Randers FC 0:4 FC København: xG & match report · Superliga (Denmark)

Sunday, 16/08/2026 at 18:00

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Match report · Superliga (Denmark)

Randers FC Home 0.46 xG · 10 shots

FC København Away 1.30 xG · 19 shots

04 The scoreline reflected the chances

Goals

2' Birger Meling FC København · 0.02 xG chance 0–1
14' Birger Meling FC København · 0.06 xG chance 0–2
45' Mads Emil Madsen FC København · 0.04 xG chance 0–3
77' Marcos López FC København · 0.02 xG chance 0–4

The shots behind it

Randers FC FC København

Shots: 10; 19
On target: 0; 6
Inside the box: 4; 10
xG per shot: 0.05; 0.06
Open play xG: 0.46; 0.80
Set-piece xG: 0.07; 0.41
Penalties: —; —

Shot-level xG may differ from the published match total

The provider publishes the match total separately from its shot-by-shot values. Those values are rounded to two decimals and, leaving out penalties and own goals, add up to its non-penalty xG: 0.52 for Randers FC and 1.21 for FC København.

Shot map

Both teams shown attacking the same goal for comparison.

All shots

29 shots · 1.74 xG

Hover, focus or tap a shot to read it.

Randers FC
FC København
Filled = goal
Dot area = xG (the smallest chances drawn at a minimum size)

Recent form

Last 6 · xG per match

Randers FC

1.17 xG created 1.75 xGA

W 1.94 created, 0.70 conceded against Vejle
D 1.99 created, 0.94 conceded against Odense Boldklub
L 0.22 created, 2.21 conceded against FC København
D 0.60 created, 1.09 conceded against Silkeborg IF
L 0.74 created, 3.70 conceded against FC Nordsjælland
W 1.51 created, 1.85 conceded against Lyngby

FC København

1.97 xG created 1.54 xGA

D 2.39 created, 3.01 conceded against FC Fredericia
W 2.90 created, 1.13 conceded against Silkeborg IF
W 2.21 created, 0.22 conceded against Randers FC
D 1.59 created, 1.02 conceded against Brøndby IF
W 1.56 created, 2.34 conceded against Lyngby
W 1.20 created, 1.52 conceded against Silkeborg IF

xG created xG conceded (xGA) W / D / L: the match result

Head to head

Randers FC: 1 won · 0 drawn · 3 lost

Model details

Show

The model never priced this match before kick-off, so there is no projection to read the result against.

Limitations

The model reads expected goals and nothing else. It does not know the lineups, who is injured or suspended, what either side has to play for, the weather, or how congested the schedule around this match is.