First of all, we will compute the top 10 wallets with the highest value locked in liquidity pools right now on Sushiswap (removing the removed liquidations). The table below shows it. The top 10 whales are formed by wallets with over 75M deposited liquidity in USD each. The top 2 wallets have more than 45M USD each.

Once the top 10 whales are obtained, we will extract some info about them in order to study their behavior since becoming a liquidity provider. The analyzed metrics are:

• Percentage of liquidity provided per user
• Top 10 pools with the most liquidity provided by them
• The total number of days with liquidity provided per user by pool
• Correlation between total transactions and the average days on a pool

Analyzing the results obtained we can observe different interesting things:

• From the liquidity distribution donut, we can observe how the distribution is not equal, so the top 2 wallets have almost the 40% of the entire liquidity deposited by the top 10 wallets.
• From the top 10 pools, avoiding router, almost 30% of the total whales' lp provided is deposited in a unique pool, the SUSHI-WETH. The other top 10 pools are: WETH-ALCX, DAI-WETH, USDC-WETH, WBTC-WETH, WETH-USDT, CREAM_WETH and YFI-WETH.
• The distribution of staking days for each pool looks similar.
• There is a negative correlation between the average days in pools and the total transactions, so when a user stays in a pool for a long time, tends to be fewer transactions, which indicates that save more on fees.

Finally, we will take a look at the activity of each top 10 users, in order to analyze specifically the behavior of each one. The metrics analyzed are:

• Distribution in the percentage of total transactions per user
• Distribution in % of the average day in a pool by user Relation between the number of pools joined per user and the average days in a pool
• Relation between the total number of transactions and the number of joined pools

Interpreting the results:

• The top 4 users with the higher number of transactions, represent more than 75% of the total transactions.
• The distribution between the total average days in a pool seems heterogenous. We can see that there is more than a 10% difference between the top and the bottom of the users.
• There is no clear correlation between the total number of transactions per user and the average day in a pool.
• There is a positive correlation between the total number of transactions and the number of pools joined per user.

This final table shows the whole data of the activity performed by the top 10 users.

To conclude, some basic notes to take into account. First of all, the top 10 lp providers in Sushiswap are whales, and the top 2 are BIG whales. From their activity, we have observed that users who realize more transactions, tend to join more pools. Finally, we have also concluded that the most interesting pool for this kind of people is the SUSHI-WETH.

Conclusion