Overview ~ 7 min read

In this dashboard, I will answer this question from Flipside Crypto:

Time to do some NFT analysis.

Compare and contrast the NFT market and ecosystem across at least three different chains (you can do more if you wish, but there must be at least three included.) Explain how the various chains differ in terms of metrics such as:

• Total Sales
• Total Unique Buyers
• Total Volume
• Average Sales/Day
• Average Buyers/Day
• Average Sales Price/Day

Introduction

Introducing to Flow

Flow, the blockchain for open worlds \n ‍ \n ‍Flow is a fast, decentralized, and developer-friendly blockchain, designed as the foundation for a new generation of games, apps, and the digital assets that power them. It is based on a unique, multi-role architecture, and designed to scale without sharding, allowing for massive improvements in speed and throughput while preserving a developer-friendly, ACID-compliant environment. \n \n Flow empowers developers to build thriving crypto- and crypto-enabled businesses. Applications on Flow can keep consumers in control of their own data; create new kinds of digital assets tradable on open markets accessible from anywhere in the world; and build open economies owned by the users that help make them valuable.  \n \n Smart contracts on Flow can be assembled like Lego blocks to power apps serving billions of people, from basketball fans to businesses with mission-critical requirements.  \n ‍ \n There are four pillars that make Flow unique among existing blockchains: \n

• Multi-role architecture: Flow’s design is unique, allowing the network to scale to serve billions of users without sharding or reducing decentralization of consensus.
• Resource-oriented programming: smart contracts on Flow are written in Cadence, an easier and safer programming language for crypto assets and apps.
• Developer ergonomics: from upgradeable smart contracts and built-in logging support to the Flow Emulator, this network is designed for results.
• Consumer onboarding: Flow was designed for mainstream consumers, with payment onramps catalyzing a safe and low-friction path from fiat to crypto.[1]

Introducing to Algorand

Algorand is a new-gen blockchain that is bridging the decentralized and centralized world of finance. Its native crypto is ALGO. \n \n Algorand Standard Assets run on the blockchain's "Layer-1" and include fungible, non-fungible, restricted fungible, and restricted non-fungible assets.[2] \n

Introducing to Solana

The Solana docs describe the Solana open source project, a blockchain built from the ground up for scale. They cover why Solana is useful, how to use it, how it works, and why it will continue to work long after the company Solana closes its doors. The goal of the Solana architecture is to demonstrate there exists a set of software algorithms that when used in combination to implement a blockchain, removes software as a performance bottleneck, allowing transaction throughput to scale proportionally with network bandwidth. The architecture goes on to satisfy all three desirable properties of a proper blockchain: it is scalable, secure and decentralized.

The architecture describes a theoretical upper bound of 710 thousand transactions per second (tps) on a standard gigabit network and 28.4 million tps on 40 gigabit. Furthermore, the architecture supports safe, concurrent execution of programs authored in general-purpose programming languages such as C or Rust.[3]

Introducing Optimistic Ethereum

Like other L2 solutions, the Optimism team saw an opportunity to be a new scaling solution for Ethereum without compromising security and decentralization as shown in the trilemma. Optimistic Ethereum is an EVM compatible rollup chain designed to be fast, simple, and secure, and allows users to interact with any Ethereum application at a fraction of the cost by using optimistic rollups.

You can think of optimistic rollups as a smart contract that bundles (or “rolls up”) transaction data and moves it off chain for faster processing. Optimistic rollups are distinct from zero knowledge (ZK) rollups, because they use a different method of verification: while ZK rollups rely on each batch of transactions to have their own ‘validity proof,’ optimistic rollups assume all transactions are valid.[4]

Introducing to Ethereum

Ethereum is a blockchain with a computer embedded in it. It is the foundation for building apps and organizations in a decentralized, permissionless, censorship-resistant way.

In the Ethereum universe, there is a single, canonical computer (called the Ethereum Virtual Machine, or EVM) whose state everyone on the Ethereum network agrees on. Everyone who participates in the Ethereum network (every Ethereum node) keeps a copy of the state of this computer. Additionally, any participant can broadcast a request for this computer to perform arbitrary computation. Whenever such a request is broadcast, other participants on the network verify, validate, and carry out ("execute") the computation. This execution causes a state change in the EVM, which is committed and propagated throughout the entire network.

Requests for computation are called transaction requests; the record of all transactions and the EVM's present state gets stored on the blockchain, which in turn is stored and agreed upon by all nodes.

Cryptographic mechanisms ensure that once transactions are verified as valid and added to the blockchain, they can't be tampered with later. The same mechanisms also ensure that all transactions are signed and executed with appropriate "permissions" (no one should be able to send digital assets from Alice's account, except for Alice herself).[5]

Method

To solve this question,

I first got the total number of transactions per day and the number of unique users per day and the dollar volume of the daily transaction and set the date condition '2022-06-15' to date. Then, to get Average Sales/Day, I divided the average number of sales transactions by the number of historical days I considered (approximately 95 days). To get Average Buyers/Day, I did a similar operation, with the difference that this time I divided the number of unique users To get the Average Sales Price/Day, I divided the total dollar amount obtained by the number of transactions I did this for 5 chains with the same dates For the Ethereum and Optimism chains, I only considered sales that were made with ETH The reason for this was that in some transactions tokens were traded whose prices were not found in the ethereum.core.fact_hourly_token_prices table. The date I set for this analysis is from '2022-06-15' to today's date 2022-09-17

Please accept my apologies for my inappropriate accent and dialect.

