× 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 #1104 - 2018-10-20 00:54:21 to 00:54:37; Start Time 1539996861.3086908

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 50.000000006 () 0x498B859D2e59958E209d7DD262324C8D31b12B12/74 500000 0x0dbef5e07a32ede7a08d6383b2632a83d57f7beae7289111a0fac901d051f5d7
0.637 100.000000013 (66.66666666755555) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/687 101635 0x8e5b4d05e7af15e94c1d6502fad11568b1fbfda014ba6361c2a62f1e84e7665d
1.488 100.000000018 (4.999999999225e-09) 0x498B859D2e59958E209d7DD262324C8D31b12B12/74 500000 0xe95b990ffa4c0379970b12a19cfb1854eed338db617d93354375ee0d9b1a1c63
3.286 200.000000037 (66.6666666671111) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/687 101635 0x4d215ea88b383722a57578023dcd939516d2ba54439677151db1449d52dfa97f
4.725 6.500000000 (-187.40920097077898) 0xBE708D227F6DfA0B8f2698bf543B949Dfe4e28fB/15494 504344 0x1e1ccc7f09db812ecfafabd2bfa14ec224a28f066c2d8606f64d26d1521a1b6b
5.568 200.000000042 (187.40920097108383) 0x498B859D2e59958E209d7DD262324C8D31b12B12/74 500000 0xe1b0ec4ce11a0f462ee65a763bf0ca288db11d9e46d3184d7317da44d1e349de
8.531 400.000000085 (66.66666666688889) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/687 101635 0x9b5da0253c12908633c9e79784da8adba7d3c8694997efd821263a37b8fb0424
10.782 400.000000090 (1.2499999997265627e-09) 0x498B859D2e59958E209d7DD262324C8D31b12B12/74 500000 0xb56692d77148d07c5535fd521f2c080e30c28a30538c339ceb46b8a3653ad5f0
13.987 800.000000181 (66.66666666677777) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/687 101635 0x62367f0dd7bd2e0973b5d3fd76a90fb202bc48adcca67e404edec0b0a3a186ea
15.161 1600.000000373 (66.66666666727778) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/687 101635 0x430bba4085b105b68678d5395963b627a0bc88680df932c961aa0a0a2f1ffd5e
16.613 3200.000000757 (66.66666666697222) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/687 101635 0xa99e3a98070149db57764bc0e6d54cca097cc62acb526564e616f1b1021b4ae3
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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.