Before I begin the analysis, I have to give endless credit to @arcange, the creator of the http://steemsql.com/ database. Without making this amazing project free, I could not have done this analysis. All the data used here is extracted from the SteemSQL database by Tableau. If you like analyses like these, please go and support him if you can, even in the smallest way, so that people can continue to produce transparent and interesting analyses about the world of Steem.
Let's evaluate democracy: Looking for patterns in the witness voting data.
(provided by the SteemSQL database)
Witness vote accumulation over time:
The size of the bubble shows the amount of votes that day (range 1-500 in a day). The gradient from red to green has it's midpoint at 1000 votes (to see how quickly witnesses earned their first 1000 votes) and maximum at 8478 votes.
Ok, so what kind of patterns can we see from the data?
- The barriers to entry seem to be quite tough since witnesses that have been successful for over a year are very likely to be successful today.
The only notable exceptions to this pattern are @ausbitbank, @aggroed, @jerrybanfield in the top 30. - The most meteoric rise can be attributed to @utopian-io. Most other witnesses among the top 30 have been around since 2016.
- Most witnesses among the top gained their first 1000 votes in mid 2017, and they continue to grow steadily all together - one might say "with the market".
Conclusion: I'm tempted to infer from this quick analysis that much of the success of the top witnesses is largely due to them already being popular and seen. However, it is possible to break through as we've seen with the notable exceptions mentioned above.
All signs of a healthy and stable democratic environment some might argue. What do you think?
If you like these kind of posts, I would be super duper happy about a follow: @remuslord
Since these analyses take time, I don't post much, so you don't have to be afraid of me spamming up your feed :)
Thank you and happy steeming! :)
This is a very interesting analysis and a beautiful way to present the results. I am going to do some related analysis on witnesses votes in an upcoming post. This captures a big aspect of what I wanted to look at. I'll be referencing this post. 👍
Cool! thank you! :) glad it was interesting
Looking forward to reading your analysis. I think there's a lot more to look at definitely :)
So true. I don't know what language you use, but when you get into the weeds with some of this stuff, it can be a slog!
This was all in Tableau! I don't know any languages. Just a bit of VBA. I hope that's not my downfall as a data visualiser haha
nice analysis!
maybe you should team up with @witnesswatch
We would be very open to that! This is cool!
Good idea! :)
Could you explain to me who is the witnesses and what exactly they do. I know that my question probably sounds little stupid , but I want to understand world of Steemit and how it works. I was looking for plain and simple explanation for the past two weeks on many Steemit structure related subjects and could't fined it.I need hellllpppppppppp!
@pfunk's post on Steem witnesses is worth a read, although it was from 2 years ago: https://steemit.com/steemit-guides/@pfunk/a-full-steemit-user-s-guide-to-steem-witnesses
Also, a more recent version from @steemitguide: https://steemit.com/steem/@steemitguide/steemitguide-what-is-a-exactly-is-a-steem-witness-and-why-every-user-should-vote
Thanks for reply. It is hard to fined info with search.
At least Steemit has good visibility on Google Searches, which is how I usually find my own posts :D I also use Steemtracked.com if I want to look at other people's posts.
what tool do you use ?