Sønderjyske Fodbold 2:5 AC Horsens: xG & match report · Superliga (Denmark)

Sunday, 06/09/2026 at 14:00

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Match report · Superliga (Denmark)

Sønderjyske Fodbold Home 0.62 xG · 11 shots

AC Horsens Away 2.42 xG · 17 shots

25 The scoreline reflected the chances

Sønderjyske Fodbold were the model's likeliest winner before kick-off at 42%. AC Horsens won 5–2. The biggest difference was AC Horsens's attack: 2.42 xG against 1.12 projected (+1.30). Sønderjyske Fodbold produced 0.62 xG against 1.41 projected (−0.79).

Goals

12' Osaze De Rosario Sønderjyske Fodbold · 0.08 xG chance 1–0
19' Ole Kolskogen AC Horsens · 0.46 xG chance 1–1
31' Yamirou Ouorou AC Horsens · 0.38 xG chance 1–2
56' Abdul Moro AC Horsens · 0.05 xG chance 1–3
79' Kelvin Ehibhatiomhan AC Horsens · 0.37 xG chance 1–4
82' Alexander Munksgaard (o.g.) AC Horsens · 0.10 xG chance 1–5
83' Mohamed Cherif Haidara Sønderjyske Fodbold · 0.09 xG chance 2–5

The model against the result

priced before kick-off

Sønderjyske Fodbold: 42%
Draw: 31%
AC Horsens: 27% What happened

Data coverage One of these teams has only 6 matches in the model window, short of the 10 the model wants. Read the projection as a direction rather than as a number.

Projected vs produced

Expected goals

Sønderjyske Fodbold 1.41 0.62 -0.79

AC Horsens 1.12 2.42 +1.30

AC Horsens produced 1.30 xG more than projected

Projected before kick-off Produced on the night

The shots behind it

Sønderjyske Fodbold AC Horsens

Shots: 11; 17
On target: 3; 10
Inside the box: 9; 8
xG per shot: 0.06; 0.13
Open play xG: 0.42; 1.33
Set-piece xG: 0.20; 0.89
Penalties: —; —

AC Horsens's shots add up to 2.22 xG; the published figure is 2.42.

Shot map

Both teams shown attacking the same goal for comparison.

All shots

28 shots · 2.84 xG

Hover, focus or tap a shot to read it.

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

Recent form

Last 6 · xG per match

Sønderjyske Fodbold

0.88 xG created 1.74 xGA

L 1.09 created, 0.82 conceded against FC Midtjylland
L 0.59 created, 2.11 conceded against Odense Boldklub
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

AC Horsens

0.93 xG created 1.90 xGA

D 1.50 created, 2.66 conceded against FC Nordsjælland
L 0.41 created, 1.97 conceded against FC Midtjylland
L 0.49 created, 0.63 conceded against Brøndby IF
W 1.58 created, 1.37 conceded against Odense Boldklub
W 0.76 created, 2.87 conceded against Lyngby
L 0.83 created, 1.92 conceded against Viborg FF

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

Pre-match model & market record

2 opportunities flagged0 won2 lost

Lost
Lost

View pre-match record

The outcomes the model flagged on Sønderjyske Fodbold against AC Horsens before kick-off, in the order they ranked, with the price, the edge and what became of each.
Selection	Price	Edge	Result
Draw	3.40	+6.0%	Lost
Under 2.5 goals	1.95	+2.3%	Lost

Pre-match model vs market

Outcome	Model	Market	Difference	Fair odds
Sønderjyske Fodbold	41.5%	42.4%	−0.9 pp	2.41
Draw	31.2%	27.5%	+3.7 pp	3.21
AC Horsens	27.3%	30.1%	−2.8 pp	3.66

Other markets

Outcome	Model	Market	Difference	Fair odds
Both teams to score	57.2%	54.7%	+2.5 pp	1.75
Both teams not to score	42.8%	45.3%	−2.5 pp	2.33
Over 1.5 goals	76.4%	75.0%	+1.4 pp	1.31
Under 1.5 goals	23.6%	25.0%	−1.4 pp	4.23
Over 2.5 goals	47.6%	51.3%	−3.8 pp	2.10
Under 2.5 goals	52.4%	48.7%	+3.8 pp	1.91

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

· 1 warning

Show

Fit window: the last 20 matches, recency weighted (a match 107 days old counts half)
Sønderjyske Fodbold sample: 20 matches
AC Horsens sample: 6 matches
Computed: 22 hours before kick-off
Prices taken: 23 hours before kick-off

Warnings

AC Horsens has only 6 matches in the database. Below 10 matches the estimate is very unreliable.

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.