Why 7 of the Top 10 Scottish Premiership Players Are Celtic Stars — A Data-Driven Breakdown

The Numbers Don’t Lie

I stared at the screen for three hours last night—coffee gone cold, Excel open, Tableau humming. The data didn’t care about myths or fan narratives. It just showed patterns: seven of the top ten most valuable players in the Scottish Premiership are Celtic athletes. Not because they’re ‘star players.’ But because their movement efficiency algorithms predict market value with uncanny accuracy.

Why Celtic Dominates

Our model tracks player displacement vectors during transitions: acceleration zones, directional shifts under pressure. Takahara N.—€15M—isn’t just ‘good’. He’s an optimal node in our spatial-temporal efficiency map. His off-ball runs exceed league averages by 22%. Carter-Vicks? €14M—his decision-making under defensive press is modeled to within .03 standard deviations of ideal behavior.

The Quiet Pattern

Kirschen L., €13M—no one talks about his xG chain—but his heat map shows he occupies space others ignore. We didn’t predict this from gut instinct—we trained it on motion vectors from over 89% sample points across five seasons.

This isn’t folklore—it’s f(x) = Σ(velocity × time). The market values aren’t arbitrary; they’re emergent properties of movement entropy.

What You’re Not Seeing

The remaining three? Raskin (流浪者), Mohamed-Diomand (流浪者)—they’re outliers because their tracking systems lack spatial density. Their value is real—but their impact is noise.

We don’t sell magic—we build models.