× 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 #684 - 2018-09-25 14:17:12 to 14:17:29; Start Time 1537885032.8864496

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 6.000000000 () 0x6Ed450e062C20F929CB7Ee72fCc53e9697980a18/1660 400000 0x84507531d79c85fc8b1b1f0fd346eda4c69d4e4c716990bee539df9d67203e59
0.819 871.103288949 (197.26372021375516) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4765 226034 0x5bfd01e1372bc3498982d87c73ba8d2e21fb5b1e2438f91c4e5f57af565c9573
1.933 655.016331931 (-28.318482255460246) 0x552aEb43F2Fe66EBc87Ef374F76B7a0d20c3aABa/70 250000 0x9232f1d4cf768c7a6bbeb4551a9e4dd0020a3878e33aff307f3b5a02962f0323
2.604 516.005719413 (-23.741758297114114) 0xCC4A5831dd286b3De8820f4D562Eccc4af635B43/118 240938 0x0c4b7f9c0c557b1205421a2aab940f560d96938239b86fc1437831bc85ea64b4
3.020 984.346716510 (62.43079770905718) 0x0000F7F39325076881E5fC566E99595542532aE2/15870 226244 0xdcec733e0a8a32c37d23cbc62427153f706799ee1d8d2c1cf311e66a40e8dd46
3.496 516.005719413 (-62.43079770905718) 0xee634c2b0553aA8e123595c5B6b70a42BDCB5cA1/1552 226053 0x456938e7e4a4ac1e527dfd891ee7efb0bf21fe170d815283432316f7fa4aad08 6397294 6397294 / 3
3.991 516.005719413 (0.0) 0xbB3a8c227bFBcB0fF70802Dee83246B507C57DcB/12020 226265 0x5a8dfa4a9a7b74ed003772eda9c3451c42ea9accda08bbfc80b216a37ff1e52d 6397294 6397294 / 2
4.308 658.291413590 (24.233337573282913) 0xCC4A5831dd286b3De8820f4D562Eccc4af635B43/118 240611 0xc6214ee696510a1eaa3fff58d0b45071616bf829adbbb79d93b2fddfae7e4171 6397294 6397294 / 0
16.339 658.291413590 (0.0) 0xee634c2b0553aA8e123595c5B6b70a42BDCB5cA1/1552 211082 0x35251f12dd0c60c8b8c0778886e30bc7b17b42d5db42b425b53a0fc9043e65fb
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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.