× 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 #1545 - 2018-11-24 22:59:35 to 22:59:41; Start Time 1543100375.861942

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 9.000300000 () 0xe7d97A788E6a0a0B4F27bEBEfBD0F95623e98E2F/373 2000000 0x1cf742d37a2ec05f8e24e12c4acdfd7efec847efb78addea634e9f284a61ef1e
0.200 9.000300000 (0.0) 0x042EABCc257ff6df43dB036Fd6Ef2FF5Dbae1512/104 2000000 0x63bd09650d15215dabce0d971f9188ae272d9e90a8ae6614ea6bec9ff45fe154
0.335 6.000000000 (-40.00319993600128) 0xBE708D227F6DfA0B8f2698bf543B949Dfe4e28fB/20326 490686 0x6fe3f55055ed06d1f7a13ee2253c6c797f2c68522c3623c7df7c52f0f0424eb2
0.379 41.000000008 (148.93617022145767) 0xb314371A82BD900fd70B83902665B2af93dc3294/10 2000000 0xf98f9d4a328e9b4207e6bfbe8ddf641de0f49e74596dd10740a970feab23b7a7
0.546 6.000000000 (-148.93617022145767) 0xfF59364722A4622A8D33623548926375B1B07767/46707 600000 0x698357234c3df1b2b7dd134798ee25704100a1224828a2be086710b9153a07d0
0.682 41.000300008 (148.93649615871618) 0xe7d97A788E6a0a0B4F27bEBEfBD0F95623e98E2F/373 2000000 0xfa240e6df2863d41ee7dcb349c943ae6da48ed7ae0c162d037b6562e585934ab
0.686 41.000300008 (0.0) 0x042EABCc257ff6df43dB036Fd6Ef2FF5Dbae1512/104 2000000 0x7892f58c6b6b5469fa0c896330e80ec61027fa687e94478da8df4c1dfaa2effd
1.136 41.100000004 (0.24287364598041075) 0x36F94220C57E34b6b3a294523De1977602Aa625c/745 2000000 0x5cfe5dcaae9c13ac1b9349a7c12ba8ba44f66e43bf862f4442e0c97827ccfd80
1.138 41.100000004 (0.0) 0x36F94220C57E34b6b3a294523De1977602Aa625c/746 2000000 0x594edae174ed85a20f4f1c0254d38c100098eef565b54ed96cab3877572a3d90
1.423 46.125747512 (11.523541273356509) 0x042EABCc257ff6df43dB036Fd6Ef2FF5Dbae1512/104 2000000 0xb682839ab72d056793d8910c663c924ef897f6733707e8088ff5f0aa3cfd0a1d
1.431 46.125747512 (0.0) 0xe7d97A788E6a0a0B4F27bEBEfBD0F95623e98E2F/373 2000000 0x52b351cc40944ab23c0cea246132ee0b7f9e230012214cefcaf2ddb6106108ce
2.277 608.250000000 (171.80473286953278) 0xb1b68a82dfA780E7490E2DfFF0886aFd839fe705/21 750000 0x1bf19befdc9c1da8d9e3eb43b2e2971dbecc37bf8b24feb71fc337ee1dcc453c
2.317 608.250000000 (0.0) 0x036c49ABd9665F2A4fa28f7767F13d2Ab0822FA2/23 750000 0x6c47a01795f6786d038e416e1c47e6ef0ddffd5234a277c1dfd70b8d8ed754e3
2.866 11.000000000 (-192.89463060153412) 0x0005ABcBB9533Cf6F9370505ffeF25393E0D2852/51682 340554 0x2becc6a967cf28f94f9fecd73829b8c42e0de89ed6f47b32ca92f2b892e86b1d
3.967 46.125747520 (122.97693787797633) 0xb314371A82BD900fd70B83902665B2af93dc3294/10 2000000 0x58216ad8b0d90b5837fc61ed74cb74b11ec0d4f7596d96935c006956f4ac72e6
5.742 610.000000000 (171.87993448247113) 0x5e0337086931BaA32fF51f7Ce25840d9a3C572D0/28 750000 0x26036022f826868438fd9dd44d4498976910d12b11e6684ecb91f05f8282d66d
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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.