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…

Portrait of Nathan Tella

Nathan

Tella

RWNigeria · Bayer 04 Leverkusen

№ 340322Elite €12.6M +0.17

Image: FootyLogos

Player dossierBundesliga

NathanTella

RWRight WingerAttack

Club
Bayer 04 Leverkusen
League
Bundesliga
Nationality
Nigeria
Age
26
Height
173 cm
Foot
Right
Valued
27 May 2026

Reading the valuation

The market values Tella at €15M. Model A, which never saw Tella during training, estimates €12.6M: the market value is 19% above that estimate.

That is a factor of ×1.19. 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 €6.9M, 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.

▲ +0.17A €12.6M▲ +0.77B €6.9M
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
62
Minutes
3,049
Goals
5
Assists
11
Started
36/62
Min / app
49
Cards
5Y 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 Tella…

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 €12.6M

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 1,583 attackers

Nathan Tella's percentiles among attackers: Goals per 90 23rd, Assists per 90 93rd, Minutes, two years 67th, Starts 49th, Minutes, last 365 days 40th, European club minutes 73rd23rdGoals93rdAssists67thMinutes49thStarts40thRecent73rdEurope

Dashed ring: the attacker median (50th percentile)

Assists per 90

Club assists per 90 minutes in the same window.

0.32 this player0.15 attacker median93rd percentile

0.00peer deciles · median line · this player ●0.73

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
€750k
Oct 2020
Peak
€23M
Oct 2023
Model target
€15M
May 2026
Valuations
16
3 club changes
May 2026€15MBayer 04 LeverkusenValuation the model is trained to reproduce

May 2026: €15M, Bayer 04 Leverkusen. Dotted lines mark the €2M, €10M and €30M tier boundaries.

Show as a table
DateMarket valueClub
2020-10-13€750kSouthampton FC
2021-03-18€1.5MSouthampton FC
2021-06-08€2MSouthampton FC
2021-12-23€2MSouthampton FC
2022-06-15€1.8MSouthampton FC
2022-09-14€3MBurnley FC
2022-10-27€4.5MBurnley FC
2023-03-24€12MBurnley FC
2023-06-01€18MSouthampton FC
2023-10-18€23MBayer 04 Leverkusen
2023-12-14€23MBayer 04 Leverkusen
2024-05-29€23MBayer 04 Leverkusen
2024-12-20€18MBayer 04 Leverkusen
2025-06-05€15MBayer 04 Leverkusen
2025-12-19€15MBayer 04 Leverkusen
2026-05-27€15MBayer 04 Leverkusen

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
+0.175

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 74% of the 451 Bundesliga players.

€300k€2M€15M€100M€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 Tella 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).