× 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.
*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 #328 - 2018-08-26 10:08:42 to 10:08:44; Start Time 1535278122.6979513

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 296.056555013 () 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4536 151488 0x7ffca5be5f08aead183db341d5f5fa224c953b336a103fe9b29eb90d850a9975
0.230 310.044182742 (4.615611517257094) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4145 128840 0xfd029f8dae16afba034cfb1e5d0bc9cb5648bc3c06c7ad60f63742ca019c07d1
0.492 503.296143522 (47.5205653868619) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4536 151488 0x3a663d90ffa7ea697b8527f98752b3af280b4a9440fd7807dc03711062ed21d1
0.592 296.056555014 (-51.85185185151825) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/12236 445894 0x2baf8381f6553d27ebbb6779ceaa81f6c1f2606100c1b929650c3d4519c65a1f
0.659 504.302735809 (52.03817415072746) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4145 143704 0x72e445de00d2d79bdaff54f1003da34e1114f17c18498a3570a882380a8157bb
0.824 358.228431568 (-33.87107846438041) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/12236 445894 0xcb8d5a7851c3d4a9ceac46c687d06d731c8b34d115be201d37ac40f4457f4782
0.864 855.603443987 (81.95121951161241) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4536 151488 0x58a625587c330c3979021219449304cb30d0567139333f4de3d9920d3331454f
0.885 553.625757876 (-42.85714285678806) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/12236 445894 0x568736d89285700c9f324fefa57c1adaafc273205e43d2725d608801bc3a8226
1.339 941.163788387 (51.85185185163381) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/12236 445894 0x2f6d2092a08cad69ea03ffb57a73f9d092e2b512c379189be6ac80648e32de00
1.567 1454.525854777 (42.85714285695204) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4536 151488 0x12919475e06423475147fd58159d6a2a7759ae760f49b8499c901a0a5dbbef2a
1.730 1599.978440256 (9.52380952389059) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/12236 445894 0xd3d121645f0b37d2830c0a2c9614cfeccafe136fe7b259db048ab4aa3ae3523b
1.975 2472.693953120 (42.85714285703061) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4536 151488 0x3f24d3d84dc508d9896022e4e44a1c8aef4fad47ef8a8bf6c2402f3fc1699fc5 6216359 6216359 / 0
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This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. .
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.