For the last three RF rounds, my team at OSO has published a report that analyzes the voting data. We just published our report on RF5 here. Here is our report on RF4 and RF3.
Key points:
- This was the flattest reward distribution yet. The top project (geth) received 235K OP. The median project received 93K. Even the lowest ranked project received 37K, which was more than 70% of projects received in Round 4. Basically, it was a good round for “average” projects.
- The reward distribution was flat because … voters voted on flat distributions. There’s a lot of dataviz illustrating this point in the report. Have a look. There’s an important idealogical question about how voters feel about power law distributions, ie, is it a “good” outcome for a handful of projects to receive most of the retro funding.
- Experts voted differently than non-experts. In this round, there was an expertise dimension (as well as expert guest voters from the developer community). There are meaningful differences in how these groups allocated rewards and differentiated among projects. This raises important questions about the composition of voters in a given round
The report has lots of data visualizations and statistics. You can also view my data science notebooks if you’re curious about the methods used. I hope it helps you draw your own conclusions.
@SPop is running a RF retro workshop on 30 October and I’ll be sharing the analysis in that forum too. Feel free to come with questions or drop them below so I can try to answer them.
