not the one one that feels that YouTube sponsor segments have develop into longer and extra frequent not too long ago. Typically, I watch movies that appear to be attempting to promote me one thing each couple of seconds.
, it positive is annoying to be bombarded by adverts.
On this weblog put up, I’ll discover these sponsor segments, utilizing knowledge from a well-liked browser extension referred to as SponsorBlock, to determine if the perceived improve in adverts truly did occur and in addition to quantify what number of adverts I’m watching.
I’ll stroll you thru my evaluation, offering code snippets in Sql, DuckDB, and pandas. All of the code is out there on my GitHub, and because the dataset is open, I may even educate you the way to obtain it, as a way to comply with alongside and play with the information your self.
These are the questions I can be attempting to reply on this evaluation:
- Have sponsor segments elevated through the years?
- Which channels have the best share of sponsor time per video?
- What’s the density of sponsor segments all through a video?
To get to those solutions, we must cowl a lot floor. That is the agenda for this put up:
Let’s get this began!
SponsorBlock is an extension that means that you can skip advert segments in movies, much like the way you skip Netflix intros. It’s extremely correct, as I don’t keep in mind seeing one improper section since I began utilizing it round a month in the past, and I watch plenty of smaller non-English creators.
You is perhaps asking your self how the extension is aware of which elements of the video are sponsors, and, imagine it or not, the reply is thru crowdsourcing!
Customers submit the timestamps for the advert segments, and different customers vote if it’s correct or not. For the common person, who isn’t contributing in any respect, the one factor you must do is to press Enter to skip the advert.
Okay, now that you already know what SponsorBlock is, let’s speak concerning the knowledge.
Cleansing the Knowledge
If you wish to comply with alongside, you’ll be able to obtain a duplicate of the information utilizing this SponsorBlock Mirror (it’d take you fairly a couple of minutes to obtain all of it). The database schema could be seen right here, though most of it gained’t be helpful for this challenge.
As one may anticipate, their database schema is made for the extension to work correctly, and never for some man to mainly leech from an enormous neighborhood effort to seek out what share of adverts his favourite creator runs. For this, some work will must be carried out to wash and mannequin the information.
The one two tables which might be vital for this evaluation are:
sponsorTimes.csv: That is crucial desk, containing thestartTimeandendTimeof all crowdsourced sponsor segments. The CSV is round 5GB.videoInfo.csv: Comprises the video title, publication date, and channel ID related to every video.
Earlier than we get into it, these are all of the libraries I ended up utilizing. I’ll clarify the much less apparent ones as we go.
pandas
duckdb
requests
requests-cache
python-dotenv
seaborn
matplotlib
numpyStep one, then, is to load the information. Surprisingly, this was already a bit difficult, as I used to be getting plenty of errors parsing some rows of the CSV. These had been the settings I discovered to work for almost all of the rows:
import duckdb
import os
# Connect with an in-memory DuckDB occasion
con = duckdb.join(database=':reminiscence:')
sponsor_times = con.read_csv(
"sb-mirror/sponsorTimes.csv",
header=True,
columns={
"videoID": "VARCHAR",
"startTime": "DOUBLE",
"endTime": "DOUBLE",
"votes": "INTEGER",
"locked": "INTEGER",
"incorrectVotes": "INTEGER",
"UUID": "VARCHAR",
"userID": "VARCHAR",
"timeSubmitted": "DOUBLE",
"views": "INTEGER",
"class": "VARCHAR",
"actionType": "VARCHAR",
"service": "VARCHAR",
"videoDuration": "DOUBLE",
"hidden": "INTEGER",
"fame": "DOUBLE",
"shadowHidden": "INTEGER",
"hashedVideoID": "VARCHAR",
"userAgent": "VARCHAR",
"description": "VARCHAR",
},
ignore_errors=True,
quotechar="",
)
video_info = con.read_csv(
"sb-mirror/videoInfo.csv",
header=True,
columns={
"videoID": "VARCHAR",
"channelID": "VARCHAR",
"title": "VARCHAR",
"printed": "DOUBLE",
},
ignore_errors=True,
quotechar=None,
)
# Ignore warnings
import warnings
warnings.filterwarnings('ignore')Here’s what a pattern of the information seems like:
con.sql("SELECT videoID, startTime, endTime, votes, locked, class FROM sponsor_times LIMIT 5")
con.sql("SELECT * FROM video_info LIMIT 5")
Understanding the information within the sponsorTimes desk is ridiculously vital, in any other case, the cleansing course of gained’t make any sense.
