× 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 #1636 - 2018-11-29 02:05:07 to 02:05:16; Start Time 1543457107.600195

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 2535.988380952 () 0x1365e0eEbB4D29c7De1a8ae8ddc9d1545BaF927c/43 21000 0x339abbaea3ac70a1f8065d0ca5aa1ef5fe876efe1c794a8fde1e5c991f16cdcf
5.364 16.000000000 (-197.49215159137498) 0x8Da4F82Dc4D03c5421BB2087F858750c650d8571/3954 150000 0x4af3fa775d638ac46f98615ec3659da7fe1989f8a3d41ed27f16069142576324
6.196 2527.319428571 (197.48360354263644) 0x1365e0eEbB4D29c7De1a8ae8ddc9d1545BaF927c/43 21000 0x73dd477e91687be65beca857fd1bc495cc90338ba6eab6c54777467e2a23fd28
6.295 12.100000000 (-198.09405254384322) 0x0016BD4cb70Bd98CA07A341DA66450b5d22A55aa/62651 196465 0x0b7545ae1c3921dbaf83f61894c0b4af4ce51a6e6200eceee9624b6e1b1561d4
6.492 2527.319428571 (198.09405254384322) 0x1365e0eEbB4D29c7De1a8ae8ddc9d1545BaF927c/44 21000 0xa337261ab08cba31e609fd670a78fd448508466b6b01c8cc71ca4e1c0c107ea3
6.990 2527.319428571 (0.0) 0x1365e0eEbB4D29c7De1a8ae8ddc9d1545BaF927c/45 21000 0x2d8e5f3f48f3a86a50f3b52c4668b31c57ecda25b0918799cbf2cf77ce7ebaf2
7.187 2527.319428571 (0.0) 0x1365e0eEbB4D29c7De1a8ae8ddc9d1545BaF927c/46 21000 0xa95330476fe0eddd2c9415c77a5cf6167291800ae86bfe62baaf9c895031604f
7.713 2527.319428571 (0.0) 0x1365e0eEbB4D29c7De1a8ae8ddc9d1545BaF927c/47 21000 0xd23408e8b142c071882d250cc69060ffb7fa85667819b8930a329f743447c175
7.904 2527.319428571 (0.0) 0x1365e0eEbB4D29c7De1a8ae8ddc9d1545BaF927c/48 21000 0x291bcce4025635ebbcbe9db621395e6d5a19e69775742b83ac43102bcdd57953
9.206 9.000000000 (-198.58062042207817) 0x93Ed41743f151bf8F1D46e23F65605dA45388FFF/1986 78798 0xf32a40b169db3cc4fcec499b9353b61aafa3902d88ca325a60fd1a4cfde1d5d4
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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.