### **Update:**
**We thought that a video would provide a more detailed explanation of the product we offer. To make it more engaging for you, we've enhanced it with AI.**
[![image](https://i.ibb.co/r6jtS9H/7263-783d14c1f75f4ea191a4e0ad8-1696936700840.png)](https://www.youtube.com/watch?v=funiC0HSgxc)

### **We appreciate your support!**
-----

Reseath overview
Nominators hold a crucial role in Polkadot owing to the innovative NPOS approach. They fortify the economic security of the system with genuine decentralization through their constant actions to select the best nodes and personal commitment.
This approach demands significantly more involvement and decisions from them compared to any other staking yield (L1, farming, etc.). To incentivize their contributions, economic tools are employed: nominators receive the highest APY in the market for successful actions, while strict disincentivization tools (loss of reward or slashing of personal stake) ensure adherence to the system's stability.
This blend, along with the platform's openness, enables the realization of a high level of decentralization, offering true freedom of action for participants and full permissionlessness. Any DOT holder can enter the system, undertake tasks, and earn rewards for their successful completion.
However, the attributes of decentralization, openness, and permissionlessness mean every potential or existing nominator must engage in continuous self-education and perpetual optimization of their role. The complexity and dynamism of the system, key actions specific to the Polkadot ecosystem, necessitate proactive engagement from the nominator's side with almost zero feedback loop (because of decentralization).
Indirect data (that was partially available before this research) show that nominator behavior is somewhat suboptimal. We have conducted an extensive analysis of the structure of the nomination system from the nominators' behavior perspective and its dynamics.&nbsp;
Key findings (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener">full details are available in the full proposal</a>):
<ul>
<li>The staking ensemble is currently "decentralizing," with the contribution of "regular accounts" growing at the expense of a transition from "whales."</li>
<li>The growth in the role of regular nominators correlates with competition among them. Moreover, the closer the personal stake of the nominator is to the disincentivization level (min-reward), the higher the competition.
<ul>
<li><img src="https://i.ibb.co/2ZK4jts/9.png" alt="Graph 9" width="300" height="228" /> (<a href="https://i.ibb.co/2ZK4jts/9.png" target="_blank" rel="noopener">Zoom in new window</a>)</li>
</ul>
</li>
<li>A significant number of accounts are near the disincentivization border, with 50% in the min-reward growth zone of 100%.
<ul>
<li><img src="https://i.ibb.co/34zzFjN/6.png" alt="Graph 6" width="300" height="180" /> (<a href="https://i.ibb.co/34zzFjN/6.png" target="_blank" rel="noopener">Zoom in</a>)&nbsp;</li>
</ul>
</li>
<li>These accounts risk falling into the potential disincentivization zone (or are already in), possibly not receiving a reward while having staked funds if their actions are not optimal.&nbsp; 
<ul>
<li><img src="https://i.ibb.co/Mcdc9BM/19.png" alt="Graph 19 " width="300" height="188" />(<a href="https://i.ibb.co/Mcdc9BM/19.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>Our research showed that, in their mass, nominators indeed behave suboptimally. This concerns both stake size control and validator set control. Therefore, during the system's transformation, a significant number of accounts lose the active set &ndash; disincentivization occurs.
<ul>
<li><img src="https://i.ibb.co/SxgGfKx/26.png" alt="Graph 26 " width="300" height="168" />(<a href="https://i.ibb.co/SxgGfKx/26.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/Mcdc9BM/19.png" alt="Graph 19" width="300" height="188" /> (<a href="https://i.ibb.co/Mcdc9BM/19.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>The non-receipt of rewards as a disincentivization tool works harshly: currently, 40% of nominators do not return (dropout) to the active set after its loss, a number that is increasing.
<ul>
<li><img src="https://i.ibb.co/ThFc33z/17.png" alt="Graph 17" width="300" height="252" /> (<a href="https://i.ibb.co/ThFc33z/17.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/9qSnd0Z/16.png" alt="Graph 16" width="300" height="164" /> (<a href="https://i.ibb.co/9qSnd0Z/16.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>Simultaneously, our research showed that there is a set of super-effective regular nominators in the system (not just basically performing target tasks in the system, but doing it super-effectively).
<ul>
<li><img src="https://i.ibb.co/drmr2xd/25.png" alt="Graph 25" width="300" height="192" /> (<a href="https://i.ibb.co/drmr2xd/25.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>These accounts proficiently manage both stake size and validator choice over the long term. A nonzero number of them even never missed the active set for the last 500 eras with growing median reward.&nbsp;
<ul>
<li><img src="https://i.ibb.co/rmBdm70/24.png" alt="Graph 24" width="300" height="198" /> (<a href="https://i.ibb.co/rmBdm70/24.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/HTdmmwZ/31.png" alt="Graph 31" width="300" height="198" /> (<a href="https://i.ibb.co/HTdmmwZ/31.png" target="_blank" rel="noopener">Zoom in</a>)&nbsp;</li>
</ul>
</li>
<li>Their actions are easily observable and visible on-chain (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener">data can be found in the full proposal</a>).</li>
</ul>
Proposed solution
Given the presence of these efficient nominators, we can implement benchmarking based on their activities, continuously showcasing optimal actions and outcomes. By utilizing unbiased on-chain data in an open-source format, this method demonstrates superior strategies without necessitating centralized actions or permissions.
