× 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 #724 - 2018-09-29 22:24:12 to 22:24:19; Start Time 1538259852.0004845

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 26.000000000 () 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/6221 359615 0xefb3e103e67c1c1fb130c5e169c36503b34cc14dc0568b598273329b4f2a8e25
0.149 932.578304858 (189.15059943742352) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/6221 22000 0xf64657e06fa5d5dfeb759fe0710044e3f8200966d222a75e0cd211d4a07b60f3
0.207 26.000000000 (-189.15059943742352) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/19492 359615 0x6fabee8f66afb693cab3f98773bb19c239cefd8581253e05731ecf4a8858a31d
0.299 932.578304858 (189.15059943742352) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/19492 22000 0xbbec43bb42f1a7576e0d71f5c5a090875eaf0d30c2ab87f75f0997ea3924b55a
0.375 26.000000000 (-189.15059943742352) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/17632 359487 0xb4f9cf5e78ab0d652361037675ccf31baaf3a11d6d994951b5ac0fe98a45699f
0.509 932.578304858 (189.15059943742352) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/17632 22000 0xbd1bc092f2e8218709af8269520906c03192fb3916101760c54d9a7de1e9b1c2
0.582 26.000000000 (-189.15059943742352) 0xA045E11fB1551D7196B5B0526431688EfD49CFEA/7418 359231 0x8ba43ec6fb680f38f7a35929a0c9b4015b9c77a28b1c3561c25c9a4c1d4ac97d
0.644 932.578304858 (189.15059943742352) 0xA045E11fB1551D7196B5B0526431688EfD49CFEA/7418 22000 0x36afaa83a46320c724ff5a927f14763a3373c7ba80046ca729d84c2a719f2163
2.736 318.185227700 (-98.24288303344973) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/17965 359551 0x06c97213b05852a0bfac77b17654fa691d198b904a5d7be3bbde20ae7b5bf076
7.234 1072.465050586 (108.47872174097756) 0xA045E11fB1551D7196B5B0526431688EfD49CFEA/7418 22000 0xdeabd04cfd5203a8b27d5efa9c38e916cbb80ead2589c74804419c3c0f8629ad
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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.