FairValue

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  • 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.
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Valuation story

Inside a valuation: Bernardo

From the model's €2.2M starting point to €4.3M, one input at a time, and where that lands against an observed €4M.

Built from Published SHAP output for the served Model A (reports/explainability); the player was in its training data.10 chapters

Observed €4MModel A €4.3M (served, in-sample)Bundesliga · Left-Back

Bernardo

LB

№ 364258Established €4M 0.00

The valuation

Bernardo: 31, left-back in Bundesliga. Transfermarkt's valuation is €4M, in the Established tier. This story follows how Model A arrives at its own figure, one input at a time.

Where every estimate starts

Before it looks at this player, Model A sits at €2.2M: its average output across the players it was trained on. Every player's journey starts here. Each input then multiplies the estimate up or down.

Age

Age (31.0 yrs) lowers the estimate ×0.56: from €2.2M to €1.2M.

Across all modelled players this input moves Model A's estimate by ×1.60 on average, in one direction or the other. Here it moves it more than usual.

League

League (Bundesliga, Germany) raises the estimate ×1.70: from €1.2M to €2.1M.

Across all modelled players this input moves Model A's estimate by ×1.96 on average, in one direction or the other. Here it moves it less than usual.

Club points per game

Club points per game (1.79) raises the estimate ×1.65: from €2.1M to €3.4M.

Across all modelled players this input moves Model A's estimate by ×1.38 on average, in one direction or the other. Here it moves it more than usual.

Minutes (last 365 days)

Minutes (last 365 days) (2,529) raises the estimate ×1.23: from €3.4M to €4.2M.

Across all modelled players this input moves Model A's estimate by ×1.19 on average, in one direction or the other. Here it moves it more than usual.

European-cup minute share

European-cup minute share (0%) lowers the estimate ×0.88: from €4.2M to €3.7M.

Across all modelled players this input moves Model A's estimate by ×1.20 on average, in one direction or the other. Here it moves it less than usual.

Everything else

The remaining 15 inputs are individually small. Together they raise the estimate ×1.16, to €4.3M.

The estimate

After all 20 inputs, Model A says €4.3M. Transfermarkt says €4M. The residual, log(observed) − log(estimate), is −0.064: the observed value is below the estimate, so the model over-predicts by ×1.07.

The served model was trained on this player, so this is an in-sample figure; on unseen players a typical miss is ×1.50.

What this is not

SHAP describes the model's arithmetic for these inputs. It is not a price, a scouting judgement or a statement about what causes value, and the order of the steps is by size, not sequence.

The same record, with Model B and the player's traits, is in the dossier.

Starting point€2.2Mobserved €4M

Every Model A valuation starts at €2.2M, the model's average output over the players it was trained on (log 14.592).

raises the estimate lowers it Model A estimate observed value (dashed line)Log scale: equal lengths are equal multiplications. Inputs are ordered by size of effect, not in any causal sequence. Published SHAP run for the served Model A; this player was in its training data.