Benchmarking works as a self-learning tool for nominators (what the best nominators do, when they increase the stake, which validators and when they choose), as well as a feedback loop in the nomination process (the nominator can understand their effectiveness, compare it with the best, etc.).
We propose the implementation of nominator leaderboards &ndash; dashboards that visualize nominator behavior, offering a comprehensive view of actions and results within the system. The leaderboard allows showing the entire range of actions and results that exist in the system.
These leaderboards serve as a user-friendly interface over the core incentivization mechanism, aiming to prompt nominators to act before facing disincentivization, thus softening the tool's impact without compromising its efficacy.
By centering on regular accounts, this tool amplifies the decentralization effect, making it universally accessible and comprehensible, regardless of language, location, or depth of blockchain/ecosystem knowledge.
Key components of the system include:
<ol>
<li>Nominator Dashboard: A leaderboard for nominator rewards with insights and behavior analysis.</li>
<li>Validator Dashboard: A leaderboard for validator long-term rewards and real payouts with insights and trends.</li>
<li>Nomination Pool Dashboard: A leaderboard for nominator rewards and payouts with insights and trends.</li>
</ol>
Team and traction 
Our team specializes in researching user behavior in the web3 ecosystem. We have a track record with research in the blockchain industry, specifically in DeFi traders' behavior, dApp user experience, and utilizing AI&amp;ML. Our team already has relevant experience with different methods of blockchain research. We have conducted research to determine the most suitable method for this case. Astar, dYdX, Lido done and methods that were tested (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1">Samples can be found in the Appendix.1 at the end of this proposal</a>).
Interface sketches 
The implementation of the interface will take the form of a &ldquo;desktop&rdquo; webapp, providing users with a convenient way to explore the system's capabilities. The design will follow the style of "Similarweb," known for its user-friendly and visually appealing approach. The following is a basic layout and visual example of an internal project page. Please note that the dataset provided in the<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener"> full proposal</a> is illustrative and subject to change.
Benefits for Polkadot ecosystem from Motif 
The Motif platform empowers key stakeholders with valuable data, insights, and educational resources. It aligns interests, optimizes engagement, and fosters a stronger and more collaborative Polkadot community.
<ul>
<li>Benefits for Nominators:
<ul>
<li>Nominators self-education and benchmarking</li>
<li>Higher stickiness of system participants due to Confidence and Understanding of the system's operation</li>
<li>Nominating based on data-driven insights (not just the minimum stake or a known name)</li>
</ul>
</li>
<li>Benefits for Validators:
<ul>
<li>Competition Beyond Low Fee</li>
<li>Data-Driven Strategy:</li>
<li>Optimized Engagement</li>
<li>Potential reward growth due to improving the work of nominators in the system.</li>
</ul>
</li>
<li>Community-Wide Benefits
<ul>
<li>Decentralization focus</li>
<li>Onboarding newcomer nominators and Retaining Members</li>
</ul>
</li>
</ul>
Also, an open-source platform for the ecosystem. Based on this solution, additional elements or separate products can be based, which will improve the effectiveness of the feedback loop.
Thanks for support&nbsp;
We extend our heartfelt gratitude to those who have significantly assisted us during the nine months we prepared this proposal: Bill Laboon, Jonas Gehrlein, Raul Romanutti, Bryan Chen, Otar, Anaelle, Alex Promoteam, Tim, Denis Pisarev, Kirill Pimenov, Alice Faultcore, Subsquid guys and Dmitry, Jay Chrawnna, Tugy and Ra from Amforc, Tom, Ilhan, CoinStudio, Greg from Ryabina, and many more members from our community.

Reseath overview
Nominators hold a crucial role in Polkadot owing to the innovative NPOS approach. They fortify the economic security of the system with genuine decentralization through their constant actions to select the best nodes and personal commitment.
This approach demands significantly more involvement and decisions from them compared to any other staking yield (L1, farming, etc.). To incentivize their contributions, economic tools are employed: nominators receive the highest APY in the market for successful actions, while strict disincentivization tools (loss of reward or slashing of personal stake) ensure adherence to the system's stability.
This blend, along with the platform's openness, enables the realization of a high level of decentralization, offering true freedom of action for participants and full permissionlessness. Any DOT holder can enter the system, undertake tasks, and earn rewards for their successful completion.
However, the attributes of decentralization, openness, and permissionlessness mean every potential or existing nominator must engage in continuous self-education and perpetual optimization of their role. The complexity and dynamism of the system, key actions specific to the Polkadot ecosystem, necessitate proactive engagement from the nominator's side with almost zero feedback loop (because of decentralization).
