# Odds of Becoming a YouTube Celebrity

Twenty hours of video are uploaded to YouTube every minute; in turn, users stream seventy-five billion videos per year. With all those videos, what are your chances of making a dent in the YouTube universe and garnering, say, one million views?

Slate.com writer Chris Wilson recently addressed just that question, concluding that “[y]ou might have better odds playing the lottery than of becoming a viral video sensation.” Wilson gathered data on about ten thousand randomly selected YouTube videos and found that 1 in 2.39 videos was viewed no more than ten times after one month, while just 1 in 401 reached ten thousand streams.

The odds are heavily stacked against the user, so much so that in Wilson’s sample, just one video cleared one hundred thousand views, and none went over a million. His article’s title was “Will My Video Get 1 Million Views on YouTube?” The answer, according to Wilson’s data, was a resounding no.

And yet, anyone who has visited YouTube enough times knows that there are plenty of videos with at least one million views—too many, in fact, to count. Can we use Wilson’s data to estimate a video’s odds of getting one million views? We can, but first we must apply some corrections.

As with many data sets, Wilson’s YouTube stats are heavily skewed, with a huge number of videos bunched at no more than a few views (most commonly zero), and an ever-decreasing total as the numbers get higher. There are, for example, 2,226 videos with no views in their first month, 237 with one view, 158 with ten views, and just 23 with one hundred. Any statistical estimation demands a more or less normal distribution, where the data resembles a bell curve rather than the power law.

This can be accomplished by taking the natural logarithm of the video streams plus 0.5 (which is there to make all numbers positive, as you cannot take the natural logarithm of zero—it also results in an almost perfect skewness). We can then find the average of that number for the sample as well as the standard deviation; from there, we can find the odds of a video getting any number of views.