Every row represents a user-submitted timestamp for a sponsored section. Since a number of customers can submit segments for a similar video, the dataset incorporates duplicate and doubtlessly incorrect entries, which can must be handled throughout cleansing.
To search out incorrect segments, I’ll use the votes and the locked column, because the latter one represents segments that had been confirmed to be appropriate.
One other vital column is the class. There are a bunch of classes like Intro, Outro, Filler, and so on. For this evaluation, I’ll solely work with Sponsor and Self-Promo.
I began by making use of some filters:
CREATE TABLE filtered AS
SELECT
*
FROM sponsor_times
WHERE class IN ('sponsor', 'selfpromo') AND (votes > 0 OR locked=1)Filtering for locked segments or segments with greater than 0 votes was a giant determination. This decreased the dataset by an enormous share, however doing so made the information very dependable. For instance, earlier than doing this, all the Prime 50 channels with the best share of adverts had been simply spam, random channels that ran 99.9% of adverts.
With this carried out, the following step is to get a dataset the place every sponsor section reveals up solely as soon as. For instance, a video with a sponsor section originally and one other on the finish ought to have solely two rows of knowledge.
That is very a lot not the case thus far, since in a single video we will have a number of user-submitted entries for every section. To do that, I’ll use window features to determine if two or extra rows of knowledge signify the identical section.
The primary window operate compares the startTime of 1 row with the endTime of the earlier. If these values don’t overlap, it means they’re entries for separate segments, in any other case they’re repeated entries for a similar section.
CREATE TABLE new_segments AS
SELECT
-- Coalesce to TRUE to cope with the primary row of each window
-- because the values are NULL, nevertheless it ought to depend as a brand new section.
COALESCE(startTime > LAG(endTime)
OVER (PARTITION BY videoID ORDER BY startTime), true)
AS new_ad_segment,
*
FROM filtered
The new_ad_segment column is TRUE each time a row represents a brand new section of a video. The primary two rows, as their timestamps overlap, are correctly marked as the identical section.
Subsequent up, the second window operate will label every advert section by quantity:
CREATE TABLE ad_segments AS
SELECT
SUM(new_ad_segment)
OVER (PARTITION BY videoID ORDER BY startTime)
AS ad_segment,
*
FROM new_segments
Lastly, now that every section is correctly numbered, it’s straightforward to get the section that’s both locked or has the best quantity of votes.
CREATE TABLE unique_segments AS
SELECT DISTINCT ON (videoID, ad_segment)
*
FROM ad_segments
ORDER BY videoID, ad_segment, locked DESC, votes DESC
That’s it! Now this desk has one row for every distinctive advert section, and I can begin exploring the information.
If these queries really feel difficult, and also you want a refresher on window features, try this weblog put up that may educate you all you should find out about them! The final instance lined within the weblog put up is sort of precisely the method I used right here.
Exploring and Enhancing the Knowledge
Lastly, the dataset is nice sufficient to start out exploring. The very first thing I did was to get a way of the dimensions of the information:
- 36.0k Distinctive Channels
- 552.6k Distinctive Movies
- 673.8k Distinctive Sponsor Segments, for a mean of 1.22 segments per video
As talked about earlier, filtering by segments that had been both locked or had at the very least 1 upvote, decreased the dataset massively, by round 80%. However that is the value I needed to pay to have knowledge that I may work with.
To verify if there’s nothing instantly improper with the information, I gathered the channels which have probably the most quantity of movies:
CREATE TABLE top_5_channels AS
SELECT
channelID,
depend(DISTINCT unique_segments.videoID) AS video_count
FROM
unique_segments
LEFT JOIN video_info ON unique_segments.videoID = video_info.videoID
WHERE
channelID IS NOT NULL
-- Some channel IDs are clean
AND channelID != '""'
GROUP BY
channelID
ORDER BY
video_count DESC
LIMIT 5
The quantity of movies per channel seems practical… However that is horrible to work with. I don’t wish to go to my browser and lookup channel IDs each time I wish to know the identify of a channel.
