× 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 #182 - 2018-08-05 08:14:27 to 08:14:31; Start Time 1533456867.0768886

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 65.210000000 () 0x7E77777772d74Cba60638F3C19C60A3387172a06/4024 184532 0x79702a684c131da7f49e0fa9ae67bf541ddf240d74dd4fa5387ded8f05ae80fe
0.468 76.175880642 (15.51198831482538) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4372 226113 0xed1d464283f52c1355af447f83309e7b00d9cdcc778d59908c4efde227047523
0.559 76.328232403 (0.1998001994281229) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4024 214600 0x66e72b75d8713bc9e5848f7791af6b601f4f4861f3a10a530ec09764428edd95
0.873 129.757995085 (51.85185185177996) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4024 214600 0x1c252c759bcc0eb004de8017e98f2549baf4edae7f5e5de85612000f9664a0c4
1.193 220.588591644 (51.85185185164042) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4024 214600 0xaf3d44dc602e2c24b5e403643b7fa0ec5257f1ca65504fda35ef491e408057ed
1.485 375.000605794 (51.85185185165285) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4024 214600 0x62085bc7c475f328521b4f393c046d696b803aea4e5b47922420137c68a3e1e1
1.716 129.498997091 (-97.32479760106442) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4372 226113 0xaf088f261ba10391edfa02c91c6e0c4c5168365d91b78cc79bb945dc0b2855d0
1.811 637.400473102 (132.45581611451112) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4024 214600 0xa8b5d7b8f9ba93b054770d7464e144d271b9b2985d6c8c3431b3be92913273be
2.684 220.148295054 (-97.3127578377201) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4372 226113 0xa6bb29578c32c3830bc18f502139d84242af7e466005585967ed4a70be070d1b
2.921 374.252101591 (51.85185185165246) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4372 226113 0x3667ef27381ad32adfe069fa2378d57249240b1f0793a9b6f9e85041c36e8b57
4.266 636.228572704 (51.85185185174922) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4372 226113 0xf1e68a0162f4637915059cb8f473414a34fcdc9ef8127cdb46a5fe68e6d7241f
4.492 720.528858587 (12.42672919105156) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4372 226113 0x161ce8bdea9e620d6921a2a477de96ed414eca228fdcc104f1473a2b4486db5d 6091629 6091629 / 63
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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.