Youtube Shorts Analysis

By gerald · March 19, 2026

Analyzes YouTube Shorts performance using data insights on views, engagement, growth, and viral trends across channels and videos. Highlights key factors…

Hey! 👋 I'm Datastam AI, here to help with whatever you need. Whether you want to:

- Analyze data (CSV, Excel, databases, etc.)

The analysis identified the top 10 YouTube channels generating the most total views across their Shorts content. A horizontal bar chart has been created showing each channel's total view count, ranked from highest to lowest, making it easy to compare performance at a glance.

The analysis identified the 15 videos with the highest growth velocity (Views Per Day) from the dataset. A horizontal bar chart has been generated showing each video's daily view rate, color-coded by channel, making it easy to compare which channels dominate the top spots. A data table is also available with full details for each video.

The analysis reveals which videos drive passive engagement (likes) versus active engagement (comments) by calculating the likes-to-comments ratio. A bar chart shows the top 15 videos with the highest ratios, and data tables provide detailed breakdowns. The overall median ratio is 88.6, meaning a typical video gets about 89 likes for every comment — while the mean of 248.7 suggests some videos skew much higher, attracting massive passive engagement with relatively few comments.

Viral YouTube Shorts (top 5% by views, requiring 127M+ views) show a surprising pattern: their titles are virtually identical in length to non-viral Shorts, but their descriptions are dramatically shorter. Two data tables were generated summarizing these findings across 40 viral videos compared to the rest.

The analysis produced two visualizations and a data table showing how Engagement Rate % varies across Low (<100K), Medium (100K–1M), High (1M–10M), and Viral (10M+) views tiers. A box plot displays the full distribution and spread of engagement rates within each tier, while a bar chart highlights the average engagement rate per tier.

The analysis identified channels with multiple YouTube Shorts that maintain the most consistent high engagement rates. A bar chart was generated showing the top 20 channels ranked by their consistency (lowest coefficient of variation) while having above-median mean engagement rates. Each bar displays the mean engagement rate with error bars representing variability — shorter error bars indicate more consistent performance.

Newer Shorts (under 30 days old) significantly outperform older ones on both engagement and views. The analysis compared 312 newer Shorts against 487 older ones, and the results are striking. A box plot visualization and summary tables were generated to illustrate the differences clearly.

The analysis reveals which videos achieve the highest engagement rates and what makes their titles distinctive. A bar chart shows the top 20 videos ranked by engagement rate, and the data highlights clear title patterns among high performers.

The analysis reveals how Views Per Day declines as videos age, shown in a scatter plot with a median trend line on a log-log scale. The chart displays individual videos as light blue dots alongside a red median line that traces the decay curve across age bins from 0–7 days all the way to 4+ years. The visualization clearly shows the steepest decline occurring in the earliest days of a video's life, with a more gradual tapering as videos mature.

Description Length does not strongly correlate with views, engagement rate, or comments. The analysis reveals mostly weak to negligible relationships across all three metrics. Two scatter plot charts and binned bar charts were generated to visualize these patterns clearly.

The analysis examined how title length — short (≤30 chars), medium (31–60 chars), and long ( 60 chars) — affects views, engagement rate, and comments on YouTube Shorts. A bar chart and data table were generated showing the performance breakdown across all three categories.

The analysis identified 99 'hidden gem' videos that have high engagement rates (at or above the 75th percentile of 2.45%) but low view counts (at or below the 25th percentile of 544,312 views). These videos are resonating strongly with their audiences but haven't yet reached a wide viewership — making them prime candidates for boosted distribution. A scatter plot was generated showing engagement rate vs. views across all videos, with the hidden gems highlighted as red stars, and threshold lines marking the key cutoffs.

The analysis successfully identified the top 15 channels with the most YouTube Shorts in the dataset. Two visualizations were generated: a bar chart showing the number of Shorts per channel, and a grouped bar chart comparing average views and likes across those top channels. These charts reveal which creators are most prolific with Shorts and how their content performs on average.

Here are the percentile benchmarks across three key metrics for YouTube Shorts. The data reveals a wide spread between low and high performers, indicating significant variability in content performance.

The analysis compared average Views Per Day across different age cohorts (weekly, monthly, quarterly, yearly, and older). A grouped bar chart was generated showing both the mean and median Views Per Day for each cohort, making it easy to compare how video viewership rates differ based on content age.

The analysis produced a scatter chart showing how Comments relate to Views across all videos, with each point colored by Engagement Rate and sized by Likes. This gives a clear visual picture of whether high-view videos automatically rack up comments — or if other factors play a role.

Shorts with zero descriptions actually outperform detailed ones in raw views and speed, but detailed descriptions win on engagement rate. The two bar charts and comparison tables break down performance across all five description length groups — from zero characters all the way to 1,000+ characters.

The analysis successfully identified statistical outliers in your YouTube Shorts dataset using the IQR (Interquartile Range) method. A scatter plot was generated showing all videos plotted by Views (log scale) vs. Engagement Rate, with outliers color-coded: red for Views Outliers, blue for Engagement Outliers, purple for videos that are outliers in both, and gray for normal videos. Additionally, data tables were produced highlighting the top views outliers and top engagement outliers, making it easy to spot which videos stand out from the crowd.