FairValue

Checking model

Esc

  • EnvironmentOverviewWhat FairValue measures, what it found, where to go next.
  • EnvironmentTerminalExplore, compare and track players; market, league and model views.
  • EnvironmentStoriesValuations, head-to-heads and research findings, told as stories.
  • TerminalPlayer ExplorerSearch, filter and sort the player export.
  • TerminalComparison StudioTwo to four players on one scale.
  • TerminalWatchlistsNamed collections, kept in this browser.
  • TerminalMarket LandscapeHow observed value is distributed, and where the model drifts.

↑ ↓ to move · Enter to open · Esc to closeLoading player index…

Prosper

Peter

CFFrance · Angers SCO

№ 1103958Elite €6.7M +0.80

Player dossierLigue 1

ProsperPeter

CFCentre-ForwardAttack

Club
Angers SCO
League
Ligue 1
Nationality
France
Age
18
Height
188 cm
Foot
Right
Valued
1 Jun 2026

Reading the valuation

The market values Peter at €15M. Model A, which never saw Peter during training, estimates €6.7M: the market value is 123% above that estimate.

That is a factor of ×2.23. 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 €5.8M, 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.

▲ +0.80A €6.7M▲ +0.95B €5.8M
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
31
Minutes
1,291
Goals
5
Assists
0
Started
14/31
Min / app
42
Cards
0Y 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 Peter…

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 €15M

Checking API
EmergingEstablishedEliteWorld Class€2M€10M€30M€100k€1M€100MMarket €15MModel A €6.7M

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

Prosper Peter's percentiles among attackers: Goals per 90 67th, Assists per 90 6th, Minutes, two years 27th, Starts 27th, Minutes, last 365 days 49th, European club minutes 28th67thGoals6thAssists27thMinutes27thStarts49thRecent28thEurope

Dashed ring: the attacker median (50th percentile)

Goals per 90

Club goals per 90 minutes played in the two years to the valuation date.

0.35 this player0.26 attacker median67th percentile

0.00peer deciles · median line · this player ●1.17

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
Jun 2025
Peak
€15M
Mar 2026
Model target
€15M
Jun 2026
Valuations
5
1 club change
Jun 2026€15MAngers SCOValuation the model is trained to reproduce

Jun 2026: €15M, Angers SCO. Dotted lines mark the €2M, €10M and €30M tier boundaries.

Show as a table
DateMarket valueClub
2025-06-03€50kAngers SCO B
2025-10-10€1MAngers SCO
2025-12-15€5MAngers SCO
2026-03-27€15MAngers SCO
2026-06-01€15MAngers SCO

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
+0.802

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 →

Ligue 1423 players · median €5M

Valued above 76% of the 423 Ligue 1 players.

€100k€750k€5M€30M€15MSee them in the Explorer →

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

Valued above 81% of the 1,583 attackers.

€20k€300k€5M€75M€15MSee them in the Explorer →

Where Peter 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).