× 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 #5 - 2018-06-28 13:50:35 to 13:50:39; Start Time 1530193835.4166067

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 39.940089865 () 0x7E77777772d74Cba60638F3C19C60A3387172a06/3814 198252 0x2ed41f138570a2f2bc93c3ab69d3f82506c5b8e7371ccec56096a233c3055a05
0.319 5.000000000 (-155.49630617099345) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/5134 185240 0x42fc25ac9ae8cc0d9d626911e4dbd1c23f5f2836ecb9b98e043c202f197cd89c
0.537 92.938071166 (179.5789321130278) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4053 149319 0x7ecf8ddda2d9e606d5142dd84b896f2a6342cb72ca2886f3817fc8be947616cb
0.851 40.940089865 (-77.67955714443922) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/5134 185240 0x2ffbfc355cd6dafdfbf77d3639a41a4738065c07eea6723247ac82ebb4c1fbd2
1.036 93.123947308 (77.84915111225614) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3814 233569 0xb9bf59b5c54e7d2b25a9489c5a44b85f77aeebe4313acd64f5311c7b429fbaae
1.237 158.310710423 (51.851851851498324) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3814 233569 0x466389831349db011669c4544681dcaa66dc5065143295a733a8105de4c284d0
1.452 157.994720982 (-0.19980019919127132) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4053 149319 0x1d0b0cc2ae3f263c73458ef1b0b288ca5fa3047a5db8b491708e50c2ad55e6a6
1.606 92.746384373 (-52.04438778924677) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4053 149484 0xde8bce970e667f99389f666c4b73f8dd7212c77bc4938678fd94945071f80985
1.667 268.711467075 (97.3640948714125) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4053 149282 0x9d19e1f929ddad1ac4ed7e85c3705bc85c30b086f49ddd78af5ce6c9e5d65e0e
1.876 269.248890009 (0.19980019974448932) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3814 233569 0x157e6db11059a82befca6a34362402466b577b971e0372554e77c016f0234a6e
2.068 456.604743637 (51.623590471511996) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4053 149319 0xe914dfa6bf9681bda03e1b43069a7bf3e81cbdb0b9aad2ffb41c4c51433e3a5f
2.156 457.517953124 (0.19980019974031152) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3814 233569 0xb97678d2163fec3d3c1919aa07214987b5057d95624120436fac0ad66a86a85a
2.545 776.576139845 (51.70727070792561) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4053 149282 0xc3327ccfba78ead7dda0f4481196f2d51a09a6ec7a554ea580c556aa3a790378
3.366 776.228064182 (-0.04483188055484524) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4053 149319 0x601db196b877e32ba6eb56dec42e90736779a323a9b4d6ebbb32c7a6b1c34bb8 5869384 5869384 / 31
4.574 16.010000010 (-191.91657117544406) 0x00003e132a49A9C0F066cE2Fe2aB48B886B340cF/898 631200 0xa05a81cf4a5d35ec0ac2ca6e1976f0269da531c22b77fb13dbc33bd6dcdc94cb
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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.