Superliga · Denmark · Relegation Round - 2

Aalborg–Odense

2–3

HT 0–2

Kick-off: Monday, 10 April 2023 at 12:00
Status: Match Finished
Venue: Aalborg Portland Park, Aalborg

Goals and red cards

13'OdenseJ. Tverskov (assist: M. Frokjaer-Jensen)
39'OdenseM. Frokjaer-Jensen (assist: E. Sabbi)
48'AalborgL. Prip (assist: J. Ahlmann)
71'AalborgAllan Sousa
75'OdenseB. Kadrii (penalty)

Match statistics

Match statistics
Aalborg		Odense
16	Shots	16
4	Shots on target	7
12	Shots inside the box	12
4	Goalkeeper saves	2
8	Corners	8
4	Offsides	1
53%	Possession	47%
346	Passes	323
243	Accurate passes

What we expected, and what happened

Expected goals before kick-off: Aalborg 1.36 · Odense 1.31. On the pitch: 2-3.
Shots Aalborg 16 · Odense 16; on target 4 and 7.

Referee

Lasse Laebel Graagaard, Denmark→

0.00

yellows per match

0.00

reds per match

matches of data

1 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 37.2%: “Aalborg to win”. Second comes “Odense to win” at 34.7%.
Behind that distribution are the expected goals: 1.36 for Aalborg, 1.31 for Odense.
The highest double chance value is 71.9% (12 — any result other than a draw).
These values were computed from 136 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.

Aalborg to win37.2%

Draw28.1%

Odense to win34.7%

When the model has said 35–40% in the past, 2,090 of 5,638 predictions were correct (37.1%). Accuracy record →

13	Fouls	10

2	Yellow cards	2

0	Red cards	0

First half

Actual: 0–2

Aalborg lead at half-time26.5%

Level at half-time46.6%

Odense lead at half-time26.9%

First-half double chance

1X — Aalborg ahead or level73.1%

12 — not level at half-time53.4%

X2 — level or Odense ahead73.5%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 136 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

Aalborg: at least 9Actual: 13
Odense: at least 9Actual: 10

Corners

Aalborg: at least 5Actual: 8
Odense: at least 3Actual: 8

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 Aalborg score1.36

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

Opponent's defence: Odense concede 30% more than the league average, which pushes the expectation up.

Result: These two effects give an expectation of 1.36 goals (including home advantage).

How many goals Odense score1.31

Attack: Odense 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 1.31 goals (including the away penalty).

Most likely scorelines

1–114.2%2–19.1%1–28.8%

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 2 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.