When it comes to public chain data, the first things that come to mind are definitely TPS and gas fees—one is about speed, and the other is about cost. However, these two indicators are far from sufficient. If we only focus on these two metrics, we might overlook the most essential issues. I would like to discuss the basic data of a public chain from three dimensions: performance, security, and ecology.
1. Performance Dimension: Is the chain fast and cost-effective?#
Performance indicators determine whether this chain can support large-scale applications. If gas is too expensive and TPS is too low, users can't afford it, and developers can't operate it—that's just theoretical talk.
What data should we look at?
TPS (Transactions Per Second): The number of transactions that can be processed per second.
Block Time: The faster the block time, the quicker the transaction confirmation.
Average Gas Fee: The actual cost of users' daily transactions.
Finality Time: How quickly can a transaction be "locked" and confirmed without being altered?
How to analyze + what tools to use?
Token Terminal: Enter the target chain name (e.g., Ethereum, Solana) to view Gas Fee, daily transaction volume trends, etc.
Blockchair, Etherscan, Solana Beach: Detailed chain-level data.
L2Beat (for Layer 2): TPS and fee comparison charts for Layer 2.
How to read the charts: If the gas trend chart looks "like an ECG," fluctuating up and down, it may indicate chain congestion or instability; a steady decline may be due to optimization or subsidies, which should be interpreted in conjunction with the mechanism.
2. Security Dimension: What is its level of decentralization and resistance to attacks?#
Many public chains sacrifice security for performance, such as having too few validators or overly centralized nodes. If something goes wrong, they have to "restart the chain"—this is absolutely unacceptable in Web3.
What data should we look at?
Number of Validators: The more validators there are, the more decentralized it is.
Number of Full Nodes / Distribution Area: The more nodes there are and the more dispersed they are, the more stable the chain is.
Staking Concentration: For example, if 90% of the staking is controlled by a few large holders or institutions, the chain can easily be manipulated.
Downtime History: Has the chain experienced any major failures? What is its recovery mechanism like?
How to analyze + what tools to use?
Validator Dashboard (official sites of each chain like Ethereum Beaconcha.in, Solana Validator App)
Messari Reports / Chainflow Validator Decentralization Ratings
Rated.network: Ethereum PoS validator distribution and performance data.
Reminder: A "large" number of validators does not equal "decentralization." If 80% are concentrated in a few node operators, it is still centralized.
3. Ecology Dimension: Are there people using this chain, and are there developers building on it?#
Chains are meant for applications to run on, not just for technical show. If there are no DApps and no developers, even the best technology is useless.
What data should we look at?
Number of DApps and Leading Projects: One is the number of DApps, and the other is whether there are iconic applications (whether in DeFi, NFT, or GameFi).
Daily Active Users: How many people are using this chain every day?
Transaction Volume and Number of Addresses: Key indicators for assessing the activity and growth potential of blockchain projects.
TVL (Total Value Locked): How many assets are running on the chain, reflecting user trust and actual usage.
How to analyze + what tools to use?
DeFiLlama: View the total TVL ranking of public chains and DApp contributions.
DappRadar: Browse popular applications on each chain and user activity.
Dune Analytics: Custom dashboard queries for user activity trends, address growth, etc.
To judge whether the ecology is "hollow":
User numbers surge but transaction volume doesn't increase? It might be due to airdrop tasks; TVL surges but quickly flows out? It could be a Ponzi scheme. Long-term retention is the reliable metric.
Let's summarize: How to interpret public chain data? Remember these three keywords:#
Stable operation: Look at TPS, fees, and confirmation times to see if they can support real usage.
Resilience: Look at nodes, validators, and security mechanisms to see if they are sufficiently decentralized.
Active usage: Look at DApps, TVL, and user numbers to assess whether the ecology is vibrant.
Alright, the next time you encounter a new public chain, you can make a simple judgment using this method. I believe you can get about 70-80% accurate assessments. I will share more about tool usage in the community later. Thank you.
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