× Welcome to frontrun me, the premier site for visualizing Ethereum gas auctions. These gas auctions are often bids by frontrunners, arbitrageurs, and other programmatic network actors that seek to exploit inefficiencies in on-chain systems, ultimately profiting miners. On this site, we monitor these behaviors on the network in real-time as these bots compete for block priority in rapid-fire all-pay auctions*, showing users and mechanism designers the potential rent-seeking economy created by orderbook inefficiencies on-chain. Timing data is sourced from a global network of 8 nodes peered deeply with Ethereum. Read our full paper!
*Modern auctions are a special kind of all-pay auction, in which the winner of the auction pays some constant percentage of their bid; they do not pay full gas, as they do not pay for execution, but they must pay for attempted execution.

WARNING: THIS IS AN ALPHA VERSION OF THE SITE WITH PRELIMINARY DATA AND KNOWN BUGS. Use at your own risk and follow @ProjectChicago_ for announcements on release and more data being added.

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Individual Real-Time Auction Data

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Note: only auctions with a single detected "winner" (mined with 2+ log events) are displayed in this view. Other auctions may contain multiple sub-auctions, as the split is heuristic.

Gas Auction #1098 - 2018-10-19 16:20:57 to 16:21:02; Start Time 1539966057.1831207

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 9.000000000 () 0x8bC3da587DeF887B5C822105729ee1D6aF05A5ca/184676 98160 0xebac32388b5b36b88175954f1633bf5ed5acd094fbeb56e367e7074a5b372702
0.173 109.820854622 (169.70228827715022) 0xf666666666E8025c503209ac580a5aA4b39A090c/467 199116 0xa378e4c6f0940550bb419003928ffb2ec8636f4bc2645b95c835e47562326bb8
0.758 114.213688806 (3.921568626680791) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4879 267153 0x73e56cac8d320d16dcdf66e491347b2fd174c89254c251abd1a2a71f55e8250a
0.780 114.213688806 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4879 267201 0x8478f2ade6e87dadaaa21cc201e54bbb719210d26750f628cec6d968e1190ca8
1.208 278.913858568 (83.78968650869602) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4879 267153 0x52c72c5a1828b5934f180eb820964d875efad7335ef84979ce25686bd33ec71a
1.613 474.153559565 (51.85185185173382) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4879 267153 0xd811e13ebb00d20ba1f63494acd2911c04fed598b46c6cf6d26f7a78ef51a312
1.836 321.823682964 (-38.274932614157024) 0x9d084C384bE364Ffa6e81FeB319a9C6FEDb4acD2/1 249961 0xc77132b53180d5fe29c675d8a5cc45491e6b6d6714b409173b65752cf0fb711d
4.151 268.186402470 (-18.181818181818183) 0x9d084C384bE364Ffa6e81FeB319a9C6FEDb4acD2/1 249961 0x73011122113201d0a2307bdda68ecfa4a2858f5dd3e5e66b51d4e65749a1c8ca
5.163 9.000000000 (-187.01234992798888) 0x00A2aEbC7E233cd2fFE5AB5856F90f0ad2Fa3CcD/3857 1600000 0x32dbb5e85c26b7e699f75217846a6647851fb30de38b020cb5209df89b54a3af
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This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1650441.
We would also like to thank NSF CNS-1330599, CNS-1514163, CNS-1564102, and CNS-1704615, ARL W911NF-16-1-0145, and IC3 Industry Partners.
Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation.