Mar 2023 - Governance Call OP Rewards Analytics Update

• chuxin_h - No Role

  March 21, 2023, 8:56 a.m.
  Magic
  Background The data team at OP Labs has been working on tracking OP distributions across governance grants, partner funding, and other sources. We first shared early insights in Oct 2022 . Takeaways then: Projects were deploying OP too slowly ( 34 % of approved OP was deployed). Proposals approved via governance were less effective than other programs. This doc serves to update data and case studies, and begin open-sourcing the data so others can analyze & contribute. A snapshot of program data was taken on Mar 13 , 2023 Current Deployment Status - Growth Experiments We project that 56 % of allocated OP ( 30 . 7 M) has been deployed (not in projects’ wallets) We’ve observed 38 growth experiment proposals launch or complete, with 32 to be launched. Live & Completed programs represent 80 % ( 43 . 7 M) of Allocated OP (i.e. 24 % of allocated OP is “live” and to-be-deployed). # Programs # OP Allocated (M) % OP Allocated Live ‎? Subtotal 33 41 . 1 M 75 % Governance - Season 3 - - Governance - Season 2 10 7 . 1 M Governance - Season 1 9 4 . 5 M Governance - Phase 0 14 29 . 5 M Coming soon ‎⏳ Subtotal 32 10 . 9 M 20 % Governance - Season 3 12 2 . 1 M Governance - Season 2 13 5 . 2 M Governance - Season 1 4 1 . 3 M Governance - Phase 0 3 2 . 2 M Completed Subtotal 5 2 . 6 M 5 % Governance - Season 3 - - Governance - Season 2 1 240 . 0 K Governance - Season 1 - - Governance - Phase 0 4 2 . 4 M Grand Total 70 54 . 6 M Source: OP Summer Programs 3 Stats by Season Aggregate by Gov Fund Season Stats were measured at the Latest Date (Note: Many programs still ongoing) Source # OP Allocated Net OP Deployed Net $ Inflow Net $ Inflow / OP Incremental # Txs Annualized # Txs / OP Incremental Gas Fee ($) Annualized Gas Fee / OP Governance - Phase 0 31 . 0 M 16 . 8 M 128 . 1 M $ 7 . 61 16 , 775 0 . 36 123 , 940 2 . 6872 Governance - Season 1 4 . 5 M 2 . 0 M 111 . 6 M $ 54 . 46 3 , 378 0 . 60 16 , 887 3 . 0086 Governance - Season 2 2 . 5 M 978 . 0 K 16 . 9 M $ 17 . 23 1 , 214 0 . 45 5 , 154 1 . 9234 Multiple 8 . 9 M 6 . 9 M 227 . 0 M $ 32 . 69 10 , 701 0 . 56 33 , 508 1 . 7607 Revisited Case Studies & Early Theories Note: We are mentioning specific programs. Some were more successful than others, but the intent is to learn from their examples, not to accuse or blame. Retention Problem: Separate “Usage Acquisition” vs “Longer-Term Impact” Theory: Incentives are great at “usage acquisition” (transaction volume, liquidity, etc), but this is not a good predictor of longer-term impact. DEXs: Uniswap Phases 1 + 2 (selected managers), Revert Finance, Rainbow image 992 × 451 16 . 4 KB image 988 × 452 17 . 4 KB image 988 × 450 32 . 8 KB Lending & Borrowing: Aave & WePiggy TVL measured as “available liquidity” (deposits - borrows) Aave had 18 % retention from the local max before incentives turned off (+$ 431 M) to 30 days post-incentives (+$ 78 . 5 M) WePiggy had 6 % TVL retention by the same methodology (+$ 2 . 8 M to $ 165 k). image 991 × 452 32 KB image 992 × 450 29 . 5 KB Value Extractive-Resistant Design: Can someone create fabricated activity to maximize rewards? Theory: When Rewards > Costs, value-maximizing actors will spend to eat up the rewards. Anything that can be gamed, will be gamed. Aave - Oversized Emissions Led to Recursive Borrowing Aave ‘Deposit APY’ + Rewards > Aave ‘Borrow APY’, so actors borrowed and re-deposited the same asset over and over to maximize rewards Learning: Unless ****we can design a system where Rewards < ‘Borrow APY’ - ‘Deposit APY’, lending rewards may always be gamed. Snapshot ~ 1 day in to the Aave Liquidity Program image 1016 × 500 89 . 8 KB Rainbow Wallet - Swap Volume Leaderboard Led to Inorganic Volume Rainbow Wallet incentivized bridging to and swapping on Optimism. Base rewards were partial gas rebates, but there was an additional 52 K OP bonus to the top 100 addresses by trade volume (as of Mar 5 ). On the last day, trade volume spiked to $ 12 M, likely by addresses trying to get in the top 100 . Trade volume fell to $ 25 k Trade Volume / Day post-program (vs ~$ 4 k prior), showing that the increased volume did not sustain. image 1025 × 476 47 . 3 KB This was similar to Slingshot’s Flash Programs we observed last time: Rewards were offered either per trade or per $ of volume until they ran out. Transactions spiked up following program announcements and then return to normal levels afterward. image 1022 × 347 56 . 