To repair this, I created a small script with features to get these values from the YouTube API in Python. I’m utilizing the library requests_cache to verify I gained’t be repeating API calls and depleting the API limits.
import requests
import requests_cache
from dotenv import load_dotenv
import os
load_dotenv()
API_KEY = os.getenv("YT_API_KEY")
# Cache responses indefinitely
requests_cache.install_cache("youtube_cache", expire_after=None)
def get_channel_name(channel_id: str) -> str:
url = (
f"https://www.googleapis.com/youtube/v3/channels"
f"?half=snippet&id={channel_id}&key={API_KEY}"
)
response = requests.get(url)
knowledge = response.json()
strive:
return knowledge.get("objects", [])[0].get("snippet", {}).get("title", "")
besides (IndexError, AttributeError):
return ""In addition to this, I additionally created very related features to get the nation and thumbnail of every channel, which can be helpful later. If you happen to’re within the code, verify the GitHub repo.
On my DuckDB code, I’m now in a position to register this Python operate and name them inside SQL! I simply must be very cautious to all the time use them on aggregated and filtered knowledge, in any other case, I can say bye-bye to my API quota.
# This the script created above
from youtube_api import get_channel_name
# Strive registering the operate, ignore if already exists
strive:
con.create_function('get_channel_name', get_channel_name, [str], str)
besides Exception as e:
print(f"Skipping operate registration (presumably already exists): {e}")
# Get the channel names
channel_names = con.sql("""
choose
channelID,
get_channel_name(channelID) as channel_name,
video_count
from top_5_channels
""")
A lot better! I regarded up two channels that I’m conversant in on YouTube for a fast sanity verify. Linus Tech Ideas has a complete of seven.2k movies uploaded, with 2.3k current on this dataset. Players Nexus has 3k movies, with 700 within the dataset. Appears adequate for me!
The very last thing to do, earlier than transferring over to truly answering the query I set myself to reply, is to have an concept of the common period of movies.
This matches my expectations, for probably the most half. I’m nonetheless a bit stunned by the quantity of 20-40-minute movies, as for a few years the “meta” was to have movies of 10 minutes to maximise YouTube’s personal adverts.
Additionally, I assumed these buckets of video durations used within the earlier graph had been fairly consultant of how I take into consideration video lengths, so I can be sticking with them for the following sections.
For reference, that is the pandas code used to create these buckets.
video_lengths = con.sql("""
SELECT DISTINCT ON (videoID)
videoID,
videoDuration
FROM
unique_segments
WHERE
videoID IS NOT NULL
AND videoDuration > 0
"""
).df()
# Outline customized bins, in minutes
bins = [0, 3, 7, 12, 20, 40, 90, 180, 600, 9999999]
labels = ["0-3", "3-7", "7-12", "12-20", "20-40", "40-90", "90-180", "180-600", "600+"]
# Assign every video to a bucket (trasnform period to min)
video_lengths["duration_bucket"] = pd.reduce(video_lengths["videoDuration"] / 60, bins=bins, labels=labels, proper=False)The large query. It will show if I’m being paranoid or not about everybody attempting to promote me one thing always. I’ll begin, although, by answering a less complicated query, which is the share of sponsors for various video durations.
My expectation is that shorter movies have a better share of their runtime from sponsors compared to longer movies. Let’s verify if that is truly the case.
CREATE TABLE video_total_ads AS
SELECT
videoID,
MAX(videoDuration) AS videoDuration,
SUM(endTime - startTime) AS total_ad_duration,
SUM(endTime - startTime) / 60 AS ad_minutes,
SUM(endTime - startTime) / MAX(videoDuration) AS ad_percentage,
MAX(videoDuration) / 60 AS video_duration_minutes
FROM
unique_segments
WHERE
videoDuration > 0
AND videoDuration < 5400
AND videoID IS NOT NULL
GROUP BY
videoIDTo maintain the visualization easy, I’m making use of related buckets, however solely as much as 90 minutes.
