All previews

Trust layer

Data Quality Score

A visible score and warning list that explains what DivotData can and cannot say from the uploaded file.

The product earns trust by saying when the data is not good enough.

See compatibilityUpload a file
Live scaffold

Mock screen state

This is a product scaffold, not a claim that the full workflow is already live.

Score

82/100

good beta file

Club labels

Present

20/20

Face/path

Missing

limits mechanics

Sample

20 shots

matched 8i

Workflow mock

How this should work.

The page exists to align product, engineering, and beta feedback before the full backend workflow is built.

  1. 1Inspect headers and rows.
  2. 2Score club labels, distance, offline, launch, spin, delivery metrics, shot order, and sample size.
  3. 3Warn about missing optional metrics.
  4. 4Set ball-flight and mechanical confidence separately.

Mock rows

Can say

Right miss widened

Carry, offline, launch, and spin are present.

Cannot say

Exact swing fault

No video, impact, or face/path columns.

Best next upload

Same source retest

Use same simulator, target, club, and ball type.

User should see

  • Score out of 100
  • Missing metrics
  • Confidence by claim type
  • Manual review fallback

Not live yet

  • Simulator-specific confidence calibration
  • Facility assignment confidence in same score
  • Automated parser QA labels