× 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 #1176 - 2018-10-23 23:04:23 to 23:04:32; Start Time 1540335863.9101458

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 101.000000001 () 0x33F8a639B0a6a89943DCb570661674b331A0C7be/17 30000 0x37441fc35af7b2ee598861de61e7afc674463641a8deffaad6c5860853f2da68
1.886 1721.409466688 (177.8315462365328) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4905 241056 0x8690d1fa026be4d67ebb11234eb3bb3e568fb5f6826725245924df08ed81749e
2.097 1721.409466690 (1.1618386204456327e-10) 0x33F8a639B0a6a89943DCb570661674b331A0C7be/17 30000 0x94357cddecf187ec887529f32ef544189dbed727f547ab241b1056cac5b620b5
2.345 101.000000000 (-177.83154623676447) 0x822d0101ff9eaA29B19546B8374Aa064e56D5332/1 250380 0x9e5129584d3e7a5d6fb8cc7243bde7a1d50dfd857a1f946e9c6294d01f8eb01e
2.467 1722.584569632 (177.84583140657264) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4905 240955 0xc5f9d87976f9e423433bd27137f01f67d334ba405b65435584f61f07191e6f53
5.135 76.618870750 (-182.96604618903294) 0x29BAf3e6Aca05316613F91f56E3f3B6C76bC3846/3743 1300000 0x8f06dad5507f18923f8427b83da6c8ec9bbb4cda8d47c69ff5625464f704df44
5.776 21.840000000 (-111.27259602456895) 0xbB3a8c227bFBcB0fF70802Dee83246B507C57DcB/12296 226123 0xbfe72e6809c09d7e722d54810f0b28b3802d6e5a7e0411976b3bdc81fcb69733
5.948 79.683625580 (113.95106360621365) 0xbB3a8c227bFBcB0fF70802Dee83246B507C57DcB/12296 226123 0x15cc05bb6295e670917c468c3519ff94f5b6efabc0c5b36f3e8df6717b2241fe
6.392 79.683625580 (0.0) 0xbB3a8c227bFBcB0fF70802Dee83246B507C57DcB/12296 226041 0x698f3470ba508fba6c527834ecf61cb33be196febacdcdc68474264a6f9a2aee
6.455 79.683625580 (0.0) 0xbB3a8c227bFBcB0fF70802Dee83246B507C57DcB/12296 225955 0x0ca94a67744f526a87e709fb2f8e8f68819f09ef23e0e2c93109e2aef92bec43
8.736 2.850000000 (-186.1874479402944) 0x822d0101ff9eaA29B19546B8374Aa064e56D5332/2 21000 0x50a3ea85aa9463c6a0c8fa3f5f939157f62108d299b7190b1fe99769e1e62bd6
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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.