× 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 #1134 - 2018-10-22 06:19:51 to 06:20:01; Start Time 1540189191.5304844

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 229.488090232 () 0xf666666666E8025c503209ac580a5aA4b39A090c/474 199340 0xfc4406d920c8583075f3e84d6008b82357932e4dc93289fe0b94a51078ac3bb4
0.362 238.667613841 (3.9215686273337074) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4888 240940 0x8ff903df89842be7161fea93a10629d263ebdeb9c4addb98e31e1950f30d5a6a
0.488 261.616422864 (9.174311926539486) 0xf666666666E8025c503209ac580a5aA4b39A090c/474 234461 0x30258ae85267d201260ed7e190064dc47e75861db1f7ba8208ae7ddb126961f1
0.660 406.546413417 (43.38163773360444) 0xf666666666E8025c503209ac580a5aA4b39A090c/474 234461 0x61decd1bb27c32208829d7d0cef107e91fd8b91da5f82cffb407832173728f3b
0.682 405.734943529 (-0.19980020003190743) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4888 240940 0xd2639e8e021fdcc91b8c24629767b82d177ee96fb178942c0dc55b9bc4b1cac0
0.763 691.128902807 (52.03817415102875) 0xf666666666E8025c503209ac580a5aA4b39A090c/474 234461 0xa0414b0088b90767eb2055fc73af41349ee6fcfa31f9e4214095a1ef7faf8e71
0.851 689.749403999 (-0.1998001998004892) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4888 240940 0x51689d63321ce0a3b3f95b6bf8812570d3155656c5fd377f931c0e00e18f2244
0.893 405.734943529 (-51.85185185181128) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4888 240910 0x641c602a244efbaa29816398c5a4c65afda09dba16f8fa1bafc4b142b2a31a9a
0.924 1172.573986798 (97.17223650380326) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4888 240940 0x2a715bd6fbccd572f4c9f5b67baefcf64678e31fb471ef4afde30feb8835c03a 6560930 6560930 / 0
0.977 1172.573986798 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4888 240850 0x87027e8ecfe5643f26e75d6b40278a168dc3d2620a5cf1aaefb85124d961261c
1.340 1172.573986798 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4888 240744 0x7f939220c465ec307d8330c4fe4f9e92f80a7eb3310c743aead54f87ee387be7
1.622 1172.573986798 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4888 240910 0x547e7143de38f68b365722755970ad76a47e82ff174c7396580543cce69638ce
1.832 689.749403999 (-51.85185185182799) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4888 240910 0xc1e013912dd89645b8de2068e83c4060ec8d65d1fe2ceb8803bcef5dbbc7340b
2.239 689.749403999 (0.0) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4888 240850 0x3d0b05d4c502626c69ce80fe3551df308979168c74bd59d2eb2690076a8a93c2
8.009 238.667613841 (-97.17223650369101) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4888 240910 0x320e690ee8354c135c0faa834826ff1426d0185553a5b4d570240a0e296fc5b8
9.594 689.749403999 (97.17223650369101) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4888 240744 0xdd215381e764ecc3932dfae33f3a5ea80d0af2d4ab9cac75c4b02efb227c59c4
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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.