Definition
What this measures
Trainer outcomes reveal which cases create persistent recognition or execution mistakes instead of relying on anecdotal difficulty lists.
SupaKewber first-party research
Most commonly failed OLL and PLL cases
Trainer outcomes reveal which cases create persistent recognition or execution mistakes instead of relying on anecdotal difficulty lists.
SUPAKEWBER PRACTICE DATA
Research question
Which OLL and PLL cases fail most often in training?
The live dataset will publish a quantified answer automatically after it reaches at least 20 distinct users and 100 eligible observations.
Published results
Aggregate benchmark table
Every displayed cell has passed the public sample threshold. Empty tables are a deliberate “not enough evidence yet,” never a fabricated estimate.
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Methodology
How it is calculated
Failure rate divides explicit unsuccessful attempts by all recorded attempts for a case after case-level stability thresholds.
Inclusion rules
What enters the sample
- Eligible SupaKewber observations with valid measurement fields.
- Only aggregate cells above the stated privacy and stability threshold.
Exclusion rules
What is removed
- Deleted, invalid or structurally inconsistent records.
- Small cells that could be misleading or identify a narrow group.
Interpretation limit
What this dataset cannot prove
Trainer users are self-selecting and may deliberately practise their weakest cases; the results describe practice difficulty, not universal case difficulty.
SupaKewber data describes people who use this platform; it is not automatically representative of every speedcuber. Definitions and thresholds stay attached to every JSON download so the number cannot be separated from its method.
Source separation
This is practice data, not an official competition result.
SupaKewber records browser practice and platform interactions. Official WCA results appear only on separately labeled WCA analysis pages with the required WCA attribution and export date.