# Quality of Tracking Data

**URL:** <https://community.podlove.org/t/quality-of-tracking-data/50>\
**Category:** Podlove Publisher\
**Created:** [February 15, 2015, 3:05am UTC](https://community.podlove.org/t/quality-of-tracking-data/50 "2015-02-15T03:05:01Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![ericteubert](https://community.podlove.org/user_avatar/community.podlove.org/ericteubert/32/1568_2.png) [@ericteubert](https://community.podlove.org/u/ericteubert)\
**Post date:** [February 15, 2015, 3:16am UTC](https://community.podlove.org/t/quality-of-tracking-data/50/2 "2015-02-15T03:16:41Z")

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> [@katrinleinweber](#):
>
> Podlove reports almost twice as many downloads as we measured with our old system

That’s a huge difference! Would be interesting to compare and find out why.

Here’s the relevant excerpt on how we handle aggregation:

> Before tracking data is presented in the analytics area, it is cleaned up. Cleanup involves the following steps:

> - Based on the UA analysis bots are filtered out.
> - Duplicate requests are filtered out. A request is considered a duplicate if it contains
> - the same File ID
> - and the same Request ID
> - and was made within the same hour
> 
> - Pre-Release downloads are filtered out. They may happen if you test downloads before publishing the episode.
> 
> (Source: [http://docs.podlove.org/guides/download-analytics/](http://docs.podlove.org/guides/download-analytics/))

… which immediately raises the question how much of a difference it would make if I changed “within the same hour” to “within 24 hours”. I will investigate.

> [@katrinleinweber](#):
>
> There are about 40% unknown podcast clients & operating systems. If these were all bots of some kind, we’d have a similar number of “real” downloaders as we thought.

Unlikely that they are all bots. I use the same UA (user agent) parser as Piwik and they already have a pretty decent bot detection. They only had a completely blind eye for podcast clients. That’s why I [forked](https://github.com/eteubert/device-detector) their library and started adding my own detection rules for popular clients. This is far from complete, because it’s a slow and tedious process. We [have a crowd-sourced solution in mind](https://twitter.com/podlove_org/status/566268161788760066) but lack the manpower to execute at the moment.

When building the system, I only had data from the Metaebene to work with. I actually worked until the “Unknown” podcast client disappeared from the top 10 most of the time. That’s why your report of such a high number surprises me. If you want to help, throw this against your database:

```
SELECT
	COUNT(ua.id) cnt, ua.*
FROM
	wp_podlove_downloadintentclean di
	JOIN `wp_podlove_useragent` ua ON ua.id = di.`user_agent_id`
WHERE client_name IS NULL
GROUP BY ua.id
ORDER BY cnt DESC

```

It looks for unknown clients and orders them by popularity.

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