Indirect data (that was partially available before this research) show that nominator behavior is somewhat suboptimal. We have conducted an extensive analysis of the structure of the nomination system from the nominators' behavior perspective and its dynamics.&nbsp;
Key findings (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener">full details are available in the full proposal</a>):
<ul>
<li>The staking ensemble is currently "decentralizing," with the contribution of "regular accounts" growing at the expense of a transition from "whales."</li>
<li>The growth in the role of regular nominators correlates with competition among them. Moreover, the closer the personal stake of the nominator is to the disincentivization level (min-reward), the higher the competition.
<ul>
<li><img src="https://i.ibb.co/2ZK4jts/9.png" alt="Graph 9" width="300" height="228" /> (<a href="https://i.ibb.co/2ZK4jts/9.png" target="_blank" rel="noopener">Zoom in new window</a>)</li>
</ul>
</li>
<li>A significant number of accounts are near the disincentivization border, with 50% in the min-reward growth zone of 100%.
<ul>
<li><img src="https://i.ibb.co/34zzFjN/6.png" alt="Graph 6" width="300" height="180" /> (<a href="https://i.ibb.co/34zzFjN/6.png" target="_blank" rel="noopener">Zoom in</a>)&nbsp;</li>
</ul>
</li>
<li>These accounts risk falling into the potential disincentivization zone (or are already in), possibly not receiving a reward while having staked funds if their actions are not optimal.&nbsp; 
<ul>
<li><img src="https://i.ibb.co/Mcdc9BM/19.png" alt="Graph 19 " width="300" height="188" />(<a href="https://i.ibb.co/Mcdc9BM/19.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>Our research showed that, in their mass, nominators indeed behave suboptimally. This concerns both stake size control and validator set control. Therefore, during the system's transformation, a significant number of accounts lose the active set &ndash; disincentivization occurs.
<ul>
<li><img src="https://i.ibb.co/SxgGfKx/26.png" alt="Graph 26 " width="300" height="168" />(<a href="https://i.ibb.co/SxgGfKx/26.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/Mcdc9BM/19.png" alt="Graph 19" width="300" height="188" /> (<a href="https://i.ibb.co/Mcdc9BM/19.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>The non-receipt of rewards as a disincentivization tool works harshly: currently, 40% of nominators do not return (dropout) to the active set after its loss, a number that is increasing.
<ul>
<li><img src="https://i.ibb.co/ThFc33z/17.png" alt="Graph 17" width="300" height="252" /> (<a href="https://i.ibb.co/ThFc33z/17.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/9qSnd0Z/16.png" alt="Graph 16" width="300" height="164" /> (<a href="https://i.ibb.co/9qSnd0Z/16.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>Simultaneously, our research showed that there is a set of super-effective regular nominators in the system (not just basically performing target tasks in the system, but doing it super-effectively).
<ul>
<li><img src="https://i.ibb.co/drmr2xd/25.png" alt="Graph 25" width="300" height="192" /> (<a href="https://i.ibb.co/drmr2xd/25.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>These accounts proficiently manage both stake size and validator choice over the long term. A nonzero number of them even never missed the active set for the last 500 eras with growing median reward.&nbsp;
<ul>
<li><img src="https://i.ibb.co/rmBdm70/24.png" alt="Graph 24" width="300" height="198" /> (<a href="https://i.ibb.co/rmBdm70/24.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/HTdmmwZ/31.png" alt="Graph 31" width="300" height="198" /> (<a href="https://i.ibb.co/HTdmmwZ/31.png" target="_blank" rel="noopener">Zoom in</a>)&nbsp;</li>
</ul>
</li>
<li>Their actions are easily observable and visible on-chain (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener">data can be found in the full proposal</a>).</li>
</ul>
Proposed solution
Given the presence of these efficient nominators, we can implement benchmarking based on their activities, continuously showcasing optimal actions and outcomes. By utilizing unbiased on-chain data in an open-source format, this method demonstrates superior strategies without necessitating centralized actions or permissions.
Benchmarking works as a self-learning tool for nominators (what the best nominators do, when they increase the stake, which validators and when they choose), as well as a feedback loop in the nomination process (the nominator can understand their effectiveness, compare it with the best, etc.).
We propose the implementation of nominator leaderboards &ndash; dashboards that visualize nominator behavior, offering a comprehensive view of actions and results within the system. The leaderboard allows showing the entire range of actions and results that exist in the system.
These leaderboards serve as a user-friendly interface over the core incentivization mechanism, aiming to prompt nominators to act before facing disincentivization, thus softening the tool's impact without compromising its efficacy.
By centering on regular accounts, this tool amplifies the decentralization effect, making it universally accessible and comprehensible, regardless of language, location, or depth of blockchain/ecosystem knowledge.
