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…

Abakar

Sylla

CBCote d'Ivoire · FC Nantes

№ 962555Established €1.6M +0.44

Player dossierLigue 1

AbakarSylla

CBCentre-BackDefender

Club
FC Nantes
League
Ligue 1
Nationality
Cote d'Ivoire
Age
23
Height
188 cm
Foot
Left
Valued
1 Jun 2026

Reading the valuation

The market values Sylla at €2.5M. Model A, which never saw Sylla during training, estimates €1.6M: the market value is 55% above that estimate.

That is a factor of ×1.55. 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 €936k, 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.

▲ +0.44A €1.6M▲ +0.98B €936k
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
24
Minutes
1,010
Goals
1
Assists
0
Started
11/24
Min / app
42
Cards
2Y 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 Sylla…

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 €2.5M

Checking API
EmergingEstablishedEliteWorld Class€2M€10M€30M€100k€1M€100MMarket €2.5MModel A €1.6M

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

Abakar Sylla's percentiles among defenders: Goals per 90 83rd, Assists per 90 17th, Minutes, two years 12th, Starts 5th, Minutes, last 365 days 3rd, European club minutes 30th83rdGoals17thAssists12thMinutes5thStarts3rdRecent30thEurope

Dashed ring: the defender median (50th percentile)

Goals per 90

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

0.09 this player0.03 defender median83rd percentile

0.00peer deciles · median line · this player ●0.41

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
€200k
Jul 2022
Peak
€15M
Oct 2023
Model target
€2.5M
Jun 2026
Valuations
12
2 club changes
Jun 2026€2.5MFC NantesValuation the model is trained to reproduce

Jun 2026: €2.5M, FC Nantes. Dotted lines mark the €2M, €10M and €30M tier boundaries.

Show as a table
DateMarket valueClub
2022-07-20€200kClub Brugge KV
2022-09-07€1.5MClub Brugge KV
2022-11-04€9MClub Brugge KV
2023-06-09€8MClub Brugge KV
2023-10-16€15MRC Strasbourg Alsace
2023-12-18€12MRC Strasbourg Alsace
2024-06-03€12MRC Strasbourg Alsace
2024-12-10€12MRC Strasbourg Alsace
2025-06-03€10MRC Strasbourg Alsace
2025-10-10€5MRC Strasbourg Alsace
2025-12-15€3MRC Strasbourg Alsace
2026-06-01€2.5MFC Nantes

Audit context group-level, not about this player

Audit group
non-UEFA
Population gap, Model A
+0.093 95% CI +0.063 to +0.124
This player, market vs held-out A
+0.436

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 24% of the 423 Ligue 1 players.

€100k€750k€5M€30M€2.5MSee them in the Explorer →

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

Valued above 54% of the 2,065 defenders.

€20k€200k€2M€20M€2.5MSee them in the Explorer →

Where Sylla 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).