× 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 #204 - 2018-08-08 17:54:15 to 17:54:20; Start Time 1533750855.864655

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 65.210000000 () 0x7E77777772d74Cba60638F3C19C60A3387172a06/4052 214276 0x5b2884f03946a6726f748d18b8767618b72e81deebfd0f5eda42469bdb749182
0.673 84.529266214 (25.803874564724865) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4395 240918 0x3cf4cbea7a78cc3185ee98537f15d02f6793760415ca9e6d1cc83d8cf31899bc
1.448 84.698324746 (0.19980019929487747) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4052 249355 0x95426b037bc51c1c63a290c112b92e0896fb91c59af3e548db4ec84c0f79a197
1.951 143.987152068 (51.85185185172229) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4052 249355 0x7fa685a5d4487b0fd6af3f395a46e5b961483a22e5b63e43e7045d50b532a410
2.003 143.699752563 (-0.19980019971269206) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4395 240918 0xc4cb9d96024e97aeaec426e127237841fdb2c3e909fa7b76f26384b2091635c5
2.554 244.289579357 (51.85185185181367) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4395 240918 0xe3604253b7ce947b852e09d8e26d9aca1812f5b3243b061e95ccb4e0a02753a8
2.560 244.778158515 (0.1998001995085074) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4052 249355 0x2f1430407e44dc68dd3733da5b578a97c8ae32ee1b361b3f271488cbd46aca67
3.040 416.122869475 (51.851851851739774) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4052 249355 0xaab32b9e09657cc6ce8399ceb6efdc869aff99ddbf600621436eb6d68a0807ab
3.174 415.292284906 (-0.19980019960506532) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4395 240918 0x0f45daa3197289b957c9f9709cefb639ed29fec6102c633968c4b23bcde86753
3.502 705.996884340 (51.85185185182542) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4395 240918 0x95368cd6c787501ba36cbee9ced8498d0d0704787259d00d46e07db96762aea1 6111797 6111797 / 0
3.743 111.922300000 (-145.26486130029437) 0x29BAf3e6Aca05316613F91f56E3f3B6C76bC3846/3572 300000 0x0c57ea6b0d4a835737a9ba960e158891c8da1ec6e4c8a9da144e697e42d74b54
4.163 705.724934202 (145.2466563484518) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4395 240975 0xc97caf514ab25248d2d35e6ecc426c0737c836f5852e7cbc2bee4eb378617c86
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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.