× 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 #1 - 2018-06-27 19:42:46 to 19:42:49; Start Time 1530128566.9849615

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 96.908832405 () 0x7E77777772d74Cba60638F3C19C60A3387172a06/3808 168860 0xde5ec888d8a9a868ba0a3e3e02976f657f1b97a9c6dab7fb004f292eb736ff1b
0.266 123.582308851 (24.194601464764435) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3808 198538 0xced54f5584cb7608c7f485db9d723ad0f07766ee9cc648bcb84c73ffcf2e891d
1.627 139.648009001 (12.206572769503596) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3808 198538 0x03972a9e94cbd7efa08887adc4bfaf671da5b7b3c7116b360a5f972dc301d1c1
1.646 130.265327918 (-6.952365666773782) 0xDd5e4dbcff073A5f12ADb90e156db5Ea96557982/1367 223104 0x6230183e86549df30436d234e02d4e66e05bfa3361eec0e532876aab7363b7f5
1.847 157.802250171 (19.118376622371798) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3808 198538 0x527a44b8e76670fc5a9715237dbe3011de21afc7b534cba0dd192164826d28dc
1.937 210.089925046 (28.425543350661524) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3808 198538 0xdd297d77ce337f8a7cb27c059a482f893505f9b0c31e5e1bd2782d5d94b41977
2.353 145.233929361 (-36.50528658889968) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4049 208209 0xa77bbebb6d07ed1a9a828f42bf60a62592e3456aa9704779f8dbabe0dc9a4d08
2.382 100.785185701 (-36.13438219936556) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4049 223174 0xe06d867cd7e554b395ac366fe467f470dd81377d9e0b262816199c4b80a7a51c
2.542 171.334815691 (51.851851851470755) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4049 223174 0xb6899408c3ba37b5e9c576726a4d8c4641f0e350e6b749b1b5a1cfedf75e47b3
3.006 5.000000000 (-188.65794033831244) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/5127 170386 0xf1e7a420cc0ca9df6fde6154f634cf344d2b70bd4b7912abda04371b35b0fbe1
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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.