Eliteserien · Norway · Regular Season - 3

Ham-Kam–Brann

1–5

HT 0–2

Kick-off: Monday, 6 April 2026 at 15:00
Status: Match Finished
Venue: Briskeby Arena, Hamar

What the model said, and what happened

Prediction 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

32'BrannN. Holm (assist: B. Finne)
37'BrannJ. Þorsteinsson
59'BrannB. Finne (assist: J. Sorensen)
74'Ham-KamM. Niang (assist: H. Opsahl)
80'BrannM. Haaland (assist: K. Eriksen)
85'BrannK. Eriksen (assist: U. Mathisen)

Match statistics

Match statistics
Ham-Kam		Brann
15	Shots	20
5	Shots on target	9
11	Shots inside the box	15
4	Goalkeeper saves	4
7	Corners	6
0	Offsides	0
45%	Possession	55%
393	Passes	489
339	Accurate passes

What we expected, and what happened

Expected goals before kick-off: Ham-Kam 1.36 · Brann 1.69. On the pitch: 1-5.
Shots Ham-Kam 15 · Brann 20; on target 5 and 9.
Brann kept the ball more (55%).

Unavailable players

Ham-Kam

L. Maresout · Groin Injury
O. Ryeout · injury

Brann

E. Gudmundssonout · Knee Injury
J. Torsvikout · Groin Injury
N. Boakyeout · Knee Injury
N. Castroout · Achilles Tendon Injury
S. Magnussonout · Knee Injury
S. Opsahlout · Foot Injury

Provider data — may change until the line-ups are announced. Last updated: Saturday, 19 September 2026 at 15:10.

Referee

Sigurd Smehus Kringstad, Norway→

4.35

yellows per match

0.12

reds per match

matches of data

Model prediction

Why this prediction?

The model puts the most likely outcome at 44.8%: “Brann to win”. Second comes “Ham-Kam to win” at 29.6%.
Behind that distribution are the expected goals: 1.36 for Ham-Kam, 1.69 for Brann.
The highest double chance value is 74.4% (12 — any result other than a draw).
These values were computed from 973 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.

Ham-Kam to win29.6%

Draw25.6%

Brann to win44.8%

When the model has said 40–45% in the past, 2,356 of 5,296 predictions were correct (44.5%). Accuracy record →

11	Fouls	10

1	Yellow cards	0

0	Red cards	0

The measurements our data provider sent for this match. A row that was not measured shows “—”; writing 0 into an empty field would claim we measured it and found none.

When the model has said 60–65% in the past, 2,215 of 3,677 predictions were correct (60.2%). Accuracy record →

Double chance

1X — Ham-Kam win or draw55.2%

12 — any result other than a draw74.4%

X2 — draw or Brann win70.4%

These three values add up to 200%, not 100% — and that is not a mistake. The options are not mutually exclusive; every outcome is counted in two of them. For the same reason 1X plus X2 exceeds 100% by exactly the probability of a draw (25.6%).

First half

Actual: 0–2

Ham-Kam lead at half-time42.9%

Level at half-time37.4%

Brann lead at half-time19.7%

First-half double chance

1X — Ham-Kam ahead or level80.3%

12 — not level at half-time62.6%

X2 — level or Brann ahead57.1%

These values are not a fraction of the full-match prediction; they come from a separate model trained on the first-half scores of 973 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

Ham-Kam: at least 11Actual: 11
Brann: at least 10Actual: 10

Corners

Ham-Kam: at least 4Actual: 7
Brann: at least 6Actual: 6

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.

Goals market

The 3+ goals prediction is not published for this match.

The model gives a probability below the baseline (60.6%). In backtesting the model's edge in the "fewer goals" direction could not be confirmed; we don't publish an unconfirmed prediction.

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 Ham-Kam score1.36

Attack: Ham-Kam score close to the league average.

Opponent's defence: Brann's defence is close to the league average (0.86×), so it doesn't move the expectation much.

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

How many goals Brann score1.69

Attack: Brann score above the league average (21% more).

Opponent's defence: Ham-Kam's defence is close to the league average (1.04×), so it doesn't move the expectation much.

Result: These two effects give an expectation of 1.69 goals (including the away penalty).

Most likely scorelines

1–112.3%1–210.1%2–18.0%

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.

Ratings were computed from 973 matches played in this league before kick-off; the team with fewer has 92 matches. No data from after the match was used. The model's track record is published unfiltered.

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.