× 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 #319 - 2018-08-24 19:09:09 to 19:09:12; Start Time 1535137749.3246202

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 213.206768321 () 0x7E77777772d74Cba60638F3C19C60A3387172a06/4138 184340 0x3364fed9aaec36513f5977c92778c9e1c82935f07f238bef282dd8f9d3dee916
0.543 240.923648202 (12.20657276965118) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4138 214300 0x45f1989285f2d1b7ccc0e7161680f8499e96fb83c23d02f4f3f14c0c4d8acda1
0.560 221.735039053 (-8.294930875651737) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4522 225848 0xa8801c0e2b569e038b57152caf359c915faad4f01ab484a4bd51d5411e2d62a9
0.927 377.703465522 (52.03817415085176) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4138 214300 0x5fee3ebb6400bdae9b68059cfd739c8c074e9c44800875284b9cc1e80e0abd09
1.009 376.949566390 (-0.19980019959368878) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4522 225962 0xb8e03b195e235a521b992ce72df9393fee3041586e15ea77c23a6c346b4d8228
1.036 642.095891388 (52.03817415096361) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4138 214300 0xe85476b3ee48200d5d19db104cdc9b87f6172445428995fba885c8ebccf4d506
1.251 640.814262863 (-0.1998001996871327) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4522 225659 0xe0e110beb35dc120662ced37184ad42ffc907bde0f11be0f4724bf414387d6c1
1.404 1089.384246867 (51.85185185184329) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4522 225659 0xb9a9850a6324f1002cd46979fc8e1c564ace196c308853789a1136cdf3e2435d 6206718 6206718 / 0
2.752 376.949566390 (-97.17223650384904) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4522 225848 0x820e2baa99e36e5fe8fa002718ecbe029041a3c4a54d6b0338691123467437e8
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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.