× 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 #1558 - 2018-11-25 09:44:59 to 09:45:12; Start Time 1543139099.692488

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 22.000000000 () 0x0016BD4cb70Bd98CA07A341DA66450b5d22A55aa/57359 196465 0xe09465143fbe63055948191a178feb73e93cca582dde7a981d48c50e20fcc6a7
0.363 20.000000000 (-9.523809523809524) 0x10E9d62C705181c0964d7FeCC29F06FF0d7596E9/5885 21000 0xfcab146613e6bc18cc3237c8d8ed1da1efcadd62648a3185f7aa055311c8b81b
0.889 12.000000000 (-50.0) 0x5204B0980Cf54a17ad0C19D45B028295659Aa505/4975 250000 0xe5d5f334c08c507aba44a8ca03f69ce5812472111f21be6de0dc024e675cffeb
1.888 20.000300000 (50.00140623681653) 0xe7d97A788E6a0a0B4F27bEBEfBD0F95623e98E2F/400 2000000 0x7cfeee49d95dbe1429732cc118487ad9ba25fe3734061f7af57e2b71456cb282
3.868 80.500000001 (120.39705354192579) 0xDF4E525428eF1cd68a438f1d0E6A64f9163089F8/1381 1430000 0xd18cc4895ca13f230e1a1be111456f30b8d2391a6ef58e7e283ba49c11dbfdc1
3.871 80.500000001 (0.0) 0xDF4E525428eF1cd68a438f1d0E6A64f9163089F8/1382 1430000 0x29ead2b2e84e1b8962474ddaa291c382cbc9d9b4b09288f660c27749ce52667a
4.014 20.000300000 (-120.39705354192579) 0xC3Cd47aAbbab45EC41D9D46E159c2c0f55c47Bb7/96 2000000 0x5a20bc8fc07cd2709f857d13ca5f9d333996447952e3a12d461158b8e6ecfbbb
4.994 80.500300001 (120.39729116124285) 0xC3Cd47aAbbab45EC41D9D46E159c2c0f55c47Bb7/96 2000000 0x68ab186e67251a4b9a01fa1fd0dc19c1736c8f34cc61306382ec386ec7958528
4.995 80.500300001 (0.0) 0xe7d97A788E6a0a0B4F27bEBEfBD0F95623e98E2F/400 2000000 0xad1cb993014d8047b9a145fc7e544c463820a3d4949df3c643dc3ba4367503cb
12.757 256.000300000 (104.30887790302808) 0xC3Cd47aAbbab45EC41D9D46E159c2c0f55c47Bb7/96 2000000 0x3c084220cf20b57ded61075e4eb9fce06c78feed0a4cf1e508f1bb992ec0e738
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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.