× 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 #662 - 2018-09-24 13:50:35 to 13:50:43; Start Time 1537797035.8217897

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 122.000000000 () 0xD2bB24e348651970e0A3aE50EA89E5ff2B37C09B/189 45000 0xe9055d481eb79c3ab70724aaf94d180e326418ca43827c90be01db7cd9e00c0e
0.413 8.415377970 (-174.18900101815962) 0x18a0451Ea56Fd4FF58f59837e9EC30f346ffDCa5/1849 300000 0xc532fa908ae1e778c5a4057f3355be308c72e2f1b34fcdb901e0a4b7e7ef76db
2.062 134.000000000 (176.3638503370817) 0xa8660c8ffD6D578F657B72c0c811284aef0B735e/49094 121000 0x9d6a46eaa71ae8d4beb515b1ed00e27d503b43ab4f66f383cdb2182a3f270837
4.398 1.469512221 (-195.66098025479656) 0xaDA1ED8Dea59b7B082937460C0863AbFFbB0484F/11208 800000 0x068f3e0d266ebdde5dcd48c52c93cef2392170832374fd02cb485389ca89f2d3
4.538 5.284996034 (112.97591679381256) 0xaDA1ED8Dea59b7B082937460C0863AbFFbB0484F/11208 800000 0x8a685a7cd37bf348fcce0f636a32338c1d8b3fcaaa1226128d073b403822ef93
5.302 3.501299335 (-40.6017923160944) 0xaDA1ED8Dea59b7B082937460C0863AbFFbB0484F/11209 800000 0x147d0849d6cff2b77bb9a22baab8273b5dcf6eeb10a880224616f2b5211a672c
6.973 4.000000000 (13.296380872927797) 0xae1a19ba2285c4D840Fd0c83C047355D5c9bFaf6/83 29900 0x56db6af5df8dc3f21ea600e3830522605ea4c3cd1ff23ddf5e161ea5fa434d91
7.316 22.041941550 (138.5606485243033) 0xaDA1ED8Dea59b7B082937460C0863AbFFbB0484F/11208 800000 0xcd83bf9ee220264fafd00962cdd03bef6c272e92c165e9126c12c3afb2b8a1c7
7.487 22.041941550 (0.0) 0xaDA1ED8Dea59b7B082937460C0863AbFFbB0484F/11209 800000 0x9419cb54c218342068d150cc69fc6e0239c0d3a0a76c1d338b30ea6237f52ce2
7.657 12.000000000 (-58.997466611888946) 0x71f35825a3B1528859dFa1A64b24242BC0d12990/3907 139894 0x7573bcc74ff6b32ca559c212995341d6520cbc29f86ce4c99e4b867e8a2811aa
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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.