Molde’s strong home attack should set the tempo, with their scoring threat likely to trouble Fredrikstad’s steady but not impenetrable defence. Fredrikstad’s attack looks less potent, yet Molde’s forward drive gives the match a clear route to an open finish: the over appears the most logical outcome.
Match GoalsOver 2.5
Best Pick
Over 2.5
65%
Correct Score
Top
2-1
9.8%
8.50
Top
2-0
9.2%
9.50
1-1
8.5%
9.00
1-0
8%
11.00
3-1
7.5%
11.00
3-0
7.1%
12.00
2-2
5.2%
13.00
1-2
4.5%
19.00
165%
1.50TOP
X19%
4.20
216%
5.50
1X84%
X235%
1281%
Combined Bet Builder
3 legs
Molde’s profile points to early control and a home win: their 1.53 first-half xG dwarfs Fredrikstad’s 0.34, while a +42% attack-power rating gives them the edge to strike first. Molde’s 1.77 xG plus Fredrikstad’s 1.02 supports a three-goal game, making Molde, over 2.5 and Molde at half-time a strong fit.
1Match WinnerMolde
2Match GoalsOver 2.5
3First Half WinnerMolde
Confirmed XI4-5-1
22
Posiadala
25
Linnes
19
Haugan
27
Dahl
8
Løvik
11
Sery
17
Dæhli
23
Granaas
16
Kilen
10
Breivik
20
Lonebu
Impact Player Hot Head
Molde33 players
52.5/ 99Average
Top Rated
1Trent Kone DohertyFWD85
2Oskar Spiten-NysæterFWD82
3Noah Ljøen ByeDEF75
Fredrikstad FK37 players
48.6/ 99Average
Top Rated
1Samuel Leach HolmMID77
2Jakub JezierskiMID74
3Rocco SheinMID71
Player Rankings
Snipers
Top goal threats
1
Trent Kone DohertyMolde
99
2
Oskar Spiten-NysæterMolde
85
3
S. DiopMolde
77
4
Caleb Zady SeryMolde
64
5
Kristian LonebuMolde
63
Wall
Top defenders
1
Noah Ljøen ByeMolde
99
2
Ulrik FredriksenFredrikstad FK
74
3
S. HansenMolde
69
4
Fredrik HolméFredrikstad FK
69
5
Eirik HauganMolde
68
Control
Top playmakers
1
Eirik Julius GranaasFredrikstad FK
92
2
Samuel Leach HolmFredrikstad FK
81
3
Mats Møller DæhliMolde
79
4
Max NilssonFredrikstad FK
78
5
Sondre GranaasMolde
68
Bad Boys
Most likely to get booked
1
Mathias LøvikMolde
1
Aggression 41
2
Sondre GranaasMolde
1
Aggression 42
3
Fanuel Ghebreyohannes Yrga-AlemFredrikstad FK
1
Aggression 82
4
Simen RafnFredrikstad FK
1
Aggression 45
5
Eirik Julius GranaasFredrikstad FK
1
Aggression 99
Power Rankings
Strongest overall
1
Trent Kone DohertyMolde
85
2
Oskar Spiten-NysæterMolde
82
3
Samuel Leach HolmFredrikstad FK
77
4
Noah Ljøen ByeMolde
75
5
Daniel DagaMolde
74
Frequently Asked Questions
Our Poisson model calculates the probability of every possible scoreline for Molde vs Fredrikstad FK based on each team's expected goals (xG). The top-3 most likely scorelines are shown above with their probability percentages. Even the highest-probability score rarely exceeds 12-15% chance, as football is a low-scoring sport.
The Bet Builder combines multiple selections from the Molde vs Fredrikstad FK match into one bet. Our AI identifies outcomes that complement each other — for example, a home win paired with over 2.5 goals and both teams to score. All selections must win for the combined bet to pay out, which means higher combined odds but also higher risk.
xG (expected goals) measures the quality of scoring chances. If Molde has an xG of 1.8, it means they are expected to score around 1-2 goals based on chance quality. The model uses pre-match xG to build a probability table for all possible scorelines. A team can overperform xG (score more than expected) or underperform xG (score fewer).
Yes, all predictions on ProSoccer are completely free. You can view predicted scorelines, over/under probabilities, and bet builder suggestions for Molde vs Fredrikstad FK without paying or registering. Odds values are revealed after a tap to keep the page compliant with search engine guidelines.
A value bet occurs when the probability of an outcome is higher than what the bookmaker odds suggest. For example, if our model gives Molde a 55% chance of winning but the odds imply only 40%, that is a value bet. Our AI highlights selections where the model probability exceeds the implied probability from the odds.
AI predictions use statistical modeling (Poisson distribution) based on historical data and pre-match metrics. They are more reliable over large samples than for any single match. No prediction is guaranteed — football has inherent unpredictability. Our model typically identifies the correct top-3 scorelines in 25-30% of matches, which is significantly better than random chance.