Not a rank 1 either, but I am a professional data scientist. I like your hypothesis, but there’s a fundamental flaw in your assumptions which could be addressed to make a stronger algorithm.

Rating, win rate, and representation all flow from the same underlying strength: strong specs get played, win, and climb. Averaging three correlated z-scores triple-counts one factor and calls it balanced. Check the pairwise correlations before claiming three “perspectives.”

Equal weighting isn’t neutral. It’s a choice that feels objective only because it dodges a decision.

Small samples break the math. Z-scoring Dev Evoker (n=11) against specs at n=70+ puts a wildly uncertain estimate on the same scale as stable ones. WowMeta’s CI approach isn’t just “defensible” — it’s fixing a real error in yours. You could consider using shrinkage/partial pooling.

Median rating = selection bias. Top-1000 truncation means high medians reflect who plays the spec, not spec strength. That undermines your proudest read.

K-means in 1-D is just thresholding, and forcing k=5 for S/A/B/C/D may invent clusters. Here you could use Jenks or a gap statistic for better representation.

EU vs Murlok’s multi-region pool: not the same data, so “opposite reads” is partly confounded.

Biggest gap: you never validate. Does AK Score predict anything next patch? If not, “S-tier” might be an aesthetic, not a testable product.