The aggregate data behind the champion select findings

Published so the numbers can be checked rather than taken on trust. The findings page shows them as charts; the method page explains how they were produced.

These are the aggregates the r/summonerschool post was built from. They are measurements, not conclusions: per champion-role averages and per matchup numbers computed from 30,000 ranked solo queue matches. If a claim in that post is wrong, it should be wrong in here too.

Provenance

Source	Riot Match-V5 API, matches plus timelines
Queue	420 (ranked solo/duo)
Region	EUW
Rank band	Silver to Platinum, pooled
Patch	16.16
Matches	30,000
Aggregated	21 August 2026

Files

matchv5-metrics.json · 1.2 MB gd15-matchups.json · 645 KB

matchv5-metrics.json

173 champions, split by role, with 10,806 matchup rows. One row looks like this:

"Mordekaiser": { "TOP": {
  "games": 2192, "wr": 0.509, "dpm": 882,
  "teamDmgShare": 0.235,
  "physShare": 0.109, "magShare": 0.837, "truShare": 0.054,
  "ccPerMin": 0.579, "healPerMin": 0, "shieldPerMin": 0,
  "mitPerMin": 1391, "takenShare": 0.249, "killPart": 0.341,
  "gd15": 221, "csd15": 3.825, "xpd15": 388, "at15Samples": 2175,
  "wrByLen": { "<25m": 0.52, "25-30m": 0.511, "30-35m": 0.483, ">35m": 0.528 }
} }

wrByLen	Win rate split by game length. This is the scaling finding: draft hard for late game and you trade short-game losses against long-game wins.
gd15, csd15, xpd15	Average gold, CS and XP difference at 15 minutes.
phys/mag/truShare	Share of the champion's damage output that is physical, magic and true.
matchups	Keyed Champion|ROLE|Opponent, each with n, wr and gd15. Sample sizes are included precisely so you can discard the thin ones.
rolePickShare	How often each champion is actually played in each role, measured rather than assumed.

gd15-matchups.json

The same 30,000 matches reshaped for lane lookups, plus two derived sets.

data	Gold difference at 15 by champion, role and opponent, with sample size.
pairRes	Pair residuals: how much better or worse two champions do together than their individual rates predict. This is the basis of the synergy finding. Near zero for most pairs on teams of strangers.
kitM	Measured kit values per champion: s shield, f frontline, p peel.

What is not here

• Nothing that identifies a player. No PUUIDs, no summoner names, no match IDs. These files are aggregates only, and that is deliberate.
• The scoring engine. How these measurements get weighted into a win probability is the tool, and that stays private. Everything the weighting is applied to is on this page.
• Raw match records. Anyone can pull those from the Riot API themselves. The API is free and the queue is 420.

Caveats, stated up front

• Silver through Platinum is pooled, not split by rank. The collection carries no tier field, so it cannot be split after the fact. Every number here is an average across that band.
• One patch, one region. Other patches and regions may look different.
• It is correlational. Real drafts are already sensible, so this measures ranking among real drafts, not what would happen if you drafted at random.
• Matchup rows with small n are noisy. They are published with their sample sizes rather than filtered, so you can set your own threshold.