5 KB Elsewhere: Demand-Side Incentives Have Led to NFT Wash Trading Wash Trading: People trading NFTs back and forth with themselves to create fabricated volume. With LooksRare and X 2 Y 2 introducing tokens rewards for trading, we’ve seen a significant increase in NFT wash trade volumes ( 58 % of the NFT secondary volume was wash trading in 2022 ). Wash trade volume may disappear once the incentives become less attractive or profitable for traders (starting Sep 2022 ). image 970 × 474 28 . 3 KB Source: NFT Wash Trading Dashboard (hildobby) 2 What drives long-term impact? (The real unanswered question) Theory: We can bootstrap a network with supply incentives, but demand needs to follow, and that comes from natural product usage (need to be careful to not create fabricated demand) Aave - Non-Recursive Borrowing had ~ 60 % Retention While only 18 % of Aave TVL retained, 58 % of “non-recursive” borrow volume retained 30 -days later (+$ 30 . 6 M vs pre-incentives). Hypothesis: The “Non-Recursive Borrow” demand comes from other use cases on/offchain image 1036 × 278 54 . 4 KB Aave User Journey Mapping 1 OP Quests - ~ 8 % of transactions come from addresses new to Optimism via Quests While Quests appeared to have driven a high-volume of fabricated activity, total Optimism daily transactions increased ~ 50 % Post-Quests, and 15 / 18 apps saw increased transactions 8 % of Transactions (Last 30 Days) came from addresses new to Optimism via Quests ( 18 % of transacting addresses) Quests on Coinbase Wallet launched Mar 9 (requires Coinbase authentication per wallet) image 1060 × 490 53 . 9 KB Optimism Quests - App Growth on Optimism After Quests :tv::sparkles: 3 Breakdown by Program - Liquidity Top Inflows - Acquisition Period For Liquidity Inflows, we can segment programs by where the incentives were deployed (i.e. to the native app, to an external DEX pool). Inflows Cutoff at Program End Date (Latest Date if still Live) App Product Incentivized Net TVL Inflows Projected OP Deployed Net Inflows per OP Aave App $ 342 . 0 M 5 . 0 M $ 68 Velodrome App $ 241 . 9 M 5 . 1 M $ 47 Synthetix DEX Pools $ 120 . 6 M 2 . 4 M $ 50 Rocket Pool DEX Pools $ 79 . 4 M 222 . 0 k $ 357 Pooltogether App $ 56 . 4 M 842 . 5 k $ 67 Beefy Finance App $ 32 . 2 M 172 . 1 k $ 187 Stargate Finance App $ 27 . 0 M 469 . 7 k $ 57 Beethoven X App $ 26 . 3 M 209 . 5 k $ 125 Pika Protocol App $ 11 . 0 M 672 . 6 k $ 16 Rubicon App $ 9 . 1 M 791 . 1 k $ 11 Top Inflows - Post-Incentives Period Only Showing Programs which Have Ended App Product Incentivized Net TVL Inflows (End Date + 30 ) Projected OP Deployed Net Inflows per OP (End Date + 30 ) Aave App $ 77 . 3 M 5 . 0 M $ 15 Defiedge Uniswap - Phase 2 $ 2 . 1 M 25 . 0 k $ 85 Revert Finance App $ 1 . 6 M 240 . 8 k $ 7 Xtoken Uniswap - Phase 1 + 2 $ 1 . 2 M 41 . 7 k $ 28 Gamma Uniswap - Phase 1 + 2 $ 372 . 0 k 41 . 7 k $ 9 Layer 2 Dao DEX Pool $ 235 . 0 k 20 . 8 k $ 11 Wepiggy App $ 166 . 8 k 300 . 0 k $ 1 Breakdown by Program - App Usage Top Usage - Acquisition Period For usage, we aggregate all incentive programs and observe the activity on each apps’ contracts. For a broader view, see the Project Usage Trends 3 dashboard and project <> contract mappings. Cutoff at Program End Date (Latest Date if still Live) App # OP Allocated OP Deployed (All Programs) Incremental # Txs Annualized # Txs / OP Incremental # Txs After Annualized # Txs / OP After Velodrome 7 . 0 M 5 . 1 M 8 , 045 0 . 58 - - Uniswap 1 . 0 M 150 . 0 K 5 , 666 13 . 79 - - Pika Protocol 900 . 0 K 672 . 6 K 4 , 782 2 . 59 - - Rubicon 900 . 0 K 791 . 1 K 4 , 110 1 . 9 1 , 584 0 . 73 Synthetix 9 . 0 M 4 . 9 M 4 , 092 0 . 3 - - Aave 5 . 0 M 4 . 8 M 3 , 123 0 . 24 4 , 744 0 . 36 Hop Protocol 1 . 0 M 152 . 6 K 3 , 003 7 . 18 - - Beethoven X 500 . 0 K 164 . 7 K 2 , 297 3 . 96 - - 1 inch 300 . 0 K 300 . 0 K 2 , 101 2 . 56 390 0 . 47 PoolTogether 1 . 0 M 842 . 5 K 1 , 910 0 . 83 - - Top Usage - Post-Incentives Period Cutoff at Program End Date + 30 days (Latest Date if not yet reached 30 days) App # OP Allocated OP Deployed Incremental # Txs Annualized # Txs / OP Incremental # Txs After Annualized # Txs / OP After Rubicon 900 . 0 K 791 . 1 K 4 , 110 1 . 9 1 , 584 0 . 73 1 inch 300 . 0 K 300 . 0 K 2 , 101 2 . 56 390 0 . 47 Revert Finance 240 . 0 K 240 . 8 K 218 0 . 33 247 0 . 37 Aave 5 . 0 M 4 . 8 M 3 , 123 0 . 24 4 , 744 0 . 36 WePiggy 300 . 0 K 300 . 0 K 39 0 . 05 12 0 . 01 Aelin 900 . 0 K 900 . 