× 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 #1640 - 2018-11-29 20:24:34 to 20:24:53; Start Time 1543523074.4178808

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 22.000000112 () 0x498B859D2e59958E209d7DD262324C8D31b12B12/488 40358 0x543dad247c93e338d3e484982d83e58275255177d1243c0c77295a85d26bacb9
2.488 44.000000340 (66.6666669010101) 0x498B859D2e59958E209d7DD262324C8D31b12B12/488 40358 0xbd12329ed610dc450cc8c403c528ebd7ed4daa88383fff6c2b06f99ff0efb8df
4.726 88.000000796 (66.66666678383838) 0x498B859D2e59958E209d7DD262324C8D31b12B12/488 40358 0x73cab3781cb8f65e4e6d1ded9bb561818221cedb941dbef25c6426ad2e3387fc
8.305 176.000001708 (66.66666672525253) 0x498B859D2e59958E209d7DD262324C8D31b12B12/488 40358 0x0bb31e9176e50a8af885aba29b8080651adaec090c3a600480a732d2c3c4e271
11.584 352.000003527 (66.66666669469697) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/1027 40358 0xc11566a296e9d13ec34171181e62603681c03623e8f1006c9a03cce37896a652
11.886 352.000003532 (1.420454531211664e-09) 0x498B859D2e59958E209d7DD262324C8D31b12B12/488 40358 0x7b5ac438731bffe225a8fbc339dc52b87d8a8519c927a23953a04074e5709cca
13.905 704.000007175 (66.66666668068181) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/1027 40358 0x518d7eb95d0f81e29c0db7ed6c1f99213593301ec1174a2a17a4c61ca34fa7bf
14.274 704.000007180 (7.102272654862838e-10) 0x498B859D2e59958E209d7DD262324C8D31b12B12/488 40358 0xe33246be99e6652287feec09d16528c84e2a20d43338dfaeda9875a9aef59bf7
15.440 1408.000014471 (66.66666667367424) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/1027 40358 0x75e2a396cba8658bebe4489df3acc65cfc01f806d4a6e196e65c1bf693444e1f
15.797 1408.000014476 (3.5511363271325484e-10) 0x498B859D2e59958E209d7DD262324C8D31b12B12/488 40358 0x97e3a75d7cf6c5e7a85c39c751b6abad5ff663bce315f76ee0e1a0d3413892f0
16.626 2816.000029063 (66.66666667017044) 0x541fB7476aab8d6E19904461932Cc9C9cf0F8d7C/1027 40358 0xdd6620bcb37e3c78fe2cad1accfb1ca0e22544b0eda5a423a8ae1397b55145fb
17.034 2816.000029068 (1.7755681634915568e-10) 0x498B859D2e59958E209d7DD262324C8D31b12B12/488 40358 0xae74471a86ce71e41efa93bb1428f91c8678e5259b31efb8c4d7cb14ae89c451
18.600 22.000000000 (-196.89922483796101) 0x0005ABcBB9533Cf6F9370505ffeF25393E0D2852/54754 224838 0xd0c81e23c0e8607ea2c2e45c7f1d9843e515d1469fe2915f83d922d2d2b429db
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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.