Battle Statistics
Move Performance Analysis
Top 10 moves used in 10+ battles
⚠️ Important Note:
- This data shows moves actually used in battles, not move adoption rates in team building.
- Some moves may be on the Pokémon's moveset but never used during a battle, so they won't appear here.
- Win rate reflects performance in battles where the move was used.
- For actual move adoption rates, please refer to Smogon Stats or other official sources.
Usage Trend
Win Rate Trend
Performance by Rating Tier
Ogerpon-Cornerstone Competitive Analysis
Performance Metrics
Ogerpon-Cornerstone currently holds a D tier ranking in the OU (Over Used) metagame with a 2.1% usage rate and 51.5% win rate. This data represents 55,693 games analyzed over 14 days from 08/15/2026 to 08/29/2026.
Usage Trends
Usage has been trending upward over the past two weeks, indicating growing popularity in the metagame. Win rate has decreased recently, which may indicate emerging counters or declining effectiveness.
Rating Tier Performance
Usage remains consistent across all rating tiers, demonstrating broad appeal at all skill levels.
Key Moves
The most frequently used move in competitive battles is Ivy Cudgel used in 53.1% of battles. Knock Off demonstrates the highest win rate at 54.5% While Ivy Cudgel is the most popular choice, Knock Off shows promising results as an underexplored option.
Team Synergies
Frequently partners with Great Tusk, Raging Bolt, and Gholdengo in successful team compositions. The strongest synergy is observed with Ribombee, achieving a 60.4% win rate when used together.
Matchup Analysis
Favorable matchups include Hoopa-Unbound, Ninetales, and Pelipper, while caution is advised against Scizor, Lokix, and Landorus-Therian. Particularly effective against Hoopa-Unbound with a 67.7% win rate in direct matchups.
Strategic Recommendations
Ogerpon-Cornerstone maintains a balanced presence in the metagame. Its moderate usage and win rate suggest viability in specific team archetypes. Experiment with different builds and partner Pokemon to optimize performance.
Frequent Partners
Frequent Matchups
Strong Partners
Challenging Partners
Favorable Matchups
Difficult Matchups
