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
~2,133
solves remaining
≈ 96 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 7, 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 9.61 seconds, while the recent median of the last 3 AO12s is 9.07 seconds. This indicates a degree of variability in execution, as reflected by the 1.256 second standard deviation. The lack of change in standard deviation suggests this variability is currently stable.
Improve solve efficiency to reduce average solve time. The 'efficiency_bottleneck' diagnosis, combined with the 0.54 second gap to the sub-8 second target, indicates that reducing inefficiencies within existing execution is the most direct path to improvement. The current performance plateau, indicated by 0 s/day on both 14-day and 30-day slopes, reinforces this focus.
Implement block building drills focusing on lookahead and fingertrick efficiency.
Practice slow, deliberate execution of cross and F2L, prioritizing optimal move counts.
Incorporate timed solves with a focus on minimizing pauses and maximizing moves per second, aiming for consistent execution of efficient algorithms.
Progress requires targeted refinement of existing skills. The current tier gap of 0 indicates you are operating at the expected level for your goal, but sustained improvement necessitates addressing identified limitations. The stable state suggests consistent effort will yield predictable results.
Continued performance at the current velocity may result in a prolonged plateau. A stable standard deviation, while not immediately detrimental, could indicate a lack of focused practice on reducing variability.