FairValue

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  • EnvironmentOverviewWhat FairValue measures, what it found, where to go next.
  • EnvironmentTerminalExplore, compare and track players; market, league and model views.
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  • TerminalPlayer ExplorerSearch, filter and sort the player export.
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  • TerminalMarket LandscapeHow observed value is distributed, and where the model drifts.

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Isaac

Romero

CFSpain · Sevilla FC

№ 687609Established €4.3M +0.14

Player dossierLaLiga

IsaacRomero

CFCentre-ForwardAttack

Club
Sevilla FC
League
LaLiga
Nationality
Spain
Age
26
Height
183 cm
Foot
Left
Valued
5 Jun 2026

Reading the valuation

The market values Romero at €5M. Model A, which never saw Romero during training, estimates €4.3M: the market value is 15% above that estimate.

That is a factor of ×1.15. 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 €4.1M, 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.

▲ +0.14A €4.3M▲ +0.21B €4.1M
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
63
Minutes
3,931
Goals
10
Assists
4
Started
47/63
Min / app
62
Cards
17Y 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 Romero…

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 €5M

Checking API
EmergingEstablishedEliteWorld Class€2M€10M€30M€100k€1M€100MMarket €5MModel A €4.3M

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

Isaac Romero's percentiles among attackers: Goals per 90 43rd, Assists per 90 28th, Minutes, two years 80th, Starts 75th, Minutes, last 365 days 62nd, European club minutes 28th43rdGoals28thAssists80thMinutes75thStarts62ndRecent28thEurope

Dashed ring: the attacker median (50th percentile)

Minutes, two years

Total club minutes in the two-year window: how much the player was used.

3,931 this player2,242 attacker median80th percentile

450peer deciles · median line · this player ●8,788

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
€50k
Oct 2020
Peak
€18M
Jun 2024
Model target
€5M
Jun 2026
Valuations
17
1 club change
Jun 2026€5MSevilla FCValuation the model is trained to reproduce

Jun 2026: €5M, Sevilla FC. Dotted lines mark the €2M, €10M and €30M tier boundaries.

Show as a table
DateMarket valueClub
2020-10-08€50kSevilla Atlético
2021-01-08€50kSevilla Atlético
2021-06-30€100kSevilla Atlético
2021-12-30€100kSevilla Atlético
2022-06-29€100kSevilla Atlético
2022-12-29€100kSevilla Atlético
2023-06-30€150kSevilla Atlético
2023-10-13€250kSevilla Atlético
2023-12-20€400kSevilla Atlético
2024-03-21€15MSevilla FC
2024-06-07€18MSevilla FC
2024-12-27€15MSevilla FC
2025-03-21€10MSevilla FC
2025-06-09€6MSevilla FC
2025-12-12€6MSevilla FC
2026-03-16€5MSevilla FC
2026-06-05€5MSevilla FC

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
+0.144

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 →

LaLiga509 players · median €4M

Valued above 57% of the 509 LaLiga players.

€100k€1M€10M€100M€5MSee them in the Explorer →

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

Valued above 64% of the 1,583 attackers.

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

Where Romero 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).