Eliteserien · Norway · Regular Season - 1

Aalesund–Lillestrom

1–3

HT 1–1

Kick-off: Sunday, 15 March 2026 at 16:00
Status: Match Finished

Goals and red cards

6'AalesundK. Lonebu (assist: J. Orsahl)
18'LillestromVá
58'LillestromT. Olsen
79'LillestromT. Olsen

Match statistics

Match statistics
Aalesund		Lillestrom
14	Shots	17
7	Shots on target	7
10	Shots inside the box	7
4	Goalkeeper saves	6
3	Corners	7
2	Offsides	1
40%	Possession	60%
382	Passes	581
290	Accurate passes

What we expected, and what happened

Expected goals before kick-off: Aalesund 1.58 · Lillestrom 1.60. On the pitch: 1-3.
Shots Aalesund 14 · Lillestrom 17; on target 7 and 7.
Lillestrom kept the ball more (60%).

Referee

Espen Eskas, Norway→

2.50

yellows per match

0.25

reds per match

matches of data

12 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 37.8%: “Lillestrom to win”. Second comes “Aalesund to win” at 36.7%.
Behind that distribution are the expected goals: 1.58 for Aalesund, 1.60 for Lillestrom.
The highest double chance value is 74.5% (12 — any result other than a draw).
These values were computed from 962 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.

Aalesund to win36.7%

Draw25.5%

Lillestrom to win37.8%

When the model has said 35–40% in the past, 1,562 of 4,187 predictions were correct (37.3%). Accuracy record →

12	Fouls	9

1	Yellow cards	1

0	Red cards	0

First half

Actual: 1–1

Aalesund lead at half-time29.1%

Level at half-time40.1%

Lillestrom lead at half-time30.8%

First-half double chance

1X — Aalesund ahead or level69.2%

12 — not level at half-time59.9%

X2 — level or Lillestrom ahead70.9%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 962 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

Aalesund: at least 10Actual: 12
Lillestrom: at least 9Actual: 9

Corners

Aalesund: at least 4Actual: 3
Lillestrom: at least 5Actual: 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 Aalesund score1.58

Attack: Aalesund score below the league average (24% fewer).

Opponent's defence: Lillestrom's defence is close to the league average (1.15×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 1.58 goals (including home advantage).

How many goals Lillestrom score1.60

Attack: Lillestrom score close to the league average.

Opponent's defence: Aalesund concede 33% more than the league average — the defence is the weakest link in this match.

Result: These two effects give an expectation of 1.60 goals (including the away penalty).

Most likely scorelines

1–111.9%1–29.2%2–19.0%

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 1 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 and we're not hiding it: in the opening week of past seasons the model came in 3.3 points below the accuracy it claimed (n=220) — for the rest of the season it beats its own claim. The tendency points this way, though it is not proof on its own: in the opening week it slightly over-calls home wins (said 42.7%, got 39.5%) and slightly under-calls draws (said 26.1%, got 28.2%).

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.