Sønderjyske Fodbold 4:2 Randers FC: xG & match report · Superliga (Denmark)

Sunday, 20/09/2026 at 14:00

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Match report · Superliga (Denmark)

Sønderjyske Fodbold Home 1.36 xG · 10 shots

Randers FC Away 1.57 xG · 14 shots

42 The chances were evenly matched

Sønderjyske Fodbold were the model's likeliest winner before kick-off at 38%. They won 4–2. The biggest difference was Randers FC's attack: 1.57 xG against 1.19 projected (+0.38).

Goals

25' Osaze De Rosario Sønderjyske Fodbold · 0.06 xG chance 1–0
36' Elies Mahmoud Randers FC · 0.10 xG chance 1–1
45' Brynjar Ingi Bjarnason Sønderjyske Fodbold · 0.02 xG chance 2–1
49' Mohamed Cherif Haidara Sønderjyske Fodbold · 0.14 xG chance 3–1
66' Mohamed Sankoh Randers FC · 0.83 xG chance 3–2
78' Ismaïl Seydi Sønderjyske Fodbold · 0.45 xG chance 4–2

The model against the result

priced before kick-off

Sønderjyske Fodbold: 38% What happened
Draw: 31%
Randers FC: 31%

Data coverage Both teams have the full 20-match model window available.

Projected vs produced

Expected goals

Sønderjyske Fodbold 1.33 1.36 +0.03

Randers FC 1.19 1.57 +0.38

Randers FC produced 0.38 xG more than projected

Projected before kick-off Produced on the night

The shots behind it

Sønderjyske Fodbold Randers FC

Shots: 10; 14
On target: 7; 6
Inside the box: 6; 9
xG per shot: 0.13; 0.13
Open play xG: 0.92; 1.61
Set-piece xG: 0.36; 0.19
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: 1.28 for Sønderjyske Fodbold and 1.85 for Randers FC.

Shot map

Both teams shown attacking the same goal for comparison.

All shots

24 shots · 3.08 xG

Hover, focus or tap a shot to read it.

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

Recent form

Last 6 · xG per match

Sønderjyske Fodbold

1.00 xG created 1.96 xGA

D 0.62 created, 1.47 conceded against Viborg FF
L 0.92 created, 1.63 conceded against Brøndby IF
L 1.28 created, 1.97 conceded against FC Nordsjælland
L 0.78 created, 2.43 conceded against FC København
L 0.62 created, 2.42 conceded against AC Horsens
D 1.80 created, 1.87 conceded against Lyngby

Randers FC

1.11 xG created 1.46 xGA

W 1.51 created, 1.85 conceded against Lyngby
L 0.46 created, 1.30 conceded against FC København
L 1.03 created, 1.28 conceded against FC Midtjylland
W 1.10 created, 1.97 conceded against AGF
W 0.85 created, 1.19 conceded against Brøndby IF
L 1.68 created, 1.14 conceded against Odense Boldklub

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

Head to head

Sønderjyske Fodbold: 0 won · 1 drawn · 1 lost

Pre-match model & market record

4 opportunities flagged1 won3 lost

Lost
Lost
Won
Lost

View pre-match record

The outcomes the model flagged on Sønderjyske Fodbold against Randers FC before kick-off, in the order they ranked, with the price, the edge and what became of each.
Selection	Price	Edge	Result
Under 2.5 goals	2.10	+11.0%	Lost
Draw	3.50	+10.2%	Lost
Sønderjyske Fodbold to win	2.88	+8.3%	Won
Under 1.5 goals	4.33	+4.3%	Lost

Pre-match model vs market

Outcome	Model	Market	Difference	Fair odds
Sønderjyske Fodbold	37.7%	32.8%	+4.8 pp	2.65
Draw	31.5%	27.0%	+4.5 pp	3.18
Randers FC	30.8%	40.2%	−9.4 pp	3.24

Other markets

Outcome	Model	Market	Difference	Fair odds
Both teams to score	57.1%	58.9%	−1.8 pp	1.75
Both teams not to score	42.9%	41.1%	+1.8 pp	2.33
Over 1.5 goals	75.9%	78.3%	−2.4 pp	1.32
Under 1.5 goals	24.1%	21.7%	+2.4 pp	4.15
Over 2.5 goals	47.1%	55.3%	−8.1 pp	2.12
Under 2.5 goals	52.9%	44.7%	+8.1 pp	1.89

Prices and probabilities are the ones stored before kick-off; nothing here is recalculated. Edge is the model's probability times the price, minus one, so a flagged outcome that lost is the ordinary case rather than a contradiction.

Model details

Show

Fit window: the last 20 matches, recency weighted (a match 107 days old counts half)
Sønderjyske Fodbold sample: 20 matches
Randers FC sample: 20 matches
Computed: 1 day before kick-off
Prices taken: 1 day before kick-off

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.