0 K 8 0 - 5 0 Top Gas Spend - Acquisition Period Cutoff at Program End Date (Latest Date if still Live) App # OP Allocated OP Deployed Incremental Gas Fee ($) Annualized Gas Fee / OP Incremental Gas Fee ($) After Annualized Gas Fee / OP After Synthetix 9 . 0 M 4 . 9 M 91 , 133 6 . 76 - - Velodrome 7 . 0 M 5 . 1 M 32 , 018 2 . 31 - - Hop Protocol 1 . 0 M 152 . 6 K 17 , 055 40 . 79 - - Uniswap 1 . 0 M 150 . 0 K 9 , 268 22 . 55 - - Beethoven X 500 . 0 K 164 . 7 K 7 , 638 16 . 93 - - Aave 5 . 0 M 4 . 8 M 6 , 832 0 . 52 12 , 297 0 . 93 Rubicon 900 . 0 K 791 . 1 K 6 , 519 3 . 01 8 , 511 3 . 93 QiDao 750 . 0 K 342 . 9 K 5 , 729 6 . 10 - - Stargate Finance 1 . 0 M 469 . 7 K 4 , 774 3 . 71 - - 1 inch 300 . 0 K 300 . 0 K 4 , 511 5 . 49 - 49 - 0 . 06 Top Usage - Post-Incentives Period Cutoff at Program End Date + 30 days (Latest Date if not yet reached 30 days) App # OP Allocated OP Deployed Incremental Gas Fee ($) Annualized Gas Fee / OP Incremental Gas Fee ($) After Annualized Gas Fee / OP After Rubicon 900 . 0 K 791 . 1 K 6 , 519 3 . 01 8 , 511 3 . 93 Aave 5 . 0 M 4 . 8 M 6 , 832 0 . 52 12 , 297 0 . 93 Revert Finance 240 . 0 K 240 . 8 K 99 0 . 15 263 0 . 40 WePiggy 300 . 0 K 300 . 0 K 132 0 . 16 132 0 . 16 1 inch 300 . 0 K 300 . 0 K 4 , 511 5 . 49 - 49 - 0 . 06 Aelin 900 . 0 K 900 . 0 K 49 0 . 02 - 66 - 0 . 03 Key Takeaways Usage Acquisition Efficiency has improved Post-Phase 0 Incentives have been effective at attracting usage, but not retaining it (yet). Game-able program designs will be gamed - how can we mitigate this? Open design space with how to drive longer-term impact post-incentives. Analytics Resources Things are still super messy, but a lot of the code and scripts powering our analysis are listed below! [Readmes & how-to-contribute writeups coming soon] TVL Flows by Program Flows are shown by token at the latest price (unless otherwise indicated) | Sources: Defillama & TheGraph APIs Time-Series Chart 3 of TVL Flows by Program from Start to End + 30 Days Folder of charts specific to each program Onchain Usage by Program Incentive Program Usage Summary from Start to End + 30 Days Other Metrics & Resources Dashboards that publicly sharable Optimism Popular Apps and Project Usage Trends - Dashboard 3 NFT Marketplace Volume on Optimism - Dashboard DEX Volume on Optimism - Dashboard Overall Optimism Protocol Metrics - Dashboard 1 OP Analytics GitHub - See Readme for more Google Sheet Summary of Results Gov Fund Incentive Program Performance Sheet 2 Token Distribution Transfer Mappings [WIP] We can map token transfers involving known (or suspected) project addresses to determine when tokens are deployed (and to where). Intermediate Addresses <> Program Mapping 2 (can help here!) | Mapping Scripts 1 Closing Notes Tracking this stuff is super difficult to do as a small group. Please help :slight_smile: There are also infinite more rabbit holes we could go down We’re thinking about better metrics than raw transactions, volume, and TVL (i.e. app fees, transfer volume, incentivized vs native yield) and deeper-dive methods (i.e. segment by behavior type). Open to ideas! Splitting by grants and by season may get increasingly difficult over time, since protocols are re-applying for grants and using the same addresses. In a perfect world, every proposal uses completely distinct addresses, but may be infeasible. For simplicity: Thales and Overtime Markets were combined since they each used the same proposal address (we can’t easily tell the grants apart) This post is coauthored with @MSilb 7 .

  The post provides an update from OP Labs on their data regarding OP distributions across various programs, focusing on growth experiments, deployment status, and case studies. They share data on the current status of OP deployment, observations on growth experiment proposals, and insights from revisited case studies on issues like retention problems and value-maximizing actors. The post also delves into what drives long-term impact, theories, breakdown by program, app usage, and gas spend analysis. Key takeaways include improved usage acquisition efficiency and the need to mitigate game-able program designs. The post ends by highlighting analytics resources and closing notes on the challenges of tracking data and the need for collaboration.
  Likes: 13 Replies: 0
  Likers: DELETEDUSER, FractalVisions, 0xzenodotus, diligit, MSilb7, forrest, Mery, Houchens, Kety, jackanorak, Nazia, raho, ScaleWeb3
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• chuxin_h - No Role

  March 21, 2023, 8:56 a.m.
