× 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 #1099 - 2018-10-19 16:35:34 to 16:36:33; Start Time 1539966934.6337168

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 9.000000000 () 0xA045E11fB1551D7196B5B0526431688EfD49CFEA/8090 1150000 0xb57f556fad359b2860084b15306099b11d6ff79e506f2985fb0c4c03ffbd02f3
49.679 134.000000000 (174.82517482517483) 0xa8660c8ffD6D578F657B72c0c811284aef0B735e/52434 121000 0x3773668442556566abd14bcdd90ed111a000036532676782b728561cb723a775
52.441 41.000000000 (-106.28571428571429) 0x274F3c32C90517975e29Dfc209a23f315c1e5Fc7/95652 58300 0x6dfcef6bb335124f055a9cc7240ceb2168cbf0211c0328f658886c0f7fcf53dd
52.924 10.350000000 (-119.37682570593964) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/20336 22000 0x721581e85ac24a7c2a49e7b0bd06df0dd15c1a9cf13c72d1e934eab72850525d
52.955 10.350000000 (0.0) 0xA045E11fB1551D7196B5B0526431688EfD49CFEA/8090 22000 0x17ab87c32a27e0302fb57e75fc5d3537c416b2d6ca393b1df4e2a13f6bfbd831
53.126 10.350000000 (0.0) 0xC9D81352fBdb0294b091e51d774A0652ef776D99/18248 22000 0x0ef6c1156e30b440c1d27bd5fbd40e82d7e9cd97a20431e1a1232ffac26b845e
54.287 9.000000000 (-13.953488372093023) 0x9D22a13c36E10B9630dDa6A3A3C0aF2076AB88D5/3673 850000 0xd8a3452ad8fceb10d69b314d516de934c5966ea1d11c44a6b0d7091df61fdff9
56.694 10.000000000 (10.526315789473683) 0x44E05b25115Fb86F0e9861ceA0Bf27E50f2338BF/13922 504183 0x5dbb86f65b46b72df434396bf03651969831189cd326dc0c90fcf25c473516c2
56.794 9.999999999 (-1.00000000005e-08) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/23913 600000 0x50a944ca11031945fe8f84962bf28c3436d1e784059dc25632c4731231204486
57.301 9.000000000 (-10.526315779501386) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/7938 747381 0xd122d8384ae8c70b17d29692ec495664925a6281a7b2f793874b04541581c540
58.437 9.000000000 (0.0) 0x3bd189a9b1c11684b08f6464866dFa431362A06e/1967 1300000 0x811d0cf318d9ec1504e86329584994e7224aea4ac8739b45f9362df08163c869
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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.