× 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 #1208 - 2018-10-26 03:05:29 to 03:05:32; Start Time 1540523129.6952276

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 181.782742183 () 0x0000bAA8F700aF2476492b19e378d61b90454982/268 129016 0x85642af03dd5ead9882d6569f7d0312a5c6d1197ca8762001981e88ee1c2f2b3
0.237 189.054051870 (3.921568627281782) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151424 0x0dcda1f72e91b58719d27f7ed7e3a07cffeb93964651d9004b7a52acd7b013c1
0.402 189.054051870 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151408 0x1d0564cbec2042578c1fc99ed9b92a3a6e88ea5271decab44b5b4d5e0eacfdec
0.534 189.054051870 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151467 0x19f819689f4d38ff7f642b46ba4e6117c0612e09e9224a928fbb01b7731934d6
0.606 174.059524104 (-8.258863759516197) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151439 0x7eb77a2c6836186c1c57264c2468cd4168ecda3f98eb186c6b188648fbda86a6
0.648 296.492993357 (52.03817415051761) 0x0000bAA8F700aF2476492b19e378d61b90454982/268 144116 0x0e9c844fa98f7b3339237ef896a2aad7073cac526b54e573d9de1e585ebfa320
0.650 295.901190976 (-0.1998001994791133) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151439 0xbad2f360972b9c62b3cfeaeb2f218b4c36313daf14c4eb83f1f4d79dcadc0a2f
0.701 547.458942324 (59.6560689592179) 0x0000bAA8F700aF2476492b19e378d61b90454982/268 144116 0xdeb22a1359f15fdfa68332bbceeaa887eed9f9f56467c79fae49c27fe001a3d2
0.702 546.366209904 (-0.19980019983527086) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151424 0xc2733f4e4974c1ab91ef742f6cb93f175205035d09b1e09a8634da1a5d0ab2e2
0.828 321.391888179 (-51.85185185180063) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151424 0x6afae03257b041e5ff0aa001b38749cb186f2efb004dce660484627aa1b48440
0.834 207.232326088 (-43.19119669888589) 0x0000bAA8F700aF2476492b19e378d61b90454982/268 144116 0xe152499c8659a2cc236f2666e1ac3a10e0c8865445f49be4c3432c2cc80caafa
0.983 928.822556836 (127.03439624162472) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151424 0x2ef69b5158e43a5ae63d674beeccf7496ec7ee00f962785c3177df558418294e 6584620 6584620 / 0
1.092 321.391888179 (-97.1722365037483) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151408 0x4177148db23bc32bf8f39663d4990b018f89312cb7bea832739b56ff2239fa12
1.148 855.154441920 (90.7337926413976) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151439 0x34ef6cc759a805e0a6ea9c75f7c6017230871f7f3b07e1d448b1f6d16b05ca90
1.297 321.391888179 (-90.7337926413976) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151467 0x97a0947cb30ef4a96aa9768f24e31b02a72436ee5dfb29bb4c9e2ab5fabb9f61
1.809 338.002012426 (5.037997540395827) 0x0000bAA8F700aF2476492b19e378d61b90454982/268 144116 0x65acc98b19b3d70acd970d5f3e38a49f976586b79540b553520c822d2c57f0d4
1.902 546.366209904 (47.12159306878609) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151408 0xa8738615e5ebb3f778fccb88e58d181a687941fef78916cce05428d7bcefbbe4
2.028 503.032024659 (-8.258863759770973) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151439 0x3d5be9dd236128878a2ac94366fa86db683ec3fedc8cbe5a3690264b1e8953bf
2.363 546.366209904 (8.258863759770973) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151467 0x8de2bc8be1cfd799f7a57f3b1ebbde9319bd39e2ab0a25ac275091f94103d3c1
3.022 928.822556836 (51.851851851771514) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151408 0x37f4dd502115c475998912c1bbf844489acbdad548ad62c2ca30f521409c2ad9
3.157 928.822556836 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4916 151467 0x8425d6bb1986c4d94509338d7708b3b40cb39515536033a82dc61fc6fffd5787
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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.