  Background The data team at OP Labs has been working on tracking OP distributions across governanc… Background The data team at OP Labs has been working on tracking OP distributions across governance grants, partner funding, and other sources. We first shared early insights in Oct 2022 . Takeaways then: Projects were deploying OP too slowly ( 34 % of approved OP was deployed). Proposals approved via governance were less effective than other programs. This doc serves to update data and case studies, and begin open-sourcing the data so others can analyze & contribute. A snapshot of program data was taken on Mar 13 , 2023 Current Deployment Status - Growth Experiments We project that 56 % of allocated OP ( 30 . 7 M) has been deployed (not in projects’ wallets) We’ve observed 38 growth experiment proposals launch or complete, with 32 to be launched. Live & Completed programs represent 80 % ( 43 . 7 M) of Allocated OP (i.e. 24 % of allocated OP is “live” and to-be-deployed). # Programs # OP Allocated (M) % OP Allocated Live ‎? Subtotal 33 41 . 1 M 75 % Governance - Season 3 - - Governance - Season 2 10 7 . 1 M Governance - Season 1 9 4 . 5 M Governance - Phase 0 14 29 . 5 M Coming soon ‎⏳ Subtotal 32 10 . 9 M 20 % Governance - Season 3 12 2 . 1 M Governance - Season 2 13 5 . 2 M Governance - Season 1 4 1 . 3 M Governance - Phase 0 3 2 . 2 M Completed Subtotal 5 2 . 6 M 5 % Governance - Season 3 - - Governance - Season 2 1 240 . 0 K Governance - Season 1 - - Governance - Phase 0 4 2 . 4 M Grand Total 70 54 . 6 M Source: OP Summer Programs Stats by Season Aggregate by Gov Fund Season Stats were measured at the Latest Date (Note: Many programs still ongoing) Source # OP Allocated Net OP Deployed Net $ Inflow Net $ Inflow / OP Incremental # Txs Annualized # Txs / OP Incremental Gas Fee ($) Annualized Gas Fee / OP Governance - Phase 0 31 . 0 M 16 . 8 M 128 . 1 M $ 7 . 61 16 , 775 0 . 36 123 , 940 2 . 6872 Governance - Season 1 4 . 5 M 2 . 0 M 111 . 6 M $ 54 . 46 3 , 378 0 . 60 16 , 887 3 . 0086 Governance - Season 2 2 . 5 M 978 . 0 K 16 . 9 M $ 17 . 23 1 , 214 0 . 45 5 , 154 1 . 9234 Multiple 8 . 9 M 6 . 9 M 227 . 0 M $ 32 . 69 10 , 701 0 . 56 33 , 508 1 . 7607 Revisited Case Studies & Early Theories Note: We are mentioning specific programs. Some were more successful than others, but the intent is to learn from their examples, not to accuse or blame. Retention Problem: Separate “Usage Acquisition” vs “Longer-Term Impact” Theory: Incentives are great at “usage acquisition” (transaction volume, liquidity, etc), but this is not a good predictor of longer-term impact. DEXs: Uniswap Phases 1 + 2 (selected managers), Revert Finance, Rainbow image 992 × 451 16 . 4 KB image 988 × 452 17 . 4 KB image 988 × 450 32 . 8 KB Lending & Borrowing: Aave & WePiggy TVL measured as “available liquidity” (deposits - borrows) Aave had 18 % retention from the local max before incentives turned off (+$ 431 M) to 30 days post-incentives (+$ 78 . 5 M) WePiggy had 6 % TVL retention by the same methodology (+$ 2 . 8 M to $ 165 k). image 991 × 452 32 KB image 992 × 450 29 . 5 KB Value Extractive-Resistant Design: Can someone create fabricated activity to maximize rewards? Theory: When Rewards > Costs, value-maximizing actors will spend to eat up the rewards. Anything that can be gamed, will be gamed. Aave - Oversized Emissions Led to Recursive Borrowing Aave ‘Deposit APY’ + Rewards > Aave ‘Borrow APY’, so actors borrowed and re-deposited the same asset over and over to maximize rewards Learning: Unless ****we can design a system where Rewards < ‘Borrow APY’ - ‘Deposit APY’, lending rewards may always be gamed. Snapshot ~ 1 day in to the Aave Liquidity Program image 1016 × 500 89 . 8 KB Rainbow Wallet - Swap Volume Leaderboard Led to Inorganic Volume Rainbow Wallet incentivized bridging to and swapping on Optimism. Base rewards were partial gas rebates, but there was an additional 52 K OP bonus to the top 100 addresses by trade volume (as of Mar 5 ). On the last day, trade volume spiked to $ 12 M, likely by addresses trying to get in the top 100 . Trade volume fell to $ 25 k Trade Volume / Day post-program (vs ~$ 4 k prior), showing that the increased volume did not sustain. image 1025 × 476 47 . 3 KB This was similar to Slingshot’s Flash Programs we observed last time: Rewards were offered either per trade or per $ of volume until they ran out. Transactions spiked up following program announcements and then return to normal levels afterward. image 1022 × 347 56 . 5 KB Elsewhere: Demand-Side Incentives Have Led to NFT Wash Trading Wash Trading: People trading NFTs back and forth with themselves to create fabricated volume. With LooksRare and X 2 Y 2 introducing tokens rewards for trading, we’ve seen a significant increase in NFT wash trade volumes ( 58 % of the NFT secondary volume was wash trading in 2022 ). Wash trade volume may disappear once the incentives become less attractive or profitable for traders (starting Sep 2022 ). image 970 × 474 28 . 