Research on uncaptured backrunning opportunities for privately submitted transactions.

Author:

EigenPhi

contact at [eigenphi.io]

About You:

As a technology company focused on analyzing and researching transaction data on the blockchain, EigenPhi is committed to providing the best analytical tools for tackling emerging financial problems in the blockchain world with state-of-the-art data science approaches. Especially when it comes to economic attacks on protocols, our analysis based on real-time liquidity - token flow data - will allow us to draw transparent and credible conclusions and assist in effective decision-making.

We have launched a token-flow-oriented data platform, EigenPhi, with dashboards covering the main MEV types (Arbitrage & Sandwich), Lending, Liquidation, and Flashloan, and EigenTx, an on-chain transaction analysis and visualization tool to improve the efficiency of data analysis for professional DeFi users.

To further leverage the value of our tools, we use them to investigate the problems DeFi protocols concern and specific cases that occur, and we expect to benefit stakeholders and interested parties as a result.

Additional Links:

• Website: eigenphi.io
• Twitter: EigenPhi

Grant Category:

MEV Data research

Grant Description:

MEVblocker is leading the market of MEV-protection RPCs. It has generated the most amount of rebates to its users, at the time of writing >800 ETH.

Unfortuntaely some users and projects are losing on the opportunity to re capture their transaction’s back-running opportunity and therefore the value is captures by MEV bots instead of being captured by the user in the form of a rebate.

This grant is offering to conduct an analysis aiming to quantify the value of “lost back-running value” in a given set of transactions privately-submitted by Maestro and Unibot.

• Dataset: list of transactions made from the router contract addresses associated with Maestro and Unibot, and submitted through Flashbots. Current analysis will focus on transactions made in the month of November 2023.
• Methodology:
  • Every AMM pool interaction contained within the transactions of the data set will be identified
  • Backrunning resulting from the MEVshare auction will be detected and ignored
  • The pool will be monitored for backrunning happening in the same block and the following block.
  • The value of the backrunning trades will be counted

Grant Goals and Impact:

The analysis results will help to compare the efficiency of capturing backrunning rebates by MEVblocker vs MEVshare.

Milestones:

• Conduct the analysis
• Share numeric results and visualizations with the CoW team
• Potentially - collaborate on publishing the results in a shared post/paper

Grant Timeline:

10 working days

Funding Request:

2000 xDAI

Gnosis Chain Address (to receive the grant):

0x20Fd2beC717c8544f209C4c50EE31F8bDaC134B4