Analyzing Our Token Generation Event (TGE)
In this analytical review, we study how our genesis supply of ZTG tokens were distributed.
In this post, we will analytically explore the allocation of tokens that will be distributed as a result of our TGE. The purpose of this article will be to explore the values and make a diagnosis of the distribution of tokens in order to measure the success of community participation. Here we will evaluate the distribution of Tokens in relation to 3 categories of our Tokenomics: Shape the Future, Marketing Incentives, and Parachain Lease .
A total of 17,470,683.1317 tokens will be distributed under these three areas, among which are the following main categories:
Clarification: the Bifrost category is also linked to the Crowdloan.
Token Distribution Analysis
The strategic and well-designed distribution of tokens is one of the key factors contributing to the long-term health and prosperity of a blockchain project. In this section, we will evaluate the number of addresses that contributed during our initial stage, as well as the average rewards of these contributors.
Methodology: the balance of the wallets was added, as well as the number of shares they had during the process. These participations can be from both contributions from the wallet itself, as well as the use of referral links provided by its user.
Let’s make an initial descriptive statistics:
We will be assigning tokens to more than 14,000 wallets. As a first observation, there is no doubt this is beneficial for the project since the user base upon which we will distribute the rewards is considerably large. Now we will separately analyze the two numbers that we are interested in evaluating.
Let's take a tour of the numbers generated in the table. The first thing we need to see is the difference between the median and the mean. When the mean is greater than the median, it indicates that we have extreme values (known as outliers) that weigh the value of the mean, increasing it. The proof of this is that the value corresponding to 75% (last quartile) is 800 ZTG allocated, while the user who contributed the most will receive a reward of 808.9K ZTG (analysis of whales later). Therefore, we are talking about users who will mostly receive between 0.317 (min) and 800 (75%) ZTG.
At this point, it is clear that there are some users (at least one) who will receive significantly higher rewards than most contributors. For this reason, we will divide the analysis between outliers (whales) and non-outliers.
What criteria will we use to differentiate one from the other? Based on statistical conventions, we will consider an outlier to be any direction that exceeds the median of the distribution by two or more standard deviations ( \( outlier(reward) > median + 2std \) ). In numerical terms, anyone who will receive more than 31,934.05 ZTG will be an outlier. Before starting the separate analyses, we must indicate that we have 14,010 non-outlier addresses and only 30 outliers (0.214% of the sample).
At this point let’s stop and appreciate what looks to so far be a highly satisfactory distribution of tokens. A red flag for any project can be the number of token holders who own more than 4% of the total token supply. Currently we do not have any user that meets these characteristics (in fact, the largest holder owns less than 0.85% of the initial genesis).
The first thing we will do is visualize the distribution of these users using a histogram.
From this group, we already had information obtained in the descriptive statistics generated at the beginning of the section. Most users (more than 10,000) will receive less than 800 tokens (corresponding to the last quartile), and practically all users in this group will receive less than 5,000 (we have 13,849 addresses that meet that condition, and 160 that will receive more than 5,000 ZTG but not enough to be considered outliers).
Now let’s also visualize the distribution of outliers using a histogram:
Of the 30 users considered outliers, 20 will have less than 200k ZTG and only 10 (0.07% of the total) will have more.
If we look again at our descriptive statistics table, we will see that both the information corresponding to the quartiles (median included) and the mean hover between 1 and 2 shares, meaning that it is highly likely that users were motivated to participate based on their own interests.
Here too we can see a very large gap between the beginning of the last quartile (2) and the address with the highest number of holdings (731). For this reason, we will also use the same concept to differentiate the outliers, whom we will call “Influencers”.
The majority of users, 12,853 to be precise (91.5% of the total) have had less than 3 participations.
We see something similar here, whereby most of the Influencers are located between the minimum of the category (17 participations) and a value that is much closer to the category threshold (we used 100 in this case). Here, 29 of the 35 cases of the category are concentrated. Under this group, we can consider Crypto Influencers or Applications that refer you to Crowdloans under a referral link.
From Zeitgeist, we feel very satisfied with how the allocation of tokens has been coordinated. We believe that we have managed to achieve a uniform distribution that, added to the vesting strategy, will allow us to maintain a healthy economic state with regards to the token’s stability in the market. However, we are aware that it is not something over which we have control, hence we will constantly monitor possible supply and demand interactions, and act as quickly as possible when anomalies or red flags are raised.
Just a quick reminder of the vesting periods:
Shape the Future: One month cliff, full unlock after 1 month.
Parachain Lease: 30% at launch, the rest vesting linear over the lease term.