× 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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Global Market Overview

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 #143 - 2018-07-30 03:36:34 to 03:36:42; Start Time 1532921794.0586028

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 82.145624660 () 0x7E77777772d74Cba60638F3C19C60A3387172a06/3979 199212 0x96c4a27d43b374d6df6d11f162887b51c4324b0eb6dc8ee2a5407cd27b8101be
0.562 115.990141416 (34.162955458549646) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4275 226082 0xc49dcb88015d1dea9af0284f7ac52467f13ba4ae7cf2bfaf9f62d81bb6b30ca9
1.128 116.222121698 (0.1998001990843299) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3979 219227 0xee33d580c286f2e8bb33ec786ecc16897cfe93f776921cdaac7c9cc754eceb8d
1.696 131.330997518 (12.206572769391688) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3979 219227 0xb3f490465436b1fec7c7de707c6a9b25d205a5914d0684c1c01dcc81849e8c7a
1.701 197.306295532 (40.150828533000535) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4275 225999 0x9f2ddcf185d3069289305f8aa6bed7e9e208dac2f66b8483553ca9bc841bda6d
2.776 197.700908123 (0.1998001997678277) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3979 219227 0xc035ad55bf4c8dc6459da8a4e58fceb7e2d18fa1a3b5240bfa82905159dc2865
3.344 335.100716608 (51.57634740861392) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4275 226126 0x530de4497105e76c97373fd1fa5e5d86dc8b2ac7bcd027612583b7bb89d11727
3.901 335.881931708 (0.2328570200617426) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3979 219227 0x2a18afabd2777cfb04bc03421dd800b869b76ea98e4667eee1aed5ac77fe0239
4.484 570.215194086 (51.723652069342364) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4275 225999 0x05b5636c84a7289889f3cf91d822b34107ea0284def434e9eb4ae3a5c04f8b6f
5.039 571.355624474 (0.19980019977009597) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3979 219227 0x36b617d4a3022856e2346a8c3efe9b37db8e2f1142d7ad0d8a7acf43c04fc388
5.674 969.365829946 (51.66543301259211) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4275 225999 0x19477a8815459d8384352d914ffc5ad7979eb5f2e414ecdd817b66fe26923145 6054992 6054992 / 0
8.215 60.342879930 (-176.55924268630625) 0xe0Fca959877F8E328DA4694C7833EA4831b828Ab/1601 550000 0xa0ace5347f3ea9b33c008e0fa145f9e36e0b59d630c2816ae888488bfdf511ea 6054992 6054992 / 12
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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.