× 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 #1137 - 2018-10-22 07:08:02 to 07:08:09; Start Time 1540192082.0585558

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 4.000000000 () 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/8580 692396 0xfa920ba2e4e3d66d668ee8086c06f72fdc5f497df3b316415ca84358493b91ee 6561131 6561131 / 90
0.000 4.000000000 () 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/8580 692396 0xfa920ba2e4e3d66d668ee8086c06f72fdc5f497df3b316415ca84358493b91ee 6561131 6561131 / 90
0.000 4.000000000 () 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/8580 692396 0xfa920ba2e4e3d66d668ee8086c06f72fdc5f497df3b316415ca84358493b91ee 6561131 6561131 / 90
1.832 50.283929688 (170.52534683476136) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/25176 22000 0x6c9affd91fdace0378ef9c4dea3808894ef5c0fc7781fadda35d3fe65ca2549a
3.145 4.000000001 (-170.5253468279357) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20688 359361 0x861bfd44c3d6980396b3c80c31d4c3d354f281e5b0b78033eeca252b57741d34
4.954 134.023765152 (188.40779340698037) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20688 22000 0x7167f4035bd60a6d0d5dd539a8ad2421ef46b4f3572459bf781699a0584a9d16
5.163 4.000000001 (-188.40779340698037) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/18465 359233 0x308caf42ee6830713f7d36589dc689959aa3af324ea96e0553bf4afc60901fb9
5.336 154.127329924 (189.8815720144084) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20688 22000 0x042a54037f6030590015ee5212638c3a61e6f9ec399c172d19e5b1f01a52064d
5.814 6.000000001 (-185.0119276857729) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/18465 22000 0x13fdd6b8d1517389d298e56ad5ffb810259115857d40496a02290b9c3b81108a
7.216 4.600000001 (-26.41509433463866) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/18465 22000 0x50ca3ac38c60a27b8c1bdea6de6e05397d5d0087afe44115132dee4adacf6708
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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.