Price Action

Among these three investigated weeks, the highest average price was related to the second week (target week). ($2,477)

At the same time, the highest percentage of increase and decrease of the price compared to the previous day was also seen in the target week.

The highest daily price was in the second week. (January 23: $2.577) while the lowest daily price in these three weeks was only 4 days before. (January 19: $2.05)

In the target week, there were the highest number of days of price increase compared to the previous day. (4 days)

Near at a Glance

What seems remarkable is that the price did not have a clear and direct relationship with the number of transactions. Although it was seen in the price action section that there were mostly positive price parameters in the second week, no notable trend can be mentioned in this section that has a clear relationship with the price of the native token of this ecosystem.

It is very interesting that the high intensity of fluctuations in the number of transactions in the first week had a similar effect on the intensity of fluctuations in the success rate of transactions in the same week.

The number of daily transactions has decreased over time. The number of transactions exceeded 500k three times in the first week, but the number of daily transactions was significantly lower in the second and third week.

But what is important here is the success rate of transactions. On the first and second days, in terms of the highest number of daily transactions (January 14 and 18, respectively), the lowest transaction success rate was also recorded. (81.33% and 83.6% for January 14th and 18th respectively - on January 14th there were about 100k failed transactions, which is completely unprecedented for these 21 days). But the success rate of 93% on the third day in terms of the highest number of transactions in one day (January 20) has caused the highest number of successful transactions on this day (476.6k).

The chart of “Daily Transactions and Their Success Rate (%)” shows that from the beginning of this 21-day period, as we move towards the end, the number of transactions decreases while the success rate increases. It seems that the users of the Near ecosystem do not consider encountering a transaction error as a serious problem and continue their efforts to successfully complete their request.

With the decrease in the number of transactions, the number of blocks has also decreased. (Especially in the third week when you can clearly see a dramatic decrease in the number of daily blocks. In all days of the third week, the number of daily blocks is less than the previous two weeks.

The average number of successful transactions per block is highly volatile in the first week. This is in one direction with the fluctuation of the number of daily transactions and the success rate of daily transactions that were in the same situation. It is noteworthy in the second and third weeks of Essen that the number of successful transactions in each block has been an upward trend with less fluctuations.

The highest number of users (in the role of sender or source of a transaction) corresponds to the week of the first day of this 21-day period. (January 14 - 87.46k). A specific fluctuating trend can also be observed in the number of transmitter users.

Regarding the number of receiving users (or the destination of a transaction), the trends are not very similar to the number of sending users. The most interesting point of this chart is on January 20. Where more than 135k unique users have been the destination of at least one transaction. A number that is at least 4 times more than any other day in this 21-day window.

Interestingly, in the third week, despite the decrease in the number of successful transactions, the first three positions in the list of the highest fees paid in a day belong to this week. Likewise, the highest average fee paid per sender in one day also belongs to this week.

In terms of the percent of gas used from the gas limit defined for each transaction, in general, in these 21 days, the average gas used by transactions in one day has always been less than 15%. While, for example, in the Ethereum network, this percentage is generally more than 50. (link)

Source:

Image: Paras

Actions and Portions

Every three weeks, in more than 60% of successful transactions, a sign of transfer can be seen.

Among these three weeks, in the third week despite the decrease in the number of successful transactions, it has the largest portion compared to the other two weeks.

On January 20th (which has the highest number of successful transactions in one day out of these 21 days), there was at least one NEAR asset transfer in over 70% of transactions. (more than 336k transactions)

On other days, the number of successful transactions containing at least one NEAR token transfer is constantly fluctuating in the range of 210-270k.

The ratio of the number of unique users who have had at least one transfer in these one-week intervals to the total number of users is more than 90%. The highest percentage is related to the second week, when about 93% of users had a transaction this week, in which at least one of them was a NEAR token transfer.

From the point of view of the number of daily transferers, we can see a great similarity with the graph of the number of daily senders, which is not far from the expected result considering these numbers are close to 100 in the mentioned ratio.

The ratio of the number of swaps in each week to the total number of transactions in that week has decreased respectively. From 1.12% in the first week, it has reached 0.82% with a decrease of more than 27%.

The ratio of the number of swappers in each week to the total number of swappers in that week has decreased respectively. From 0.74% in the first week, it has reached 0.54% with a decrease of about 29%.

The number of daily swaps has also had a downward trend in a general trend. Despite the fact that relatively regular fluctuations can be observed in the trends of every three weeks.

The ratio of the number of stake+unstakes in each week to the total number of transactions in that week is similar to the situation of transfers (third, first and second week respectively).