3 KB Source: NFT Wash Trading Dashboard (hildobby) What drives long-term impact? (The real unanswered question) Theory: We can bootstrap a network with supply incentives, but demand needs to follow, and that comes from natural product usage (need to be careful to not create fabricated demand) Aave - Non-Recursive Borrowing had ~ 60 % Retention While only 18 % of Aave TVL retained, 58 % of “non-recursive” borrow volume retained 30 -days later (+$ 30 . 6 M vs pre-incentives). Hypothesis: The “Non-Recursive Borrow” demand comes from other use cases on/offchain image 1036 × 278 54 . 4 KB Aave User Journey Mapping OP Quests - ~ 8 % of transactions come from addresses new to Optimism via Quests While Quests appeared to have driven a high-volume of fabricated activity, total Optimism daily transactions increased ~ 50 % Post-Quests, and 15 / 18 apps saw increased transactions 8 % of Transactions (Last 30 Days) came from addresses new to Optimism via Quests ( 18 % of transacting addresses) Quests on Coinbase Wallet launched Mar 9 (requires Coinbase authentication per wallet) image 1060 × 490 53 . 9 KB Optimism Quests - App Growth on Optimism After Quests :tv::sparkles: Breakdown by Program - Liquidity Top Inflows - Acquisition Period For Liquidity Inflows, we can segment programs by where the incentives were deployed (i.e. to the native app, to an external DEX pool). Inflows Cutoff at Program End Date (Latest Date if still Live) App Product Incentivized Net TVL Inflows Projected OP Deployed Net Inflows per OP Aave App $ 342 . 0 M 5 . 0 M $ 68 Velodrome App $ 241 . 9 M 5 . 1 M $ 47 Synthetix DEX Pools $ 120 . 6 M 2 . 4 M $ 50 Rocket Pool DEX Pools $ 79 . 4 M 222 . 0 k $ 357 Pooltogether App $ 56 . 4 M 842 . 5 k $ 67 Beefy Finance App $ 32 . 2 M 172 . 1 k $ 187 Stargate Finance App $ 27 . 0 M 469 . 7 k $ 57 Beethoven X App $ 26 . 3 M 209 . 5 k $ 125 Pika Protocol App $ 11 . 0 M 672 . 6 k $ 16 Rubicon App $ 9 . 1 M 791 . 1 k $ 11 Top Inflows - Post-Incentives Period Only Showing Programs which Have Ended App Product Incentivized Net TVL Inflows (End Date + 30 ) Projected OP Deployed Net Inflows per OP (End Date + 30 ) Aave App $ 77 . 3 M 5 . 0 M $ 15 Defiedge Uniswap - Phase 2 $ 2 . 1 M 25 . 0 k $ 85 Revert Finance App $ 1 . 6 M 240 . 8 k $ 7 Xtoken Uniswap - Phase 1 + 2 $ 1 . 2 M 41 . 7 k $ 28 Gamma Uniswap - Phase 1 + 2 $ 372 . 0 k 41 . 7 k $ 9 Layer 2 Dao DEX Pool $ 235 . 0 k 20 . 8 k $ 11 Wepiggy App $ 166 . 8 k 300 . 0 k $ 1 Breakdown by Program - App Usage Top Usage - Acquisition Period For usage, we aggregate all incentive programs and observe the activity on each apps’ contracts. For a broader view, see the Project Usage Trends dashboard and project <> contract mappings. Cutoff at Program End Date (Latest Date if still Live) App # OP Allocated OP Deployed (All Programs) Incremental # Txs Annualized # Txs / OP Incremental # Txs After Annualized # Txs / OP After Velodrome 7 . 0 M 5 . 1 M 8 , 045 0 . 58 - - Uniswap 1 . 0 M 150 . 0 K 5 , 666 13 . 79 - - Pika Protocol 900 . 0 K 672 . 6 K 4 , 782 2 . 59 - - Rubicon 900 . 0 K 791 . 1 K 4 , 110 1 . 9 1 , 584 0 . 73 Synthetix 9 . 0 M 4 . 9 M 4 , 092 0 . 3 - - Aave 5 . 0 M 4 . 8 M 3 , 123 0 . 24 4 , 744 0 . 36 Hop Protocol 1 . 0 M 152 . 6 K 3 , 003 7 . 18 - - Beethoven X 500 . 0 K 164 . 7 K 2 , 297 3 . 96 - - 1 inch 300 . 0 K 300 . 0 K 2 , 101 2 . 56 390 0 . 47 PoolTogether 1 . 0 M 842 . 5 K 1 , 910 0 . 83 - - Top Usage - Post-Incentives Period Cutoff at Program End Date + 30 days (Latest Date if not yet reached 30 days) App # OP Allocated OP Deployed Incremental # Txs Annualized # Txs / OP Incremental # Txs After Annualized # Txs / OP After Rubicon 900 . 0 K 791 . 1 K 4 , 110 1 . 9 1 , 584 0 . 73 1 inch 300 . 0 K 300 . 0 K 2 , 101 2 . 56 390 0 . 47 Revert Finance 240 . 0 K 240 . 8 K 218 0 . 33 247 0 . 37 Aave 5 . 0 M 4 . 8 M 3 , 123 0 . 24 4 , 744 0 . 36 WePiggy 300 . 0 K 300 . 0 K 39 0 . 05 12 0 . 01 Aelin 900 . 0 K 900 . 0 K 8 0 - 5 0 Top Gas Spend - Acquisition Period Cutoff at Program End Date (Latest Date if still Live) App # OP Allocated OP Deployed Incremental Gas Fee ($) Annualized Gas Fee / OP Incremental Gas Fee ($) After Annualized Gas Fee / OP After Synthetix 9 . 0 M 4 . 9 M 91 , 133 6 . 76 - - Velodrome 7 . 0 M 5 . 1 M 32 , 018 2 . 31 - - Hop Protocol 1 . 0 M 152 . 6 K 17 , 055 40 . 79 - - Uniswap 1 . 0 M 150 . 0 K 9 , 268 22 . 55 - - Beethoven X 500 . 0 K 164 . 7 K 7 , 638 16 . 93 - - Aave 5 . 0 M 4 . 8 M 6 , 832 0 . 52 12 , 297 0 . 93 Rubicon 900 . 0 K 791 . 1 K 6 , 519 3 . 01 8 , 511 3 . 93 QiDao 750 . 0 K 342 . 9 K 5 , 729 6 . 10 - - Stargate Finance 1 . 0 M 469 . 7 K 4 , 774 3 . 71 - - 1 inch 300 . 0 K 300 . 0 K 4 , 511 5 . 49 - 49 - 0 . 06 Top Usage - Post-Incentives Period Cutoff at Program End Date + 30 days (Latest Date if not yet reached 30 days) App # OP Allocated OP Deployed Incremental Gas Fee ($) Annualized Gas Fee / OP Incremental Gas Fee ($) After Annualized Gas Fee / OP After Rubicon 900 . 0 K 791 . 1 K 6 , 519 3 . 01 8 , 511 3 . 93 Aave 5 . 0 M 4 . 8 M 6 , 832 0 . 52 12 , 297 0 . 93 Revert Finance 240 . 0 K 240 . 8 K 99 0 . 15 263 0 . 40 WePiggy 300 . 0 K 300 . 0 K 132 0 . 16 132 0 . 16 1 inch 300 . 0 K 300 . 0 K 4 , 511 5 . 