× 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 #149 - 2018-07-30 23:20:17 to 23:20:26; Start Time 1532992817.0896108

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 114.608986255 () 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4282 255181 0x2acf7e3c96aa0eb7eec3d15d7e0690e6e999904b5f04549341db3fa2c40e36ac
4.949 114.701635297 (0.08080658573330873) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4282 255050 0xd6ef987079107faa53478d064835868bdecd582e6edbb98f8e32204e6d3c2460
5.019 114.931038567 (0.19980019928336865) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3984 248705 0xd1d786ea8da6bcd4965ca2f48590411b17cb274a0993574e3827d2878f47b4fd
6.122 194.992780004 (51.66543301263486) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4282 255050 0x94e3010c27b76ab4caf6a10d8aa522ef2161e816ffd99dfe77f52062ca42ef8f
6.671 195.382765564 (0.19980019979610528) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3984 248705 0xf4c7d821832f93ee76687c3dda4d5cdf7579159a2895a4b9901ab7e0869d2008
7.226 331.487726006 (51.66543301236186) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4282 255050 0xef85364e1bedcfa3a35b2126a0a56ac85306cc33a0098193538d82291019bc5d
7.946 332.150701458 (0.19980019979658697) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3984 248705 0x6ec6625c775331e6367884402c1246c7a72bd33601e7f0c489a2139166ac68e7
8.520 563.073949469 (51.59001101497379) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4282 255181 0xc7621c05ad169a15f490a2ee33389fb736729f520071b8e165191655dcfcbc7f
9.083 564.200097367 (0.1998001996339469) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3984 248705 0x2ec5f4396c3d5dd3d9a50cda5f355f8fcee90a3ab653b7af4822ddbf4815d30d
9.636 957.999528157 (51.74083926796907) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4282 255050 0xe0d398a234fb451d115985ef12016c6884ebb7e21621c894fe5a16082e5fc932 6059895 6059895 / 0
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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.