× 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.

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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 #1149 - 2018-10-22 16:00:34 to 16:00:55; Start Time 1540224034.8264804

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 163.992259279 () 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/25232 22000 0xd08a8c6441e9397050d4f92f530b9d474e75d5807c9728d2657bfd5d24a2c913
0.679 13.251414013 (-170.09447216506516) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/8593 359489 0x5eceb567caa21c0940dfd0b878e99e71fde9687460050563cb54908af985dc2f
0.820 46.411153418 (111.15760126598431) 0x521dB06bF657Ed1D6C98553A70319a8DdBAc75A3/28355 210000 0x0cc5bef44d4c2bf6d63ada32ec4f014d05ef2ae347193bac8470ad3b05f1a2cf
1.190 124.249711757 (91.22016141097417) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/8593 22000 0x48b526a8e177d786abc92425acbcebce23a93aaa724599e7c12e372f4878e27c
1.684 13.251414013 (-161.45074758103198) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20692 359745 0xd63873acc7e28cdb9d57edec733ac89181f8fc19320348c4018a6f48ab62d53b
1.865 124.249711758 (161.45074758131233) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/8593 22000 0x8875b88c490b5e45600dadcf4ca223f4697d81629721576470205046ac5ee9c1
2.147 124.249711757 (-8.048308409372528e-10) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20692 22000 0x8f912eeca0e313f2d9bee130239d7c1570d710a600e76932ae4c2ba201349dcd
2.385 13.251414013 (-161.45074758103198) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/18466 359553 0xdd5ac29b37c96afc028c66f6747219bd191fecdf7c158a8a6f5073842e5ebef2
2.770 13.251414013 (0.0) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/8593 359361 0xe93062df7616d343a7870ce54acb71c51cc8d80c31180d5e51db23b92e7b953d
2.911 124.249711757 (161.45074758103198) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/18466 22000 0x6c3f662aa9500b7d5106194ee7ed4a534a53999790e2edcb33c16dd1a211ad1a
21.097 142.887168520 (13.953488371709977) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/18466 22000 0x9a0d21b321b3a833aab0733251552f44e339606eafe4a10220747845c095d81c
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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.