× 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.
*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 #21 - 2018-07-01 12:44:27 to 12:44:31; Start Time 1530449067.0707684

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 36.185002452 () 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/511 171410 0x6ff2087aa16768dc884c391f40d0b9029e167f43dfe8833189e80dd443b5652c
0.065 41.950545238 (14.757797075601456) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/511 201804 0x36604a9798d02294738ed68208a8564009912e49439f6dcb32b3ddc9df06bb38
0.164 36.185002453 (-14.757797072852927) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11320 362773 0xfde7ee4df5e19d4e3c39976ba2e8375c759c1ba26193d7385e51fbaf752d0a43
0.294 101.000000001 (94.49283287323387) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11320 377773 0x07e123c0d1e89a405d0ccdf791e01c7c7da87334f416216e3d27bf1b02e5466d
0.320 47.404116118 (-72.22964602952571) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/511 201804 0x9df00258219bff4d7ef2df1c74d2706f60b15a9fc763f3b533bda35f98614764
1.209 101.202000000 (72.40332401834934) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/511 201804 0xf612eb68dc45e545ae27ccfd80315952bac4c9c00a2f9c84aac8b81f8c6fa23e
2.309 122.210000003 (18.806509948183535) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11320 377773 0x36d4c46e19c6bfac6411a459ac22c2ec7523e0df39a0661b9010082ca1e482d6
3.435 114.358260000 (-6.638033354855322) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/511 201804 0x5c9ad73a44161bb97e4cf80393f41637223cf30b0ba39c6c584827b2247020f4
3.567 134.969261728 (16.533274453740614) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/511 201804 0xfab435cee10255fc115c9af9c799cd1a20d65a4046cc72da429294a660605a59
3.571 134.699862004 (-0.19980019979427252) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11320 362773 0x6f7f50b0c76da138d11ebf7fcc75d314ad6b03a75511df80300d888df80e07e5
3.758 163.312806692 (19.20250223804264) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/511 201804 0xe56ea9ea03e66cd83511cccca743451734174d810d36c5d9ee113b9d6fda1361
3.761 162.986833026 (-0.19980019976835908) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11320 362773 0x1d6e57fb77fdae6a0c50113fa5de3b21f9e840b16ef7571e0d3929a2d4917b8f
4.035 197.608496097 (19.202502237177043) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/511 201804 0x67d444e91a8eca967e956d617eaf0744e70cf933bc734e976ed682ff21f15d27
4.041 197.214067962 (-0.19980019933260904) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11320 362773 0xb0445bb1769b95d12344ace0b5062166cfcd60c16bc686cd99c39ef6ee17313a
4.166 239.106280279 (19.202502237581175) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/511 201804 0x53ea02dd7d75763608e3d370198167afd6eaeaca1fddf57f772bec1b0b94e5d7
4.169 238.629022235 (-0.19980019960363468) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11320 362773 0x0f35fe5f1f7303ceb93073f6d643763c9827afb316a5a301b54b41ebe34debfc
4.331 289.318599138 (19.202502237314686) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/511 201804 0xbba4f7324b077d68378a77e0c1bc34d259bde7cd175a05a9c5fe46adf53ed3f4
4.337 288.741116905 (-0.19980019952023192) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11320 362773 0xf7200f11f34df8f0fd1802c8aa801f4972e76a859ff9c19011d69da6e464411a
4.510 350.075504958 (19.20250223738033) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/511 201804 0x01dbc6cec8283d372e14a8f4f56b6951e408342cb22e4072545cacc15ccd42af
4.517 349.376751456 (-0.19980019953968484) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11320 362773 0x7e56918923e03ea7f81068b93ae3bae1311ab54f5e9beeaa9aee310d70384262
4.821 395.585320602 (12.405616575444226) 0x6BEcAb24Ed88Ec13D0A18f20e7dC5E4d5b146542/511 201804 0xf3b4b564e5840d785a503c00cf6a192f602e4c08e54be229d8f397670ab542cc
4.829 384.314426602 (-2.890344314229364) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11320 362773 0x8f608d65a4049246a2e8b32fde6596554491d7ce7912ecf962e04cb371a6b933
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This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. .
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.