YouTube Shorts Analyzer result
PURPLE😈VS GREEN🟢 #roblox #robloxshorts
Eazy
V/S ratio
2.9x
views / subscribers
Views
7.3M
Subscribers
2.5M
Published
10/21/2026 03:50
Duration
0:11
How to read these metrics
This tool shows public YouTube metrics, not a manual review of this video. Views are cumulative video plays; subscribers describe the channel. These counts may refresh at different times and are not guaranteed to be synchronized. Subscriber counts may be rounded or hidden. The timestamp refers to collection or update of the video data, not a simultaneous subscriber observation.
V/S divides the currently displayed views by the currently displayed subscribers. It does not measure the share of subscribers who watched. If subscribers are zero or unavailable, no ratio is shown.
A high V/S does not prove originality, explain why traffic grew, or predict future performance. Publication date is not collection time. Compare videos with similar age and format, then watch the source and check rights before drawing conclusions.
Video data collected/updated at:
ShortsMonkey analysis
Computed from the public counts ShortsMonkey has collected for this video and its channel.
- Reach band
- High (1.3–5×)
- Channel size
- 1M+ subscribers
- Views per hour since publishing
- 107.1K
- Growth between collections
- +2.6M
V/S is at least 1.3 and below 5. Compare similar videos from this channel at a similar time since publication. The same ratio corresponds to more views when the subscriber count is larger. Compare similar videos from channels of similar size.
Only 1 of this channel’s videos are in ShortsMonkey’s data so far — too few for a fair comparison, so none is shown.
Between 10/23/2026 08:11 and 10/24/2026 00:11 the video gained 2.6M views, about 162.7K per hour.
Bands are defined by ShortsMonkey, not YouTube. V/S means views divided by subscribers; it cannot identify unique viewers, their subscription status, or traffic from recommendations or search. Counts may be collected at different times and differ from YouTube now. How we measure outliers →
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