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
Kangaskhan Competitive Analysis
Performance Metrics
Kangaskhan currently holds a E tier ranking in the BSS Regulation M-B (Pokemon Champions) metagame with a 1.2% usage rate and 48.8% win rate. This data represents 5,224 games analyzed over 14 days from 06/18/2026 to 06/20/2026.
Usage Trends
Usage has been trending upward over the past two weeks, indicating growing popularity in the metagame. Win rate has improved over the analysis period, suggesting either better optimization or favorable metagame conditions.
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 Fake Out used in 37.6% of battles. Double-Edge demonstrates the highest win rate at 91.7% While Fake Out is the most popular choice, Double-Edge shows promising results as an underexplored option.
Team Synergies
Frequently partners with Garchomp, Gardevoir, and Gengar in successful team compositions. The strongest synergy is observed with Meganium, achieving a 75.0% win rate when used together.
Matchup Analysis
Favorable matchups include Meganium, Sinistcha, and Lopunny, while caution is advised against Annihilape, Ditto, and Gyarados. Particularly effective against Meganium with a 85.7% win rate in direct matchups.
Strategic Recommendations
Kangaskhan 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
