And the possible effects of K=1000
Today, I want to bring you something I have been working on regarding the K and minPoolCost poll
As a business (data) analist, I believe in measurable arguments. Especially when it comes to things like changing the rewards formula. One could argue that changing something that has a big mathematical aspect should at least be supported by SOME data. Otherwise we might change this very important formula purely on gut feelings or past assumptions, while ignoring available information.
As I was trying to decide which option to vote for, I started gathering 3 data sources and combining them in a way that might help me decide what I felt is best for the protocol.
These data sources were:
Extract of all active pools and their pool parameters. Thank you APEX Pool for your great Koios script!
A list of pool groups. Thank you BALANCE for your API that I am using.
The Poll results. Thank you ADASTATS for you poll results API
My goal was to try to get some sense of what would happen with the various pools if K would be increased. How many Single pools would saturate and how much stake would that be. The same for the Multi pool groups.
I combined these data sources and built 5 Google Looker Studio interactive Dashboards.
Here is the link for all to use:
This initial rough report helped me to make a voting choice that I could be comfortable with. And which I could explain to my delegates.
I am here to share this dashboard and underlying data with you.
The first dashboard shows a General overview of the pool landscape with some filters that are preconfigured and some that you can change for yourself. Note that you can also filter the dashboard by clicking on a pool or pool group:
The second dashboard zooms in on the multi pool groups and shows only those MPOs that will not be affected when K moves to 1000. The rationale here is that even if some pools of a group will get saturated, the group as a whole is only “affected” if that saturated stake cannot move to one of their other pools.
The other 3 dashboards consequently show the Unaffected Single pools, Affected MPOs and Affected Single Pools
I used the data to make up my own mind. I have my own arguments why I think that increasing K might not be the best way forward, but that’s not the reason why I am posting these reports. I hope that others find it useful and use the information to adhere to the Cardano way, which imho is:
“Measure twice, cut once”
The important word for me here is “Measure” and I hope I contributed a bit to this ethos.
Eventhough these dashboards show data, I have my own filters and assumptions in there.
There probably are errors in these reports. It’s not meant to be a work of art but started as a quick way to help my own voting choice. Please let me know and I will see if I can improve them
The data is not dynamic. I have to manually update them.
I have used 2 filters to rule out pools that I think should be ruled out in this analysis. You might not agree with this. One of the filters is the “Regular pool” filter. It rules out pools with higher than 100k average stake per delegator. The other filter is the “margin” filter where I rule out all pools with higher than 20% margin.
The reports are based on this datasource in Google Sheets: Cardano Pools Stats and K=1000 - Google Sheets
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Please leave your comments below and let me know what other subjects you would want me to cover without Jargon & within 199 words.