× 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 #685 - 2018-09-25 14:58:06 to 14:58:11; Start Time 1537887486.3776424

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 383.887669498 () 0xf666666666E8025c503209ac580a5aA4b39A090c/402 183028 0x6d82c2aa468db368109dd35a866f8f07a55c2f04c206e0289c91ce60203700ee
0.313 399.243176277 (3.9215686272206325) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4768 224939 0x10a7e6262933e55531844ebdc86927fcf96c9a90fd5a0d6835733b1cea1f1854
0.415 433.793066532 (8.294930875635782) 0xf666666666E8025c503209ac580a5aA4b39A090c/402 212942 0x7735dfc4ad5daadaea02f5a1333d427c6b4ec735e7d0680a7364d1845538bf08
0.712 678.713399670 (44.03036576931308) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4768 224647 0x16a1cab785b38b370a6793f69f3bc9d51cab9430dd2adb0acec843bbd4106b6c
1.254 680.070826469 (0.19980019975020508) 0xf666666666E8025c503209ac580a5aA4b39A090c/402 212942 0x0564515e9870a28c70707ad5dc30de0d0777537d963f73b3859e23ae1a33f433
1.675 1153.812779439 (51.66543301262993) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4768 224647 0xa36ec12f1a87293dd72c4ceb5516b22a9c51ae97c17907db7ac1be2cb9e28d58 6397470 6397470 / 0
3.191 1153.812779439 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4768 224939 0x7f95bc2779752dfb23631ee1d12b0196a865b088b4c85ef070cbcef3459cdeca
3.266 1153.812779439 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4768 224638 0xf81447f6d3585c866cad7c51916222c3efd929625ae16a1bc02b6d4688f0c252
5.023 24.000000000 (-191.8492988295028) 0xc951D3463EbBa4e9Ec8dDfe1f42bc5895C46eC8f/7524 108762 0x3ea9ef128b813b401700ce832bff2a9a7d7ecbc50eeb0d05246d535c0708a274
5.340 678.713399670 (186.33866949953105) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4768 224939 0x75d20c8020f0ae1f3a34c69856930d23b954bb8864950ede4a5680473cd641b8
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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.