× 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 #1599 - 2018-11-26 15:40:28 to 15:40:30; Start Time 1543246828.6968002

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 11.000000000 () 0x4cF7Ad3B79dd781F32788dbb14dF8f016eb81f92/54 850000 0xddbb4565fadb46c8ed900019563e44e7eb31a698eb781a4cda9830cf5ec68bff
0.043 11.000000000 (0.0) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/680 500000 0x02a4bf8caf451bdd7cd0221517f9d47e99250485d8b64a690e88fde2b0885b37
0.096 144.000000000 (171.61290322580643) 0xEf43f36E97D51216dC6740fFdF7F7Ee65D9Aaaba/11 21000 0xe507586c895f9aac2a1269610501b37ce40bf36803abaa70a0decfcd630e8df2
0.336 52.009688746 (-93.86302467242427) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/680 500000 0xb831532a1ceeacbf788c45037ad8543faad3b9ddc0d620fca0ce5ec3a23dc16f
0.707 3640.000000000 (194.36516226871927) 0xEf43f36E97D51216dC6740fFdF7F7Ee65D9Aaaba/11 21000 0xd2e4aed959275a6d1989c6445459f575de0ab9f50ed549851929188675c6ad22
1.103 58.770948282 (-193.64427274856823) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/680 42000 0xb01847b04a372af9196fb75cbdd40c085f5fb4847924a9fd9f822533b8d6167d
1.421 4982.000000000 (195.33635240442493) 0xEf43f36E97D51216dC6740fFdF7F7Ee65D9Aaaba/11 21000 0x7386993fc583c6f7fe6387a3251353dbe799f3d2825d585e1f07be20dd825965
1.886 7.800000000 (-199.37472443785322) 0xEf43f36E97D51216dC6740fFdF7F7Ee65D9Aaaba/11 21000 0x0db30394cd65f32dce8e4e3dfbcbfa6162c2b97557eea21a9f1424bf2116befc
1.974 12.429999999 (45.77360355144704) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/520 500000 0xbdee24b350d900d407f5a9521e3c6678048a1d319a6b32bed30d42216e6299cd
1.985 11.000000000 (-12.206572761937966) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/520 500000 0xceea6efea2802ee3b7fa2a8f42ad56e2f7a696e0089d7078b0b19208a77a4c4e
2.099 52.009688746 (130.16946936943373) 0x2152d20E8E66E65aaF336B7b53E04433F0967939/520 500000 0x5e9bc49aab259a1911133914b10c5493f3471567cd69c5bac3ea6d60f5f0b8ac
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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.