× 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 #244 - 2018-08-14 22:38:09 to 22:38:17; Start Time 1534286289.6220853

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 114.642292869 () 0x7E77777772d74Cba60638F3C19C60A3387172a06/4090 169532 0x7c56b87668f38212b23f3e82b2c1584ccd6800efd75cdb1ba59703b3c2683477 6148441 6148441 / 0
0.316 585.767088254 (134.52840812315304) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4451 210997 0x9c86add6c2df735f28c091caa7ac4d8716a0fc380d4a5a7fe3f86882fe083d6a
0.551 129.545790941 (-127.55853014317961) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4090 199254 0x3b02ecdd19abb3a11caed66a26c61f80ba90b018e46212f5f28ee3e5a6cb08c4
0.810 119.227984583 (-8.294930875464892) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4451 211113 0x817826e3c89b2515e6e66606a753fabc5ac4d9c4dc4bf764ae9388df6ddffdd6
1.097 203.092948938 (52.03817415075586) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4090 199254 0x3377fa26095a896badbe3e715e6c6fcb65a4cb927796fd479e434ae856fbb2b1
1.761 345.258013194 (51.851851851689744) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4090 199254 0x416e598c89eb0d69023cfc79b4192f1b1ccfdc0d93a2491e7e68ac88e985c025
1.994 202.687573791 (-52.03817415063984) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4451 211113 0x89b2b24da285e16e135391468203f5d643b417e3491cd8e3a5302fa8b70bc8bf
2.010 202.687573791 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4451 210997 0xee7bd1fdae09b0b75cef24b29e1dc30820c9b62e88f023d134cfc72eeb32a842
3.038 119.227984583 (-51.85185185180583) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4451 211129 0x71a0943d542d1a5cdd0af2429b0ec803c89b5706e8be3a0a3570ee7510b011db
4.000 344.568875444 (97.17223650366316) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4451 210997 0x5c804b9c66175d59fc789b4c840dfc37f1233d2dd511379834658d4b9fed7eb8
4.212 586.938622430 (52.038174150861) 0x7E77777772d74Cba60638F3C19C60A3387172a06/4090 199254 0x6560067dde5f23835d79a5c8837e652fe695450f42465749ab00f758b0ad86e3
4.617 995.804050031 (51.66543301258907) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4451 210997 0x07d61e89fac1ac47c2a680cd28409bc480d6738e61baf184b2eb87f3afd67e21
6.957 202.687573791 (-132.35244376773278) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4451 211174 0x57a94b0650ad79ea3572233bdd420edb73510835397b0e9693a7b1a53377276a
7.255 344.568875444 (51.85185185166235) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4451 211174 0xe7859bb1586b8e6ccec8428802f14de06e51c9dfea90949b4aaeea8a39f33330
8.055 585.767088254 (51.85185185172446) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4451 211174 0x1fa09be37784040e1bc0bb171c6c986939b6405480073cb18fa9e0f1765093a1
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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.