CFOP stage percentages by skill level
Smart-cube stage timestamps turn a general training rule into an observed benchmark for distinct skill brackets.
Stable public data catalog
CSV files support quick analysis. JSON downloads keep the sample, date range, definitions, methodology, inclusion and exclusion rules beside the data.
SupaKewber first-party data
Smart-cube stage timestamps turn a general training rule into an observed benchmark for distinct skill brackets.
Move-aware solves show how turning rate and move count vary across real solve-time bands.
Trainer outcomes reveal which cases create persistent recognition or execution mistakes instead of relying on anecdotal difficulty lists.
Separated trainer phases reveal whether identifying a case or turning it is the larger part of the delay.
Move timestamps around detected stage boundaries quantify the transitions that a stopwatch total hides.
Aggregate attempt volume shows which cases attract the most deliberate work across the training community.
Coarse anonymous outcomes reveal where browser-based Bluetooth succeeds and where setup still creates friction.
Validated error buckets show whether unavailable APIs, permissions, GATT state or initialization failures dominate.
Milestone cohorts connect sustained practice volume to the change in a player’s own rolling performance.
Immutable timer-source labels make it possible to describe two timing populations without mixing them into one average.
User-day totals describe the practice dose people actually record when they open a session.
Entry and submission records reveal which event windows turn interest into a completed competitive average.
WCA-derived data
The analysis groups each competitor’s official 3×3 personal-best average by the country on their primary WCA person record.
The series filters event-333 result rows carrying the WCA world-record marker and joins official competition dates and competitor records.
Field depth is described with recent active competitors, official sub-10 and sub-15 PB averages, and global top-100 representation.
The page documents why this analysis is not computed from the public export: dates of birth are deliberately excluded.
A competitor enters the series in the year of their first positive official 3×3 average below 10.00 seconds.