Use EigenPhi Label on Etherscan to Identify MEVs in the Daily 1 Million Transactions
According to the data of Etherscan, the daily transaction number on Ethereum has mostly been more than 1M since around July 2020, shown in the chart below.
Among these 1 million transactions, thousands are MEVs including arbitrage, sandwich, and liquidation. The following chart was captured from https://eigenphi.io/, from which you can tell that the daily MEV number is usually less than 10K.
10K in 1 million, which means when you are browsing transactions on Etherscan, the chance to find an MEV is less than 1 in 100.
However, it’s not hard anymore.
Using EigenPhi’s API and data, Etherscan has added a label-EigenPhi-to the transaction detail page:
The label tells you that this is a transaction recognized by EigenPhi as an MEV.
You can also search “EigenPhi” on Etherscan and get the result below:
Click the “Block,” and you will be directed to the block list page showing all the blocks containing MEV identified by EigenPhi. We can read that there are 13,025 such blocks.
And 50,085 transactions were marked as MEV as of the moment of the writing.
On Etherscan, once you encounter an MEV transaction, how can you know the way it works? Sometimes, one transaction contains tens of transfers due to the composability of Smart Contract. Moreover, occasionally, some sneaky MEVs could hide their trails disguised in simple actions on Etherscan.
The best companion is EigenTx Chrome Extension, which can visualize the asset flows in an intuitive way like this.
To understand more about the EigenPhi label on Etherscan, you can check this short video:
As a DeFi trade visualizer, EigenTx provides 4 pivotal advantages:
Visualize Token Full Life Cycle
Streamline Complex Transactions Asset Flow
Support Mint, Burn, Flash Loan, Liquidation, Arbitrage, Sandwich
Support Ethereum, BSC, Fantom, Avalanche, Polygon
Here is a short video giving more detailed instructions on how to use it.
Last but not least, our Everything Money-making MEV illustrates lucrative MEVs by using EigenTx to dissect them. Don’t forget to subscribe!
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