× 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 #786 - 2018-10-06 01:52:32 to 01:52:33; Start Time 1538790752.1795368

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 343.127209650 () 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/151 200000 0x6e8e9aa22535102332c1a18e98a8a5b749904019e5e3bd1c6a8f7e9c1b1cfe1e
0.206 411.752651580 (18.181818181818183) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/151 200000 0x97a16f79fa42426752d84b3db7ee073d29239b973ad26827b08b61a0bdb0a9af
0.208 46.180800000 (-159.66156231595383) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/152 200000 0xad2fe47a46a28d4bfc083f4df2462bd231670228526a272d4c0c01d1f39cb864
0.232 55.416960000 (18.181818181818183) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/152 200000 0xa6186b5275b540bc75c8dc0bd4f4bb81b2bc2ffd2e9b35da46f2aed4fd81391b
0.236 66.500352000 (18.181818181818183) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/151 200000 0xc9f975e06672a940e4f2b358b5119b4eb4ed932a89ceed100594cd0d123f39c7
0.281 198.568987066 (99.6483678809159) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/151 200000 0xfcb097de4684632816a894f056c2e7d5fccd9e9c787f310453ad406defadaa8f
0.305 79.800422400 (-85.33162095205464) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/152 200000 0x431d64627fb1d55ed21231913157ae0a55f8ad96c511e159aab4848fd993e543
0.308 114.912608255 (36.06557376964987) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/151 200000 0xf1ec126c98e3a5db3fa3109681516f8513d40165d4f2eacff9a1d2974a4f5655
0.343 95.760506879 (-18.18181818199079) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/152 200000 0x2201684ef4951b1b0d837b587671ef309f1eec0a39ab4ad966525395a63ca199
0.746 285.939341375 (99.64836788169042) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/151 200000 0x4652537371f0dafb11d85735a3bd7a589465a516f25a857d6e7d3f19d07c7ad5
1.302 66.500352000 (-124.52569531747623) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/152 200000 0x544dcc76971c235e7f9990b1675b091f4c0456e9f29616ed4c1a5015bff1ce50
1.433 238.282784479 (112.7243681940625) 0x9CC7D6FCE44eff82fe561c9587C65346AE6130a1/151 200000 0xc6e30d98be7b290f030ec0f99c3b8505bf43f78f8c8f723fc4686aa0280e9a02
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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.