× 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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Global Market Overview

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 #788 - 2018-10-06 03:19:33 to 03:19:35; Start Time 1538795973.5188541

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 159.600844799 () 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/227 200000 0x0808f17b12337988c5c01b71ce64b3fa1db63dd6aca9cc8025bf6651d687e4c1
0.131 133.000704000 (-18.181818181196796) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/227 200000 0xa8d97660e20d23d2bdd284b13a639058102b63aa18419ff416c3c9dad245f962
0.294 25.784280000 (-135.04604944255937) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/228 200000 0x5d878e504ef3d710372043f5583f9484b199561a64429e1cdb2770dd3125283c
0.356 30.941136000 (18.181818181818183) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/228 200000 0x4f78879f5569bbbfde32b24e019ac53dfb61e9c7837dac30c723a4ace5d9e3ad
0.368 159.600844799 (135.0460494422185) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/228 200000 0xff1bb227ad5bb2e2e7b81800b9a74350779ef3e298fed6ba65723ed9466bdb7d
0.389 30.941136000 (-135.0460494422185) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/228 200000 0xb0d08ca69cdac7419a8f3675fc7c3243b302719f9dd4409a0e3a0d54d268f888
0.508 73.889280000 (81.93832599118943) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/227 200000 0xc2d0da927d00a2eb316cb6ec4449ae6e2bc782160dcf1c87f8b98dc800b20b8c
0.520 106.400563200 (36.0655737704918) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/228 200000 0x9806b81bf3b4f7bbb0472eb60ceb076e30f12b9f267b5817fd71807838e80c6d
0.621 40.639104000 (-89.44723618090453) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/228 200000 0x90975219ea806e38bbb4d0e51a85d6cd7748a700eb104b770876d76a9bb9e4e0
0.682 88.667136000 (74.28571428571429) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/227 200000 0x907dd8503848cb5a5eb17984dadae13fc4367e37660f0f917a465a112f31ec47
0.800 25.784280000 (-109.88567585743108) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/227 200000 0x295832728dec57c2be5c98b8c6b6bf9ca9221c8962fe33ba62fc0fb2b535a6c1
0.904 159.600844799 (144.36602175507647) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/228 200000 0xa0c188a71cfe177c59afbeb381e0abc9442eef6d1f924d1a3a35dc74f275efed
1.511 88.667136000 (-57.142857142281734) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/228 200000 0xaab0b67431f8af446d80f13886b0c5931192030dd563d7fd57f8409f663f5ca5
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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.