Performance Over Time
Latest 20 Solves
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Daily AO12
Daily AO100 & Mean
Monthly Stats
Includes historical & solve-based data
Growth Prediction
🎯 Chasing Sub-20
AO100 is trending upward while inconsistency is also rising. Fatigue, overtraining, or technique regression are common causes.
Sub-30 → Sub-20
🧭 Typical at this level
- • Smooth Cross-to-F2L transition
- • Continuous solve flow
- • Reduced hesitation before the first pair
🎯 Recommended focus
- • Focus on fluid transitions rather than burst TPS
- • Eliminate the pause after finishing the cross
- • Drill F2L cases until automatic
~219
solves remaining
≈ 4 sessions at your current pace
Sub-25s
~10
solves to next milestone
≈ 0 sessions
Your improvement rate is the biggest variable — a slower rate dramatically increases the estimate even when the gap is smaller.
You're entering advanced-level gains. Progress naturally slows.
Estimate based on recent trends. More sessions improve accuracy.
Based on solve data as of Mar 22, 2026
Performance Trend & Forecast
AI Performance Insight
Recent performance indicates a decline in consistency. The solve standard deviation of 62.612 seconds, coupled with a 61.156 second increase from the prior measurement, suggests increased time variability. This inconsistency is reflected in the gap between the average of 100 solves (25.56s) and the recent median of 12 solves (56.77s), indicating a performance drift.
Reduce solve-to-solve time variation. The 14-day slope of 1.067442 seconds per day demonstrates a clear upward trend in average solve times, exceeding the slower 30-day slope of 0.706007 seconds per day. Addressing this increasing variability is critical to regaining performance stability.
Implement slow-speed solves focusing on execution accuracy.
Perform 3 sets of 10 solves at a deliberately slow pace, prioritizing correct fingertricks and algorithm execution.
Follow this with blockbuilding practice, completing 5 sets of 15 blockbuilding exercises to reinforce muscle memory and reduce pauses.
The current performance phase aligns with the established goal of transitioning to sub-20 second solves. Despite the current instability, the tier gap remains at 0, indicating the target remains attainable. Focus on the technical adjustments outlined to address the observed performance drift.
The ‘regression_risk’ stability state and ‘regression_warning’ diagnosis tag indicate a high probability of continued performance decline. Without intervention, the increasing standard deviation may further widen the gap to the target time.