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-9
Consistency is strong, but improvement has stalled. Sub-optimal move efficiency or technique ceiling is likely limiting further AO100 drops.
Sub-10 → Sub-8
🧭 Typical at this level
- • Consistent Full Cross+1 inspection
- • Efficient F2L (pseudo-slotting/keyhole)
- • Strong 1-look PLL recognition
🎯 Recommended focus
- • Improve Cross+1 success rate
- • Reduce move count in F2L
- • Eliminate micro-pauses through advanced look-ahead
~9,708
solves remaining
≈ 111 sessions at your current pace
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 26, 2026
Performance Trend & Forecast
AI Performance Insight
Current average solve times, indicated by the 9.34s AO100, are slightly below the recent median of 9.49s from the last three sessions. However, the standard deviation of 1.049s suggests considerable time variation in solves. The 14-day trend of 0.006667 s/day indicates recent improvement, but this is offset by a 30-day decline of -0.003281 s/day, signaling potential instability.
The 'efficiency_bottleneck' state, coupled with a stable standard deviation change of 0s, suggests that solve times are limited by move count or execution rather than random fluctuations. Addressing inefficiencies in execution will yield more consistent results than attempting to reduce variability. The tier gap of 0 indicates performance is at the boundary of the current goal.
Practice slow, deliberate execution of algorithms, focusing on minimizing pauses and unnecessary movements.
Incorporate block building drills to improve lookahead and reduce total move count.
Target 30 minutes daily dedicated to these drills, tracking the average move count for specific cases.
The recent performance trend demonstrates a capacity for improvement, but the 30-day slope indicates this is not guaranteed. Maintaining focus on efficient execution, rather than solely on speed, will be critical for progressing from the current sub-10 to sub-8 second goal. Consistent effort is required to counteract the identified regression risk.
The 'regression_risk' status indicates a potential for performance decline. Continued worsening of the 'efficiency_bottleneck' state could lead to a sustained increase in solve times.