Blockchain InsightsOsmosis in Graphs: Validator Parity

There is definitely a non-normal spread of voting power among validators. That's not necessarily a bad thing. This is not a critique of anyones staking selection or of any validators in general! Currently, there's no incentive to stake w/ a smaller validator aside from your own desire to decentralize voting power and support the individal validators efforts. Larger validators have pre-established reputations and are trusted for that reason.

Validator Power

I was inspired by a lot of the recent Prop 120 discussions and a recent post by u/Gohodoshii plus the spreadsheet u/JohnnyWyles maintains

To assist in these discussions, I decided to bring out the relevant stats and graphs to demonstrate the lack of symmetry in voting power. In Statistics, a lot of things are compared to the Normal Distribution (AKA Bell Curve). There are two metrics that indicate how far off a set of numbers, in this case voting power of validators, is from the Normal Distribution.

The first of these metrics is skew. Skew tells us how symmetrical a set of numbers is. The more skewed the numbers, the higher or lower the skew is depending on how it's skewed. Typically, any skew greater than 1 or less than -1 is considered "highly skewed" (Though some people permit skews up to 3/-3, we don't need to sweat that too much here). Here is the Skew of the Osmosis Validator Set:

Skew Graph Blockchain
Skew of Validator Voter Power over time, sampled every 25,000 Blocks

Note that for the most part, we sit around 4. That's highly skewed. This indicates that the mean or average voting power for a validator is much higher than the median. You can see that here:

Mean: 681939

Median: 184210

What's the difference between the mean and median? Think of the mean as a completely even splitting up of the votes/staked Osmos. If we gave all the validators the same number Osmos from the currently staked amount, they'd all have ~682k Osmos staked to them. But the median tells us that half the validators actually have less than ~184k Osmos, roughly 1/4th of that mean.

That large difference is demonstrated by our skew!

While the skew looks at the middle of our set of numbers - the median and mean, the kurtosis looks at the extremes, also known as the tails. In a bell curve, 99% of data points occur within 3 standard deviations of the median/mean. . The normal distribution/bell curve has a kurtosis of 3, so anything higher than that would indicate that we have more voting power in the edges of our dataset (i.e. smaller validators or larger validators) than in a bell curve. That said, lets take a look at Osmosis today:

Kurtosis Graph Blockchain
Kurtosis of Validator Voting Power sampled every 25,000 Blocks

In Statistics, we sometimes refer to those edge numbers as the tails of a distribution. And in this case, we'd call these some pretty fat tails. There's definitely been a thinning out of the edges over time though, so perhaps that will continue to be the case as we go through 2022.