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-10
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
~7,626
solves remaining
≈ 229 sessions at your current pace
Sub-13s
~273
solves to next milestone
≈ 8 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 13, 2026
Performance Trend & Forecast
AI Performance Insight
Recent performance demonstrates a decreasing average solve time. The 30-day slope of -0.288662 s/day indicates a more substantial improvement than the 14-day slope of -0.255679 s/day, suggesting accelerating progress. A solve standard deviation of 1.658s, coupled with a reduction of -0.218s from the prior measurement, shows increasing consistency.
Maintain the current trajectory of decreasing solve times and improving consistency. The current AO100 of 13.65s is 0.13s below the target, indicating a strong position to continue progressing towards the next phase goal. Focus on solidifying the gains in consistency as evidenced by the decreasing standard deviation.
Perform 30 slow solves, focusing on look-ahead and minimizing pauses.
Then, complete 50 timed solves, concentrating on executing algorithms smoothly without rushing.
Finally, analyze the 10 fastest solves for patterns in successful execution and areas for refinement.
Progress is evident in both speed and consistency. The current balanced growth state, combined with the negative slopes, suggests that the current training approach is effective. Continued adherence to this approach is projected to yield further improvements.
While the standard deviation is decreasing, a value of 1.658s still indicates variability. A sudden reversal in this trend could indicate fatigue or a need to adjust practice methods. Monitor solve times closely for any signs of plateauing.