1. gas_price is in gwei (1e-9)

• e.g. 20 gwei = 20 / 1e9 ether

2. tx_fee = gas_price * gas_used / 1e9

• tx_fee is denominated in ether

We're recreating the '3 hour' portion of the gas tracker dashboard on Etherscan https://etherscan.io/gastracker . This dashboard tracks the contracts that addresses spent the most fee on in the past 3 hours.

Most fees could be due to:

• high gas prices that addresses are willing to pay for
• high volume of transactions

Examples on topics for insights / trends:

• addresses who sent ETH to ben.eth - what nfts did they buy (saw this on Twitter)
• addresses who buy new tokens on Uniswap - what other tokens did they buy?
• addresses who bought a high number of BAYC nfts, what other collections do they splurge into?

Additional step by step guide - on sale trends for users who minted a hyped nft collection

1. Search for nft address on etherscan.
2. Use nft mints to filter for that nft address.
3. Using min and max timestamp gives you the total mint time of this nft - filter for the first day - so you can filter for sales after this day
4. Get the mint to address - this gives you the users who minted this 'trendy' nft
5. Use nft sales to see the top 10 collection this subset of users also buy

Create a dashboard with the following content:

1. Recreate the gas guzzlers dashboard on a 1 month time period. Essentially answering: which protocols did users spent the most tx fee on in the past 1 month?
2. Trends about a specific user subset

• define a user subset - e.g. recent buyers on opensea / high gas payers , etc
• analyze behavior from those user subset - could be nfts bought or minted / tokens bought or sold

Submit your dashboard here: https://metricsdao.notion.site/Segment-3-Behavior-of-a-subset-of-users-e-g-who-s-causing-high-eth-gas-prices-93477d43d3d54222859cf37f33c43703