Key components of the system include:
<ol>
<li>Nominator Dashboard: A leaderboard for nominator rewards with insights and behavior analysis.</li>
<li>Validator Dashboard: A leaderboard for validator long-term rewards and real payouts with insights and trends.</li>
<li>Nomination Pool Dashboard: A leaderboard for nominator rewards and payouts with insights and trends.</li>
</ol>
Team and traction 
Our team specializes in researching user behavior in the web3 ecosystem. We have a track record with research in the blockchain industry, specifically in DeFi traders' behavior, dApp user experience, and utilizing AI&amp;ML. Our team already has relevant experience with different methods of blockchain research. We have conducted research to determine the most suitable method for this case. Astar, dYdX, Lido done and methods that were tested (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1">Samples can be found in the Appendix.1 at the end of this proposal</a>).
Interface sketches 
The implementation of the interface will take the form of a &ldquo;desktop&rdquo; webapp, providing users with a convenient way to explore the system's capabilities. The design will follow the style of "Similarweb," known for its user-friendly and visually appealing approach. The following is a basic layout and visual example of an internal project page. Please note that the dataset provided in the<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener"> full proposal</a> is illustrative and subject to change.
Benefits for Polkadot ecosystem from Motif 
The Motif platform empowers key stakeholders with valuable data, insights, and educational resources. It aligns interests, optimizes engagement, and fosters a stronger and more collaborative Polkadot community.
<ul>
<li>Benefits for Nominators:
<ul>
<li>Nominators self-education and benchmarking</li>
<li>Higher stickiness of system participants due to Confidence and Understanding of the system's operation</li>
<li>Nominating based on data-driven insights (not just the minimum stake or a known name)</li>
</ul>
</li>
<li>Benefits for Validators:
<ul>
<li>Competition Beyond Low Fee</li>
<li>Data-Driven Strategy:</li>
<li>Optimized Engagement</li>
<li>Potential reward growth due to improving the work of nominators in the system.</li>
</ul>
</li>
<li>Community-Wide Benefits
<ul>
<li>Decentralization focus</li>
<li>Onboarding newcomer nominators and Retaining Members</li>
</ul>
</li>
</ul>
Also, an open-source platform for the ecosystem. Based on this solution, additional elements or separate products can be based, which will improve the effectiveness of the feedback loop.
Thanks for support&nbsp;
We extend our heartfelt gratitude to those who have significantly assisted us during the nine months we prepared this proposal: Bill Laboon, Jonas Gehrlein, Raul Romanutti, Bryan Chen, Otar, Anaelle, Alex Promoteam, Tim, Denis Pisarev, Kirill Pimenov, Alice Faultcore, Subsquid guys and Dmitry, Jay Chrawnna, Tugy and Ra from Amforc, Tom, Ilhan, CoinStudio, Greg from Ryabina, and many more members from our community.

Polkadot Nominator Insight Hub. Spreading the Best Nominator Strategies through TOP Nomination Leaderboards

Reseath overview
Nominators hold a crucial role in Polkadot owing to the innovative NPOS approach. They fortify the economic security of the system with genuine decentralization through their constant actions to select the best nodes and personal commitment.
This approach demands significantly more involvement and decisions from them compared to any other staking yield (L1, farming, etc.). To incentivize their contributions, economic tools are employed: nominators receive the highest APY in the market for successful actions, while strict disincentivization tools (loss of reward or slashing of personal stake) ensure adherence to the system's stability.
This blend, along with the platform's openness, enables the realization of a high level of decentralization, offering true freedom of action for participants and full permissionlessness. Any DOT holder can enter the system, undertake tasks, and earn rewards for their successful completion.
However, the attributes of decentralization, openness, and permissionlessness mean every potential or existing nominator must engage in continuous self-education and perpetual optimization of their role. The complexity and dynamism of the system, key actions specific to the Polkadot ecosystem, necessitate proactive engagement from the nominator's side with almost zero feedback loop (because of decentralization).
Indirect data (that was partially available before this research) show that nominator behavior is somewhat suboptimal. We have conducted an extensive analysis of the structure of the nomination system from the nominators' behavior perspective and its dynamics.&nbsp;
Key findings (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener">full details are available in the full proposal</a>):
<ul>
<li>The staking ensemble is currently "decentralizing," with the contribution of "regular accounts" growing at the expense of a transition from "whales."</li>
<li>The growth in the role of regular nominators correlates with competition among them. Moreover, the closer the personal stake of the nominator is to the disincentivization level (min-reward), the higher the competition.
<ul>
<li><img src="https://i.ibb.co/2ZK4jts/9.png" alt="Graph 9" width="300" height="228" /> (<a href="https://i.ibb.co/2ZK4jts/9.png" target="_blank" rel="noopener">Zoom in new window</a>)</li>
</ul>
</li>
<li>A significant number of accounts are near the disincentivization border, with 50% in the min-reward growth zone of 100%.
