If you’re looking at an Ethereum-based tokens (ie Tron, EOS, Ziliqa, 0x, etc), looking at the token distribution can be useful. It won’t tell you everything of course about the future success of the token but it can lead you to investigate further.
Let’s take two cases: QuarkChain and EXRNChain. EXRNChain has been since October 2017 via a free airdrop plan and Quarkchain has been with us the last couple months driven by an ICO.
I am not suggesting that you should or should not invest in the below examples. They are only used as examples.
QuarkChain’s Token Analytics:
There is a total supply of 10B coins at a current price of $0.18 which suggests a potential marketcap of $1.85B if all token were released into circulation.
Notice also that the 10th highest token holder has only 0.008% of the total supply. From a supply/stability point of view, the investor is absolutely reliant on the project controlling the supply of tokens in the market. Much like the diamond industry.
EXRNChain’s Token Analytics:
There is a total supply of 100B coins at a current price of $0.000126 and a circulating supply of 92B which is close to the total supply. Therefore the current marketcap of $11m is close to the potential marketcap of $12.6m which is reasonable close.
Excluding the 8m tokens not in circulation, the distribution of token holders is ‘safer’ insofar that not a small proportion of token holders can dump the value of the token. This is assuming that the token holders are not holding too many multiple wallets of course.
Looking at the graph attached, you can visually see that OTHER ACCOUNTS for QKC comprise a very small amount whilst for EXRN, OTHER ACCOUNTS comprises the majority. I think this is a very important fact to consider if you’re looking at it from a token metrics point of view.
Of course, if you were to compare the projects based on the above alone, you’d know what to look for; however, as I stated earlier, there are many more factors to decide whether to invest in a coin or a token rather than just looking at token metrics.