tl;dr
The gm-count of a community is positively correlated to the project token price. (gm-count goes up <-> token price goes up)
In our last article, we introduced the gm-index. Since it got quite some traction and was well received we listened to the community and are now developing it further.
With this article, we will be exploring if the gm (good morning) count of a community can be used as an indicator to predict the token price. In theory, there should be a positive correlation between them. When the gm count goes up the token price should also be on an upward trajectory indicating the health and success of the project.
From our experience, the majority of web3 projects only succeed if they have a strong community that is backing them. There are two aspects to this thesis (1) contribution and (2) demand.
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Contribution
For a project to succeed it needs to be enhanced and developed continuously. The more smart contributors a project can attract the higher the likelihood of them driving it forward. Depending on the project contribution types can vary from coding and tokenomics all the way to designing and community management.
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Demand
Looking at economics 101, an asset increases in value if the demand for it increases. This is also true for most tokens, the higher the demand for it the higher the price. A project's community plays a vital role in this dynamic. On the one hand, they generate demand themselves by buying and holding the project”s token. On the other hand, they promote the project to the outside either actively or passively. They actively shill the project on platforms like Twitter and Discord. Furthermore, they passively generate demand by having a high-energy community that other people hear or read about in tweetstorms, newsletters, or videos. In turn, this creates a sense of FOMO promoting other people to join the project.
The Ohmies are a very good example for both, contributing to and generating demand. They have very smart contributors that are constantly pushing the project forward with new innovations and features. In addition, they also have a fire community that generates tremendous active and passive demand.
We believe a project”s success is highly correlated with the strength of the community. Therefore a measure is needed to quantify this strength. As we already stated in the last article we believe that the most basic interaction that lays the foundation of every web3 project is the basic act of saying gm. This action has been so ingrained in the crypto culture that one can use it to measure the activity and energy within a community.
In our analysis, we took the top 3 Discords, in terms of gm’s sent over the past year, from our past article to calculate the correlation between gm count and token price. To compare the two curves consistently we normed each time series by its largest value.
A positive correlation for the Ohmie’s
Both normed curves seem to follow the same trajectories. Between May and October 2021 their curves are almost identical apart from a small lag. This lag is intuitive, explaining that increases in the price drive higher emotions and engagement with the community. The two last spikes are credited to the start and end of the V2 migration. Additional partnerships announcements were also a boost to the Olympus brand in January. Overall, the correlation between the gm-index and price token for the Ohmie’s is 0.41.
A strong positive correlation for Aavegotchi gamers
The champions of this quest are the Aavegotchi gamers. They have a positive correlation of 0.69🤓. This is a good indicator that there is a strong relationship between their gm-index and token price.
A positive correlation for Index Coop
The asset managers got a positive correlation of 0.42 positioning them in the second place. This correlation is notorious in January of 2022 when both the gm-index and the token prices decreases when the whole crypto market entered a short bear market.
In conclusion, we can say that there is definitely a positive correlation between the gm-index and token price. This is strengthening the hypothesis that the success of web3 projects is driven by the product but also by community strength, energy, and of course memes. These will be the indicators separating noise in web3 projects from signals.
What comes next? We want to go deeper in leveraging AI models to surface signals in all of the web3 noise. If you have any crazy ideas about what we should analyze next send us a DM!
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