I work on models like those of Sensor Tower and App Annie use to estimate downloads. They are extremely precise, the cross-validated error is << 10% for apps that are this small, and in the US (it increases with much bigger apps in countries where little data is available).
In general, could you tell a bit about how do app downloads get estimated? Now that I'm thinking about it, I think the first way to do it is to correlate the amount of comments in your particular app section to downloads numbers that you do know and extrapolate that out, assuming that comments/downloads ratio stay the same within a particular app category.
One could test this hypothesis out on different YouTube video categories for which this obviously is known to give the assumption some quick strength or falsification.
Anyways, this is the first time I'm thinking about this question at all. I'm curious what you're allowed/able to tell us.
There is a public ranking of apps ranked by downloads in the app store. If you know how many downloads correspond to what position in the chart (e.g. number 100 makes ~1000 downloads), you can fit a log regression to determine the number of downloads given the ranking in the category. I am oversimplifying because you also need to take into account seasonality and time trends (e.g. the app store is always expanding), but overall fits are very very precise (unless you get near the top positions, which are those for which they have more problems in estimating). Ah, and if you wonder where the data comes from, App Annie and Sensor Tower get data for downloads to fit their models directly from tens of thousands of developers that share it with them in exchange for free analytics,
For Android apps that are popular in rest-of-world geographies (including my own) those tools (and analogs like similarweb and apptopia) are way off in both download counts and active users, sometimes by 2-3x.
Maybe iOS apps in US can get here but I'm doubtful.