<ul>
<li><img src="https://i.ibb.co/34zzFjN/6.png" alt="Graph 6" width="300" height="180" /> (<a href="https://i.ibb.co/34zzFjN/6.png" target="_blank" rel="noopener">Zoom in</a>)&nbsp;</li>
</ul>
</li>
<li>These accounts risk falling into the potential disincentivization zone (or are already in), possibly not receiving a reward while having staked funds if their actions are not optimal.&nbsp; 
<ul>
<li><img src="https://i.ibb.co/Mcdc9BM/19.png" alt="Graph 19 " width="300" height="188" />(<a href="https://i.ibb.co/Mcdc9BM/19.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>Our research showed that, in their mass, nominators indeed behave suboptimally. This concerns both stake size control and validator set control. Therefore, during the system's transformation, a significant number of accounts lose the active set &ndash; disincentivization occurs.
<ul>
<li><img src="https://i.ibb.co/SxgGfKx/26.png" alt="Graph 26 " width="300" height="168" />(<a href="https://i.ibb.co/SxgGfKx/26.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/Mcdc9BM/19.png" alt="Graph 19" width="300" height="188" /> (<a href="https://i.ibb.co/Mcdc9BM/19.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>The non-receipt of rewards as a disincentivization tool works harshly: currently, 40% of nominators do not return (dropout) to the active set after its loss, a number that is increasing.
<ul>
<li><img src="https://i.ibb.co/ThFc33z/17.png" alt="Graph 17" width="300" height="252" /> (<a href="https://i.ibb.co/ThFc33z/17.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/9qSnd0Z/16.png" alt="Graph 16" width="300" height="164" /> (<a href="https://i.ibb.co/9qSnd0Z/16.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>Simultaneously, our research showed that there is a set of super-effective regular nominators in the system (not just basically performing target tasks in the system, but doing it super-effectively).
<ul>
<li><img src="https://i.ibb.co/drmr2xd/25.png" alt="Graph 25" width="300" height="192" /> (<a href="https://i.ibb.co/drmr2xd/25.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>These accounts proficiently manage both stake size and validator choice over the long term. A nonzero number of them even never missed the active set for the last 500 eras with growing median reward.&nbsp;
<ul>
<li><img src="https://i.ibb.co/rmBdm70/24.png" alt="Graph 24" width="300" height="198" /> (<a href="https://i.ibb.co/rmBdm70/24.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/HTdmmwZ/31.png" alt="Graph 31" width="300" height="198" /> (<a href="https://i.ibb.co/HTdmmwZ/31.png" target="_blank" rel="noopener">Zoom in</a>)&nbsp;</li>
</ul>
</li>
<li>Their actions are easily observable and visible on-chain (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener">data can be found in the full proposal</a>).</li>
</ul>
Proposed solution
Given the presence of these efficient nominators, we can implement benchmarking based on their activities, continuously showcasing optimal actions and outcomes. By utilizing unbiased on-chain data in an open-source format, this method demonstrates superior strategies without necessitating centralized actions or permissions.
Benchmarking works as a self-learning tool for nominators (what the best nominators do, when they increase the stake, which validators and when they choose), as well as a feedback loop in the nomination process (the nominator can understand their effectiveness, compare it with the best, etc.).
We propose the implementation of nominator leaderboards &ndash; dashboards that visualize nominator behavior, offering a comprehensive view of actions and results within the system. The leaderboard allows showing the entire range of actions and results that exist in the system.
These leaderboards serve as a user-friendly interface over the core incentivization mechanism, aiming to prompt nominators to act before facing disincentivization, thus softening the tool's impact without compromising its efficacy.
By centering on regular accounts, this tool amplifies the decentralization effect, making it universally accessible and comprehensible, regardless of language, location, or depth of blockchain/ecosystem knowledge.
Key components of the system include:
<ol>
<li>Nominator Dashboard: A leaderboard for nominator rewards with insights and behavior analysis.</li>
<li>Validator Dashboard: A leaderboard for validator long-term rewards and real payouts with insights and trends.</li>
<li>Nomination Pool Dashboard: A leaderboard for nominator rewards and payouts with insights and trends.</li>
</ol>
Team and traction 
Our team specializes in researching user behavior in the web3 ecosystem. We have a track record with research in the blockchain industry, specifically in DeFi traders' behavior, dApp user experience, and utilizing AI&amp;ML. Our team already has relevant experience with different methods of blockchain research. We have conducted research to determine the most suitable method for this case. Astar, dYdX, Lido done and methods that were tested (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1">Samples can be found in the Appendix.1 at the end of this proposal</a>).
Interface sketches 
The implementation of the interface will take the form of a &ldquo;desktop&rdquo; webapp, providing users with a convenient way to explore the system's capabilities. The design will follow the style of "Similarweb," known for its user-friendly and visually appealing approach. The following is a basic layout and visual example of an internal project page. Please note that the dataset provided in the<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener"> full proposal</a> is illustrative and subject to change.
