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Data Methodology |
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Data Methodology |
There's no inherent way to know exactly what client a validator is running. Researchers use other metrics to make deductions on which client a validator is most likely operating. The problem is they cannot distinguish with 100% certainty which client a validator is running.
Blockprint - Developed by Sigma Prime's Michael Sproul, Blockprint analyzes each client's block proposal style as described in this Twitter thread (Nitter).
Miga Labs - A crawler is used to count beacon nodes and their self-reported identity. However, this means that validators sharing a node are counted only once and nodes with fewer validators have a greater influence on the estimate.
Rated - Methodology unknown.
Ethernodes - Methodology unknown.
{% assign supermajority = site.data.supermajority | last %} {% assign supermajority_geth = supermajority.data.other.validators_percentage | times: 100 | round: 1 %} {% assign supermajority_other = 100 | minus: supermajority_geth %}
supermajority.info - Through social effort, supermajority.info (lead by Sonic) gathers self-reported client breakdown data and weighted against how many validators each entity has. This accounts for {{supermajority_geth}}% of the network. To estimate the remaining {{supermajority_other}}%, two steps were taken. The values that are "unknown" from the self-reported data are assumed to be 100% Geth. The remaining validators on the network are assumed to be 40% Geth, 40% Nethermind, and 20% split evenly among the other clients.