× 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 #248 - 2018-08-15 02:50:31 to 02:50:34; Start Time 1534301431.3986735

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 53.040000000 () 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4455 240085 0x0985c3fd2268980167bb91d6e0451f40e8768b52a6a077ffe8e17473bf42e98b
0.365 6.000000001 (-159.34959348984842) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/17330 469319 0x988dca933abe1beab56b1640903f65989b972ff0984fa7b46cf33dcdb3f21284
0.607 53.040000001 (159.34959349053693) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11994 431267 0xfa437d9ce946ac9af0e0d4b5b14c6b21409fdedac93715b7cf9dcd99426131fc
1.012 90.168000000 (51.85185185009321) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4455 240038 0xe9d3b2f504541954000f947628eb7a7d217fe4425095de162dcadd01b2b4e549
1.356 99.184800002 (9.523809525821388) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11994 431267 0x4cadacff44d371bc2acfbfd6fb32aa267d241cc4e7142cad4fa8fddc58c5e407
1.864 153.285600000 (42.85714285521901) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4455 240038 0x8539cf957c89e379c2a5959cae7160586435c2137df8e77eb44e0cfe96023769
2.140 168.614160002 (9.523809524992974) 0x67d1f31eeEF182cFB40c0719A8c0000fA7DDCAA9/11994 431267 0x1423abf89216b394389fd81e5d8d920a057ae94d43198c3dd9ecb333b7f05776
2.358 50.000000000 (-108.51461771818884) 0xC533a4e3350deecaA0bf0fcba92D4a6138013b18/17330 469319 0xbce6464863b4c9ce815d7d992c3122c6cf661c53ed0e744fe4a80d03a8f1cacc
2.614 260.585520000 (135.60549764200212) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4455 240286 0x8af7e6f4b5d330ba239092c12d5d94ad1ed0f70d43c9b52325e0ea347cd3e194 6149472 6149472 / 44
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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.