Benefits for Polkadot ecosystem from Motif 
The Motif platform empowers key stakeholders with valuable data, insights, and educational resources. It aligns interests, optimizes engagement, and fosters a stronger and more collaborative Polkadot community.
<ul>
<li>Benefits for Nominators:
<ul>
<li>Nominators self-education and benchmarking</li>
<li>Higher stickiness of system participants due to Confidence and Understanding of the system's operation</li>
<li>Nominating based on data-driven insights (not just the minimum stake or a known name)</li>
</ul>
</li>
<li>Benefits for Validators:
<ul>
<li>Competition Beyond Low Fee</li>
<li>Data-Driven Strategy:</li>
<li>Optimized Engagement</li>
<li>Potential reward growth due to improving the work of nominators in the system.</li>
</ul>
</li>
<li>Community-Wide Benefits
<ul>
<li>Decentralization focus</li>
<li>Onboarding newcomer nominators and Retaining Members</li>
</ul>
</li>
</ul>
Also, an open-source platform for the ecosystem. Based on this solution, additional elements or separate products can be based, which will improve the effectiveness of the feedback loop.
Thanks for support&nbsp;
We extend our heartfelt gratitude to those who have significantly assisted us during the nine months we prepared this proposal: Bill Laboon, Jonas Gehrlein, Raul Romanutti, Bryan Chen, Otar, Anaelle, Alex Promoteam, Tim, Denis Pisarev, Kirill Pimenov, Alice Faultcore, Subsquid guys and Dmitry, Jay Chrawnna, Tugy and Ra from Amforc, Tom, Ilhan, CoinStudio, Greg from Ryabina, Alexey cp287 and many more members from our community.

Reseath overview
Nominators hold a crucial role in Polkadot owing to the innovative NPOS approach. They fortify the economic security of the system with genuine decentralization through their constant actions to select the best nodes and personal commitment.
This approach demands significantly more involvement and decisions from them compared to any other staking yield (L1, farming, etc.). To incentivize their contributions, economic tools are employed: nominators receive the highest APY in the market for successful actions, while strict disincentivization tools (loss of reward or slashing of personal stake) ensure adherence to the system's stability.
This blend, along with the platform's openness, enables the realization of a high level of decentralization, offering true freedom of action for participants and full permissionlessness. Any DOT holder can enter the system, undertake tasks, and earn rewards for their successful completion.
However, the attributes of decentralization, openness, and permissionlessness mean every potential or existing nominator must engage in continuous self-education and perpetual optimization of their role. The complexity and dynamism of the system, key actions specific to the Polkadot ecosystem, necessitate proactive engagement from the nominator's side with almost zero feedback loop (because of decentralization).
Indirect data (that was partially available before this research) show that nominator behavior is somewhat suboptimal. We have conducted an extensive analysis of the structure of the nomination system from the nominators' behavior perspective and its dynamics.&nbsp;
Key findings (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener">full details are available in the full proposal</a>):
<ul>
<li>The staking ensemble is currently "decentralizing," with the contribution of "regular accounts" growing at the expense of a transition from "whales."</li>
<li>The growth in the role of regular nominators correlates with competition among them. Moreover, the closer the personal stake of the nominator is to the disincentivization level (min-reward), the higher the competition.</li>
</ul>
<img src="https://i.ibb.co/2ZK4jts/9.png" alt="Graph 9" width="200" height="152" /> (<a href="https://i.ibb.co/2ZK4jts/9.png" target="_blank" rel="noopener">Zoom in new window</a>)
<ul>
<li>A significant number of accounts are near the disincentivization border, with 50% in the min-reward growth zone of 100%.
<ul>
<li><img src="https://i.ibb.co/34zzFjN/6.png" alt="Graph 6" width="200" height="120" /> (<a href="https://i.ibb.co/34zzFjN/6.png" target="_blank" rel="noopener">Zoom in</a>)&nbsp;</li>
</ul>
</li>
<li>These accounts risk falling into the potential disincentivization zone (or are already in), possibly not receiving a reward while having staked funds if their actions are not optimal.&nbsp; 
<ul>
<li><img src="https://i.ibb.co/Mcdc9BM/19.png" alt="Graph 19 " width="200" height="125" />(<a href="https://i.ibb.co/Mcdc9BM/19.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>Our research showed that, in their mass, nominators indeed behave suboptimally. This concerns both stake size control and validator set control. Therefore, during the system's transformation, a significant number of accounts lose the active set &ndash; disincentivization occurs.
<ul>
<li><img src="https://i.ibb.co/SxgGfKx/26.png" alt="Graph 26 " width="200" height="112" />(<a href="https://i.ibb.co/SxgGfKx/26.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/Mcdc9BM/19.png" alt="Graph 19" width="200" height="125" /> (<a href="https://i.ibb.co/Mcdc9BM/19.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>The non-receipt of rewards as a disincentivization tool works harshly: currently, 40% of nominators do not return (dropout) to the active set after its loss, a number that is increasing.