Chart & Analyze

In this dashboard you will read:

1. Total Sales in past 3 month (Flow , Algorand , Solana , Optimism , Ethereum)
2. Total Unique Buyers in past 3 month (Flow , Algorand , Solana , Optimism , Ethereum)
3. Total Volume in past 3 month (Flow , Algorand , Solana , Optimism , Ethereum)
4. Average Sales/Day in past 3 month (Flow , Algorand , Solana , Optimism , Ethereum)
5. Average Buyers/Day in past 3 month (Flow , Algorand , Solana , Optimism , Ethereum)
6. Average Sales Price/Day in past 3 month (Flow , Algorand , Solana , Optimism , Ethereum)
7. cumulative of The first 3 cases in past 3 month (Flow , Algorand , Solana , Optimism , Ethereum)

1. Total Sales in past 3 month (Flow , Algorand , Solana , Optimism , Ethereum)

• Total sales amount in the last 3 months: The highest sales were in the Solana chain The lowest sales were in the Optimism chain
• Solana > Ethereum > Flow > Algorand > Optimism

2. Total Unique Buyers in past 3 month (Flow , Algorand , Solana , Optimism , Ethereum)

observations

observations

• The total number of Unique Buyers in the last 3 months: The most Unique Buyers were in the Ethereum chain. Optimism has the lowest number of Unique Buyers.
• Ethereum > Solana > Flow > Algorand > Optimism

3. Total Volume in past 3 month (Flow , Algorand , Solana , Optimism , Ethereum)

observations

• Total Volume in the last 3 months:

  The highest sales were in the Ethereum chain

  The lowest sales were in the Optimism chain

• Ethereum > Solana > Flow > Algorand > Optimism

now this information in chart over time (past 3 month) section 1, 2 , 3

observations

• So far, it can be understood that Solana competes with the Ethereum chain in terms of the number of unique users
• The statistics of the Optimism chain have been strangely reduced since August 5. Since this date, until today, the volume of sales in this chain has been under 1000 dollars on most days.
• The volume of sales in Flow and Algorand is different on different days
• But Solana and Ethereum operate very close to each other and there is not much difference in sales volume on most days
• On September 13 (2 days before the Merge), the Ethereum chain had a very high volume of sales

4. Average Sales/Day in past 3 month (Flow , Algorand , Solana , Optimism , Ethereum)

observations

• Average Sales/Day in the last 3 months:

• The highest sales were in the Solana chain

  The lowest sales were in the Optimism chain

• Solana > Ethereum > Flow > Algorand > Optimism

5. Average Buyers/Day in past 3 month (Flow , Algorand , Solana , Optimism , Ethereum)

observations

• Average Buyers/Day in the last 3 months:

• The highest sales were in the Ethereum chain

  The lowest sales were in the Optimism chain

• Ethereum > Solana > Flow > Algorand > Optimism

6. Average Sales Price/Day in past 3 month (Flow , Algorand , Solana , Optimism , Ethereum)

observations

now this information in chart over time (past 3 month) section 4 , 5 , 6

• Average Sales Price/Day in the last 3 months:

• The highest sales were in the Ethereum chain

  The lowest sales were in the Optimism chain

• Ethereum > Solana > Algorand > Flow > Optimism

observations

7. cumulative of The first 3 cases in past 3 month (Flow , Algorand , Solana , Optimism , Ethereum)

• Average Sales/Day It is obtained from the average result (dividing the total number of sales by days).

• The increase in the number of sales in some days causes violations in this criterion Ethereum and Solana, which had a lot of sales on some days, can be seen in these graphs that the average of this measure has also had deviations.

  • This criterion ranges from 300 to 700 in Ethereum
  • 400 to~ 2000 in Solana
  • 70 to~ 580 in Flow
  • 9 to~ 55 in Algorand
  • 0.02 to~ 22 in Optimism

  Average Buyers/Day It is obtained from the average result (dividing the total number of users by days).

  • This criterion ranges from 170 to~ 300 in Ethereum
  • 100 to~ 290 in Solana
  • 25 to~ 237 in Flow
  • 3 to~ 13 in Algorand
  • 0.02 to~ 6 in Optimism

  \

• Average Sales Price/Day It is obtained from the average result (dividing the total volume of sales by the total number of sales).

• The higher the selling price and the smaller the number, the more numbers this criterion shows us

• On September 13, due to the sale of 104 billion dollars and the number of Ethereum sales of 34 thousand, this criterion shows a high number

• In the rest of the chains, like Solana on July 2, due to the sale of 42 million dollars and the number of sales of 45,000 Ethereum, this criterion shows a high number.

observations

Conclusion

1. Total sales amount in the last 3 months:
   • Solana > Ethereum > Flow > Algorand > Optimism
2. The total number of Unique Buyers in the last 3 months:
   • Ethereum > Solana > Flow > Algorand > Optimism
3. Total Volume in the last 3 months:
   • Ethereum > Solana > Flow > Algorand > Optimism
4. Average Sales/Day in the last 3 months:
   • Solana > Ethereum > Flow > Algorand > Optimism
5. Average Buyers/Day in the last 3 months:
   • Ethereum > Solana > Flow > Algorand > Optimism
6. Average Sales Price/Day in the last 3 months:
   • Ethereum > Solana > Algorand > Flow > Optimism
7. On September 13 (2 days before the Merge), the Ethereum chain had a very high volume of sales

• In these graphs, you can see sudden changes in the volume and number of users and the number of sales
• Sudden changes in sales volume affect the value of Average Sales Price/Day
• Sudden changes in the number of sales affect the value of Average Sales/Day
• Sudden changes in the number of Unique Buyers affect the value of Average Buyers/Day

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Appendix

The solution of this question and the queries are completely used from the filipside database

The construction of the dashboard was also used from the site

app.flipsidecrypto.com/velocity, which belongs to flipside

Contact data

-> the writer : sajjadsmoke -> discord : Sajjadiii#5567

Thanks for reading !