The ratio of the number of staker+unstakers in each week to the total number of staker+unstakers in that week is similar to the high ratio situation.

Fairness

Fairness metrics have been used extensively in resource allocation in wireless networks. They measure the fairness level of resource decisions in allocations . As the objective of a consensus protocol in Blockchain is to be fair among the miners, we can use the Fairness index to quantify decentrality.

When a system is completely distributed, the normalized fairness is 1. When it is completely central, the normalized fairness will be 0

Entropy

Entropy has been employed in various fields to quantify uncertainty or randomness of an event or mechanism . If we consider the Blockchain system as an information-source, we can model it as a random variable. Here, the amount of information emanating from a source is the amount of uncertainty that existed before the source released the information. In Blockchain systems, we can estimate the \n probability that a miner will create the next block based on its ability to add a block in the past. With respect to this model, we can use Shannon’s entropy ,H(x), to quantify decentrality.

Intuitively, a higher Shannon entropy means a higher degree of randomness of the distribution of mining power, thus indicating a higher degree of decentralization

Gini Coefficient

The Gini coefficient is often used as a gauge of economic inequality, measuring wealth distribution among a population. In the scenario of measuring decentralization in blockchains, the Gini coefficient could be used to indicate the inequality of the distribution of mining power among miners. Therefore, we adopt Gini coefficient as the decentralization measurement metric

Intuitively, a lower Gini coefficient means that more miners need to collude to compromise a blockchain system, thus indicating a higher degree of decentralization

Euclidean Distance

We can use Euclidean distance to compare the resource distribution with the best case scenario .

Intuitively, a lower Euclidean distance means that the lower distance with the best case scenario (the fraction of total blocks mined by each node is 1/N), thus indicating a higher degree of decentralization

Kullback–Leibler Divergence

The Kullback-Leibler divergence (hereafter written as KL divergence) is a measure of how a probability distribution differs from another probability distribution. In the scenario of measuring decentralization in blockchains, we have two probability distribution:

• P : distribution of total blocks mined by nodes
• S: the best distribution of total blocks mined by nodes (the fraction of total blocks mined by each node is 1/N)

Intuitively, a lower KL divergence means that the lower distance with the best case scenario (the fraction of total blocks mined by each node is 1/N), thus indicating a higher degree of decentralization

In Search of Decentralization

Fairness:

The second week, the second rank has the most fairness among the other two weeks. Its daily trend shows a downward trend. This could be the first sign of the ecosystem moving towards less decentralization.

Entropy:

The second week has the second highest entropy with a slight difference compared to the first week, but its daily trend is downward. This decline has continued even more strongly in the third week. This could also be the second sign of decentralization easing this week.

Gini Coefficient:

The second week has the lowest Gini coefficient among the other two weeks, also its daily trend shows that this coefficient has been decreasing more during the week. This is the first sign of increased decentralization this week.

Euclidean Distance:

The second and first weeks have the lowest Euclidean distance and their daily trend is also similar. Although the value of this distance has increased in the third week, there are no clear signs that the second week has an effect on this trend or gives a sign.

Kullback–Leibler Divergenc:

The second week has the second lowest value of Kullback–Leibler Divergenc, and its daily trend is also a positive trend, which is also well followed in the third week. This is the second positive sign of increased decentralization in the ecosystem.

Final result:

From the obtained results, it can be said that in the second week, a clear advantage cannot be distinguished between increasing or decreasing the decentralization of the ecosystem. But it is possible to decide on a more scientific conclusion by determining the appropriate weight for each of the parameters and determining the percentage of changes.

An Introduction to the NEAR Blockchain Ecosystem

NEAR is a layer-1 blockchain that is built to empower creators and developers. This Proof-of-Stake protocol serves as the base layer and enables the creation of decentralized applications (dApps). It is a community-run computing platform, meaning that a distributed network of setups maintains it.

Overview of the Near ecosystem

> Launched in 2020 by Alexander Skidanov and Illia Polosukhin, NEAR was originally formulated as a machine learning platform. The founders started NEAR.ai in early 2017 to automate programs from a human specification. For the idea to be materialized, there was a need for a smart contract platform. After thorough research in late 2017 and early 2018, disappointed by the lack of tools, Alexander and Illia decided to design their own blockchain. The process of building the NEAR protocol started in August 2018. \n

The NEAR token and tokenomics

> $NEAR is the native utility token of the NEAR ecosystem. 1 billion NEAR tokens were created with the genesis block of the NEAR blockchain. From this initial supply, a large portion is subject to long-term lock-ups. To support the network, every year 5% of additional supply is distributed, with 0.5% going to the NEAR treasury. The remaining 4.5% represents the annual inflation rate. The inflation rate refers to the rate at which new tokens are issued to pay the network operators (validators). To calculate the real value of issued NEAR, we must take into account that the network burns any transaction fees that are collected. As a result, the inflation is 5% minus transaction fees. As network usage increases, the protocol could become deflationary. The current circulating supply of NEAR tokens as of 23 Nov 2022 is around 829M NEAR, or around 75% of the maximum supply.

