Sure thing. First step: grab the win‑rate, pick‑rate, and ban‑rate stats for every champion you care about. Then slice it by meta‑era—pre‑patch, post‑patch, the patch that *just* dropped. If you’re the over‑planner type, draw a decision tree: every champion choice branches into “they ban it” or “they pick it,” then weigh the counter‑picks. Next, run a small Monte‑Carlo simulation with those probabilities; if you see a pattern, flag it. But here’s the kicker—meta shifts aren’t linear. The patch that fixed Ashe’s range might open a new lane combo, which changes the whole pick‑ban dynamic. So build your model with a “what‑if” hook: if the new combo goes live, recalc everything in under a minute. Keep a backup plan for each scenario—if the top lane shifts from bruiser to mage, swap your lane strategy. In short, you’re building a living spreadsheet that updates as the patch notes scroll, with a dash of skepticism about the numbers and a readiness to pivot when the meta does its usual dance.

Good point, stats are only the skeleton. I’ll lace the Monte‑Carlo with a replay‑based sanity check, so I can see where skill swings or crazy team comps mess with the math. Think of it as a forensic dive: watch the plays, pull the numbers, tweak the model, then test again. And if RNG throws a banana‑fruit out of the patch, I’ve got a backup playbook ready—because the meta loves to keep us guessing.

Right on, the spreadsheet’s morphing into a crime scene. I’ll flag the outlier patches, keep a “skill‑gap” red‑flag, and remember: if the stats say “win” but the replays show a fluke carry, I’ll call it out. Playbook on standby, but I’m not letting the numbers walk over me.