# Outline period buckets (in minutes, as much as 90min)
bins = [0, 3, 7, 12, 20, 30, 40, 60, 90]
labels = ["0-3", "3-7", "7-12", "12-20", "20-30", "30-40", "40-60", "60-90"]
video_total_ads = video_total_ads.df()
# Apply the buckets once more
video_total_ads["duration_bucket"] = pd.reduce(video_total_ads["videoDuration"] / 60, bins=bins, labels=labels, proper=False)
# Group by bucket and sum advert instances and whole durations
bucket_data = video_total_ads.groupby("duration_bucket")[["ad_minutes", "videoDuration"]].sum()
# Convert to share of whole video time
bucket_data["ad_percentage"] = (bucket_data["ad_minutes"] / (bucket_data["videoDuration"] / 60)) * 100
bucket_data["video_percentage"] = 100 - bucket_data["ad_percentage"]
As anticipated, for those who’re watching shorter-form content material on YouTube, then round 10% of it’s sponsored! Movies of 12–20 min in period have 6.5% of sponsors, whereas 20–30 min have solely 4.8%.
To maneuver ahead to the year-by-year evaluation I want to affix the sponsor instances with the videoInfo desk.
CREATE TABLE video_total_ads_joined AS
SELECT
*
FROM
video_total_ads
LEFT JOIN video_info ON video_total_ads.videoID = video_info.videoIDSubsequent, let’s simply verify what number of movies now we have per yr:
SELECT
*,
to_timestamp(NULLIF (printed, 0)) AS published_date,
extract(yr FROM to_timestamp(NULLIF (printed, 0))) AS published_year
FROM
video_total_ads
Not good, not good in any respect. I’m not precisely positive why however there are plenty of movies that didn’t have the timestamp recorded. It appears that evidently solely in 2021 and 2022 movies had been reliably saved with their printed date.
I do have some concepts on how I can enhance this dataset with different public knowledge, nevertheless it’s a really time-consuming course of and I’ll go away this for a future weblog put up. I don’t intend to accept a solution based mostly on restricted knowledge, however for now, I must make do with what I’ve.
I selected to maintain the evaluation between the years 2018 and 2023, on condition that these years had extra knowledge factors.
# Limiting the years as for these right here I've an honest quantity of knowledge.
start_year = 2018
end_year = 2023
plot_df = (
video_total_ads_joined.df()
.question(f"published_year >= {start_year} and published_year <= {end_year}")
.groupby(["published_year", "duration_bucket"], as_index=False)
[["ad_minutes", "video_duration_minutes"]]
.sum()
)
# Calculate ad_percentage & content_percentage
plot_df["ad_percentage"] = (
plot_df["ad_minutes"] / plot_df["video_duration_minutes"] * 100
)
plot_df["content_percentage"] = 100 - plot_df["ad_percentage"]
There’s a steep improve in advert share, particularly from 2020 to 2021, however afterward, it plateaus, particularly for longer movies. This makes plenty of sense since throughout these years on-line commercial grew lots as folks spent increasingly more time at residence.
For shorter movies, there does appear to be a rise from 2022 to 2023. However as the information is proscribed, and I don’t have knowledge for 2024, I can’t get a conclusive reply to this.
Subsequent up, let’s transfer into questions that don’t depend upon the publishing date, this manner I can work with a bigger portion of the dataset.
This can be a enjoyable one for me, as I’m wondering if the channels I actively watch are those that run probably the most adverts.
Persevering with from the desk created beforehand, I can simply group the advert and video quantity by channel:
CREATE TABLE ad_percentage_per_channel AS
SELECT
channelID,
sum(ad_minutes) AS channel_total_ad_minutes,
sum(videoDuration) / 60 AS channel_total_video_minutes
FROM
video_total_ads_joined
GROUP BY
channelIDI made a decision to filter for channels that had at the very least half-hour of movies within the knowledge, as a manner of eliminating outliers.