<ul>
<li><img src="https://i.ibb.co/ThFc33z/17.png" alt="Graph 17" width="200" height="168" /> (<a href="https://i.ibb.co/ThFc33z/17.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/9qSnd0Z/16.png" alt="Graph 16" width="200" height="109" /> (<a href="https://i.ibb.co/9qSnd0Z/16.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>Simultaneously, our research showed that there is a set of super-effective regular nominators in the system (not just basically performing target tasks in the system, but doing it super-effectively).
<ul>
<li><img src="https://i.ibb.co/drmr2xd/25.png" alt="Graph 25" width="200" height="128" /> (<a href="https://i.ibb.co/drmr2xd/25.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>These accounts proficiently manage both stake size and validator choice over the long term. A nonzero number of them even never missed the active set for the last 500 eras with growing median reward.&nbsp;
<ul>
<li><img src="https://i.ibb.co/rmBdm70/24.png" alt="Graph 24" width="200" height="132" /> (<a href="https://i.ibb.co/rmBdm70/24.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/HTdmmwZ/31.png" alt="Graph 31" width="200" height="132" /> (<a href="https://i.ibb.co/HTdmmwZ/31.png" target="_blank" rel="noopener">Zoom in</a>)&nbsp;</li>
</ul>
</li>
<li>Their actions are easily observable and visible on-chain (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener">data can be found in the full proposal</a>).</li>
</ul>
Proposed solution
Given the presence of these efficient nominators, we can implement benchmarking based on their activities, continuously showcasing optimal actions and outcomes. By utilizing unbiased on-chain data in an open-source format, this method demonstrates superior strategies without necessitating centralized actions or permissions.
Benchmarking works as a self-learning tool for nominators (what the best nominators do, when they increase the stake, which validators and when they choose), as well as a feedback loop in the nomination process (the nominator can understand their effectiveness, compare it with the best, etc.).
We propose the implementation of nominator leaderboards &ndash; dashboards that visualize nominator behavior, offering a comprehensive view of actions and results within the system. The leaderboard allows showing the entire range of actions and results that exist in the system.
These leaderboards serve as a user-friendly interface over the core incentivization mechanism, aiming to prompt nominators to act before facing disincentivization, thus softening the tool's impact without compromising its efficacy.
By centering on regular accounts, this tool amplifies the decentralization effect, making it universally accessible and comprehensible, regardless of language, location, or depth of blockchain/ecosystem knowledge.
Key components of the system include:
<ol>
<li>Nominator Dashboard: A leaderboard for nominator rewards with insights and behavior analysis.</li>
<li>Validator Dashboard: A leaderboard for validator long-term rewards and real payouts with insights and trends.</li>
<li>Nomination Pool Dashboard: A leaderboard for nominator rewards and payouts with insights and trends.</li>
</ol>
Team and traction 
Our team specializes in researching user behavior in the web3 ecosystem. We have a track record with research in the blockchain industry, specifically in DeFi traders' behavior, dApp user experience, and utilizing AI&amp;ML. Our team already has relevant experience with different methods of blockchain research. We have conducted research to determine the most suitable method for this case. Astar, dYdX, Lido done and methods that were tested (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1">Samples can be found in the Appendix.1 at the end of this proposal</a>).
Interface sketches 
The implementation of the interface will take the form of a &ldquo;desktop&rdquo; webapp, providing users with a convenient way to explore the system's capabilities. The design will follow the style of "Similarweb," known for its user-friendly and visually appealing approach. The following is a basic layout and visual example of an internal project page. Please note that the dataset provided in the<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener"> full proposal</a> is illustrative and subject to change.
Benefits for Polkadot ecosystem from Motif 
The Motif platform empowers key stakeholders with valuable data, insights, and educational resources. It aligns interests, optimizes engagement, and fosters a stronger and more collaborative Polkadot community.
<ul>
<li>Benefits for Nominators:
<ul>
<li>Nominators self-education and benchmarking</li>
<li>Higher stickiness of system participants due to Confidence and Understanding of the system's operation</li>
<li>Nominating based on data-driven insights (not just the minimum stake or a known name)</li>
</ul>
</li>
<li>Benefits for Validators:
<ul>
<li>Competition Beyond Low Fee</li>
<li>Data-Driven Strategy:</li>
<li>Optimized Engagement</li>
<li>Potential reward growth due to improving the work of nominators in the system.</li>
</ul>
</li>
<li>Community-Wide Benefits
<ul>
<li>Decentralization focus</li>
<li>Onboarding newcomer nominators and Retaining Members</li>
</ul>
</li>
</ul>
Also, an open-source platform for the ecosystem. Based on this solution, additional elements or separate products can be based, which will improve the effectiveness of the feedback loop.
