Cricket,
Quantified.
The most mathematically advanced evaluation engine for T20 cricket, removing era bias and narrative distortion.
The Evolution of ApexScore
From evaluating context to measuring pure statistical dominance.
Contextual Percentile
Engine V1Our foundational V1 engine proved how hard a performance was. By mathematically factoring in match pressure, phase of play, and the quality of opponents, it stripped away surface-level stats.
Pressure Impact: Heavily rewards runs scored in steep run chases.
Smart Wickets: Dismissing a premier batsman yields a significantly higher weight than a tail-ender.
The Limitation: V1 ranked players from 0-100. The problem? A percentile ceiling squashes generational, historical outliers against merely "great" seasons. It couldn't express *how much better* a player was once they hit the 99th percentile.
Apex Z-Score
Engine V2 • Current StandardTo shatter the percentile ceiling, V2 implements Uncapped Z-Score Scaling. We measure exactly how many Standard Deviations a player was above the true mean.
Uncapped Scaling: Generational seasons (like Kohli '16, Gayle '12) are allowed to stack points infinitely, proving exactly how much better they were than the rest of the league.
Specialist Floor: Negative Z-Scores are floored at 0, protecting pure specialists from being mathematically dragged down by their non-primary skill.
Empirical Weights: V2 hardcodes an economy derived from machine learning data variance (e.g., Base=1800, Pressure=300).
Z-Score Analytics,
Uncapped Performance Tracking.
Interactive Leaderboards
Explore the ApexScore architecture.
| Rank | Player | Season | Apex Z-ScoreApex Score | Impact/Match | Overall Pct | Bat Pts | Bat Pct | Bowl Pts | Bowl Pct |
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Player Comparison
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Scatter Plot Analytics
Select any two metrics to visually map out the Top 20 performers and find statistical outliers.