Superliga · Denmark · Relegation Round - 3

FC Midtjylland–Aalborg

1–1

HT 0–1

Kick-off: Friday, 14 April 2023 at 17:00
Status: Match Finished
Venue: MCH Arena, Herning

Goals and red cards

42'AalborgAllan Sousa (assist: D. Granli)
82'FC MidtjyllandGustav Isaksen (assist: Aral Şimşir)

Match statistics

Match statistics
FC Midtjylland		Aalborg
18	Shots	16
9	Shots on target	6
11	Shots inside the box	11
5	Goalkeeper saves	8
6	Corners	7
1	Offsides	2
48%	Possession	52%
421	Passes	459
325	Accurate passes

What we expected, and what happened

Expected goals before kick-off: FC Midtjylland 1.52 · Aalborg 0.95. On the pitch: 1-1.
Shots FC Midtjylland 18 · Aalborg 16; on target 9 and 6.

Referee

Jacob Karlsen, Denmark→

3.00

yellows per match

0.00

reds per match

matches of data

8 matches is a small sample — these averages are noisy and should not be read as a tendency.

Model prediction

Why this prediction?

The model puts the most likely outcome at 50.2%: “FC Midtjylland to win”. Second comes “a draw” at 27.6%.
Behind that distribution are the expected goals: 1.52 for FC Midtjylland, 0.95 for Aalborg.
The highest double chance value is 77.8% (1X — FC Midtjylland win or draw).
These values were computed from 138 past matches.

This is a prediction, not a guarantee; a probability is not a certainty.

These sentences are built by a fixed template; every number in them comes straight from the model's computation, none invented while the text was written.

FC Midtjylland to win50.2%

Draw27.6%

Aalborg to win22.2%

When the model has said 50–55% in the past, 1,798 of 3,471 predictions were correct (51.8%). Accuracy record →

7	Fouls	11

2	Yellow cards	3

0	Red cards	0

First half

Actual: 0–1

FC Midtjylland lead at half-time36.5%

Level at half-time46.5%

Aalborg lead at half-time17.0%

First-half double chance

1X — FC Midtjylland ahead or level83.0%

12 — not level at half-time53.5%

X2 — level or Aalborg ahead63.5%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 138 matches. Its edge over the base rate is markedly smaller than the full-match model's — the measured figure is on the accuracy record. Read them more cautiously. First-half goal markets are not published because they failed to beat the base rate.

Expected fouls and corners

Fouls

FC Midtjylland: at least 9Actual: 7
Aalborg: at least 10Actual: 11

Corners

FC Midtjylland: at least 4Actual: 6
Aalborg: at least 4Actual: 7

These figures are set to hold in at least 60% of matches; in the rest the count comes in below them. No upper bound is stated — we make no "at most" claim. Yellow cards were measured and are not published: there our model did not beat the historical average.

What these probabilities rest on

Expected goals comes out of a team's attack weighed against its opponent's defence. A strong attack is damped when it meets a strong defence — which is why comparing the ratings on their own is misleading.

How many goals FC Midtjylland score1.52

Attack: FC Midtjylland score close to the league average.

Opponent's defence: Aalborg's defence is close to the league average (1.05×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 1.52 goals (including home advantage).

How many goals Aalborg score0.95

Attack: Aalborg score below the league average (26% fewer).

Opponent's defence: FC Midtjylland's defence is close to the league average (0.92×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 0.95 goals (including the away penalty).

Most likely scorelines

1–114.0%1–012.1%2–110.2%

A single scoreline is always less likely than an outcome: “home win” is the sum of 1-0, 2-0, 2-1 and dozens more. So the most likely single score can be a draw while the most likely outcome is a home win; the two do not contradict each other.

Week 3 of the season. This prediction was computed from previous seasons. Summer transfers, budget changes and squad rebuilds aren't in the ratings yet — the model can only see that a team has been rebuilt once new results come in.

We measured this: the bias is only pronounced in the opening week. In this week the model's overall accuracy does not separate from the rest of the season — that is, we found no measurable flaw. The one tendency across all opening weeks is that home wins are called slightly too often (said 43.0%, got 41.9%; n=583).

This sample is small and not statistically established, which is why we do not correct the model for it. The correction we tried (pulling probabilities toward the league average) didn't win on holdout, so it wasn't added. Read opening-week predictions with a little more caution.