BPGGCounters
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Counters

What beats what · search an enemy champion to filter the ranking · min 10 games

Counters shows what beats what. It opens on the strongest head-to-head matchups for the current window; search an enemy champion to narrow the ranking to the picks that beat it.

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Counter
The champion on the winning side of the matchup — the one whose win rate the row reports. In the ranking table it is the left-hand column; after searching, it is every champion listed against the enemy you picked.
Matchup Win%
How often the Counter champion won against the Opponent champion, counting games where the two were on opposing teams. Always read from the Counter side, never the Opponent's.
Edge
How far the matchup beat what the two champions' own win rates predict, in log-odds, and the column the table is sorted by. 0 means solo strength explains the result; positive means the matchup itself added something. Roughly, +0.1 is about 2.5 percentage points of win rate.
Minimum games
Matchups below a games threshold are hidden to avoid misleading small samples.
Best counter matchupsmin 10 games

Win% is how often the Counter champion beat the Opponent.

Ranked by Edge: how far the matchup beats what the two champions' own win rates already predict. 0 means the result is explained by solo strength alone, not by the matchup.

Rows
#CounterOpponentWin%EdgeGames
1
TaliyahTaliyah
VayneVayne
81.8%+0.9011
2
Xin ZhaoXin Zhao
ZeriZeri
100.0%+0.8210
3
SyndraSyndra
Dr. MundoDr. Mundo
78.6%+0.7514
4
KarmaKarma
OriannaOrianna
67.9%+0.7128
5
WukongWukong
AnnieAnnie
69.2%+0.6926
6
AzirAzir
OriannaOrianna
70.0%+0.6710
7
SkarnerSkarner
CamilleCamille
84.6%+0.6613
8
YorickYorick
CaitlynCaitlyn
81.8%+0.6311
9
WukongWukong
LockeLocke
69.2%+0.6313
10
NautilusNautilus
YunaraYunara
72.0%+0.6225
11
OrnnOrnn
RakanRakan
70.0%+0.6210
12
AnnieAnnie
NocturneNocturne
90.9%+0.6111
13
Jarvan IVJarvan IV
VayneVayne
69.2%+0.6113
14
PykePyke
AsheAshe
58.3%+0.6012
15
ZeriZeri
Jarvan IVJarvan IV
78.6%+0.6014
16
KarmaKarma
NamiNami
70.0%+0.5910
17
TristanaTristana
AlistarAlistar
60.0%+0.5910
18
ViktorViktor
BardBard
62.5%+0.5832
19
JayceJayce
VayneVayne
54.5%+0.5822
20
JayceJayce
AlistarAlistar
63.6%+0.5822

How to read this data

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The default table ranks the strongest head-to-head matchups in the selected window. Read each row left to right: the Counter column is the champion that won, the Opponent column is the one it beat, and Win% belongs to the Counter — never to the Opponent. Search an enemy champion to narrow the table to the picks that perform best against it. Matchup Win% counts games in which the two champions were on opposing teams, and only matchups with at least the minimum number of games (currently 10) are listed, so the rates are less likely to be small-sample noise.

Rows are ordered by Edge rather than by Win%. A raw matchup win rate mostly reports which champion is stronger on its own, so ordering by it fills the top of the table with strong-champion-meets-weak-champion pairs that never interact. Edge subtracts what the two champions' own win rates already predict and reports only what is left over, in log-odds: 0 means the result is fully explained by solo strength, positive means the pair did better than their individual strength accounts for. Small samples are pulled toward the average before this is computed, so a matchup with a handful of games can no longer reach the top on a 100% win rate.

Answering the enemy's draft is a core part of professional play: teams deliberately hold their last pick so they can choose it with full information, and much of pick-and-ban strategy is about forcing the opponent to commit to a champion that can still be countered afterwards.

A typical use: a team you follow keeps struggling against an opponent's comfort pick — search that champion and see what has actually beaten it in the selected window, then use the region filter to compare what teams elsewhere pick into it.

One important caution: these are team-level results, not lane matchups. The counter and the enemy champion may never share a lane, and a favorable win rate can come from team strength or composition rather than a direct counter relationship. Treat this view as 'what has won against X in practice', not as a lane-matchup table.