SELECT
channelID,
channel_total_video_minutes,
channel_total_ad_minutes,
channel_ad_percentage
FROM
ad_percentage_per_channel
WHERE
-- At the least half-hour of video
channel_total_video_minutes > 1800
AND channelID IS NOT NULL
ORDER BY
channel_ad_percentage DESC
LIMIT 50As rapidly talked about earlier, I additionally created some features to get the nation and thumbnail of channels. This allowed me to create this visualization.
I’m undecided if this stunned me or not. A number of the channels on this record I watch very steadily, particularly Gaveta (#31), a Brazilian YouTuber who covers motion pictures and movie enhancing.
I additionally know that each he and Hall Crew (#32) do plenty of self-sponsor, selling their very own content material and merchandise, so possibly that is additionally the case for different channels!
In any case, the information appears good, and the odds appear to match my handbook checks and private expertise.
I might like to know if channels that you simply watch had been current on this record, and if it stunned you or not!
If you wish to see the Prime 150 Creators, subscribe to my free publication, as I can be publishing the complete record in addition to extra details about this evaluation in there!
Have you ever ever considered at which level of the video adverts work finest? Individuals in all probability simply skip sponsor segments positioned originally, and simply transfer on and shut the video for these positioned on the finish.
From private expertise, I really feel that I’m extra more likely to watch an advert if it performs across the center of a video, however I don’t suppose that is what creators do normally.
My purpose, then, is to create a heatmap that reveals the density of adverts throughout a video runtime. Doing this was surprisingly not apparent, and the answer that I discovered was so intelligent that it kinda blew my thoughts. Let me present you.
That is the information wanted for this evaluation. One row per advert, with the timestamp when every section begins and ends:
Step one is to normalize the intervals, e.g., I don’t care that an advert began at 63s, what I wish to know is that if it began at 1% of the video runtime or 50% of the video runtime.
CREATE TABLE ad_intervals AS
SELECT
videoID,
startTime,
endTime,
videoDuration,
startTime / videoDuration AS start_fraction,
endTime / videoDuration AS end_fraction
FROM
unique_segments
WHERE
-- Simply to verify we do not have unhealthy knowledge
videoID IS NOT NULL
AND startTime >= 0
AND endTime <= videoDuration
AND startTime < endTime
-- Lower than 40h
AND videoDuration < 144000
Nice, now all intervals are comparable, however the issue is much from solved.
I would like you to suppose, how would you remedy this? If I requested you “At 10% runtime out of all movies, what number of adverts are operating?”
I don’t imagine that that is an apparent drawback to resolve. My first intuition was to create a bunch of buckets, after which, for every row, I might ask “Is there an advert operating at 1% of the runtime? What about at 2%? And so forth…”
This appeared like a horrible concept, although. I wouldn’t be capable to do it in SQL, and the code to resolve it could be extremely messy. Ultimately, the implementation of the answer I discovered was remarkably easy, utilizing the Sweep Line Algorithm, which is an algorithm that’s typically utilized in programming interviews and puzzles.
I’ll present you the way I solved it however don’t fear for those who don’t perceive what is going on. I’ll share different assets so that you can be taught extra about it in a while.
The very first thing to do is to rework every interval (startTime, endTime) into two occasions, one that may depend as +1 when the advert begins, and one other that may depend as -1 when the advert finishes. Afterward, simply order the dataset by the “begin time”.
CREATE TABLE ad_events AS
WITH unioned as (
-- That is crucial step.
SELECT
videoID,
start_fraction as fraction,
1 as delta
FROM ad_intervals
UNION ALL
SELECT
videoID,
end_fraction as fraction,
-1 as delta
FROM ad_intervals
), ordered AS (
SELECT
videoID,
fraction,
delta
FROM ad_events
ORDER BY fraction, delta
)
SELECT * FROM ordered
Now it’s already a lot simpler to see the trail ahead! All I’ve to do is use a operating sum on the delta column, after which, at any level of the dataset, I can know what number of adverts are operating!
For instance, if from 0s to 10s three adverts began, however two of these additionally completed, I might have a delta of +3 after which -2, which suggests that there’s just one advert at the moment operating!
Going ahead, and to simplify the information a bit, I first around the fractions to 4 decimal factors and mixture them. This isn’t mandatory, however having too many rows was an issue when attempting to plot the information. Lastly, I divide the quantity of operating adverts by the whole quantity of movies, to have it as a share.
