× 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.

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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 #1582 - 2018-11-26 08:56:40 to 08:56:47; Start Time 1543222600.8840427

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 11.000000000 () 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/303 500000 0x9da83dad71e9f91047d6f8910d51533856e3ef29f9481a9c9084d0443ea63dd5
0.463 102.000000000 (161.06194690265488) 0x0fD081e3Bb178dc45c0cb23202069ddA57064258/181999 105000 0xc4607157cc82a128bb8f5b5a9bc31022625410b05f3538182003570159a39073
0.606 12.429999999 (-156.54985581015947) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/303 500000 0x3dfdcd9439a9fb0a8c0cbaebf712b49c55cbb6817155c22f74d367ca64e83018
1.053 38.139974464 (101.68078880012466) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/303 500000 0x8e8acd6e50f373a6073ae8ce1c43a7f638b57deaa4a8351069f133a62c255918
2.337 43.098171144 (12.206572769213327) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/303 500000 0xf12988c9d161aec60019684f667c8b455b8ab7764d8daa703f8dfaea2409df48
3.224 110.100000000 (87.47079466506298) 0xEcC996953e976A305ee585A9C7BBbcc85D1C467b/6489 150000 0x365c8c43d9c0c62a8c65df0942312edea35dd716edb3d6cf47e3e78b2868b44f
3.542 14.045900000 (-154.74389407946617) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/302 500000 0xbd279534b3798cde85ce8055d5cfd80d0e1cbe457a9d602ce8c0c0d0faed4e8f
3.878 48.700933392 (110.45986392810828) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/303 42000 0x71cd6a1d7d00c69514bce9ac7926db27c0edc7cf29d6584568d66fd03b651b66
4.788 10.000000000 (-131.85798301896958) 0x77521132FAD812cedB7dd5b4f34835E71821B4EB/531 150000 0xf1fca3b78158b1da7b5f0505448bb79c1bdf27b6af4f6de14b44c00a8efdb70a
5.424 43.098512145 (124.66832236133271) 0xC81064916D8c164BDC747C06b47Bc8D3Bd133152/302 500000 0xc572b79940a0b010f4d8f46014e03fe523576768778c7c44aee7de98618564da
6.857 12.000000000 (-112.8833100362478) 0x5aB9d116a53EF41063E3EaE26A7eBe736720E9bA/7164 250000 0xaa1b05f5a5a7e45d873c9039bedbf0591f08c12b8fa09f531eb01b5861bf7914
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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.