49 - 49 - 0 . 06 Aelin 900 . 0 K 900 . 0 K 49 0 . 02 - 66 - 0 . 03 Key Takeaways Usage Acquisition Efficiency has improved Post-Phase 0 Incentives have been effective at attracting usage, but not retaining it (yet). Game-able program designs will be gamed - how can we mitigate this? Open design space with how to drive longer-term impact post-incentives. Analytics Resources Things are still super messy, but a lot of the code and scripts powering our analysis are listed below! [Readmes & how-to-contribute writeups coming soon] TVL Flows by Program Flows are shown by token at the latest price (unless otherwise indicated) | Sources: Defillama & TheGraph APIs Time-Series Chart of TVL Flows by Program from Start to End + 30 Days Folder of charts specific to each program Onchain Usage by Program Incentive Program Usage Summary from Start to End + 30 Days Other Metrics & Resources Dashboards that publicly sharable Optimism Popular Apps and Project Usage Trends - Dashboard NFT Marketplace Volume on Optimism - Dashboard DEX Volume on Optimism - Dashboard Overall Optimism Protocol Metrics - Dashboard OP Analytics GitHub - See Readme for more Google Sheet Summary of Results Gov Fund Incentive Program Performance Sheet Token Distribution Transfer Mappings [WIP] We can map token transfers involving known (or suspected) project addresses to determine when tokens are deployed (and to where). Intermediate Addresses <> Program Mapping (can help here!) | Mapping Scripts Closing Notes Tracking this stuff is super difficult to do as a small group. Please help :slight_smile: There are also infinite more rabbit holes we could go down We’re thinking about better metrics than raw transactions, volume, and TVL (i.e. app fees, transfer volume, incentivized vs native yield) and deeper-dive methods (i.e. segment by behavior type). Open to ideas! Splitting by grants and by season may get increasingly difficult over time, since protocols are re-applying for grants and using the same addresses. In a perfect world, every proposal uses completely distinct addresses, but may be infeasible. For simplicity: Thales and Overtime Markets were combined since they each used the same proposal address (we can’t easily tell the grants apart) This post is coauthored with @MSilb 7 .
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  Likers: DELETEDUSER, FractalVisions, 0xzenodotus, diligit, MSilb7, forrest, Mery, Houchens, Kety, jackanorak, Nazia, raho, ScaleWeb3
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• chuxin_h - No Role

  March 21, 2023, 12:56 p.m.
  Background The data team at OP Labs has been working on tracking OP distributions across governanc… Background The data team at OP Labs has been working on tracking OP distributions across governance grants, partner funding, and other sources. We first shared early insights in Oct 2022 . Takeaways then: Projects were deploying OP too slowly ( 34 % of approved OP was deployed). Proposals approved via governance were less effective than other programs. This doc serves to update data and case studies, and begin open-sourcing the data so others can analyze & contribute. A snapshot of program data was taken on Mar 13 , 2023 Current Deployment Status - Growth Experiments We project that 56 % of allocated OP ( 30 . 7 M) has been deployed (not in projects’ wallets) We’ve observed 38 growth experiment proposals launch or complete, with 32 to be launched. Live & Completed programs represent 80 % ( 43 . 7 M) of Allocated OP (i.e. 24 % of allocated OP is “live” and to-be-deployed). # Programs # OP Allocated (M) % OP Allocated Live ‎? Subtotal 33 41 . 1 M 75 % Governance - Season 3 - - Governance - Season 2 10 7 . 1 M Governance - Season 1 9 4 . 5 M Governance - Phase 0 14 29 . 5 M Coming soon ‎⏳ Subtotal 32 10 . 9 M 20 % Governance - Season 3 12 2 . 1 M Governance - Season 2 13 5 . 2 M Governance - Season 1 4 1 . 3 M Governance - Phase 0 3 2 . 2 M Completed Subtotal 5 2 . 6 M 5 % Governance - Season 3 - - Governance - Season 2 1 240 . 0 K Governance - Season 1 - - Governance - Phase 0 4 2 . 4 M Grand Total 70 54 . 6 M Source: OP Summer Programs 3 Stats by Season Aggregate by Gov Fund Season Stats were measured at the Latest Date (Note: Many programs still ongoing) Source # OP Allocated Net OP Deployed Net $ Inflow Net $ Inflow / OP Incremental # Txs Annualized # Txs / OP Incremental Gas Fee ($) Annualized Gas Fee / OP Governance - Phase 0 31 . 0 M 16 . 8 M 128 . 1 M $ 7 . 61 16 , 775 0 . 36 123 , 940 2 . 6872 Governance - Season 1 4 . 5 M 2 . 0 M 111 . 6 M $ 54 . 46 3 , 378 0 . 60 16 , 887 3 . 0086 Governance - Season 2 2 . 5 M 978 . 0 K 16 . 9 M $ 17 . 23 1 , 214 0 . 45 5 , 154 1 . 9234 Multiple 8 . 9 M 6 . 9 M 227 . 0 M $ 32 . 69 10 , 701 0 . 56 33 , 508 1 . 7607 Revisited Case Studies & Early Theories Note: We are mentioning specific programs. Some were more successful than others, but the intent is to learn from their examples, not to accuse or blame. Retention Problem: Separate “Usage Acquisition” vs “Longer-Term Impact” Theory: Incentives are great at “usage acquisition” (transaction volume, liquidity, etc), but this is not a good predictor of longer-term impact. DEXs: Uniswap Phases 1 + 2 (selected managers), Revert Finance, Rainbow image 992 × 451 16 . 