🎯 Overall Meta Health
Usage Diversity
Measures how evenly usage is spread across different parts. Higher score = more options are being used competitively.
Shannon entropy across Blades, Ratchets, and Bits.
Win Rate Balance
Measures variance in win rates across all competitors. Lower variance means more balanced competitive options.
Win rate deviation across top-used parts.
ELO Spread
Measures the distribution of ELO ratings. Moderate spread indicates healthy skill differentiation without extreme gaps.
ELO compression ratio and distribution health.
Top Dominance
Measures how much top performers dominate matches. Lower dominance = healthier meta with more viable options.
Match share of top 3/5/10 performers.
Matchup Balance
Measures how one-sided matchups are. Fewer extreme matchups = better strategic variety and counterplay.
Frequency of highly one-sided matchups.
Usage vs Win Rate
Reveals power creep and outliers. Ideal position is high usage with moderate win rate (~50%).
ELO Distribution
Shows how ELO ratings are spread across the field. Healthy metas have moderate spread.
Meta Health Breakdown
Contribution of each metric to the overall health score.
🔍 Outlier Detection
Parts and combinations that may need attention based on performance data.
Overcentralizing
High usage combined with above-average win rates.
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Underpowered
Below-average performance across multiple metrics.
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Emerging Threats
High win rate but limited data - worth monitoring.
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🎭 Most Polarized Matchups
Matchups with highly one-sided win rates (>70% or <30%).
| Bey 1 | Bey 2 | Win Rate | Games | Severity |
|---|---|---|---|---|
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📐 Methodology
The Meta Health Score is calculated using a weighted formula combining five key metrics:
- Usage Diversity (25%) — Shannon entropy measuring how evenly parts are used
- Win Rate Balance (25%) — Inverse of win rate variance (lower variance = healthier)
- ELO Spread (20%) — How well-distributed ELO ratings are (moderate spread is optimal)
- Top Dominance (15%) — Inverse of top-3 match share (less dominance = healthier)
- Matchup Balance (15%) — Inverse of polarized matchup frequency
Outlier detection uses statistical analysis with confidence bands based on available match volume. Results with limited data are marked accordingly.