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-14
Session AO12 values are significantly faster than your AO100, and your trend is improving. This gap will close as your baseline catches up to your peak capability.
Sub-45 → Sub-30
→Sub-15 → Sub-10
3 tiers🧭 Typical at this level
- • Strong F2L look-ahead
- • Low-rotation solving
- • Consistent Full PLL
- • Cross occasionally planned with +1
🎯 Recommended focus
- • Eliminate all visible pauses during F2L
- • Track the next pair before finishing the current insertion
- • Reduce rotation count
~295,396
solves remaining
≈ 5,500 sessions at your current pace
Sub-38s
~150
solves to next milestone
≈ 3 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.
This estimate is based on stable long-term trends.
Based on solve data as of Mar 24, 2026
Performance Trend & Forecast
AI Performance Insight
Recent performance demonstrates a decreasing standard deviation, falling by 13.061 seconds over the prior measurement period. This reduction in solve time variability, coupled with a 14-day slope of -1.034211 s/day, indicates improved consistency. However, the current average of 39.46 seconds for 100 solves remains substantially above the target of 14.58 seconds, as indicated by the 24.88 second gap.
Reduce solve time while maintaining the observed improvement in consistency. The current phase transition from Sub-45 to Sub-30 suggests a focus on speed development is appropriate. The tier gap of 3 indicates a significant performance difference between current and target levels.
Implement slow-speed blockbuilding drills, focusing on efficient move sequences.
Integrate lookahead practice during solves, aiming to identify the next step while executing current moves.
Prioritize accuracy over speed during these drills to reinforce the recent reduction in standard deviation.
The 30-day slope of 0.265713 s/day suggests a positive trend, despite the current gap. This indicates that continued effort is likely to yield further improvements. The 'healthy_peak' stability status confirms the current training approach is effective.
The 'high_peak_low_stability' diagnosis suggests a potential for performance regression if consistency gains are not maintained. A shift towards a negative 14-day slope could indicate overtraining or a disruption in technique.