Ham-Kam vs Molde preview by Lars for TerraceBet
Eliteserien · Regular Season - 21 Sun 13 Sep · 16:00 BST Briskeby Arena

Ham-Kam vs Molde: Ham‑Kam to win

Ham‑Kam host Molde in a tight Eliteserien clash where the home side's 40.9% win probability edges out the visitors. Both teams boast high expected goals, setting up a potentially open encounter.

Lars's Take
1
Headline Pick
Ham‑Kam to win
Solid · Home edge
2
Both Teams to Score
BTTS — Yes
68.2% model probability
3
Total Goals
Over 2.5 Goals
67.9% model probability

Both sides arrive with contrasting recent form: Ham‑Kam have slipped to a 0‑4 defeat in their latest outing, while Molde have rattled off three wins from four matches. The disparity in momentum makes the result anything but a foregone conclusion.

Lars's Verdict

Solid
Ham‑Kam to win Solid · Home edge

The model assigns a 40.9% chance to a Ham‑Kam win, the highest single‑outcome probability among the three possible results.

Show the maths
The model’s win probability for Ham‑Kam sits at 40.9%, compared with Molde’s 36.8% and a 22.3% draw. This advantage stems from Ham‑Kam’s slightly higher expected goals (1.8 vs 1.7) and a marginally better defensive record at home (1.6 conceded per game versus Molde’s 2.0 away). Combining these figures yields the highest single‑outcome likelihood for the home side.
Pick 2 · Both Teams to Score
Both Teams to Score — Yes 68.2% model probability

With Ham-Kam averaging 1.8 expected goals and Molde averaging 1.7, the model gives a 68.2% chance both teams find the net, ahead of the 31.8% it gives to at least one side being kept out.

Show the maths
BTTS probability is derived from each side's expected goals (Ham-Kam 1.8, Molde 1.7) run through the same Poisson goal-expectancy model used for the match result: 68.2% of simulated outcomes have both teams scoring at least once, versus 31.8% where at least one side is shut out.
Pick 3 · Total Goals
Over 2.5 Goals 67.9% model probability

Combined expected goals of 3.5 (Ham-Kam 1.8 + Molde 1.7) points to an open game, with the model giving Over 2.5 Goals a 67.9% chance against 32.1% for Under.

Show the maths
Total goals probability comes from the same Poisson model summing both teams' expected goals (Ham-Kam 1.8 + Molde 1.7 = 3.5 combined): 67.9% of simulated outcomes finish with 3 or more goals, versus 32.1% finishing with 2 or fewer.
Match Result Model

Our Poisson model, built from each side's real season scoring and conceding rates, has Ham-Kam at 40.9%, the draw at 22.3%, and Molde at 36.8% to win.

Most Likely Scoreline

The single most likely scoreline in our model is 1-1 at 9.2% — treat any single scoreline price with caution given how spread out the probability distribution is across a low-scoring match.

The Numbers Behind the Headline

Ham-Kam expected goals
1.8
Molde expected goals
1.7
Model BTTS probability
68.2%

Most likely scorelines

11-1
9.2%
22-1
8.3%
31-2
7.9%

Model probabilities are derived from each team's real season goals-for and goals-against averages via a Poisson goal-expectancy model — not a fixed set of narrative numbers.

Form Guide — Last 5

Ham-Kam

WDLDL

Ham‑Kam’s last five league results (W‑D‑L‑D‑L) include a 3‑0 away win over Valerenga and a 2‑2 home draw with Kristiansund BK, showing they can score but also concede heavily, as seen in the 0‑4 away loss to Rosenborg. The mixed form underlines inconsistency, which tempers expectations for a clean sheet but supports a potential win if they replicate the Valerenga performance.

Molde

DLWWW

Molde’s recent run (D‑L‑W‑W‑W) features a 3‑2 home win over Tromso and a 2‑0 home victory against KFUM Oslo, demonstrating strong attacking output. However, the 1‑2 away loss to Kristiansund BK highlights defensive frailty on the road, suggesting they may struggle to keep a clean sheet against a motivated Ham‑Kam side.

Head-to-Head

In the five most recent meetings, Molde have won three (4‑1, 1‑0, 3‑0) while Ham‑Kam have secured one 2‑1 win, indicating a historical advantage for Molde but not overwhelming, leaving room for a home upset.

DateResultCompetition
12 Apr 2026Molde 4-1 Ham-KamEliteserien
9 Aug 2025Molde 1-0 Ham-KamEliteserien
25 May 2025Ham-Kam 2-1 MoldeEliteserien
25 Aug 2024Molde 3-0 Ham-KamEliteserien
7 Apr 2024Ham-Kam 0-1 MoldeEliteserien

Team News

Ham-Kam: M. Gjone (Red Card), A. Potur (Heel Injury), N. Alexandersson (Injury), D. Benjamin (Injury)

Molde: S. Diop (Inactive), S. Granaas (Yellow Cards), S. Hansen (Injury), E. Hestad (Injury)

Injury records reflect the current season on file and may not capture very recent knocks — confirmed lineups land closer to kickoff.

Frequently Asked Questions

Who is predicted to win Ham-Kam vs Molde?

Ham‑Kam to win. The model assigns a 40.9% chance to a Ham‑Kam win, the highest single‑outcome probability among the three possible results.

What does the model give Ham-Kam vs Molde?

Our Poisson model, built from each side's real season scoring and conceding rates, gives Ham-Kam a 40.9% chance, the draw 22.3%, and Molde 36.8% to win.

When is Ham-Kam vs Molde?

Ham-Kam play Molde on Sun 13 Sep · 16:00 BST in the Eliteserien at Briskeby Arena.

Are there any injury concerns for Ham-Kam vs Molde?

Ham-Kam: M. Gjone (Red Card), A. Potur (Heel Injury), N. Alexandersson (Injury), D. Benjamin (Injury). Molde: S. Diop (Inactive), S. Granaas (Yellow Cards), S. Hansen (Injury), E. Hestad (Injury). Injury records reflect the current season on file and may not capture very recent knocks — confirmed lineups land closer to kickoff.

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Given the slight statistical edge to the home team and Molde's recent defensive lapses, the model rates a Ham‑Kam victory as the most probable single outcome.

Predictions and model probabilities are for information and entertainment only and are not betting advice — always do your own research. BeGambleAware.org

Lars, European Football Writer at TerraceBet
Written by Lars
European Football Writer, TerraceBet — Continental European leagues from the Eredivisie to the Superliga
Priya, Fact-Checking Editor at TerraceBet
Fact-checked by Priya
Fact-Checking Editor, TerraceBet — every stat and claim verified against the underlying data