× 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 #1160 - 2018-10-23 02:12:02 to 02:12:06; Start Time 1540260722.7478836

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 295.378808912 () 0xf666666666E8025c503209ac580a5aA4b39A090c/479 184212 0xcef521ce2d94b0df7a575652088d593c4d49c4b45b0e0f1a498c33ad91c8fb05
0.190 300.452872431 (1.7031868824306555) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4897 210914 0x9d071d04a384c250b1cdc86cc2ee7ef49255b83a9319a640f2176cdc9fde1029
0.494 336.731842159 (11.387269310546442) 0xf666666666E8025c503209ac580a5aA4b39A090c/479 214278 0x32d9bfbfd374292efe2e0fe3ab2f3d3eb324884c122ed61d2b4a12e7e12f1164
0.770 511.791422899 (41.26217581742652) 0xf666666666E8025c503209ac580a5aA4b39A090c/479 214278 0xe3653b3bd8d29d67b027e00f8a69af44dd297741af51bac3432f942a920c0f50
0.835 868.308801324 (51.66543301240607) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4897 210914 0x8ee9c91077a613737ceba917e700f8ea89884ea7f37954f1a5fab3ed2352ff5b 6565994 6565994 / 14
0.837 583.442222104 (-39.24455014983884) 0xf666666666E8025c503209ac580a5aA4b39A090c/479 214278 0xa92502f10f815b8e5edefa30f3975afa6d1c38684f60084efde73ae59b430201
0.911 522.229734155 (-11.072450124557772) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4897 210943 0xc92df473560914ce6381019b5b68b5950f00830cd9c5e353bca590721bbca1ff
1.099 510.769883132 (-2.2187522301503604) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4897 210914 0x8256ba9cfc9788402f49cd6954522573d3c5471527d904dee6a673f62465a4b6
1.613 887.790548063 (53.91553436255231) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4897 210943 0x22d7a2530531f7d053a8e69e2bb406114f8b77ed77eb39e6e1759850afb4be7e
2.218 522.229734155 (-51.85185185179931) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4897 210933 0xdaf45c160cf12bc3c0b6631feb4a78439a77487b41f1b3da0f22cc92e0bbdf78
2.847 307.193961268 (-51.85185185174468) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4897 210933 0xf93da95e066b1707a05b0ac6ca5482650d981b62afe6b35b3346550c869a264b
3.410 887.790548063 (97.17223650372524) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4897 210933 0x7708e8d31883265895fb35a7c08780b3bc99b2decbec8be923d7ce6385f1d51b
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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.