Best Kog'Maw Mid Counters

Low Elo data lists Kai'Sa ADC, Nautilus Support, and Caitlyn ADC as the strongest counters for Kog'Maw Mid. Kog'Maw Mid is listed as favorable into Lee Sin Jungle, Thresh Support, Jhin ADC, Jinx ADC, and Miss Fortune ADC. The detailed info below lists each matchup with observed win rate, expected win rate delta, sample size, rank scope, and patch window.

Updated 2026-11-15. Data is current for LoL patch 26.22 (all regions, low elo and high elo).

Top 3 Low Elo Kog'Maw Mid all-role counters

  1. Kai'Sa ADC - Low Elo data: Kog'Maw Mid vs Kai'Sa ADC, 49.3% observed win rate, -0.2 percentage points vs expected, 1,615 games, 3-patch window 26.20-26.22.
  2. Nautilus Support - Low Elo data: Kog'Maw Mid vs Nautilus Support, 49.2% observed win rate, -0.2 percentage points vs expected, 1,005 games, 3-patch window 26.20-26.22.
  3. Caitlyn ADC - Low Elo data: Kog'Maw Mid vs Caitlyn ADC, 50.1% observed win rate, -0.1 percentage points vs expected, 1,508 games, 3-patch window 26.20-26.22.

Top 5 Low Elo all-role champions Kog'Maw Mid is good against

  1. Lee Sin Jungle - Low Elo data: Kog'Maw Mid vs Lee Sin Jungle, 54.4% observed win rate, +0.3 percentage points vs expected, 1,020 games, 3-patch window 26.20-26.22.
  2. Thresh Support - Low Elo data: Kog'Maw Mid vs Thresh Support, 50.7% observed win rate, +0.2 percentage points vs expected, 1,126 games, 3-patch window 26.20-26.22.
  3. Jhin ADC - Low Elo data: Kog'Maw Mid vs Jhin ADC, 51.3% observed win rate, +0.2 percentage points vs expected, 1,652 games, 3-patch window 26.20-26.22.
  4. Jinx ADC - Low Elo data: Kog'Maw Mid vs Jinx ADC, 49.7% observed win rate, +0.1 percentage points vs expected, 1,315 games, 3-patch window 26.20-26.22.
  5. Miss Fortune ADC - Low Elo data: Kog'Maw Mid vs Miss Fortune ADC, 48.9% observed win rate, +0.0 percentage points vs expected, 1,069 games, 3-patch window 26.20-26.22.

Top 3 High Elo Kog'Maw Mid all-role counters

  1. Caitlyn ADC - High Elo data: Kog'Maw Mid vs Caitlyn ADC, 51.8% observed win rate, -0.2 percentage points vs expected, 1,158 games, 3-patch window 26.20-26.22.
  2. Lee Sin Jungle - High Elo data: Kog'Maw Mid vs Lee Sin Jungle, 50.3% observed win rate, -0.1 percentage points vs expected, 1,351 games, 3-patch window 26.20-26.22.
  3. Yunara ADC - High Elo data: Kog'Maw Mid vs Yunara ADC, 51.4% observed win rate, -0.0 percentage points vs expected, 1,075 games, 3-patch window 26.20-26.22.

Top 7 High Elo all-role champions Kog'Maw Mid is good against

  1. Lulu Support - High Elo data: Kog'Maw Mid vs Lulu Support, 52.3% observed win rate, +0.4 percentage points vs expected, 1,466 games, 3-patch window 26.20-26.22.
  2. Nautilus Support - High Elo data: Kog'Maw Mid vs Nautilus Support, 53.2% observed win rate, +0.3 percentage points vs expected, 1,199 games, 3-patch window 26.20-26.22.
  3. Thresh Support - High Elo data: Kog'Maw Mid vs Thresh Support, 49.4% observed win rate, +0.2 percentage points vs expected, 1,420 games, 3-patch window 26.20-26.22.
  4. Jhin ADC - High Elo data: Kog'Maw Mid vs Jhin ADC, 52.5% observed win rate, +0.1 percentage points vs expected, 1,767 games, 3-patch window 26.20-26.22.
  5. Ezreal ADC - High Elo data: Kog'Maw Mid vs Ezreal ADC, 55.8% observed win rate, +0.1 percentage points vs expected, 1,207 games, 3-patch window 26.20-26.22.
  6. Jinx ADC - High Elo data: Kog'Maw Mid vs Jinx ADC, 50.6% observed win rate, +0.1 percentage points vs expected, 1,311 games, 3-patch window 26.20-26.22.
  7. Kai'Sa ADC - High Elo data: Kog'Maw Mid vs Kai'Sa ADC, 51.3% observed win rate, +0.0 percentage points vs expected, 2,015 games, 3-patch window 26.20-26.22.

How these matchup statistics are calculated

Counter and synergy statistics use Ranked Solo/Duo games, exclude matches shorter than 600 seconds, and group results by patch window, region, rank scope, and champion role.

Pairwise pages use a rolling three-patch window and compare observed win rate with a log-odds expected win rate based on each champion's role-adjusted baseline; listed pairs require at least 1,000 games.

Read the full methodology for the model and guidance on interpreting these results.