4 KB image 988 × 452 17 . 4 KB image 988 × 450 32 . 8 KB Lending & Borrowing: Aave & WePiggy TVL measured as “available liquidity” (deposits - borrows) Aave had 18 % retention from the local max before incentives turned off (+$ 431 M) to 30 days post-incentives (+$ 78 . 5 M) WePiggy had 6 % TVL retention by the same methodology (+$ 2 . 8 M to $ 165 k). image 991 × 452 32 KB image 992 × 450 29 . 5 KB Value Extractive-Resistant Design: Can someone create fabricated activity to maximize rewards? Theory: When Rewards > Costs, value-maximizing actors will spend to eat up the rewards. Anything that can be gamed, will be gamed. Aave - Oversized Emissions Led to Recursive Borrowing Aave ‘Deposit APY’ + Rewards > Aave ‘Borrow APY’, so actors borrowed and re-deposited the same asset over and over to maximize rewards Learning: Unless ****we can design a system where Rewards < ‘Borrow APY’ - ‘Deposit APY’, lending rewards may always be gamed. Snapshot ~ 1 day in to the Aave Liquidity Program image 1016 × 500 89 . 8 KB Rainbow Wallet - Swap Volume Leaderboard Led to Inorganic Volume Rainbow Wallet incentivized bridging to and swapping on Optimism. Base rewards were partial gas rebates, but there was an additional 52 K OP bonus to the top 100 addresses by trade volume (as of Mar 5 ). On the last day, trade volume spiked to $ 12 M, likely by addresses trying to get in the top 100 . Trade volume fell to $ 25 k Trade Volume / Day post-program (vs ~$ 4 k prior), showing that the increased volume did not sustain. image 1025 × 476 47 . 3 KB This was similar to Slingshot’s Flash Programs we observed last time: Rewards were offered either per trade or per $ of volume until they ran out. Transactions spiked up following program announcements and then return to normal levels afterward. image 1022 × 347 56 . 5 KB Elsewhere: Demand-Side Incentives Have Led to NFT Wash Trading Wash Trading: People trading NFTs back and forth with themselves to create fabricated volume. With LooksRare and X 2 Y 2 introducing tokens rewards for trading, we’ve seen a significant increase in NFT wash trade volumes ( 58 % of the NFT secondary volume was wash trading in 2022 ). Wash trade volume may disappear once the incentives become less attractive or profitable for traders (starting Sep 2022 ). image 970 × 474 28 . 3 KB Source: NFT Wash Trading Dashboard (hildobby) 2 What drives long-term impact? (The real unanswered question) Theory: We can bootstrap a network with supply incentives, but demand needs to follow, and that comes from natural product usage (need to be careful to not create fabricated demand) Aave - Non-Recursive Borrowing had ~ 60 % Retention While only 18 % of Aave TVL retained, 58 % of “non-recursive” borrow volume retained 30 -days later (+$ 30 . 6 M vs pre-incentives). Hypothesis: The “Non-Recursive Borrow” demand comes from other use cases on/offchain image 1036 × 278 54 . 4 KB Aave User Journey Mapping 1 OP Quests - ~ 8 % of transactions come from addresses new to Optimism via Quests While Quests appeared to have driven a high-volume of fabricated activity, total Optimism daily transactions increased ~ 50 % Post-Quests, and 15 / 18 apps saw increased transactions 8 % of Transactions (Last 30 Days) came from addresses new to Optimism via Quests ( 18 % of transacting addresses) Quests on Coinbase Wallet launched Mar 9 (requires Coinbase authentication per wallet) image 1060 × 490 53 . 9 KB Optimism Quests - App Growth on Optimism After Quests :tv::sparkles: 3 Breakdown by Program - Liquidity Top Inflows - Acquisition Period For Liquidity Inflows, we can segment programs by where the incentives were deployed (i.e. to the native app, to an external DEX pool). Inflows Cutoff at Program End Date (Latest Date if still Live) App Product Incentivized Net TVL Inflows Projected OP Deployed Net Inflows per OP Aave App $ 342 . 0 M 5 . 0 M $ 68 Velodrome App $ 241 . 9 M 5 . 1 M $ 47 Synthetix DEX Pools $ 120 . 6 M 2 . 4 M $ 50 Rocket Pool DEX Pools $ 79 . 4 M 222 . 0 k $ 357 Pooltogether App $ 56 . 4 M 842 . 5 k $ 67 Beefy Finance App $ 32 . 2 M 172 . 1 k $ 187 Stargate Finance App $ 27 . 0 M 469 . 7 k $ 57 Beethoven X App $ 26 . 3 M 209 . 5 k $ 125 Pika Protocol App $ 11 . 0 M 672 . 6 k $ 16 Rubicon App $ 9 . 1 M 791 . 1 k $ 11 Top Inflows - Post-Incentives Period Only Showing Programs which Have Ended App Product Incentivized Net TVL Inflows (End Date + 30 ) Projected OP Deployed Net Inflows per OP (End Date + 30 ) Aave App $ 77 . 3 M 5 . 0 M $ 15 Defiedge Uniswap - Phase 2 $ 2 . 1 M 25 . 0 k $ 85 Revert Finance App $ 1 . 6 M 240 . 8 k $ 7 Xtoken Uniswap - Phase 1 + 2 $ 1 . 2 M 41 . 7 k $ 28 Gamma Uniswap - Phase 1 + 2 $ 372 . 0 k 41 . 7 k $ 9 Layer 2 Dao DEX Pool $ 235 . 0 k 20 . 8 k $ 11 Wepiggy App $ 166 . 8 k 300 . 