP 40%

Clean sheet: POR 21% · NAP 21%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

1.4 – 2.3

Wed 20:00 UTC

most likely 1–2

RCL 23%draw 18%COM 59%

Clean sheet: RCL 10% · COM 25%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

3.1 – 0.7

Wed 20:00 UTC

most likely 3–0

VIK 85%draw 9%SAB 6%

Clean sheet: VIK 50% · SAB 5%

How we compute this

Elo plus a Dixon-Coles goals model, fitted on 54 Champions League matches with early-season shrinkage. Probabilities come from the full scoreline matrix.

Full methodology

Expected points · all positions

Updated 2026-10-26 15:51 UTC

1K. MbappéRMA · FWD · €11.0m · v AEK (A) · 59% picked6.9xPts
2L. YamalBAR · MID · €10.2m · v AVL (H) · 47% picked6.8xPts
3S. Brunstad FetBOD · MID · €5.0m · v LIL (H) · 1% picked6.4xPts
4Z. TripićVIK · MID · €5.8m · v SAB (H) · 1% picked6.2xPts
5F. WirtzLIV · MID · €8.5m · v FEN (A) · 2% picked6.0xPts
6F. SjøvoldBOD · DEF · €4.7m · v LIL (H) · 2% picked5.9xPts
7N. PazCOM · MID · €7.0m · v RCL (A) · 12% picked5.8xPts
8S. DestPSV · DEF · €5.1m · v CLU (H) · 3% picked5.6xPts
9P. ChristiansenVIK · FWD · €6.0m · v SAB (H) · 0% picked5.5xPts
10S. GuirassyBVB · FWD · €8.6m · v BET (H) · 12% picked5.4xPts
11G. StensnessVIK · DEF · €4.5m · v SAB (H) · 0% picked5.2xPts
12Bruno FernandesMUN · MID · €9.0m · v ROM (H) · 13% picked5.1xPts
13F. ValverdeRMA · MID · €7.1m · v AEK (A) · 7% picked5.0xPts
14B. MbeumoMUN · FWD · €7.0m · v ROM (H) · 8% picked4.9xPts
15D. RiceARS · MID · €7.5m · v SLP (A) · 7% picked4.8xPts
16S. AuklendBOD · MID · €6.0m · v LIL (H) · 0% picked4.8xPts
17G. TilPSV · MID · €5.5m · v CLU (H) · 1% picked4.8xPts
18R. LjubičićLAS · MID · €5.5m · v SVB (H) · 0% picked4.8xPts
19K. AskildsenVIK · MID · €5.0m · v SAB (H) · 0% picked4.7xPts
20A. HelmersenBOD · FWD · €5.0m · v LIL (H) · 1% picked4.7xPts
21VitinhaPSG · MID · €7.6m · v VIL (A) · 17% picked4.6xPts
22Gabriel VeigaPOR · MID · €5.1m · v NAP (H) · 1% picked4.6xPts
23J. McGinnAVL · MID · €6.7m · v BAR (A) · 2% picked4.6xPts
24M. LlorenteATM · MID · €6.7m · v BAY (H) · 2% picked4.5xPts
25Fabián RuizPSG · MID · €6.7m · v VIL (A) · 2% picked4.5xPts
26M. ØdegaardARS · MID · €7.1m · v SLP (A) · 7% picked4.5xPts
27A. StillerVFB · MID · €5.6m · v GAL (A) · 1% picked4.3xPts
28A. IsakLIV · FWD · €7.5m · v FEN (A) · 8% picked4.3xPts
29J. HaugeBOD · MID · €7.0m · v LIL (H) · 0% picked4.3xPts
30Ronald AraújoLIV · DEF · €4.7m · v FEN (A) · 9% picked4.2xPts
31P. BergBOD · MID · €5.0m · v LIL (H) · 2% picked4.2xPts
32A. Hadj MoussaFEY · MID · €6.6m · v INT (H) · 1% picked4.2xPts
33C. LangLAS · MID · €4.5m · v SVB (H) · 0% picked4.2xPts
34H. KaneBAY · FWD · €11.1m · v ATM (A) · 51% picked4.2xPts
35E. HaalandMCI · FWD · €11.2m · v RBL (A) · 22% picked4.2xPts
36D. UndavVFB · FWD · €7.2m · v GAL (A) · 3% picked4.2xPts
37Alisson SantanaSHK · MID · €4.6m · v SCP (H) · 1% picked4.1xPts
38GabrielARS · DEF · €6.0m · v SLP (A) · 29% picked4.1xPts
39M. BaturinaCOM · MID · €5.7m · v RCL (A) · 4% picked4.1xPts
40RaphinhaBAR · MID · €9.7m · v AVL (H) · 30% picked4.1xPts

