× 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 #443 - 2018-09-11 15:00:03 to 15:00:11; Start Time 1536678003.927093

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 6.200100100 () 0x3bd189a9b1c11684b08f6464866dFa431362A06e/666 160000 0xd743312055078cfd56474e3887182a699d001361d3eea0f495d356b15178959c
2.793 15.400000002 (85.18386357985592) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/12526 337984 0xd2dc4bfe57ad3ce4b48f6369606452d2dcf1666ddbab6642b578abe733b9920f
3.569 18.634000004 (19.00452489528039) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/12526 337984 0x1dc7ba6efc6d90338e54934efb6daf9c27b914637350a3bb5edc08cbbf58210b
4.483 79.200000002 (123.8138070492581) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/12526 337984 0xe10176da0b4a645e2477a5e6cb9c5f4bebc575b41e62b45c38ed3729b5692d83
5.506 95.832000004 (19.00452488851166) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/12526 337984 0x0c538be27cb98a4fbd11c6e30237eaf58c10b5be8179be1147285dd42a5f214a
5.624 14.000000000 (-149.0130380964013) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/13241 299807 0xb9d05c1db274613e199389330fd96b8b907a97e8ce6bcf7a90c7b7b0141a6a16
6.995 115.956720006 (156.90873084715088) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/12526 337984 0xe405d29cc069236dcd563c4e0d5f2e9c36beb67b1f2bbebfd7f6600b83d75b11
7.590 14.000000000 (-156.90873084715088) 0xA045E11fB1551D7196B5B0526431688EfD49CFEA/7083 299871 0x1ab45de2c618c150e0066d2be9662345916e15d4baaf68e821fa75e2f9af0940
7.630 16.099999999 (13.953488365912076) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/13241 22000 0xf147de60c360be99c91f401e5f7bdb894bc846fb2e416d704ed8e73005b872f4
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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.