Definition
What this measures
Aggregate attempt volume shows which cases attract the most deliberate work across the training community.
SupaKewber first-party research
Most practised speedcubing algorithms
Aggregate attempt volume shows which cases attract the most deliberate work across the training community.
SUPAKEWBER PRACTICE DATA
Research question
Which algorithms do SupaKewber users practise most?
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
Attempts are grouped by case key and category, with distinct contributors, success rate and timing reported after 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
High volume can mean a case is popular, newly introduced or difficult; volume alone is not a difficulty score.
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.