FairValue

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  • EnvironmentOverviewWhat FairValue measures, what it found, where to go next.
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Portrait of Nemanja Gudelj

Nemanja

Gudelj

DMSerbia · Sevilla FC

№ 74294Established €824k +0.89

Image: FootyLogos

Player dossierLaLiga

NemanjaGudelj

DMDefensive MidfieldMidfield

Club
Sevilla FC
League
LaLiga
Nationality
Serbia
Age
34
Height
187 cm
Foot
Right
Valued
5 Jun 2026

Reading the valuation

The market values Gudelj at €2M. Model A, which never saw Gudelj during training, estimates €824k: the market value is 143% above that estimate.

That is a factor of ×2.43. 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 €806k, 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.

▲ +0.89A €824k▲ +0.91B €806k
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
68
Minutes
5,045
Goals
3
Assists
1
Started
55/68
Min / app
74
Cards
16Y 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 Gudelj…

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 €2M

Checking API
EmergingEstablishedEliteWorld Class€2M€10M€30M€100k€1M€100MMarket €2MModel A €824k

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 1,687 midfielders

Nemanja Gudelj's percentiles among midfielders: Goals per 90 36th, Assists per 90 17th, Minutes, two years 86th, Starts 72nd, Minutes, last 365 days 82nd, European club minutes 30th36thGoals17thAssists86thMinutes72ndStarts82ndRecent30thEurope

Dashed ring: the midfielder median (50th percentile)

Minutes, two years

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

5,045 this player2,607 midfielder median86th percentile

451peer deciles · median line · this player ●9,624

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
Jul 2009
Peak
€7M
Dec 2019
Model target
€2M
Jun 2026
Valuations
33
6 club changes
Jun 2026€2MSevilla FCValuation the model is trained to reproduce

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

Show as a table
DateMarket valueClub
2009-07-11€50kNAC Breda
2010-08-20€100kNAC Breda
2011-01-17€400kNAC Breda
2011-06-28€800kNAC Breda
2013-01-17€1.5MNAC Breda
2013-06-17€3MNAC Breda
2014-02-11€4MAZ Alkmaar
2014-08-11€5MAZ Alkmaar
2015-02-16€6MAZ Alkmaar
2015-07-01€6MAjax Amsterdam
2016-02-05€6MAjax Amsterdam
2016-07-15€6.5MAjax Amsterdam
2017-03-05€5.5MTianjin Teda
2017-06-19€5.8MTianjin Teda
2017-12-01€5MTianjin Teda
2018-06-05€5MGuangzhou Evergrande Taobao
2019-01-11€6.5MSporting CP
2019-06-07€6.5MSporting CP
2019-12-20€7MSevilla FC
2020-04-08€5.5MSevilla FC
2020-10-08€6MSevilla FC
2021-01-05€6MSevilla FC
2021-06-10€6MSevilla FC
2021-12-30€4MSevilla FC
2022-06-03€4MSevilla FC
2022-11-07€4MSevilla FC
2023-06-13€4MSevilla FC
2023-12-22€4MSevilla FC
2024-06-07€3.5MSevilla FC
2024-12-27€3.5MSevilla FC
2025-06-09€3MSevilla FC
2025-12-12€2.5MSevilla FC
2026-06-05€2MSevilla 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
+0.887

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 28% of the 509 LaLiga players.

€100k€1M€10M€100M€2MSee them in the Explorer →

Midfielders, all leagues1,687 players · median €2M

Valued above 46% of the 1,687 midfielders.

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

Where Gudelj 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).