0 k $ 1 Breakdown by Program - App Usage Top Usage - Acquisition Period For usage, we aggregate all incentive programs and observe the activity on each apps’ contracts. For a broader view, see the Project Usage Trends 3 dashboard and project <> contract mappings. Cutoff at Program End Date (Latest Date if still Live) App # OP Allocated OP Deployed (All Programs) Incremental # Txs Annualized # Txs / OP Incremental # Txs After Annualized # Txs / OP After Velodrome 7 . 0 M 5 . 1 M 8 , 045 0 . 58 - - Uniswap 1 . 0 M 150 . 0 K 5 , 666 13 . 79 - - Pika Protocol 900 . 0 K 672 . 6 K 4 , 782 2 . 59 - - Rubicon 900 . 0 K 791 . 1 K 4 , 110 1 . 9 1 , 584 0 . 73 Synthetix 9 . 0 M 4 . 9 M 4 , 092 0 . 3 - - Aave 5 . 0 M 4 . 8 M 3 , 123 0 . 24 4 , 744 0 . 36 Hop Protocol 1 . 0 M 152 . 6 K 3 , 003 7 . 18 - - Beethoven X 500 . 0 K 164 . 7 K 2 , 297 3 . 96 - - 1 inch 300 . 0 K 300 . 0 K 2 , 101 2 . 56 390 0 . 47 PoolTogether 1 . 0 M 842 . 5 K 1 , 910 0 . 83 - - Top Usage - Post-Incentives Period Cutoff at Program End Date + 30 days (Latest Date if not yet reached 30 days) App # OP Allocated OP Deployed Incremental # Txs Annualized # Txs / OP Incremental # Txs After Annualized # Txs / OP After Rubicon 900 . 0 K 791 . 1 K 4 , 110 1 . 9 1 , 584 0 . 73 1 inch 300 . 0 K 300 . 0 K 2 , 101 2 . 56 390 0 . 47 Revert Finance 240 . 0 K 240 . 8 K 218 0 . 33 247 0 . 37 Aave 5 . 0 M 4 . 8 M 3 , 123 0 . 24 4 , 744 0 . 36 WePiggy 300 . 0 K 300 . 0 K 39 0 . 05 12 0 . 01 Aelin 900 . 0 K 900 . 0 K 8 0 - 5 0 Top Gas Spend - Acquisition Period Cutoff at Program End Date (Latest Date if still Live) App # OP Allocated OP Deployed Incremental Gas Fee ($) Annualized Gas Fee / OP Incremental Gas Fee ($) After Annualized Gas Fee / OP After Synthetix 9 . 0 M 4 . 9 M 91 , 133 6 . 76 - - Velodrome 7 . 0 M 5 . 1 M 32 , 018 2 . 31 - - Hop Protocol 1 . 0 M 152 . 6 K 17 , 055 40 . 79 - - Uniswap 1 . 0 M 150 . 0 K 9 , 268 22 . 55 - - Beethoven X 500 . 0 K 164 . 7 K 7 , 638 16 . 93 - - Aave 5 . 0 M 4 . 8 M 6 , 832 0 . 52 12 , 297 0 . 93 Rubicon 900 . 0 K 791 . 1 K 6 , 519 3 . 01 8 , 511 3 . 93 QiDao 750 . 0 K 342 . 9 K 5 , 729 6 . 10 - - Stargate Finance 1 . 0 M 469 . 7 K 4 , 774 3 . 71 - - 1 inch 300 . 0 K 300 . 0 K 4 , 511 5 . 49 - 49 - 0 . 06 Top Usage - Post-Incentives Period Cutoff at Program End Date + 30 days (Latest Date if not yet reached 30 days) App # OP Allocated OP Deployed Incremental Gas Fee ($) Annualized Gas Fee / OP Incremental Gas Fee ($) After Annualized Gas Fee / OP After Rubicon 900 . 0 K 791 . 1 K 6 , 519 3 . 01 8 , 511 3 . 93 Aave 5 . 0 M 4 . 8 M 6 , 832 0 . 52 12 , 297 0 . 93 Revert Finance 240 . 0 K 240 . 8 K 99 0 . 15 263 0 . 40 WePiggy 300 . 0 K 300 . 0 K 132 0 . 16 132 0 . 16 1 inch 300 . 0 K 300 . 0 K 4 , 511 5 . 49 - 49 - 0 . 06 Aelin 900 . 0 K 900 . 0 K 49 0 . 02 - 66 - 0 . 03 Key Takeaways Usage Acquisition Efficiency has improved Post-Phase 0 Incentives have been effective at attracting usage, but not retaining it (yet). Game-able program designs will be gamed - how can we mitigate this? Open design space with how to drive longer-term impact post-incentives. Analytics Resources Things are still super messy, but a lot of the code and scripts powering our analysis are listed below! [Readmes & how-to-contribute writeups coming soon] TVL Flows by Program Flows are shown by token at the latest price (unless otherwise indicated) | Sources: Defillama & TheGraph APIs Time-Series Chart 2 of TVL Flows by Program from Start to End + 30 Days Folder of charts specific to each program Onchain Usage by Program Incentive Program Usage Summary from Start to End + 30 Days Other Metrics & Resources Dashboards that publicly sharable Optimism Popular Apps and Project Usage Trends - Dashboard 3 NFT Marketplace Volume on Optimism - Dashboard DEX Volume on Optimism - Dashboard Overall Optimism Protocol Metrics - Dashboard 1 OP Analytics GitHub - See Readme for more Google Sheet Summary of Results Gov Fund Incentive Program Performance Sheet 2 Token Distribution Transfer Mappings [WIP] We can map token transfers involving known (or suspected) project addresses to determine when tokens are deployed (and to where). Intermediate Addresses <> Program Mapping 1 (can help here!) | Mapping Scripts 1 Closing Notes Tracking this stuff is super difficult to do as a small group. Please help :slight_smile: There are also infinite more rabbit holes we could go down We’re thinking about better metrics than raw transactions, volume, and TVL (i.e. app fees, transfer volume, incentivized vs native yield) and deeper-dive methods (i.e. segment by behavior type). Open to ideas! Splitting by grants and by season may get increasingly difficult over time, since protocols are re-applying for grants and using the same addresses. In a perfect world, every proposal uses completely distinct addresses, but may be infeasible. For simplicity: Thales and Overtime Markets were combined since they each used the same proposal address (we can’t easily tell the grants apart) This post is coauthored with @MSilb 7 .
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  Likers: deleteduser, fractalvisions, 0xzenodotus, diligit, msilb7, forrest, mery, houchens, kety, jackanorak, nazia, raho, scaleweb3
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