× 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 #265 - 2018-08-17 05:42:58 to 05:43:05; Start Time 1534484578.9431

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 270.631986016 () 0x7E77777772d74Cba60638F3C19C60A3387172a06/4110 198284 0x043cab58f933c13e394fa13842a65452e667980f47625761bf22e731706efaf4 6162050 6162050 / 48
1.093 305.814144198 (12.20657276992699) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4110 218211 0xa01007c0a05ffd5b12fe8c85e28dbc36d56d2f994a478bdee6dede2581aa9293
1.376 281.457265456 (-8.29493087577692) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4475 225031 0x46712661132b8af07a02c69f69603cf9ac2eaf80005dc7a9bb22fb40be40d2e9
1.849 479.434305977 (52.0381741509231) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4110 218211 0x5aa0c675bba7f4ce68fb04a0c062388cc47f303b0706ea1ebd4eaeacb17ca570
2.053 478.477351275 (-0.19980019968548138) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4475 225031 0xf4e837b255f27b3d9f26a7a81f939b774714b0f7970763919387fde9320b4137
2.594 813.411497167 (51.851851851794514) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4475 225031 0xba976897ecef6cac5ce0276f2d4e2b05105cf6cb4836b6728b910412d5081962
2.605 815.038320161 (0.19980019975922017) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4110 218211 0xecdf7f9a336011301ed831b52594db3218c9967582e56f50bda4b3f7a4de87c9
3.963 1382.799545183 (51.66543301256078) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4475 225031 0x2a874112b53e536c7c89643ca1e7ad86ddf60f1aee52b9f8376be1d366b12960
3.963 1385.565144273 (0.1998001997737846) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4110 218211 0x415259f8546dabc3d0d353d719bd0bc3d82de13d8831f9169a296355e9cf69dd
5.857 2355.460745264 (51.8518518518479) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4110 218211 0x1b81b04469c92d3461b8bbceaff27e5a623ace952be274bc196616fb22611ab2
6.074 2350.759226811 (-0.1998001997737931) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4475 225031 0x6f3ef002d84fcd0bd2634c7bbba8ec8d623f03e423c93c7794b0f5bda51f8a80
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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.