#	Player	Pos	€m	Opp	P(start)	xPts	Picked	/€m	Breakdown
1	K. MbappéRMA	FWD	11.0	AEK (A)	96%	6.90	59%▲	0.63	G 3.8A 0.7REC 0.4
2	L. YamalBAR	MID	10.2	AVL (H)	96%	6.78	47%▲	0.67	G 2.2A 1.6REC 0.7
3	S. Brunstad FetBOD	MID	5.0	LIL (H)	89%	6.38	1%▲	1.27	A 2.1G 2.0REC 0.7
4	Z. TripićVIK	MID	5.8	SAB (H)	91%	6.23	1%	1.07	A 1.7G 1.7REC 0.8
5	F. WirtzLIV	MID	8.5	FEN (A)	91%	6.05	2%	0.71	A 2.6REC 1.1G 0.6
6	F. SjøvoldBOD	DEF	4.7	LIL (H)	94%	5.94	2%	1.26	G 1.5REC 1.5CS 1.0
7	N. PazCOM	MID	7.0	RCL (A)	94%	5.78	12%▲	0.82	G 1.6A 1.2REC 1.0
8	S. DestPSV	DEF	5.1	CLU (H)	93%	5.57	3%▲	1.09	G 1.8REC 1.3CS 0.8
9	P. ChristiansenVIK	FWD	6.0	SAB (H)	94%	5.47	0%	0.91	G 3.0A 0.5REC 0.5
10	S. GuirassyBVB	FWD	8.6	BET (H)	84%	5.36	12%▲	0.62	G 2.6POTM 0.6A 0.4
11	G. StensnessVIK	DEF	4.5	SAB (H)	80%	5.17	0%	1.15	CS 1.3G 1.3REC 1.1
12	Bruno FernandesMUN	MID	9.0	ROM (H)	89%	5.07	13%	0.56	G 2.3REC 0.7A 0.3
13	F. ValverdeRMA	MID	7.1	AEK (A)	94%	4.97	7%	0.70	G 1.2REC 1.0A 0.5
14	B. MbeumoMUN	FWD	7.0	ROM (H)	95%	4.94	8%	0.71	G 1.8A 0.8REC 0.6
15	D. RiceARS	MID	7.5	SLP (A)	96%	4.83	7%	0.65	A 1.0REC 1.0G 0.7
16	S. AuklendBOD	MID	6.0	LIL (H)	92%	4.82	0%	0.80	A 1.3REC 0.9G 0.7
17	G. TilPSV	MID	5.5	CLU (H)	96%	4.80	1%	0.87	G 2.0REC 0.8
18	R. LjubičićLAS	MID	5.5	SVB (H)	76%	4.78	0%	0.87	G 1.3REC 1.0A 0.9
19	K. AskildsenVIK	MID	5.0	SAB (H)	94%	4.72	0%	0.94	A 1.1REC 0.9G 0.7
20	A. HelmersenBOD	FWD	5.0	LIL (H)	89%	4.68	1%	0.94	G 2.3A 0.5REC 0.3
21	VitinhaPSG	MID	7.6	VIL (A)	92%	4.62	17%▲	0.61	REC 1.2G 1.1POTM 0.4
22	Gabriel VeigaPOR	MID	5.1	NAP (H)	85%	4.60	1%	0.90	G 1.8REC 0.9
23	J. McGinnAVL	MID	6.7	BAR (A)	96%	4.59	2%	0.69	REC 1.0POTM 0.7A 0.7
24	M. LlorenteATM	MID	6.7	BAY (H)	94%	4.48	2%	0.67	G 1.3REC 1.0A 0.3
25	Fabián RuizPSG	MID	6.7	VIL (A)	92%	4.47	2%▲	0.67	G 1.2REC 0.9A 0.6
26	M. ØdegaardARS	MID	7.1	SLP (A)	86%	4.45	7%	0.63	G 1.4REC 0.6CS 0.4
27	A. StillerVFB	MID	5.6	GAL (A)	92%	4.34	1%	0.78	REC 1.2A 0.9G 0.5
28	A. IsakLIV	FWD	7.5	FEN (A)	64%	4.32	8%	0.58	G 2.8
29	J. HaugeBOD	MID	7.0	LIL (H)	78%	4.27	0%	0.61	G 1.7REC 0.5A 0.4
30	Ronald AraújoLIV	DEF	4.7	FEN (A)	76%	4.24	9%▲	0.90	G 1.1REC 1.0CS 0.7
31	P. BergBOD	MID	5.0	LIL (H)	94%	4.24	2%	0.85	REC 1.1G 0.7A 0.5
32	A. Hadj MoussaFEY	MID	6.6	INT (H)	90%	4.22	1%	0.64	G 1.0A 0.6REC 0.5
33	C. LangLAS	MID	4.5	SVB (H)	84%	4.21	0%	0.94	G 1.5REC 0.6A 0.5
34	H. KaneBAY	FWD	11.1	ATM (A)	96%	4.19	51%▲	0.38	G 1.0POTM 0.7A 0.5
35	E. HaalandMCI	FWD	11.2	RBL (A)	82%	4.16	22%▲	0.37	G 2.3POTM 0.3
36	D. UndavVFB	FWD	7.2	GAL (A)	93%	4.16	3%	0.58	G 1.2A 0.9REC 0.4
37	Alisson SantanaSHK	MID	4.6	SCP (H)	92%	4.10	1%	0.89	REC 0.8A 0.7G 0.4
38	GabrielARS	DEF	6.0	SLP (A)	80%	4.08	29%▲	0.68	CS 1.4REC 1.0
39	M. BaturinaCOM	MID	5.7	RCL (A)	84%	4.07	4%	0.71	G 1.0REC 0.7A 0.4
40	RaphinhaBAR	MID	9.7	AVL (H)	65%	4.06	30%▲	0.42	G 2.0REC 0.5

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How we compute this

xPts multiplies each player's share of his team's predicted goals by the UCL Fantasy scoring rules, plus clean sheets, saves and ball recoveries. Goal and assist shares blend his UCL record with his domestic-league record this season; recoveries and cards are shrunk toward position averages. Picked is the share of all UCL Fantasy managers holding him. Treat small gaps between players as noise.

Full methodology

41	J. VeermanBVB	MID	6.5	BET (H)	88%	4.06	0%	0.62	REC 1.1A 0.7G 0.4
42	H. VanakenCLU	MID	6.5	PSV (A)	91%	4.02	1%	0.62	REC 1.3G 0.5POTM 0.4
43	Jude BellinghamRMA	MID	8.5	AEK (A)	96%	3.98	22%	0.47	G 0.8REC 0.7A 0.5
44	P. GueyeVIL	MID	5.0	PSG (H)	95%	3.96	0%	0.79	REC 1.2G 1.0
45	PepêPOR	MID	6.0	NAP (H)	93%	3.94	0%	0.66	A 1.0REC 0.8G 0.3