FairValue

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  • EnvironmentOverviewWhat FairValue measures, what it found, where to go next.
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  • TerminalComparison StudioTwo to four players on one scale.
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  • TerminalMarket LandscapeHow observed value is distributed, and where the model drifts.

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Marco

Friedl

CBAustria · SV Werder Bremen

№ 156990Elite €4.4M +0.83

Player dossierBundesliga

MarcoFriedl

CBCentre-BackDefender

Club
SV Werder Bremen
League
Bundesliga
Nationality
Austria
Age
28
Height
187 cm
Foot
Left
Valued
27 May 2026

Reading the valuation

The market values Friedl at €10M. Model A, which never saw Friedl during training, estimates €4.4M: the market value is 129% above that estimate.

That is a factor of ×2.29. Model A's typical held-out miss is ×1.50 either way, so this gap is larger than its usual error.

Without league and club, Model B estimates €2.8M, further from the market. Model B is usually further off (typical miss ×2.07): league and club explain much of how the market prices players.

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

▲ +0.83A €4.4M▲ +1.27B €2.8M
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
60
Minutes
5,073
Goals
1
Assists
0
Started
58/60
Min / app
85
Cards
20Y 1R

Club matches, two years to the valuation date

The Valuation Journey

What moves Model A

Asking the prediction API to explain Model A for Friedl…

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 €10M

Checking API
EmergingEstablishedEliteWorld Class€2M€10M€30M€100k€1M€100MMarket €10MModel A €4.4M

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 2,065 defenders

Marco Friedl's percentiles among defenders: Goals per 90 38th, Assists per 90 17th, Minutes, two years 84th, Starts 89th, Minutes, last 365 days 80th, European club minutes 30th38thGoals17thAssists84thMinutes89thStarts80thRecent30thEurope

Dashed ring: the defender median (50th percentile)

Starts

Share of appearances started rather than entered from the bench.

97% this player83% defender median89th percentile

18%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
€100k
Jul 2017
Peak
€12M
Jun 2025
Model target
€10M
May 2026
Valuations
25
1 club change
May 2026€10MSV Werder BremenValuation the model is trained to reproduce

May 2026: €10M, SV Werder Bremen. Dotted lines mark the €2M, €10M and €30M tier boundaries.

Show as a table
DateMarket valueClub
2017-07-12€100kBayern Munich
2017-10-18€250kBayern Munich
2017-12-28€750kBayern Munich
2018-06-05€1.5MSV Werder Bremen
2018-12-18€1.5MSV Werder Bremen
2019-06-05€2.5MSV Werder Bremen
2019-10-02€3.5MSV Werder Bremen
2019-12-17€3.5MSV Werder Bremen
2020-04-08€3.2MSV Werder Bremen
2020-11-26€5.5MSV Werder Bremen
2021-02-10€7.5MSV Werder Bremen
2021-05-27€7.5MSV Werder Bremen
2021-10-13€4.5MSV Werder Bremen
2021-12-15€3.5MSV Werder Bremen
2022-06-16€5MSV Werder Bremen
2022-11-09€6.5MSV Werder Bremen
2023-06-22€7MSV Werder Bremen
2023-10-18€5.5MSV Werder Bremen
2023-12-14€5.5MSV Werder Bremen
2024-03-27€6.5MSV Werder Bremen
2024-05-29€7.5MSV Werder Bremen
2024-12-20€10MSV Werder Bremen
2025-06-05€12MSV Werder Bremen
2025-12-19€12MSV Werder Bremen
2026-05-27€10MSV Werder Bremen

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
+0.830

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 →

Bundesliga451 players · median €5M

Valued above 65% of the 451 Bundesliga players.

€300k€2M€15M€100M€10MSee them in the Explorer →

Defenders, all leagues2,065 players · median €2M

Valued above 81% of the 2,065 defenders.

€20k€200k€2M€20M€10MSee them in the Explorer →

Where Friedl 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).