2025/26 archive

Hwang Hee-chan

Penalties #3 · Free kicks #2

2025/26 season archive

Hee Chan is not in a 2026/27 Premier League squad, so these figures are final rather than live. Browse 2026/27 players.

Wolves

Pace

1.0

points per gameweek

Minutes

58%

reached 60' · 18 of 26 starts

Market

Buying

3,381 net transfers

How he ranks

26 games · 141 midfielders

Points

13th3.38

xGI

47th0.25

DefCon

16th6

BPS

0th10.3

Minutes

22nd1439

Final price£5.6m

Form (10 GW)1.0

Pts / Match2.1

GW38 Pts1

Total Pts543.4 per 90

Total Bonus00.0 per 90

ICT Index53.4

Selected by %0.1%

xGI3.90.2 per 90

G+A − xGI+0.1overperforming

On dutyPenalties#3Free kicks#2DefCon bonus0/26 apps

Minutes and bonus

Started

18/26

69% of appearances

Reached 60'

58%

15 of 26 games

Bonus won

in 0 of 26 games

BPS per game

6.3

0% bonus strike rate

Where the points come from

Appearance4169%

Goals1017%

Assists610%

Clean sheets23%

Cards-5

69% of his points earned came from appearance. That is a narrow base — if it stops, most of the return stops with it. 59 earned, 54 net

Are the returns real?

His goals and assists are in line with the chances behind them — +0.1 against expectation over 1439 minutes.

Full seasons on record

2023/24 +4.6

2022/23 +1.4

2021/22 +6.0

Goals and assists minus xGI, per season. Positive means he finished above the chances he had.

Defensive Contribution

He averages 3.7 against a line of 12, so DefCon is not a realistic source of points for him.

Cleared

0/26

0% of games

Average

3.7

line is 12

Versus the line

-8.3

short on average

Action mix

10% tackles · 30% CBI · 59% recoveries

Against each level of opponent

He scores 4.1 more per game against the easiest opponents he faced than the hardest — a fixture-dependent asset.

too few games(1)

5.67 per game(3)

1.67 per game(15)

1.6 per game(5)

too few games(2)

1 is the easiest opponent, 5 the hardest. Bracketed figure is appearances at that level.

Gameweek log

	Opponent
1	MCI (H)	8	1	0	0	0	0	1	3	0.0	0.0	0.0	0.2	0	£6.0m
2	BOU (A)	12	1	0	0	0	0	0	0	0.0	0.0	0.0	0.2	0	£6.0m
3	EVE (H)	75	7	1	0	0	3	2	27	0.4	0.0	0.4	1.8	0	£5.9m
4	NEW (A)	76	2	0	0	0	1	1	6	0.1	0.0	0.1	1.4	0	£5.9m
5	LEE (H)	0	0	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0	£5.9m
6	TOT (A)	45	1	0	0	0	0	2	5	0.0	0.0	0.0	0.7	0	£5.9m
7	BHA (H)	59	1	0	0	0	0	5	5	0.1	0.1	0.1	0.7	0	£5.9m
8	SUN (A)	0	0	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0	£5.9m
9	BUR (H)	22	1	0	0	0	1	1	3	0.0	0.0	0.0	0.4	0	£5.9m
10	FUL (A)	0	0	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0	£5.8m
11	CHE (A)	69	2	0	0	0	2	5	6	0.0	0.0	0.0	1.9	0	£5.8m
12	CRY (H)	24	1	0	0	0	1	1	-1	0.1	0.0	0.1	0.4	0	£5.7m
13	AVL (A)	0	0	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0	£5.7m
14	NFO (H)	23	1	0	0	0	1	1	3	0.0	0.1	0.1	0.1	0	£5.7m
15	MUN (H)	0	0	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0	£5.7m
16	ARS (A)	79	1	0	0	0	1	7	-2	0.1	0.0	0.2	1.0	1	£5.6m
17	BRE (H)	89	1	0	0	0	2	4	3	0.2	0.0	0.2	1.3	1	£5.6m
18	LIV (A)	61	2	0	0	0	2	5	5	0.0	0.0	0.0	1.2	0	£5.6m
19	MUN (A)	87	2	0	0	0	1	9	0	0.1	0.1	0.2	0.7	0	£5.6m
20	WHU (H)	60	11	1	1	1	0	4	29	0.8	0.4	1.2	0.1	0	£5.6m
21	EVE (A)	90	1	0	0	0	1	6	4	0.0	0.0	0.1	0.9	1	£5.6m
22	NEW (H)	84	3	0	0	1	0	4	8	0.0	0.1	0.1	0.6	0	£5.6m
23	MCI (A)	70	2	0	0	0	2	2	3	0.0	0.0	0.0	0.8	0	£5.6m
24	BOU (H)	69	2	0	0	0	1	3	13	0.0	0.0	0.1	0.5	0	£5.6m
25	CHE (H)	42	1	0	0	0	3	5	5	0.0	0.0	0.0	2.8	0	£5.6m
26	NFO (A)	0	0	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0	£5.6m
26	ARS (H)	0	0	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0	£5.6m
27	CRY (A)	0	0	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0	£5.6m
28	AVL (H)	0	0	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0	£5.6m
29	LIV (H)	0	0	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0	£5.6m
30	BRE (A)	0	0	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0	£5.6m
32	WHU (A)	19	1	0	0	0	1	1	3	0.0	0.0	0.1	0.7	0	£5.6m
33	LEE (A)	15	1	0	0	0	1	2	2	0.0	0.0	0.0	0.9	0	£5.6m
34	TOT (H)	5	1	0	0	0	0	1	3	0.0	0.0	0.0	0.0	0	£5.6m
35	SUN (H)	0	0	0	0	0	0	0	0	0.0	0.0	0.0	0.0	0	£5.6m
36	BHA (A)	88	1	0	0	0	3	4	-1	0.1	0.0	0.1	1.6	1	£5.6m
37	FUL (H)	78	5	0	1	0	1	9	22	0.1	0.3	0.3	1.5	0	£5.6m
38	BUR (A)	90	1	0	0	0	1	11	11	0.1	0.3	0.4	1.0	1	£5.6m

What the model expects

From the same expected-points model that runs across the whole player pool — an estimate per gameweek, not a prediction of any single one.

Expected per GW

2.4

points, average

Direction

+0.85

conditions improving

Sample

gameweeks behind it

Direction = last five gameweeks vs the five before. The model is injury-blind.

Points by gameweek · with model expectation

Points scoredModel expectation, set before kick-off

GW8–26: model expected 22 pts · he scored 22 (0 vs expectation)

Market

Price History

Selected by %

0.1%

Net transfers, latest GW

+3381

Hwang Hee-chan

How he ranks?

Minutes and bonus?

Where the points come from?

Are the returns real??

Defensive Contribution?

Against each level of opponent?

Gameweek log

What the model expects

Points by gameweek · with model expectation?

Market

Price History?

How he ranks

Minutes and bonus

Where the points come from

Are the returns real?

Defensive Contribution

Against each level of opponent

Points by gameweek · with model expectation

Price History