× 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 #113 - 2018-07-23 09:02:11 to 09:02:14; Start Time 1532336531.109758

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 87.933301943 () 0x7E77777772d74Cba60638F3C19C60A3387172a06/3946 169276 0xca7828118799c5fded8fc0ea60fdd93ddfeef06a1e68da2955d9da0a127aadad
0.221 82.270715200 (-6.65388142777203) 0xf666666666E8025c503209ac580a5aA4b39A090c/302 169404 0x264525df14936ab7a4464b34c4c7bc64e75c003a85e7cc898613755d4893dbb1
0.477 234.681375663 (96.17268026092832) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4231 211023 0xb8f0ec568e1ef208986e400e2b8ead23ce164e82cf015d2080b38ee86807453b
0.537 234.820648902 (0.05932806748982118) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4231 210953 0x0077451d3c875fbd128809476bdd6cc2634a8e2c309559b804ba4b87c1be3fce
0.624 235.760870779 (0.39960000028788295) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3946 199062 0xb0e2787f3f94b3315de05dad0c41e525252a841b842a927bc8889eae9728c601
0.766 235.290290199 (-0.19980019963138484) 0xf666666666E8025c503209ac580a5aA4b39A090c/302 199168 0xfac714c03ef3075b440170349895dfc030e244bb1787978fb9d4a329199d883b
0.837 234.108120900 (-0.5036954838565361) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4231 211312 0x043edb490460563e51c0033584e47fbeb7f9f32e90e00ca2b086525adbe46d5c
0.857 400.854652009 (52.521671576137386) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3946 199062 0xb24adbbc22119f2ff498c291213b9c41b347f932b378df03d7e8b7383a96623d
0.864 399.256030863 (-0.39960000040537996) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4231 210935 0x91797d26516f43853b7bedeb7917d69f2305ed92064308c4ca812f1b41d4f08d
0.893 400.054542924 (0.19980019961872475) 0xf666666666E8025c503209ac580a5aA4b39A090c/302 199168 0x78eb41a0ab045a737b59b76bb8b7683b4b1cf8d4a3bbf87ffd01dd71697f6c74
1.071 678.735252467 (51.66543301273888) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4231 210935 0x8c2e5dcc421c8b15eb978dab8baf20265c3a64cefa65f7530af9573f90041699 6014991 6014991 / 6
1.575 397.983805530 (-52.14943394041461) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4231 211312 0xd0dbd7ee572aa6dac401b62c767be733427256563cff8cdcbeb42f90dcba8577
3.477 234.856488743 (-51.55402342842245) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4231 210935 0xd8beb99d1f271b36201b48f6a707b8c1e7c1503d764ebb1cb022526c80315b8b
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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.