× 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 #1630 - 2018-11-28 15:28:33 to 15:28:37; Start Time 1543418913.9744751

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 110.000000000 () 0x699D53Cd35d253faEe82985019F54bC259860eAE/1249 70000 0xdf8f80ab18bca8273bb377148ba9f5b619d16c1c4f0dcbd520d5a5d7eebf1416
0.556 5.100000000 (-182.27628149435273) 0xfC624f8F58dB41BDb95aedee1dE3c1cF047105f1/22661 235412 0xa0fb3d6ebfda14c3e8805e30610cadd9a555ef54aa06308733c0386e28ab4884
0.595 9.000000000 (55.319148936170215) 0x93Ed41743f151bf8F1D46e23F65605dA45388FFF/1850 78702 0x73f02af7f5b5e1889692b792a9abd2919c77fe295a185821f50a05ed14e3ea57
1.998 9.000000000 (0.0) 0x93Ed41743f151bf8F1D46e23F65605dA45388FFF/1851 78702 0xca8e34b6a25994be191b42f12719f977185a1e18fcf7923b389e6691b241237d
2.111 8.000000000 (-11.76470588235294) 0xfC624f8F58dB41BDb95aedee1dE3c1cF047105f1/22661 235508 0xf2ff6661cdfcac45234b63fb35941864696c6f403c3301ba0259687762532c73
2.185 16.000000000 (66.66666666666666) 0xcaDd6887EBf8DADc7ACBBDf2FDaa9dDc5BCd9850/3047 250000 0xe59e39566c691d5cc0b63a7c0ca7191053700dfb9cdb37d76eb9acaf4d3cab79
2.468 5.100000000 (-103.3175355450237) 0xfC624f8F58dB41BDb95aedee1dE3c1cF047105f1/22662 235604 0xe33ddb76775fdccff2e0f52a135891a991a4782fade139851d8e1298d8633ada
2.498 8.000000000 (44.274809160305345) 0xfC624f8F58dB41BDb95aedee1dE3c1cF047105f1/22663 235412 0x88c937777322bfaf12c67be2cca320098672739444b7747bc2c7f951c3b1542e
2.659 8.000000000 (0.0) 0xfC624f8F58dB41BDb95aedee1dE3c1cF047105f1/22662 235508 0xe668dadf83f295489b67cacf64f3c9eaf8cab973f1b5a25177658f384686a248
3.562 13.000000000 (47.61904761904761) 0xF1073872C0F3888FE00Ca24791422cA0ad42Af79/562 459190 0x178400919ec980e80ad200cb6b7fae557ed0ba3b15db0c60db4b5a04afcdb47d
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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.