× 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 #213 - 2018-08-10 17:11:24 to 17:11:26; Start Time 1533921084.917396

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 107.613513073 () 0x7E77777772d74Cba60638F3C19C60A3387172a06/4061 214340 0xedbd789fc0a22e6b41e2e18d25014add8aefd951af53ef9e3c59ef7c2f85c335
0.262 148.831200453 (32.14547635884183) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4408 240844 0xa411204ed0b399ea14ec5cbfc6291eac0a361f5a8a45d6ef84897490445bbc1f
0.739 148.890618127 (0.039914893898872275) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4408 240785 0x9e6e5d379ecb379c8bcc63508ea02f4f6c1e7c49177a11d8d7c76e6e6a225629
0.791 149.128862853 (0.15988533717095396) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4061 249657 0xc20425ab5c61ae066837de4c575c59d11f79262227e9461b2bf36617379f7f2c
1.035 253.519066851 (51.851851852182996) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4061 249657 0xebe5d06def9b50162ee8e284eedbb8b4e9df3c68ddafd447017f1a0f413ca6eb
1.156 253.047272430 (-0.18627152434551658) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4408 240824 0x55df446a11fe9c6f5493ed54e4259638ebfd765c112566070009e1f2039cb478
1.157 253.013040770 (-0.013528687829140757) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4408 240844 0xd274ea3b522788a9df71a4fd28c9ecafe8e28c70932486968fab30acfe61743d
1.311 430.982413647 (52.03817415093533) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4061 249657 0xbc0932d283be1d467d8142be17a3465d46eff5db1757716519bb4fa465710dcd
1.337 430.122169309 (-0.1998001996568066) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4408 240844 0x64df63b1ca6309feddb4ae465f64d1a5f5d56bfc93645ea912e6fb716e1ad0a1
1.473 430.180363131 (0.013528687829140757) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4408 240824 0x84e03bc18df6d8873d9f0279902137afc824627554631e7032fe74492aefffec
1.570 732.520679763 (52.006544327072305) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4408 240579 0x6cd1873551ef7a5aa0c2b47306416eaedcd4b63083ad0da22058f51e427af593 6123458 6123458 / 0
1.670 731.306617322 (-0.16587509242622123) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4408 240824 0x9bb30b4401228770b94f6e0366d7c462bd042f57f7781b3b65eb1fd86e634639
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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.