FairValue

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  • EnvironmentOverviewWhat FairValue measures, what it found, where to go next.
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Villum

Berthelsen

CFDenmark · FC Nordsjaelland

№ 1088534Emerging €1.1M −0.29

Player dossierSuperliga

VillumBerthelsen

CFCentre-ForwardAttack

Club
FC Nordsjaelland
League
Superliga
Nationality
Denmark
Age
20
Height
181 cm
Foot
Right
Valued
25 May 2026

Reading the valuation

The market values Berthelsen at €800k. Model A, which never saw Berthelsen during training, estimates €1.1M: the market value is 25% below that estimate.

That is a factor of ×1.34. Model A's typical held-out miss is ×1.50 either way, so this gap is within its usual error.

Without league and club, Model B estimates €751k, closer to the market here, although it is usually further off (typical miss ×2.07).

A gap describes the model's arithmetic for this player's inputs, not what the player is worth.

▼ −0.29A €1.1M▲ +0.06B €751k
Pitch length is a log value scale fitted to this player's figures. ● observed · ◇ Model A · dashed ◇ Model B · each arc is a residual, log(observed) − log(estimate).
Apps
21
Minutes
977
Goals
1
Assists
0
Started
11/21
Min / app
47
Cards
2Y 0R

Club matches, two years to the valuation date

The Valuation Journey

What moves Model A

Asking the prediction API to explain Model A for Berthelsen…

SHAP describes the model's arithmetic for this player's inputs, not what causes market value. Model B can be explained only by the live service; Model A also has a published explanation for the research players.

Model A vs Model B against the observed €800k

Checking API
EmergingEstablishedEliteWorld Class€2M€10M€30M€100k€1M€100MMarket €800kModel A €1.1M

Asking the prediction API…

Held-out estimates come from cross-validation, with this player outside the training data: the honest comparison. Served models were refitted on every player, this one included, so they sit closer to the market. Shaded band: Model A's typical miss (×1.50 either way).

Player traitspercentiles vs 1,583 attackers

Villum Berthelsen's percentiles among attackers: Goals per 90 12th, Assists per 90 6th, Minutes, two years 18th, Starts 39th, Minutes, last 365 days 36th, European club minutes 28th12thGoals6thAssists18thMinutes39thStarts36thRecent28thEurope

Dashed ring: the attacker median (50th percentile)

Starts

Share of appearances started rather than entered from the bench.

52% this player59% attacker median39th percentile

5%peer deciles · median line · this player ●100%

No passing, progression or defensive actions exist in the dataset, so no trait claims to measure them.

Valuation history every Transfermarkt valuation on record

First valuation
€300k
Jun 2025
Peak
€850k
Dec 2025
Model target
€800k
May 2026
Valuations
3
0 club changes
May 2026€800kFC NordsjaellandValuation the model is trained to reproduce

May 2026: €800k, FC Nordsjaelland. Dotted lines mark the €2M, €10M and €30M tier boundaries.

Show as a table
DateMarket valueClub
2025-06-16€300kFC Nordsjaelland
2025-12-15€850kFC Nordsjaelland
2026-05-25€800kFC Nordsjaelland

Audit context group-level, not about this player

Audit group
UEFA
Population gap, Model A
+0.093 95% CI +0.063 to +0.124
This player, market vs held-out A
−0.289

The group comes from first-listed citizenship and is never a model input. Across the population, non-UEFA players' market values sit about 10% above Model A's held-out estimates, relative to UEFA players in the same leagues. One player's residual says nothing about that pattern (typical miss ×1.50), and none of it is evidence of discrimination: the target may itself be biased. Open the Fairness Audit

In context observed value among comparable players in this build

All players in the Terminal →

Superliga256 players · median €800k

Valued above 46% of the 256 Superliga players.

€20k€150k€1M€7.5M€800kSee them in the Explorer →

Attackers, all leagues1,583 players · median €2.5M

Valued above 25% of the 1,583 attackers.

€20k€300k€5M€75M€800kSee them in the Explorer →

Where Berthelsen appears

Watchlists and the comparison tray are kept in this browser only.

Near this valuation closest observed values: same league, then same position

Counts and ranks of observed Transfermarkt values only; model errors are never ranked here. Full player export: Every player in the modelling population (D-011).