× 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 #1014 - 2018-10-14 16:37:57 to 16:39:21; Start Time 1539535077.731017

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 4.000000000 () 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/21303 700000 0x4046b3a7659ff4362c260557615b57d0dc2f0c82d1a4d6377da70fa6d8acefc0
76.709 5.319624000 (28.31925408149513) 0x8bC3da587DeF887B5C822105729ee1D6aF05A5ca/180036 98160 0x05ca0234ae4cfd54f1a0b3f0f5b1185fa48cd41a4ef487c86c2a2f103f392728
76.964 5.319623999 (-1.87983210861142e-08) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/21302 22000 0x543b74478140c7ff62feae99345d258b8eb8e12feccb6dbc2cb51b0eb87468cb
76.972 4.600000000 (-14.509098310027587) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/21303 22000 0x49eab197f0b83a72675c6f40b39d1b9116f698be49004f923d6d32d4c3c6c717
78.584 6.117567598 (28.319254049457875) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/21302 22000 0xf94b2ffc7b288939e4230d821c57f5187dee51d12643da1208e62ca3a2e638a9
79.138 5.290000000 (-14.509098296206298) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/21303 22000 0x206831e535e732986cbb46a1013defd53bab12d05a33258c8759fe7e4f94a9f9
79.813 4.000000000 (-27.771797631862217) 0x0016eCcEcFfC25B94050187017EB59fA05C029Aa/3523 52182 0xb3b7a0ee07355463f39cdc2488577769171144bf387023957d4771bce2fc6220
82.534 5.000000000 (22.22222222222222) 0x44E05b25115Fb86F0e9861ceA0Bf27E50f2338BF/13682 504650 0xa81cb10347bece52f3180e3ad01bbea141a2a948f4e364eb47b29cff6c342138
82.705 4.999999999 (-2.0000000002e-08) 0x5a289646BFb958c3158A33b1e4b8a8c254B3B5AA/7244 600000 0x32c9b94aeb1ef9376acac488ebed6927231b16d4dc27a669751f1a144ac7e504
84.047 134.000000000 (185.61151079414108) 0xa8660c8ffD6D578F657B72c0c811284aef0B735e/52006 121000 0xe796d16a9ef37844961158dfe618e6016183ac3a1f091f4cf1ac320a0e19f7d5
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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.