Fan-made Cardano Blockchain Insights contain some compelling metrics
True Cardano enthusiasts are probably familiar with the work of this community, which is immersed in their favorite blockchain. But for those not yet familiar, we present Cardano Blockchain Insights from the Cardano Fans Stake Pool (CRFA). Here in one place, people who care about the Input Output project have compiled some of the most detailed metrics describing blockchain activity like no other.
While the full list of metrics and characteristics is presented here, we will only review the most interesting indicators that assess both Cardano’s performance and its success. First and foremost, we are interested in metrics detailing ADA staking, given the recent news of 70.6% of all token offerings deposited into staking pools.
Stacking and Blockchain Load
Blockchain Insight confirms this fact, and according to the given chart, the percentage of ADA is also higher by 0.63% than the CryptoRank data. In addition, ADA staking confidently keeps the bar of activity. The total number of wallets betting on ADA is fluctuating around 33.5%, with approximately 1.2 million addresses involved in the process. However, at this point in time, one can see a decreasing number of wallets assigned to the mortgage pool and vice versa – an increase in ADA wallets holding coins outside the pool.
The load on the Cardano network at the time of writing is estimated to be low, at 41.77%. At the same time, judging by the graph of the number of transactions/payments, we can say that this is the average “temperature” of the network load, which tends to explode from time to time with the increasing number of payments.
In short, detailed blockchain insights shed light on Cardano’s heyday but cause mixed feelings. On the one hand, everything is so good that it’s boring, and on the other, it’s puzzling that such a stable blockchain hasn’t got the scale commensurate with Solana, for example. It will be very interesting to see how Cardano’s performance may have changed under the heavy workload.
