× 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 #813 - 2018-10-08 01:31:30 to 01:31:37; Start Time 1538962290.5722709

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 239.371883830 () 0xf666666666E8025c503209ac580a5aA4b39A090c/425 113552 0x1ab9054d8adeba27aa3a63d55f42cf8d1fbc24a46105119408f8397dde426052
0.538 248.946759183 (3.921568627370673) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4813 136086 0xada11d17dba153976f90f0e1a773df470e7212ebb124572346c78da95792e2d2
0.573 270.490228727 (8.29493087532408) 0xf666666666E8025c503209ac580a5aA4b39A090c/425 123209 0x7656c459a8d6ecb005eb1e83b8b322eb37bc6bc0c8acf459ac3b5bdfc990dbb5
0.654 248.946759183 (-8.29493087532408) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4813 136057 0x282d6922371bae050e3379843a46560d5dccd527dc3548dc1e653c0c3484e628
0.732 423.209490611 (51.851851851829814) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4813 136086 0x2fd3eed4bea5cad07f623236012a8269d5546804b9650551ec796c6c07c3f66e
1.028 423.209490611 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4813 136057 0xbd15848b2ab79d5078f21aa40fef8e732a9dd44257d7d68f7ec7b2253f6a9eeb
1.217 424.055909592 (0.19980019974784827) 0xf666666666E8025c503209ac580a5aA4b39A090c/425 123209 0x452d47acafb8a6445804bc9bfd75f4405a01f6b57cfa59caed650f37272be6ef
1.301 719.456134038 (51.66543301254133) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4813 136086 0xf48bbdd524fe69f6f07e11e56f67f0e36e93541948e407132d2267e9c4684ccc
1.887 720.895046306 (0.19980019978965735) 0xf666666666E8025c503209ac580a5aA4b39A090c/425 123209 0x649534cfa58832d4b302dfb2f25b7fec82757bb4c7dd49663b562672b2f16dd6
2.010 1223.075427864 (51.665433012547325) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4813 136086 0x9d90d58309d6b5f713ebf4ec4c087d66ebfe25adba066d5f13ef7a0d2d419f53 6473734 6473734 / 0
2.645 1223.075427864 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4813 136057 0xc29fab14f578b961541f72a2ffae2b1dcd81be2541b74273fecb29de0fbe7b25
2.945 16.000000000 (-194.83485843066651) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/19198 428829 0x6598df8eb43cb84120be6b7bd77d835ae01e4428c044b1209de7d3691c6c9fa5
4.964 719.456134038 (191.29791743681437) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4813 136173 0x08d098487ac0368f33c3436fa6747b36f3f418cb653b2bafd3c51d34a6fdfc10
5.552 719.456134038 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4813 136057 0x0226610205fe2baf234407cc5b2dacbc54aa04a76039c480beb8f81159c1859d
5.967 423.209490611 (-51.8518518517611) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4813 136173 0x31f43a2a5c206830f17df686d2265220470f9ee3460ece8c34300c03ae4855f1
7.395 1223.075427864 (97.17223650374423) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4813 136173 0xb22a7e50fe354bdcb9f3dc0a6b7f11f6551424025b697d66fc9521302dcb6943
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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.