CREATE TABLE ad_counter AS
WITH rounded_and_grouped AS (
SELECT
ROUND(fraction, 4) as fraction,
SUM(delta) as delta
FROM ad_events
GROUP BY ROUND(fraction, 4)
ORDER BY fraction
), running_sum AS (
SELECT
fraction,
SUM(delta) OVER (ORDER BY fraction) as ad_counter
FROM rounded_and_grouped
), density AS (
SELECT
fraction,
ad_counter,
ad_counter / (SELECT COUNT(DISTINCT videoID) FROM unique_segments_filtered) as density
FROM running_sum
)
SELECT * FROM density
With this knowledge not solely do I do know that originally of the movies (0.0% fraction), there are 69987 movies operating adverts, this additionally represents 17% of all movies within the dataset.
Now I can lastly plot it as a heatmap:
As anticipated, the bumps on the extremities present that it’s far more widespread for channels to run adverts originally and finish of the video. It’s additionally attention-grabbing that there’s a plateau across the center of the video, however then a drop, because the second half of the video is usually extra ad-free.
What I discovered humorous is that it’s apparently widespread for some movies to start out immediately with an advert. I couldn’t image this, so I manually checked 10 movies and it’s truly true… I’m undecided how consultant it’s, however a lot of the ones that I opened had been gaming-related and in Russian, they usually began instantly with adverts!
Earlier than we transfer on to the conclusions, what did you consider the answer to this drawback? I used to be stunned at how easy was doing this with the Sweep Line trick. If you wish to know extra about it, I not too long ago printed a weblog put up masking some SQL Patterns, and the final one is strictly this drawback! Simply repackaged within the context of counting concurrent conferences.
Conclusion
I actually loved doing this evaluation because the knowledge feels very private to me, particularly as a result of I’ve been hooked on YouTube recently. I additionally really feel that the solutions I discovered had been fairly passable, at the very least for probably the most half. To complete it off, let’s do a final recap!
Have Sponsor Segments Elevated Over the Years?
There was a transparent improve from 2020 to 2021. This was an impact that occurred all through all digital media and it’s clearly proven on this knowledge. In more moderen years, I can’t say whether or not there was a rise or not, as I don’t have sufficient knowledge to be assured.
Which Channels Have the Highest Share of Sponsor Time Per Video?
I obtained to create a really convincing record of the Prime 50 channels that run the best quantity of adverts. And I found that a few of my favourite creators are those that spend probably the most period of time attempting to promote me one thing!
What’s the density of sponsor segments all through a video?
As anticipated, most individuals run adverts originally and the top of movies. In addition to this, plenty of creators run adverts across the center of the video, making the second half barely extra ad-free.
Additionally, there are YouTubers who instantly begin a video with adverts, which I believe it’s a loopy technique.
Different Learnings and Subsequent Steps
I preferred how clear the information was in displaying the share of adverts in numerous video sizes. Now I do know that I’m in all probability spending 5–6% of my time on YouTube watching adverts if I’m not skipping them since I principally watch movies which might be 10–20 min.
I’m nonetheless not totally completely satisfied although with the year-by-year evaluation. I’ve already regarded into different knowledge and downloaded greater than 100 GB of YouTube metadata datasets. I’m assured that I can use it, along with the YouTube API, to fill some gaps and get a extra convincing reply to my query.
Visualization Code
You may need seen that I didn’t present snippets to plot the charts proven right here. This was on function to make the weblog put up extra readable, as matplotlib code occupies plenty of area.
You’ll find all of the code in my GitHub repo, that manner you’ll be able to copy my charts if you wish to.
That’s it for this one! I actually hope you loved studying this weblog put up and realized one thing new!
If you happen to’re inquisitive about attention-grabbing subjects that didn’t make it into this put up, or get pleasure from studying about knowledge, subscribe to my free publication on Substack. I publish each time I’ve one thing genuinely attention-grabbing to share.
Need to join instantly or have questions? Attain out anytime at mtrentz.com.
All photos and animations by the writer except said in any other case.