Milestones
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-12
Recognition and execution are aligned within the expected range. Improvement here comes from technique refinement rather than raw speed development.
Sub-15 → Sub-10
🧭 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
~11,812
solves remaining
≈ 154 sessions at your current pace
Sub-13s
~1,611
solves to next milestone
≈ 21 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 Aug 30, 2026
Performance Trend & Forecast
AI Performance Insight
Your AO100 is 14.53s, and your recent session gap of -0.3s shows you are performing better. However, your consistency is 1.961s, and your solve pattern shows a worsening efficiency bottleneck. You are hitting a ceiling where raw speed cannot compensate for inefficient movement.
You must prioritize algorithm efficiency to resolve the efficiency bottleneck. Your current regression risk indicates that your current movement patterns are no longer sufficient to lower your average. Improving recognition will reduce the pauses that contribute to your 1.961s standard deviation.
Perform 20 slow-turning solves per session where you focus on zero pauses between steps.
Spend 30 minutes daily drilling your least efficient OLL and PLL algorithms until they are fluid.
This targets the efficiency_limited issue by automating your movements.
Your 14-day improvement rate of 0.199769 s/day shows you are still capable of rapid progress. This number proves that your current approach is working, provided you address the efficiency bottleneck.
Your momentum is flagged as regression_risk because your efficiency bottleneck is worsening. If you do not optimize your algorithm efficiency now, your progress will plateau.