Sources:

• Image : PNG ALL
• Introduction: Finoa

Methodology

In the first step, I took three 7-day time periods, all of which are within the last 21 days, and numbered them in sequence. The first week (January 14 to 20 - the week before the target week), the second week (January 21 to 28 - the target week) and the third week (January 29 to February 4 - the week after the target week). All the analyzes performed in this review were performed in these three time periods.

In the second step, for the price action section, three parameters were checked:

• NEAR Daily USD Price

• Average USD Price of NEAR in Each Period

• Percentage of Daily Changes in the USD Price of NEAR

  \

In the third step, for the Near At a Glance section, 10 parameters were checked:

• Transactions and Their Status
• Success Rate (%)
• Daily Transactions and Their Success Rate (%)
• Total Number of Blocks
• Average Number of Transactions per Blocks
• Total Number of Senders
• Total Number of Receivers
• Total Fee Paid (NEAR)
• Average Fee Paid per Sender (NEAR)
• % of Gas Used From the Gas Limit

In the fourth step, for Actions and Portions, 12 parameters were checked. Ratios were calculated to show what portion of the total these actions hold:

• Transfers / All Transactions Ratio

• Number of Transfers

• Transferrer / All Users Ratio

• Number of transferers

• Swaps / All Transactions Ratio

• Number of Swaps

• Swappers / All Users Ratio

• Number of Swappers

• Stakes+Unstakes / All Transactions Ratio

• Number of Stake+Unstake Actions

• Stakers (or Unstakers) / All Users Ratio

• Number of Stakers / Unstakers

  \

In the fifth step, for the In Search of Decentralization section, 5 parameters were checked:

• Fairness
• Entropy
• Gini Coefficient
• Euclidean Distance
• Kullback–Leibler Divergence

Price Action

Near At a Glance

Actions and Portions

In Search of Decentralization

Conclusion

Review of the target week in 4 reviewed sections:

Price action:

• The highest average price during a week
• The highest number of days with a price increase
• The highest percentage of price increase and decrease compared to the previous day's price

Near at a Glance:

• The first week is full of fluctuations in several parameters, the trends of which are not repeated in the second week.
• Lack of clear connection between the native token value of this ecosystem and the activities carried out in this ecosystem (in a general view)
• Reducing the number of transactions while increasing the success rate of transactions
• The record of the highest percentage of gas used in one day

Actions and Portions:

• Absence of notable trends compared to the other two weeks. The second week has actually always been something between the first week and the third week.
• Among the portions, only the ratio of transferers to the total number of users is higher than the second week.
• Among the actions, in stake+unstakes, the highest number in one day belongs to the second week. (January 23: 711 transactions)

In Search of Decentralization

• Fairness and Entropy indicate that the second week has moved towards decreasing decentralization compared to the other two weeks.
• Gini Coefficient and Kullback–Leibler Divergenc are parameters that showed signs of increased decentralization.
• Finally, a clear advantage cannot be distinguished between increasing or decreasing the decentralization of the ecosystem

Abstract

At the beginning of 2023, after a long time, good days have come for the crypto market. Since the first days of the new year, the USD value of most assets in this market experienced significant growth. So that the market cap of this market increased from about $850 billion at the end of 2022 to about $1100 billion in recent days. (about 30% growth). The Near ecosystem and its native token ($NEAR) were not spared from these positive changes. For this reason, it seemed interesting to me that in the first attempt of the "Insight of the Week" series, I would first analyze this ecosystem with a comprehensive view and go into details in the subsequent reviews. That's why I decided to divide this analysis into four sections in the current "Insight of the Week":

• Price Action
• Near at a Glance
• Actions and Portions
• In Search of Decentralization

Before starting this part of the analysis, I want to give ==kasadegh a big credit. Most of the queries and explanations mentioned in this section use his amazing work on this dashboard. To respect him and his great work, I chose to use only things in this dashboard that are the minimum necessary explanation for each parameter. Instead, I advise everyone who is reading this text from me to visit his dashboard. In my opinion, the approach he adopted to analyze the decentralization of the Near and Ethereum networks is very interesting.

My Reference: NEAR City Layout by kasadegh