× 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 #1087 - 2018-10-19 09:52:58 to 09:53:22; Start Time 1539942778.6115792

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 25.000000000 () 0xe89943Ec20d856F064A87349a00Ef6aB00AED042/143905 21000 0x0024f4c37eec5b8113ae3b3c34956b860befedcf0f0200ceccaa70df66370a18
2.602 50.000000000 (66.66666666666666) 0xB500a1429000AE315717390a052371fFbea59b5C/1583 74632 0xb7c5686d12e4604ce6980be25e63bac083a57eb35d901169e7114680ddefb3b8
5.420 7.000000001 (-150.8771929763004) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/7813 359423 0xb31e52b83f25b361673ab52a1c921938d968e3284065e743272a7378d4e4f1fe
5.886 60.122111237 (158.28498316341947) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/23635 374423 0x0edcb5946a563f501b961009653dc1240ee0b8f82f21adf47ac3405446ff1925
7.116 132.278155517 (75.00617904262846) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/7813 22000 0x80154f938a7e9eb176919571bf9bad01cdecc4e126ba72b7a343faf523d23238
7.343 7.000000001 (-179.89634490788376) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20270 344487 0x5713d7f04c35d5f38ff4e6700cc930eb8438b5572aa905dcb1c1abb5680f0b9f
7.658 132.278155517 (179.89634490788376) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20270 22000 0xc63f6cda4999d0d99d1e41918d1dd92e1ece78aa5b7be102316bb8f9fd2c7ef0
7.821 7.000000001 (-179.89634490788376) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/18216 359423 0x975aad51d3632e861e0cbbd4239280d257d977132e7c41eea3724c98ec3e8c6e 6543466 6543466 / 143
23.617 152.119878844 (182.40320429650714) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20270 22000 0xbc5dc8bb83a74b4e6c5d836fe52808add02d8ed639a085f79a7b1222c4f121a5
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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.