× 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 #9 - 2018-06-28 20:14:55 to 20:15:03; Start Time 1530216895.6584074

Seconds Elapsed Gas Price (gwei) Bidder (Sender Address/Nonce) Gas Limit Transaction Hash Gas Paid Block/Index Mined
0.000 42.753364108 () 0x7E77777772d74Cba60638F3C19C60A3387172a06/3819 199468 0x9c98004f6a5ad53c38ab864251c0b59ac937e1ac1dae059be6ae96d6518695a3
1.335 2.000000000 (-182.1242488482113) 0x4fCc2FF6c75923D33B4F5aF4C524461014B2EE1C/4720 780000 0xfe05a104d37e650705cb4507ca643ac5e69fc1360327025003c81791aad2a56f
1.339 54.594051386 (185.86423872460384) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3819 234591 0xb51f5d6142c4c3cb8e8ddd554d07930c95d1805aa0b74e12cd5e5b923cb4752d
1.344 54.485081224 (-0.1998001989795984) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4062 239169 0xcbc8365db39fc49bb213bbb3934a4e3aa119e2f1551096d557517beaeb72d988
1.882 92.809887356 (52.03817415010307) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3819 234591 0x47d5fcacef409d1ae7448b716c271b6ecd0d25638ff8a633da3628d731f12216
2.299 92.624638080 (-0.19980019962780454) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4062 239169 0xfad2254d1d4254b57d2a92e7fbc6eb7c67828208a3821ad08bf5fada82678947
2.488 104.875172712 (12.40561657540203) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3819 234591 0x7f2e82ce448eb4591e60a30a407ac65ff057fae54ac494af5a66156c6a7fa3be
2.675 157.776808505 (40.28268551250203) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3819 234591 0x575fa8a27ca3e1a690e655ed0005f031d776154d0d9b2304ce20ef319cdb7465
2.833 157.461884736 (-0.1998001995010433) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4062 239169 0xf01d9ce3244d3b9a7e2befde9ca80d8f651b80a44cd55e276e20fdde53b21a6e
2.880 92.736044758 (-51.739708724929464) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4062 238991 0x808e84f0e349e447aee9fde74664d5308e8880d9f5dcc21540ec56ea7d5bafb6
3.049 178.287793610 (63.13226863523099) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3819 234591 0x8232a4d3158f846c3c05b130fa88615a3f9880f967d8614ba25f60b3427252fb
3.244 268.236867555 (40.28851338706328) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3819 234591 0xfbbcd5660ec0abe2f25ea6887880975260e421f7fb59861f2f267c89e4c61ed0
3.385 268.007169349 (-0.08566928121985672) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4062 238991 0x96da964b2aefc54a4fafda1feb7b758b73133f8acc718468a9f6025e0de03f49
3.432 303.107660337 (12.291920700885429) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3819 234591 0xbef1c2c6331147407e77b6e2c5c347297d168ce4ccd05bea3ecb679d97ccfc91
3.616 267.701464626 (-12.405616575696838) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4062 239160 0x053cd963a03cd64253944fa5b7b26097c9560372490abe095f7fd0c6757eacfa
3.807 474.213975641 (55.670093869641114) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4062 239218 0x43d305b57d99d7d84b1a598d0156190e0f17b372dff33f0a78121ad06ab072f6
3.834 157.471449780 (-100.28489280084318) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4062 239160 0x319c7cbdbf2042f3257d73ca1ec74a7254ee6cff8a0f26db268d244137a5dcb0
3.994 475.162403592 (100.43438305385534) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3819 234591 0x173b500fe74ac0aa0d38e793feb2658d32e997b7849de539607de87be411f9ae
4.084 538.121422067 (12.426729190916262) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3819 234591 0xb931a7b8aae1b890e3d74f203da297c8e1e80f3e146fd537b212dd6642792a33
4.272 278.949397436 (-63.43930500140653) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4062 239218 0x6be445777082720790a952d2aa424fa83cdcd8dfc4954a916cfb2452903b46a4
4.290 537.047327413 (63.25955046566173) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4062 239218 0x8ed0137b3e89b828ad5206a83109b20481333314338d76d807ea5371a84cc5bb
4.363 267.685204051 (-66.94450959301128) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4062 239169 0xb66e26c1f7ccb71401069a246aec21d14d1a0a23edaba5208f3c73b3c0953f0b
4.558 609.422510491 (77.92368047257348) 0x7E77777772d74Cba60638F3C19C60A3387172a06/3819 234591 0xecde67606e0b770f78cce76c3e782590ccdb0a7e320d039772e30c9db6ca7c7c
4.673 608.206098295 (-0.19980019970338692) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4062 239218 0xa0e72d062013a769ef962e2356cdd0f964c718a372765c918e2ecacfe97602f4
4.853 2.000000000 (-198.68896754353108) 0x55aE3f67039c332F55bE00c1d33D989d2DA108C5/5138 171026 0x118660ea6f9495e372b43b53ba2097829ec84f24fbc2186bfef4faa0cee1998e
4.947 688.793406319 (198.84191135485366) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4062 239218 0xd367d10be1dda702204b771e2d85349b361007ab71e4d3f4248859b8483d92fc 5870932 5870932 / 0
5.235 455.092489864 (-40.86087908502585) 0xAa24325B9d108492fB5D8b8583e1705986d70E04/4062 239160 0x9bc7f2f852b0d503b85f0f3c9a847594daec29eec25c3fb64d1191bbc44b52e8
7.706 16.010000010 (-186.40635500416735) 0x13303476fec8D6A06Ce236D7badA4e359DBeCA11/3148 631200 0x4c34e307a4dce56cf3daa420d1599fbda5afceb57e2cfdfd6720098228bcbd77
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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.