Clip views scenario model.

Worked example

Assume an ordinary clip has a median of 1,000 views, a 2% chance of being a breakout that gets 20 times the median, and a spread (sigma) of 1.0. These three settings are illustrative; replace them with numbers measured from your own posts using the average views assumptions tool.

Mean views per clip become 0.98 x 1,000 x e^0.5 + 0.02 x 20,000 = about 2,016, roughly 2.02 times the median, because rare breakouts pull the mean up. Over 50 clips the chance of at least one breakout is 63.6%, and about 114 clips would be needed for a 90% chance. About 23.9% of clips land under 500 views, or roughly 12 of 50. At an assumed $2.00 net per 1,000 views, expected total pay is $201.57, against $100.00 if every clip simply got the median.

Results vary. This page is arithmetic on the assumptions you enter. It is not a forecast, offer or guarantee of what any campaign, platform or account will pay, and it cannot say how many views a clip will get.

How it works

Views for ordinary clips follow a lognormal shape, which is a common way to describe numbers that are mostly modest with a long tail of higher ones. The median is the middle clip; the spread controls how far clips stray from it. On top of that, each clip has a small chance p of being a breakout with k times the median views.

Why it matters: the mean is higher than the median, and a large share of the mean can come from rare clips. Planning around the mean overstates what a normal stretch of clips will deliver, which is explained in clipping income variance explained.

Methodology

Body mean = m x exp(s^2 / 2). Expected views = (1 - p) x body mean + p x k x m. P(at least one breakout in N) = 1 - (1 - p)^N. N for a 90% chance = ln(0.1) / ln(1 - p), shown as not reachable when p is 0. Probability below the minimum views = (1 - p) x Phi((ln(min) - ln(m)) / s), plus p if a breakout would itself fall below the minimum; Phi uses the Abramowitz and Stegun 7.1.26 approximation of the error function (error about 1.5e-7), unit-tested against known values. With s = 0 every ordinary clip gets exactly the median. Expected clips below the minimum = N x that probability. Clips are treated as independent, which real clips are not: topic, timing and account momentum link them.

What this does not cover

• A prediction of any clip, account or campaign result. Every probability and multiple here is an assumption you set.
• Platform algorithm behavior, shadow limits, audience size or posting time.
• Correlation between clips, such as a hit lifting the next posts.
• Pay terms, approval or fees; use the hourly rate calculator for those.
• Any claim that the default 2%, 20x or sigma of 1.0 is typical. They are placeholders to show the arithmetic.

Everything runs in your browser. Nothing you type is sent or stored.

Sources

No outside statistic is used because breakout odds depend on the individual account. The model is standard probability and lognormal arithmetic on your inputs. No external source is needed because no outside fact is stated.

FAQ

What is the chance of going viral on TikTok or Shorts?

No one can publish a reliable figure for your account. This tool lets you set an assumed per-clip chance and see what it implies over many clips; treat the output as scenario arithmetic, not a forecast.

Why is mean views higher than median views?

View counts have a long tail. A few large clips raise the mean while the typical clip stays near the median, so the mean overstates a normal result.

How many posts for a 90% chance of one breakout?

ln(0.1) / ln(1 - p). At an assumed 2% per clip that is about 114 posts. At 5% it is about 45. Change p to your own assumption.

What does sigma mean?

It is the spread of ordinary clips on a log scale. Zero means every ordinary clip gets the median; larger values mean more clips far above and below it.