data sources & limitations
- mev-inspect-py (by Flashbots) is used by zeromev for MEV classification.
- as such much of the information from the MEV-Explore data & metrics page applies here.
- due to known issues with sandwich profit estimation in mev-inspect-py, we recalculate sandwiches using our own AMM pool extraction method.
- other MEV amounts are sourced directly from mev-inspect-py but with dollar exchange rates for these instances recalculated by zeromev using on-chain Uniswap v2/v3 swap data.
- because of this, our USD denominated results may differ from those found in MEV-Explore which uses external data sources.
- known issues with mev-inpsect-py can be tracked on the Flashbots github.
- notably, there is a problem with detecting split arbitrages which may contribute to the disparity in arbitrage results below.
- there are also issues with overlapping MEV (eg: arbs co-mingled with sandwiches, and sandwiches that crossover with each other) which no MEV detection software fully handles at present.
- as such, this data must be considered a lower bound of MEV.
data source comparison test
- Eigenphi provide a good data source comparison test set as they employ a very different MEV classification methodology to mev-inspect-py and zeromev
- as can be seen below, the sandwich attacks recalculated by zeromev produce similar results to Eigenphi.
- mev-inspect-py arbitrage classification is not yet recalculated by zeromev and misses around 64% of instances compared to Eigenphi at time of writing.
data revision policy
- data will be subject to change as MEV classification improves.
- please bear this in mind when referencing our data and consult our change log for details of any revisions.