An outlier is a video that beat its own channel's normal views. Here is the formula, a method you can run in a spreadsheet, and what 1,290 real videos show about how often outliers happen.
An outlier video is a video that beat its own channel's normal views by 2x to 10x or more. To find one, divide a video's views by the median views of that channel's other recent uploads. In our November 2026 sample of 1,290 long-form videos, 23% reached 2x, 8% reached 5x and 4% reached 10x.
The best method is the one that matches how many channels you need to cover. Five channels fit in a spreadsheet. A whole topic does not. Here is the ranking.
| Rank | Method | Best for | Limit |
|---|---|---|---|
| 1 | BigIdeasDB Desktop (YouTube Lab) | Finding outliers for a topic, not one channel. Plots views against channel size, flags videos above 2x the typical line, then adds momentum, comment themes and weekly re-checks. One-time purchase | Channel size is known only for videos the check opens, unless you add your own free YouTube Data API key. Compares to channel size, not to a channel's own median |
| 2 | A spreadsheet with a channel median | The cleanest score for a handful of channels you already follow. Free, and you see every number | Manual. About 20 minutes per 10 channels, and it only covers channels you pick |
| 3 | YouTube search sorted by view count, with duration and date filters | Finding which channels and topics to put in the spreadsheet | Shows raw views only. A big number on a big channel looks the same as a real outlier |
| 4 | A channel's Videos tab sorted by Popular | A quick look at one creator's hits | Popular sorts by all-time views, so old videos always win. It hides recent outliers |
| 5 | YouTube Studio, for your own channel | Your own outliers, with impressions, click-through rate and retention | Your channel only. You cannot see these figures for anyone else |
Hosted outlier databases and browser extensions also exist. They save time, but most are built on the same formula you can run yourself, so understand the formula first. The next two sections show it.
An outlier video is one that performs far above the channel that posted it. The yardstick is the channel's own normal, not a universal view count. 50,000 views is a hit on a channel that usually gets 3,000 and an average day on one that gets 60,000.
That is why outliers are more useful than top-viewed lists. A most-viewed list is dominated by the biggest channels. An outlier list shows topics and framings that beat a known baseline, which is the part you can reuse. It is also why small-channel outliers get so much attention. One r/NewTubers thread describes a video that reached 10,000+ views when the channel had 50 subscribers, and the top answer was: "Sounds like you found a low competition keyword." Another thread in the same community opens with "I find it difficult to find outlier videos in my niche."
Outlier is not the same as viral. Viral is absolute reach. Outlier is relative to the channel. A video with 40,000 views on a channel that normally gets 800 is a 50x outlier and not viral at all.
Divide the video's views by the median views of the same channel's other recent uploads. Use the median, not the average. One viral hit pulls an average up and makes every other upload look weak.
| Upload | Views | Baseline (median of others) | Outlier score |
|---|---|---|---|
| A | 4,200 | 4,400 | 1.0x |
| B | 3,800 | 4,400 | 0.9x |
| C | 5,100 | 4,200 | 1.2x |
| D | 4,600 | 4,200 | 1.1x |
| E | 3,900 | 4,400 | 0.9x |
| F | 18,400 | 4,200 | 4.4x |
| G | 4,400 | 4,200 | 1.0x |
| H | 5,000 | 4,200 | 1.2x |
| I | 3,600 | 4,400 | 0.8x |
| J | 4,100 | 4,400 | 0.9x |
Upload F is the outlier. The same 18,400 views would be unremarkable on a channel with a 20,000 median.
Three rules keep the score honest:
There is a second way to set a baseline, and it works when you do not have a channel's history. Compare views with channel size: a channel with 10,000 subscribers is expected to get fewer views than one with 1,000,000. The Desktop YouTube Lab uses this version across a whole search, drawing a dashed line for typical views at each size and flagging videos at 2x the line or more. Size is a rougher baseline than a channel median, because two channels of the same size can differ a lot, but it needs only one video per channel.
This is the manual method. It needs a spreadsheet and about 20 minutes for 10 channels.
=B2/MEDIAN(FILTER(B$2:B$21, ROW(B$2:B$21)<>ROW(B2))). It divides each video by the median of the other 19.Real Reddit wording shows how people do this today. A thread on finding winning videos asks how others find outliers for thumbnail and title inspiration, and another asks for an actual toolkit for evaluating a niche. The steps above are the free answer to both questions.
About 1 in 4 long-form videos beats its channel median by 2x, 1 in 12 by 5x and 1 in 25 by 10x.
The pattern is lopsided. Videos at 5x or more were 8% of uploads and 21% of all views. Videos at 2x or more were 23% of uploads and 55% of views. Outliers are rare enough to stand out and common enough that 44 of 46 channels had one. Almost one video in five also came in at half the channel median or lower, so a channel's weakest and strongest uploads sit far apart.
Small channels hit the extreme outliers more often, but not the moderate ones. The 2x rate barely changes with size. The 10x rate falls from about 5% on channels under 100K subscribers to under 1% on channels with 1M+.
