× 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 #250 - 2018-08-15 11:17:25 to 11:17:27; Start Time 1534331845.793679

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 514.181865133 () 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4458 225745 0x66dbf32fee992267a2b23e559de737b26182c2479e7bb2c9bc3c59f39753bfad
0.079 4294.498355774 (157.22885768968712) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4458 225745 0xa23759cf0368817a360b55dac1eb6919f34cf4556133c28c63e7324d0872fed3 6151565 6151565 / 0
0.191 558.678372692 (-153.95359337193656) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4095 214044 0x337aa54b8a0282de7cf0468e5e21be85e2db5e8c2509d3fcc7dd16cddbac873c
0.198 2531.227854403 (127.67698025357281) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4095 214044 0x98fbdea09053122fa515aaafb5aaf1b8f79b5222f32fb39c6051c091e4e0ac65
0.464 874.109170726 (-97.32479760144889) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4458 225745 0xf414a5abcb31d5b265ff733883f3a7303b7958b946c4a73f5d4f24217f7e02fe
0.466 875.857389067 (0.19980019974859328) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4095 214044 0xc16fcd9e11bf384703d1beed1f14a60ffa9ec2913a03882e749fa01853f62d3d
0.884 1488.957561414 (51.85185185185812) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4095 214044 0xe18aad633da99db4162df47faedffdb3e1534c1e3b7a65a3b0b3125c037ed0fb
1.083 1485.985590234 (-0.19980019976876842) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4458 225745 0x2758dcc250ddcacd9beee6821dfb82f32dc644d87156b783bef7ae4396025b21
1.311 2526.175503397 (51.85185185182232) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4458 225745 0xc5bef57455f230c845e51e64b78d4850ab34d0086c0ecc872d8decbf87794ac9
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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.