× 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 #1009 - 2018-10-14 11:18:42 to 11:18:50; Start Time 1539515922.8799076

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 120.800000037 () 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/672 58444 0xce9e1d091935254da0a6f7f22ceacf0d3a8cfeb4a63bc65090fbfb90768393c1
0.374 120.800000042 (4.139072846328698e-09) 0x498B859D2e59958E209d7DD262324C8D31b12B12/52 72202 0xc4a1bf2bc150128d7fe50e0ca9860b81614ed39bf216ad1b31b8c653290cbc97
1.381 241.600000085 (66.66666666703458) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/672 58444 0xcf77dffe9981210a3f899233b36f5a56e8f0d283ce27769783d3f1d106d21518
1.733 15.100000001 (-176.47058824161874) 0x498B859D2e59958E209d7DD262324C8D31b12B12/52 72202 0xd1c60410cd95616c7023ad616e40aa93a6e75a6c16cf58e03b9e67e95c6ab2d8
1.826 241.600000090 (176.47058824207704) 0x498B859D2e59958E209d7DD262324C8D31b12B12/52 72202 0x1427f55195156055fc01814da1524ac379b61612b4fdd2caa1a5c7bfa8928420
2.506 30.200000006 (-155.55555556242336) 0x498B859D2e59958E209d7DD262324C8D31b12B12/52 72202 0xcd67fbb8a93417dfe1ffa775ed0f0c5f21f3086591034f9e46fdc7188a632841
2.643 483.200000181 (176.4705882391897) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/672 58444 0xdda8c13c992cffca1d0dca790af25c57dd8187d965c269eb8d68b18c65ea7141
3.016 483.200000186 (1.0347682115275665e-09) 0x498B859D2e59958E209d7DD262324C8D31b12B12/52 72202 0x2640ba3ea7524b8eec500f90a880484a66cce4c61811dfa0d722d88bb5b81e1d
3.117 102.000000000 (-130.2802460918795) 0x0fD081e3Bb178dc45c0cb23202069ddA57064258/168485 105000 0xaf3918fd45d6e10e44bd30c657ba8fdaf202d5f636d6669d8f243f97d8180115
3.932 966.400000373 (161.812055423291) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/672 58444 0x90fab889f5c5f4ce12fde59c9bc160ca06d16b4f6933496ce857d5aba43c0f56
4.156 16.987651001 (-193.09015077532663) 0x498B859D2e59958E209d7DD262324C8D31b12B12/52 72202 0xb3e9192380e074ba5a6e544669f5a2f7a586e9244d878a1dcf0e2b95b5e93c1c
4.210 966.400000378 (193.09015077536176) 0x498B859D2e59958E209d7DD262324C8D31b12B12/52 72202 0xad02c91d3cc2f111c9eabfed9c410e27cf732543e605a05138ed444ed59653e6
5.163 60.400000013 (-176.4705882391897) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/672 58444 0xdbfc886a16c93a6eb93913d8b71834d207bcd76a0a558218de66fa4a61e6f2aa
6.497 60.400000018 (8.27814569323988e-09) 0x498B859D2e59958E209d7DD262324C8D31b12B12/52 72202 0x392cd2e91d5d63a0c61117f469c5d1c959f341b40b8175685281cffbf23cde0a
7.138 30.200000001 (-66.66666669021339) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/672 58444 0x9a3e7e82b3b594846a87f4c07d400e1ead9014d2876acad0c07bdf708bb1d554
7.819 102.000000000 (108.62329803094839) 0x0fD081e3Bb178dc45c0cb23202069ddA57064258/168486 105000 0xdb1a7c12f1896a0c04660223acb280d7fa18485298e5c23ac36e7a7e84c54c6f
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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.