× 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 #846 - 2018-10-09 22:12:34 to 22:12:37; Start Time 1539123154.5699134

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 489.564752298 () 0xf666666666E8025c503209ac580a5aA4b39A090c/432 184276 0x844888a1295a7d59649272cca49a41af33b6edf7f321780335835eaeb048c7f0
0.256 2.359125954 (-198.08171462431716) 0xaDA1ED8Dea59b7B082937460C0863AbFFbB0484F/12565 211696 0x26e13f23b3a1bdd72edfa23a5fa5e188a5d219fe2788551f0e4f90e5ab21850e
0.424 509.147342389 (198.15515454837373) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4820 210735 0x4bb4b047f406b890a5ffbefe58d49b3228d22f0efe60312b5b747e1c08416e23
0.586 509.147342389 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4820 210743 0x15b7bc25ab53e86b4051aafe700218538b67a43d1309689c34e8ef4e83669d68
0.601 553.208170096 (8.294930875622885) 0xf666666666E8025c503209ac580a5aA4b39A090c/432 214104 0x20864fd649fc3eb44e0620045f5e758c134bfcb5ceccb409e4a5a3b205d53612
0.726 509.147342389 (-8.294930875622885) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4820 210693 0xf9f3e5e393a66e61a8c1ba9c62d98da20efe5929f5620a93be1797032f42ef8f
0.768 865.550482061 (51.85185185181952) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4820 210735 0x810f6c1505b6dffc53f682d7c24fb06d3c3261cb737968411c1eab4963a77836 6485169 6485169 / 0
0.867 865.550482061 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4820 210693 0x7ac6796d3f57a61ba7cb68a47570ba9c0a19ba25069377a3b98d7329335ff834
1.207 865.550482061 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4820 210617 0xccc86748c107dffa31f25d0d7852814bb644adbc890993a8a7e003626e6c3906
3.176 865.550482061 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4820 210743 0x170347c441ca525914215adfe5451a23a4e49975db18f905af159109862d7a64
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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.