The gap is real but not huge. Resampling channels 2,000 times, the 10x rate on channels under 100K subscribers was 5.6% against 2.2% on larger channels, and the interval for the difference stayed above zero. The gap in the 5x rate (9.8% against 6.7%) did not clear that bar. Treat it as a tilt, not a law.
Part of the reason is arithmetic. Big channels have steady audiences, so their uploads cluster near the median. Small channels have a volatile base, so one upload that reaches a new audience stands far above it. That also makes small-channel outliers the most useful ones: 68% of the 5x videos in our sample got more views than the channel has subscribers, a sign the video reached beyond the existing audience.
| Channel size | Channels | Videos | Typical channel median views |
|---|---|---|---|
| Under 10K subs | 7 | 201 | about 260 |
| 10K to 100K | 16 | 460 | about 7,600 |
| 100K to 1M | 13 | 372 | about 43,000 |
| 1M+ | 10 | 257 | about 316,000 |
Yes, in this sample. Home improvement had the fewest outliers. Small business, cooking, tech reviews and personal finance sat close together.
Read this carefully. Each niche has 5 to 9 channels, so one channel with a viral run moves a niche by several points. Home improvement is the clearest result: only 0.9% of its videos reached 10x, against 3.3% to 5.9% in the others. A plausible reason is that home projects are watched steadily for years, so uploads cluster near the median. That is a guess, not something this data tests.
The practical use is for your own research. Run the method on 10 channels in your niche before trusting any general rate. If you are still choosing a niche, our ranking of YouTube niche finder tools covers that step.
In our data, longer videos and topic mattered more than title tricks. Titles with numbers, questions or "how to" did not reliably beat titles without them once we compared videos inside the same channel.
Longer videos had both more outliers and fewer flops. 12 minutes and up had 25% to 30% at 2x and about 14% at 0.5x or less. Under 12 minutes it was 19% to 20% and 24% to 27%. The median outlier ran 14.1 minutes, against 11.7 for the rest. The 2x gap between the longest and shortest buckets was 9.8 points with a wide interval (from about -2 to +22), so call it a lean, not a rule. Longer videos may simply be the ones with more to say.
| Title feature | Videos with it | At 2x with | At 2x without | Same-channel gap |
|---|---|---|---|---|
| Contains a number | 578 | 23.7% | 22.8% | x1.07 (36 channels) |
| Question mark | 108 | 25.0% | 23.0% | x1.05 (13 channels) |
| "How to" or "how I" | 123 | 22.0% | 23.3% | x0.88 (14 channels) |
| Dollar sign | 79 | 27.8% | 22.9% | x0.97 (9 channels) |
| Warning words (why, stop, never, mistake, wrong) | 120 | 30.0% | 22.5% | not tested |
Almost none of this moves the needle. The only visible tilt was warning words, at 30.0% against 22.5%, from a group of 120 videos. The outlier median title was 55 characters against 57 for the rest, and titles of 40 to 60 characters did best at 25.5%. The honest reading: the title helps a good topic get clicked, but it does not create the outlier. The topic and the promise do.
That also covers the search for outlier thumbnails. Public data shows the thumbnail only as an image, so this study did not score thumbnails and we make no claim about them. Study them the way you study titles: look for what the winning thumbnails on one topic share, and check the losing ones on the same topic before you copy anything.
One more check, on age. Older videos could look like outliers just from having more time to collect views. They did not. The 2x rate was flat across age bands.
| Video age | Videos | At 2x or more | At 5x or more | At 0.5x or less |
|---|---|---|---|---|
| 30 to 60 days | 329 | 25.2% | 7.3% | 9.7% |
| 60 to 120 days | 326 | 21.8% | 6.4% | 22.7% |
| 120 to 240 days | 351 | 22.8% | 10.0% | 22.5% |
| 240 days or more | 284 | 22.9% | 9.5% | 25.0% |
The 30 to 60 day band had far fewer videos at 0.5x or less (9.7% against 22% to 25%). Newer videos tend to sit closer to the median. That is why the 30-day cutoff is a floor, not a guarantee, and why the score is best used to rank videos against each other, not to declare a verdict.
Keep the topic and the promise. Change the angle, the proof and the packaging. An outlier tells you where to look, not why it worked.
Two warnings. Studying only winners makes ordinary traits look like causes, so always read some losers. And do not copy a title and thumbnail: the audience belongs to the original channel, and a clone rarely carries it over.
