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
Consistency is strong, but improvement has stalled. Sub-optimal move efficiency or technique ceiling is likely limiting further AO100 drops.
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
~583
solves remaining
≈ 45 sessions at your current pace
Sub-26s
~18
solves to next milestone
≈ 1 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.
More recent solves improve forecast accuracy.
Solve prediction improves with consistent recent sessions.
Based on solve data as of Apr 28, 2026
Performance Trend & Forecast
AI Performance Insight
Current performance is characterized by a consistent, but slow, average solve time. The average of the last 100 solves is 26.9 seconds, with a standard deviation of 3.382 seconds. This indicates a relatively stable, but not improving, performance level. The gap of 1.11 seconds between the current average and the target suggests a need for focused improvement.
Reducing solve-to-solve variability is critical. A standard deviation of 3.382 seconds represents 12.5% of the average solve time. This high degree of fluctuation limits the potential for faster averages, as faster solves are offset by slower ones.
Implement a slow-turn drill, focusing on precise finger movements and look-ahead.
Perform 3 sets of 10 solves, aiming for consistent execution over speed.
Supplement this with block building practice, isolating specific cases to improve recognition speed and reduce pauses.
Progress is currently at a plateau, indicated by the lack of change in the solve standard deviation (0s) and the 'plateau' velocity status. This suggests that current practice methods are not yielding further improvements. A shift in training focus is required to overcome this stagnation.
The low weekly solve volume (2) may hinder progress. Insufficient practice opportunities could limit the effectiveness of any new training strategies and delay improvements in consistency.