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…

Stephen

Odey

CFNigeria · Randers FC

№ 491890Emerging €448k +0.11

Player dossierSuperliga

StephenOdey

CFCentre-ForwardAttack

Club
Randers FC
League
Superliga
Nationality
Nigeria
Age
27
Height
182 cm
Foot
Right
Valued
15 Dec 2025

Reading the valuation

The market values Odey at €500k. Model A, which never saw Odey during training, estimates €448k: the market value is 12% above that estimate.

That is a factor of ×1.12. 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 €800k, 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.

▲ +0.11A €448k▼ −0.47B €800k
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
30
Minutes
1,231
Goals
4
Assists
0
Started
11/30
Min / app
41
Cards
4Y 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 Odey…

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 €500k

Checking API
EmergingEstablishedEliteWorld Class€2M€10M€30M€100k€1M€100MMarket €500kModel A €448k

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

Stephen Odey's percentiles among attackers: Goals per 90 56th, Assists per 90 6th, Minutes, two years 25th, Starts 16th, Minutes, last 365 days 4th, European club minutes 28th56thGoals6thAssists25thMinutes16thStarts4thRecent28thEurope

Dashed ring: the attacker median (50th percentile)

Goals per 90

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

0.29 this player0.26 attacker median56th 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
€400k
Jan 2018
Peak
€2.5M
Sept 2019
Model target
€500k
Dec 2025
Valuations
21
3 club changes
Dec 2025€500kRanders FCValuation the model is trained to reproduce

Dec 2025: €500k, Randers FC. Dotted lines mark the €2M, €10M and €30M tier boundaries.

Show as a table
DateMarket valueClub
2018-01-08€400kFC Zürich
2018-04-24€400kFC Zürich
2018-07-12€400kFC Zürich
2018-10-19€1MFC Zürich
2019-01-21€1.5MFC Zürich
2019-04-15€2MFC Zürich
2019-06-04€2MFC Zürich
2019-09-13€2.5MKRC Genk
2019-12-13€2.5MKRC Genk
2020-04-08€2.3MKRC Genk
2020-11-11€2MAmiens SC
2021-06-25€1.6MAmiens SC
2021-12-28€1.5MRanders FC
2022-06-20€1.5MRanders FC
2022-12-22€1MRanders FC
2023-06-26€1MRanders FC
2023-12-21€1MRanders FC
2024-06-10€1MRanders FC
2024-12-09€800kRanders FC
2025-06-16€750kRanders FC
2025-12-15€500kRanders FC

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
+0.109

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 25% of the 256 Superliga players.

€20k€150k€1M€7.5M€500kSee them in the Explorer →

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

Valued above 15% of the 1,583 attackers.

€20k€300k€5M€75M€500kSee them in the Explorer →

Where Odey 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).