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.

Consensus Client Data

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.

Execution Client Data

Ethernodes - Methodology unknown. - Through social effort, (lead by Sonic) gathers self-reported client breakdown data and weighted against how many validators each entity has. This accounts for 66.1% of the network. To estimate the remaining 33.9%, 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 80% Geth and 20% split evenly among the other clients.