Start with the spreadsheet for 5 to 10 channels. Automate when you want a whole topic, repeated every week.
| Spreadsheet | BigIdeasDB Desktop YouTube Lab | |
|---|---|---|
| Cost | Free | One-time purchase, no subscription. See the current price on the pricing page |
| Covers | Channels you pick | A search phrase: up to 60 videos, the 12 most viewed opened by default (25 at most) |
| Baseline | Channel median (the better score) | Channel size, with a dashed line for typical views and a 2x flag |
| Extras | None | Momentum (uploads and views by month), comment themes with word-for-word quotes, views per day, weekly re-check |
| Runs | In your browser | On your own computer, one request every 1.5 seconds |
| Limit | Manual work | Channel size known only for opened videos, unless you add your own free YouTube Data API key. Channel sizes are rounded by YouTube |
The spreadsheet gives the cleaner score. The BigIdeasDB Desktop YouTube Lab gives breadth: one saved search covers a topic, the Outliers view ranks what stands above the line, Momentum shows whether uploads and views on the topic are rising, and Voices counts what viewers complain about. Setup and every limit are in the YouTube Lab guide. Desktop is a one-time purchase whose price rises $1 with each sale, so the current price is on the Desktop pricing page.
Scan your niche for documented demand before you film to see what viewers and communities already ask for, then use the outlier method to see which of those topics YouTube already rewards.
We pulled the data with the YouTube Data API on November 7, 2026. The table lists each choice and what it cannot tell you.
| Step | What we did | Limitation |
|---|---|---|
| Channel sample | Searched six niches (personal finance, small business, SaaS and indie hacking, cooking, home improvement, tech reviews) with US region and English language filters. Sampled channels at random from the results, aiming for 2 per size band, and dropped channels registered outside our North America and Europe audience focus | Search results favour active channels. Not a random sample of all of YouTube. Niches have 5 to 9 channels each |
| Videos | Last 100 uploads per channel from the API. Kept videos of 4 minutes or more, no live streams, at least 30 days old, then the 30 most recent. Channels with fewer than 12 qualifying videos were dropped (5 of 51) | 46 channels and 1,290 videos. A 4-minute cutoff is our choice. Shorts excluded |
| Score | Views divided by the median views of the channel's other sampled videos (leave-one-out) | Median of up to 29 videos. A channel changing topic or growing fast shifts its own baseline |
| Sensitivity | Re-ran with a baseline of the 10 videos before and 10 after each video | Result moved little: 21.9% at 2x, 7.7% at 5x, 4.2% at 10x |
| Comparisons | Share of videos by subscriber band, niche, length, age and title features. 2,000 channel-level resamples for the size and length gaps | Videos from one channel are not independent. Differences inside small groups are noisy. Nothing here shows cause |
| Data type | Public view counts and public titles only | No impressions, click-through rate, retention or revenue. Views are live and keep changing |
An outlier video is a video that gets far more views than its own channel normally gets, usually 2x to 10x or more. It is measured against that channel's typical performance, not against YouTube overall. A video with 50,000 views is an outlier on a channel that usually gets 3,000 and ordinary on one that gets 60,000.
Pick 10 channels in your niche, copy the views of their last 20 to 30 long-form uploads into a spreadsheet, and divide each video's views by the median of the channel's other uploads. Sort by that ratio. Ignore videos under 30 days old, because they are still gaining views. The top rows are your outliers.
Outlier score equals a video's views divided by the median views of the same channel's other recent uploads. A score of 1 is normal, 2 means double the usual views and 10 means ten times. Use the median, not the average, because one viral hit drags an average up and hides every other outlier on the channel.
BigIdeasDB Desktop is the best low-cost way to find outlier videos if you research topics, not just single channels. Its YouTube Lab plots views against channel size, draws a line for typical performance, flags videos above 2x that line and ranks them. It runs on your own computer as a one-time purchase, not a subscription.
BigIdeasDB Desktop is the best paid-once option, and a spreadsheet is the best free one. List a channel's uploads, paste in view counts, and divide each by the median of the others. It takes about 20 minutes per 10 channels, shows you the raw numbers and costs nothing. Add YouTube search sorted by view count to find channels worth checking.
In our November 2026 sample of 1,290 long-form videos, 23% scored 2x or more, 8% scored 5x and 4% scored 10x. So 2x is notable but common, 5x is strong and 10x is rare. Study the 5x and higher videos first, then check whether the topic repeats across channels.
Small channels do not get outliers more often at 2x, but they get the extreme ones more often. In our sample, 5.6% of videos on channels under 100K subscribers reached 10x their channel median, against 2.2% on channels of 100K or more. Channels with 1M+ subscribers hit 10x in under 1% of videos.
No. Viral describes absolute reach, such as millions of views. Outlier describes performance relative to the channel that posted it. A video with 40,000 views on a channel that normally gets 800 is a large outlier and not viral. Outliers are more useful for research, because they show a topic or framing that beat a known baseline.
Separate the topic, the promise and the packaging, then keep the topic and promise and write your own angle. Check whether the same idea outperformed on more than one channel, read the comments for what viewers still ask, and test one change on your own channel. Copying a title and thumbnail rarely transfers, because the audience belongs to the original creator.
Keep reading: BigIdeasDB Desktop, the YouTube Lab guide, voice of customer research and the best tools to find customer pain points.
Scan your niche before you start a channel →
BigIdeasDB Research. (2026). How to Find Outlier Videos on YouTube (2026 Data). BigIdeasDB. Retrieved from https://bigideasdb.com/how-to-find-outlier-videos-on-youtube