Leaderboard appearances are breadth, not independent wins

Singular reports 29 leaderboard appearances for Google Ads, Meta, Mintegral and TikTok across its 2026 cuts. Repetition across cuts is not the same as winning 29 independent experiments.Index announcement.

Ask how the cuts overlap

The same campaign can potentially contribute to more than one segmentation or model view. Our recommendation is to list the relevant boards, their eligibility criteria and the population behind each. Without that map, a simple appearance count can exaggerate the amount of independent evidence.

The MTA documentation explains that modelled credit depends on configured rules. A placement under one estimator should therefore not be treated as a direct replication of a placement under another unless the comparison has been specified carefully.

Use breadth for a shortlist, not an allocation formula

A network appearing across several relevant environments may deserve a closer look. The useful next step is to match the game’s geography, operating system, monetisation and measurement needs to the actual board. A global appearance count cannot establish the expected return of a new campaign.

The card separates report coverage from campaign proof. It is suitable for explaining why a buyer added a network to a test shortlist, while leaving budget size and expected payback to the studio’s own evidence. That is a stronger use of a leaderboard than converting position or frequency into an unsupported performance forecast.

Inspect the evidence

Download the source-population and comparability card. The extraction separates documented facts from our analysis and unknowns.

Further reading in the existing archive: AppsFlyer, Adjust, Singular: a buyer's comparison; The benchmark report is selling you something. These links provide background; this check does not independently verify their full contents.

Frequently asked questions

How should UA teams read leaderboard appearances when they are breadth, not independent wins?

Count the comparison behind each leaderboard placement before treating repeated appearances as independent proof. Singular reports 29 leaderboard appearances for Google Ads, Meta, Mintegral and TikTok across its 2026 cuts — repetition across cuts is not the same as winning 29 independent experiments. List the relevant boards, their eligibility criteria and the population behind each; without that map, a simple appearance count can exaggerate the amount of independent evidence. Singular’s MTA documentation explains that modelled credit depends on configured rules, so a placement under one estimator should not be treated as a direct replication of a placement under another unless the comparison has been specified carefully. A network appearing across several relevant environments may deserve a closer look; the useful next step is to match the game’s geography, operating system, monetisation and measurement needs to the actual board. A global appearance count cannot establish the expected return of a new campaign. Use breadth for a test shortlist; leave budget size and expected payback to the studio’s own evidence.