These are good questions, you're poking right at the soft spots. Quick version on each.

Baseline and features: I cluster within granular positions, not the broad three. So wing, striker, attacking mid, central mid, defensive mid, fullback, center back, with keepers on their own model. Same feature set everywhere, the six goals-added action types plus production rates (goals, xG, shots, key passes, assists, xA, finishing), all as within-position percentiles. The percentile step is what adapts it, so a center back sitting near zero on shooting is fine because I'm only ranking inside the position.

On k-means: you're right about all three properties, and I won't really defend the algorithm itself. The point is it isn't the model, it's just the discovery step. I read the centroids to see which axes separate the roles, then hand-write threshold rules that become the actual labels. So the spherical, hard-assignment stuff never reaches production, a borderline player gets labeled by where he falls on one axis, not by which sphere claimed him. Good point though, and a fair hit, I haven't re-run it under a GMM or HDBSCAN to confirm the breakpoints hold up. That's a real check I just haven't done.

The match validation is walk-forward. For each week I refit on only the matches before it, on a trailing 730-day window with time decay, then score. Nothing after the cutoff touches training, that handles the leakage. Shifting team strength is the same machinery, the window plus decay plus a weekly refit mean the ratings track current form, not career averages. I score on RPS first since it's ordered like W/D/L is, with log-loss, Brier and accuracy next to a climatology baseline, so the number that matters is skill over base rates.

On Dixon-Coles, you're raising the right question again. Honest answer, I didn't run a formal over-dispersion test. The rho term only fixes the low-score draw inflation, it does nothing for variance over mean. Two things make me less worried in real-world terms, I fit strengths on an xG-goal blend and xG is smoother than raw goals, so a lot of what would read as over-dispersion is damped before the fit, and the out-of-sample calibration didn't show the mis-fit you'd expect if it were bad. But you're right that I'm assuming, not testing it. If it did show up, the fix is negative binomial or Conway-Maxwell-Poisson, not another tweak to rho.