What’s the catch? Aggressive ad load — reported by 106 users.
Analyzed automatically from 3 independent signals that had to agree — not a hands-on test, and not human-reviewed.
This analysis describes version 5.92.0 of the app, analyzed 2026-10-05 . The app may have changed since.
The store currently lists version 5.92.0, updated 2026-09-23.
What the analysis found
The catch list 1
Charges we publish at a strength we stand behind: measured from an artefact, agreed by independent sources, or reported by enough users to clear that charge's own published bar.
One source only — shown, not confirmed 1
A single kind of evidence. We publish these because withholding them would be the bigger distortion, and we do not treat any of them as a finding we stand behind.
What reviewers said
Verbatim excerpts from public store reviews, shown as corroborating user reports. They are not Appworthy audit evidence, and a quotation is not proof of the finding it sits under.
Aggressive ad load
Wayyyy Too Many Ads — An ad before the game after the game. Can’t play another day without an ad. You spend more time playing games in the ads more than you do the sudoku. Even once your in, it looks like the game is about to start, it…
You require watching long ads — There are so many games that don’t require watching endless ads! Why don’t you reconsider that decision! Or. I won’t be playing any more!!!!!
Too — Too many ads!
Reports that the ads misrepresent the app
Adds — The add for this app claimed that there are no adds, this is a lie, although I have not yet gotten an add in the middle of a puzzle which is a step up from the sudoku app I had been using, there’s not much else of note good or bad…
Good game, awful ads — I liked the game, it is easy to use and challenging for most casual soduko players. But I uninstalled it because of the horrible ads and the pricing. The ads are misleading, making it look like the actual game needs…
False Advertising — I searched for no ads sudoku.
What this app sells
From the store listing. Billing terms are read from each purchase’s name — the stores don’t publish them — so treat them as a strong hint, not a disclosure.
Show 3 more purchases we couldn’t categorise
These are really on sale; we just can’t tell from the name whether they recur.
How the automated analysis works
This assessment is produced automatically. Three independent signals are read — user reviews, the store listing and the store privacy disclosure — and each candidate finding is filed under the sources that support it.
No charge stands on one kind of evidence — with one published exception. A finding is either a measured fact that anyone can reproduce (an ad SDK is in the package; every subscription bills weekly; the privacy label omits a tracker we can see), or it is an interpretation — and an interpretation needs independent sources to agree before it reaches the catch list. The exception is a harm only users can see: a charge may publish from reviews alone, as “Reported by N users”, once enough reviewers raise it to clear that charge’s own published bar. That agreement rule is what replaced the human reviewer, not a model’s confidence.
There is no overall score, on purpose. A 0–99 number was published here until 2026-09-15. It was withdrawn because it discriminated too weakly to be the instrument — 44.7% of every pair of apps tied — and because a single number is a verdict where a count is an observation. What publishes instead is a row of measurements, each of which says what was counted and, where we could not look, says that instead.
Findings under One source only are real reports we do not stand behind as findings. They are shown because withholding them entirely would hide a genuine complaint, and labelled because publishing them as charges would be an accusation off a single source.
Several measurements above say “Not assessed” because no artefact we hold can answer them. A human review is what resolves those — and it can remove a favourable band as easily as add one.
Hands-on inspections are not yet open.