× 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 #674 - 2018-09-25 07:44:28 to 07:44:37; Start Time 1537861468.4713442

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 10.000000000 () 0x398d0DC35A7A9ed1BD6A3F870618432678b4857A/37 250000 0x74f776991ed6646eaa186db5cf0b1d5f83ef857a8cb4361cb8d790ca102de142
0.334 21.841000000 (74.37580478000064) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/776 192021 0x4e9ac612ed7851a611d1f0e76a82ad1b2ba6315efbb4553da27348024f69980a
0.700 9.900000001 (-75.24022556708232) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/17285 359347 0x0528711a4bd408bbf61bc9acc8c378cd679dc5800473b56573f89c221f734e39
1.392 120.000000000 (169.51501154449952) 0x10E9d62C705181c0964d7FeCC29F06FF0d7596E9/2841 21000 0xdf156a46b3c8c6454c8fe6c0eac63f172ec77dcfb4d7acab82bbfec7539388a4
1.699 21.841000001 (-138.4070896261419) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/17285 359347 0x7c1056c202bd6bcd32eac5c46b0d5e0f24132809decacfb2470a1debdd98eee8
1.763 27.888772900 (24.322543805054806) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/776 227534 0xa085cde5cf9a234d6522cdace23ae8424532a22f8c658af6e168315f386b032e
1.774 25.117150001 (-10.457785648508013) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/17285 359347 0xac75cccc647a5528a595dffdea425d30b5e9372146932c1590a7b7bae69a18f9
1.880 24.680330000 (-1.7543859688933177) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/776 227534 0x57f5d98a8d077bb1a743a37ead2d80bf4cdce57811a906931274ef246d717c41
2.132 31.514313377 (24.322543809565637) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/776 227534 0xcaec3c36f9f06c5f3c36bc637b7e0b64583a000d9f01b8e84f6f3ff640abac64 6395628 6395628 / 352
2.322 28.884722501 (-8.707393546186763) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/17285 359347 0xf3987c2d3d7cdc6a5578e065b25e6bc3233b1a9d30ec981ea7dba4cc46f4508f
2.415 33.217430876 (13.953488371643651) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/17285 22000 0x25915d11c73e51eae06950db46fe923a005a3df374f77f12014047c0b68da883
9.090 38.200045507 (13.953488371051002) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/17285 22000 0xe021ceb89c4ad3200f60015b0616a925960e839306c59838755b7ddb74b2cd3a
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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.