× 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 #998 - 2018-10-14 01:03:06 to 01:03:12; Start Time 1539478986.4111845

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 4.000000000 () 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/7196 829432 0xe07a807fd310f1aabc6548b8fea9bedfb48a4b95e8457a6c65fb1b7fcbde9837
1.651 20.000000000 (133.33333333333331) 0x41FE3738B503cBaFD01C1Fd8DD66b7fE6Ec11b01/3951 7600000 0x2fb4ac970637ec020223904de92eab8c85fa8c2b8a1d8652f54a850b71674c70
1.779 22.000000000 (9.523809523809524) 0x41FE3738B503cBaFD01C1Fd8DD66b7fE6Ec11b01/3951 7600000 0x816db749b10e4a1cf4d6f61633693ce3778823209ba62bd41cf6602759f38d61
1.819 20.000000000 (-9.523809523809524) 0x41FE3738B503cBaFD01C1Fd8DD66b7fE6Ec11b01/3952 151284 0xb0ecacf5e00cc6ceb1c60e8e1b075610c7b0147d200ff74fec657abe6395c6cb
3.909 26.000000000 (26.08695652173913) 0x41FE3738B503cBaFD01C1Fd8DD66b7fE6Ec11b01/3951 7600000 0xa57bf2018e7491e043b0a0f15e212f40eaec9e07348c6aa9e9894045099b7eca
4.512 4.000000000 (-146.66666666666666) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20024 735301 0xce5d8294765aff8926ead326843f4c8ec72fe1357db17a51d0d96c10912f8f55 6510581 6510581 / 73
4.512 4.000000000 (-146.66666666666666) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20024 735301 0xce5d8294765aff8926ead326843f4c8ec72fe1357db17a51d0d96c10912f8f55 6510581 6510581 / 73
5.375 2333.000000000 (199.31536157466837) 0x9ddDE2f8a72B0511d68fd45083AC3BC004Fb61eE/373 600000 0x412fbc0767c9faff1e1caca1fcf7b0fcda2c7be18c665e7057297caa031e2256
5.786 2588.000000000 (10.36374720585247) 0x031CE360526d6be64a3Ed1e50b32889e1c7C21C5/1977 300000 0x8fa442bddaa7dec7a0ddcf597b921a7adb6e181edc7c5b0ebf54e301c9dd2f67
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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.