xgEdge

Monday, 14/09/2026 at 19:00

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Match report · Superliga (Denmark)

FC Midtjylland 4:1 Brøndby IF

FC Midtjylland Home 1.16 xG · 16 shots
Brøndby IF Away 0.80 xG · 12 shots
41 The scoreline reflected the chances

FC Midtjylland were the model's likeliest winner before kick-off at 43%. They won 4–1. The biggest difference was Brøndby IF's attack: 0.80 xG against 1.15 projected (−0.35). FC Midtjylland produced 1.16 xG against 1.49 projected (−0.33).

The model against the result

priced before kick-off
FC Midtjylland
43% What happened
Draw
30%
Brøndby IF
26%

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

Projected vs produced

Expected goals
FC Midtjylland The model projected 1.49 Produced 1.16 Difference -0.33
Brøndby IF The model projected 1.15 Produced 0.80 Difference -0.35

Brøndby IF produced 0.35 xG less than projected

Projected before kick-off Produced on the night

The shots behind it

FC Midtjylland Brøndby IF
Shots
16
12
On target
4
5
Inside the box
13
6
xG per shot
0.09
0.07
Open play xG
0.61
0.80
Set-piece xG
0.82
0.04
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: 1.43 for FC Midtjylland and 0.85 for Brøndby IF.

Shot map

Both teams shown attacking the same goal for comparison.

Brøndby IF · Max Ejdum · 69' · Goal · 12 m · 0.30 xG FC Midtjylland · Stanley Iheanacho · 74' · Goal · 6 m · 0.29 xG FC Midtjylland · Magnus Jensen · 84' · No goal · 3 m · 0.28 xG Brøndby IF · Olti Hyseni · 90' · Goal · 10 m · 0.22 xG FC Midtjylland · Friday Etim · 59' · No goal · 14 m · 0.15 xG FC Midtjylland · Philip Billing · 28' · No goal · 12 m · 0.14 xG FC Midtjylland · Gue-sung Cho · 74' · No goal · 16 m · 0.10 xG FC Midtjylland · Friday Etim · 14' · No goal · 13 m · 0.08 xG FC Midtjylland · Friday Etim · 49' · No goal · 9 m · 0.07 xG FC Midtjylland · Rasmus Kristensen · 1' · Goal · 18 m · 0.06 xG Brøndby IF · Max Ejdum · 38' · No goal · 13 m · 0.06 xG Brøndby IF · Mads Frøkjær · 57' · No goal · 16 m · 0.06 xG FC Midtjylland · Victor Bak Jensen · 79' · No goal · 11 m · 0.06 xG Brøndby IF · Mads Frøkjær · 90' · No goal · 21 m · 0.05 xG FC Midtjylland · Stanley Iheanacho · 23' · Goal · 10 m · 0.04 xG FC Midtjylland · Friday Etim · 72' · No goal · 9 m · 0.04 xG Brøndby IF · Olti Hyseni · 77' · No goal · 20 m · 0.04 xG FC Midtjylland · Beni Junior · 79' · No goal · 14 m · 0.04 xG FC Midtjylland · Magnus Jensen · 89' · Goal · 15 m · 0.04 xG FC Midtjylland · Stanley Iheanacho · 1' · No goal · 13 m · 0.02 xG Brøndby IF · Sho Fukuda · 5' · No goal · 15 m · 0.02 xG Brøndby IF · Filip Bundgaard · 29' · No goal · 28 m · 0.02 xG Brøndby IF · Mads Frøkjær · 56' · No goal · 22 m · 0.02 xG Brøndby IF · Raphael Canut · 82' · No goal · 11 m · 0.02 xG Brøndby IF · Olti Hyseni · 82' · No goal · 12 m · 0.02 xG FC Midtjylland · Philip Billing · 64' · No goal · 34 m · 0.01 xG FC Midtjylland · Magnus Jensen · 70' · No goal · 4 m · 0.01 xG Brøndby IF · Olti Hyseni · 90' · No goal · 27 m · 0.01 xG

All shots

28 shots · 2.27 xG

Hover, focus or tap a shot to read it.

  • FC Midtjylland
  • Brøndby IF
  • Filled = goal
  • Dot area = xG (the smallest chances drawn at a minimum size)

Recent form

Last 6 · xG per match
FC Midtjylland

1.73 xG created 1.06 xGA

  • W 1.97 created, 0.41 conceded against AC Horsens
  • D 1.05 created, 0.49 conceded against Lyngby
  • W 1.28 created, 1.03 conceded against Randers FC
  • D 2.50 created, 1.11 conceded against Silkeborg IF
  • W 1.71 created, 1.21 conceded against AGF
  • D 1.88 created, 2.10 conceded against FC Nordsjælland
Brøndby IF

1.45 xG created 1.42 xGA

  • W 1.34 created, 1.95 conceded against Viborg FF
  • W 0.63 created, 0.49 conceded against AC Horsens
  • W 1.63 created, 0.92 conceded against Sønderjyske Fodbold
  • W 2.14 created, 0.99 conceded against Silkeborg IF
  • L 1.76 created, 3.32 conceded against FC Nordsjælland
  • L 1.19 created, 0.85 conceded against Randers FC

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

Head to head

FC Midtjylland: 2 won · 1 drawn · 1 lost

Pre-match model & market record

6 opportunities flagged0 won6 lost

  • Lost
  • Lost
  • Lost
  • Lost
  • Lost
  • Lost
View pre-match record
The outcomes the model flagged on FC Midtjylland against Brøndby IF before kick-off, in the order they ranked, with the price, the edge and what became of each.
Selection Price Edge Result
Draw 4.00 +20.4% Lost
FC Midtjylland at most 1 goal 2.20 +20.2% Lost
FC Midtjylland not to score 6.00 +19.0% Lost
Under 2.5 goals 2.35 +16.3% Lost
Under 1.5 goals 5.00 +8.8% Lost
Brøndby IF to win 4.00 +5.7% Lost

Pre-match model vs market

Outcome Model Market Difference Fair odds
FC Midtjylland 43.5% 53.3% −9.8 pp 2.30
Draw 30.1% 23.3% +6.8 pp 3.32
Brøndby IF 26.4% 23.3% +3.1 pp 3.79

Other markets

Outcome Model Market Difference Fair odds
FC Midtjylland to score 80.2% 84.2% −4.0 pp 1.25
FC Midtjylland not to score 19.8% 15.8% +4.0 pp 5.04
Brøndby IF to score 71.1% 68.7% +2.3 pp 1.41
Brøndby IF not to score 28.9% 31.3% −2.3 pp 3.46
Both teams to score 58.7% 58.9% −0.2 pp 1.70
Both teams not to score 41.3% 41.1% +0.2 pp 2.42
FC Midtjylland 2+ goals 45.3% 57.7% −12.3 pp 2.21
FC Midtjylland at most 1 goal 54.7% 42.3% +12.3 pp 1.83
Brøndby IF 2+ goals 32.2% 35.5% −3.2 pp 3.10
Brøndby IF at most 1 goal 67.8% 64.5% +3.2 pp 1.48
Over 1.5 goals 78.2% 81.1% −2.8 pp 1.28
Under 1.5 goals 21.8% 18.9% +2.8 pp 4.60
Over 2.5 goals 50.5% 59.9% −9.4 pp 1.98
Under 2.5 goals 49.5% 40.1% +9.4 pp 2.02

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)
FC Midtjylland sample
20 matches
Brøndby IF sample
20 matches
Computed
10 hours before kick-off
Prices taken
10 hours 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.