Superliga · Denmark · Regular Season - 2

Lyngby–Aarhus

2–2

HT 0–1

Kick-off: Saturday, 1 August 2026 at 16:00
Status: Match Finished

What the model said, and what happened

Ended level — we gave the draw 26%double chance: correct

This verdict goes into the public record unchanged; we do not filter out the ones we get wrong. The model's track record

Goals and red cards

33'AarhusKristian Arnstad (penalty)
67'LyngbyCasper Winther
88'LyngbyF. Gytkjaer (penalty)
90+1'AarhusJ. Jonsson (assist: F. Emmery)

Match statistics

Match statistics
Lyngby		Aarhus
21	Shots	8
7	Shots on target	3
14	Shots inside the box	5
1	Goalkeeper saves	5
4	Corners	2
0	Offsides	3
62%	Possession	38%
464	Passes	298
355	Accurate passes

What we expected, and what happened

Expected goals before kick-off: Lyngby 1.19 · Aarhus 1.71. On the pitch: 2-2.
Shots Lyngby 21 · Aarhus 8; on target 7 and 3.
Lyngby kept the ball more (62%).

Referee

Jens Maae, Denmark→

3.21

yellows per match

0.21

reds per match

matches of data

14 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 49.0%: “Aarhus to win”. Second comes “a draw” at 25.7%.
Behind that distribution are the expected goals: 1.19 for Lyngby, 1.71 for Aarhus.
The highest double chance value is 74.8% (X2 — draw or Aarhus win).
These values were computed from 654 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.

Lyngby to win25.2%

Draw25.7%

Aarhus to win49.0%

When the model has said 45–50% in the past, 2,196 of 4,685 predictions were correct (46.9%). Accuracy record →

19	Fouls	14

2	Yellow cards	3

0	Red cards	0

First half

Actual: 0–1

Lyngby lead at half-time24.6%

Level at half-time40.2%

Aarhus lead at half-time35.2%

First-half double chance

1X — Lyngby ahead or level64.8%

12 — not level at half-time59.8%

X2 — level or Aarhus ahead75.4%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 654 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

Lyngby: at least 13Actual: 19
Aarhus: at least 10Actual: 14

Corners

Lyngby: at least 4Actual: 4
Aarhus: at least 5Actual: 2

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 Lyngby score1.19

Attack: Lyngby score close to the league average.

Opponent's defence: Aarhus concede 23% fewer than the league average, which pulls the expectation down.

Result: These two effects give an expectation of 1.19 goals (including home advantage).

How many goals Aarhus score1.71

Attack: Aarhus score above the league average (17% more).

Opponent's defence: Lyngby's defence is close to the league average (1.03×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 1.71 goals (including the away penalty).

Most likely scorelines

1–112.8%1–210.5%0–18.5%

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.