× 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 #772 - 2018-10-05 12:51:40 to 12:51:42; Start Time 1538743900.2752287

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 60.000000000 () 0x274F3c32C90517975e29Dfc209a23f315c1e5Fc7/86247 58300 0x4c55f686cd246e944dd63af0224032c5a060383445c86e30fb0e99a4143f25dc
0.910 187.974137326 (103.21571330461643) 0xf666666666E8025c503209ac580a5aA4b39A090c/419 184148 0xda359d1014cd3e29e7163cadb380d431cc90cca9da7ce539c0622bddcef67076
1.128 385.512205085 (68.8902431149548) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4802 210492 0xfb5c99a4df9ef0d52019ffceb6c36c96b264af399e038fb4721c86676b3d6dd1
1.353 386.283229495 (0.19980019975619068) 0xf666666666E8025c503209ac580a5aA4b39A090c/419 213912 0xacac47efc38fa80aa2ef2519186b7e798bd6cb3aeb3e226fe8026c82af272833
1.519 654.924812027 (51.60190313922462) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4802 210572 0x1dbb1275978447b89a9758f1e1617aaf1cbb19936bfc08ebb36853b3fc8b5040
1.539 653.646118060 (-0.19543365019043887) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4802 210802 0xcbb9f6bb3ec1ad155af4eb3cdb07fd47f417c0daf0e5eb8cf82bd1b7a320ce50
1.614 655.370748644 (0.2635001317198429) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4802 210492 0xdaeb8ee85dc00452d52d205c9d81fd63027cce420a7add778f6e7baa6cac821c
1.739 384.497716506 (-52.09755679992216) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4802 210802 0x6e459b7ae889adc53eb236909e0786ab1e8884710175800fe78c563916e9cfb9
2.024 656.234661651 (52.22033076864781) 0xf666666666E8025c503209ac580a5aA4b39A090c/419 213912 0x2911ea42642f5e43069d8d543732b9739930490fbedbd087f300b12561340260
2.125 1113.372180445 (51.66543301251551) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4802 210572 0x7b29d4229447311e9221b8d23f69e8f11c9460c716bf0fcab63a9f5d73017e24 6458063 6458063 / 0
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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.