× 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 #521 - 2018-09-17 09:04:23 to 09:04:26; Start Time 1537175063.4945657

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 405.714707910 () 0xccD7BDff63017081B5cF72993d5D84cf2a266330/666 198956 0x51aa3174b36559eef3b81f929c0afd50221772411eb865056c4412ab921ece76
0.378 421.943296226 (3.9215686273562174) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4684 240368 0x2ca3ada10e9da38a25deadb63e336fd73eb1d49e38ac331c9c7cb8a9be6887dc
0.400 421.943296226 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4684 240706 0x5dc9e92c3176668cac96dde94a35f37e043cacf1ae91f6c9359aad443d53d02f
0.443 421.943296226 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4684 225795 0xd267312db9b37bbcb8768e3f5c01f4976248c758f3fb07efb6126c5727588348
0.513 458.457619938 (8.294930875605349) 0xccD7BDff63017081B5cF72993d5D84cf2a266330/666 234316 0x14c67d77cf7fd1d9820ad34f7bc694905b6b5cd3e14ef2eb0fd83f9ac54fc301
0.789 718.738210791 (44.22044048386011) 0xccD7BDff63017081B5cF72993d5D84cf2a266330/666 234316 0xef5d97a3ff3bf9b2b736755da692ba393b36d0d7b6aa60802a55e20711d5e26b
0.818 421.943296226 (-52.03817415102123) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4684 225784 0xd0987010249e0b735b74344e5945699e73b9ac11b3f536155c072690436e9549
0.833 6.500000001 (-193.93151900544044) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/15231 492478 0x85e6e408c7c197f1d708e8c029b1fca92cd9fd6a73cc9427a6a2395a3d69a54e
0.925 1219.416126092 (197.8791371244245) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4684 225795 0xdfa980d6ab4a600655cd4fcca3b007e743a13ad5277e409fd57c5b9b6923e44a
0.996 717.303603584 (-51.851851851790656) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4684 225795 0x9bb61890e5bec6381c2136781e93b385577f6a003f51e8379d5d6e529699a700
1.089 2073.007414356 (97.17223650379118) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4684 225795 0x35955cee7fd71293d21b21485f2f70055264283ddd632cb305da2387162b8f4f 6347465 6347465 / 1
1.143 1221.854958344 (-51.66543301257932) 0xccD7BDff63017081B5cF72993d5D84cf2a266330/666 234316 0x978dc41c7ed1303d29ed2f3be7c077abed454b3f72db5329dad4c001a823ab16
1.169 2073.007414356 (51.66543301257932) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4684 240368 0xd66b81d194d20392f08457c877253e319a4779ad1c929ba794539d0e73ed3d3a
1.482 747.487739222 (-93.99198388640968) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4684 225784 0xd59402134d2d4ac2803f048fcfa53f0620da9a993c5cc5948deeb760582f873f
1.680 2077.153429184 (94.14758269719309) 0xccD7BDff63017081B5cF72993d5D84cf2a266330/666 234316 0xe337f9f4f7aae95636d4d32a6699550283662debd6e1595e404471b7ea774af2
1.794 2073.007414356 (-0.19980019976592214) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4684 240706 0x20c069ea38f3a6ee42a9d0354f440da91fc7dba81aaac3ff6f5282c55f6af954
2.179 1219.416126092 (-51.85185185183385) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4684 240706 0xc5a1971c7ff3c30faf65342b3c1e0969d33d6707700b1efa0b6cd5095296ead6
2.602 1219.416126092 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4684 240368 0x52ccb8de8a874119ff4b8df5fe80be17aa78def622452669fa347243ce866f79
3.047 1270.729156677 (4.121288098334629) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4684 225784 0xfd5b9c51e5f22c3e3c0d19c98edd0d1c69e1b4aff980dbf38f012b815dc3e229
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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.