Thanks for support&nbsp;
We extend our heartfelt gratitude to those who have significantly assisted us during the nine months we prepared this proposal: Bill Laboon, Jonas Gehrlein, Raul Romanutti, Bryan Chen, Otar, Anaelle, Alex Promoteam, Tim, Denis Pisarev, Kirill Pimenov, Alice Faultcore, Subsquid guys and Dmitry, Jay Chrawnna, Tugy and Ra from Amforc, Tom, Ilhan, CoinStudio, Greg from Ryabina, Alexey cp287 and many more members from our community.

Reseath overview
Nominators hold a crucial role in Polkadot owing to the innovative NPOS approach. They fortify the economic security of the system with genuine decentralization through their constant actions to select the best nodes and personal commitment.
This approach demands significantly more involvement and decisions from them compared to any other staking yield (L1, farming, etc.). To incentivize their contributions, economic tools are employed: nominators receive the highest APY in the market for successful actions, while strict disincentivization tools (loss of reward or slashing of personal stake) ensure adherence to the system's stability.
This blend, along with the platform's openness, enables the realization of a high level of decentralization, offering true freedom of action for participants and full permissionlessness. Any DOT holder can enter the system, undertake tasks, and earn rewards for their successful completion.
However, the attributes of decentralization, openness, and permissionlessness mean every potential or existing nominator must engage in continuous self-education and perpetual optimization of their role. The complexity and dynamism of the system, key actions specific to the Polkadot ecosystem, necessitate proactive engagement from the nominator's side with almost zero feedback loop (because of decentralization).
Indirect data (that was partially available before this research) show that nominator behavior is somewhat suboptimal. We have conducted an extensive analysis of the structure of the nomination system from the nominators' behavior perspective and its dynamics.&nbsp;
Key findings (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener">full details are available in the full proposal</a>):
<ul>
<li>The staking ensemble is currently "decentralizing," with the contribution of "regular accounts" growing at the expense of a transition from "whales."</li>
<li>The growth in the role of regular nominators correlates with competition among them. Moreover, the closer the personal stake of the nominator is to the disincentivization level (min-reward), the higher the competition.
<ul>
<li><img src="https://i.ibb.co/2ZK4jts/9.png" alt="Graph 9" width="200" height="152" /> (<a href="https://i.ibb.co/2ZK4jts/9.png" target="_blank" rel="noopener">Zoom in new window</a>)</li>
</ul>
</li>
<li>A significant number of accounts are near the disincentivization border, with 50% in the min-reward growth zone of 100%.
<ul>
<li><img src="https://i.ibb.co/34zzFjN/6.png" alt="Graph 6" width="200" height="120" /> (<a href="https://i.ibb.co/34zzFjN/6.png" target="_blank" rel="noopener">Zoom in</a>)&nbsp;</li>
</ul>
</li>
<li>These accounts risk falling into the potential disincentivization zone (or are already in), possibly not receiving a reward while having staked funds if their actions are not optimal.&nbsp; 
<ul>
<li><img src="https://i.ibb.co/Mcdc9BM/19.png" alt="Graph 19 " width="200" height="125" />(<a href="https://i.ibb.co/Mcdc9BM/19.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>Our research showed that, in their mass, nominators indeed behave suboptimally. This concerns both stake size control and validator set control. Therefore, during the system's transformation, a significant number of accounts lose the active set &ndash; disincentivization occurs.
<ul>
<li><img src="https://i.ibb.co/SxgGfKx/26.png" alt="Graph 26 " width="200" height="112" />(<a href="https://i.ibb.co/SxgGfKx/26.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/Mcdc9BM/19.png" alt="Graph 19" width="200" height="125" /> (<a href="https://i.ibb.co/Mcdc9BM/19.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>The non-receipt of rewards as a disincentivization tool works harshly: currently, 40% of nominators do not return (dropout) to the active set after its loss, a number that is increasing.
<ul>
<li><img src="https://i.ibb.co/ThFc33z/17.png" alt="Graph 17" width="200" height="168" /> (<a href="https://i.ibb.co/ThFc33z/17.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/9qSnd0Z/16.png" alt="Graph 16" width="200" height="109" /> (<a href="https://i.ibb.co/9qSnd0Z/16.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>Simultaneously, our research showed that there is a set of super-effective regular nominators in the system (not just basically performing target tasks in the system, but doing it super-effectively).
<ul>
<li><img src="https://i.ibb.co/drmr2xd/25.png" alt="Graph 25" width="200" height="128" /> (<a href="https://i.ibb.co/drmr2xd/25.png" target="_blank" rel="noopener">Zoom in</a>)</li>
</ul>
</li>
<li>These accounts proficiently manage both stake size and validator choice over the long term. A nonzero number of them even never missed the active set for the last 500 eras with growing median reward.&nbsp;
<ul>
<li><img src="https://i.ibb.co/rmBdm70/24.png" alt="Graph 24" width="200" height="132" /> (<a href="https://i.ibb.co/rmBdm70/24.png" target="_blank" rel="noopener">Zoom in</a>)</li>
<li><img src="https://i.ibb.co/HTdmmwZ/31.png" alt="Graph 31" width="200" height="132" /> (<a href="https://i.ibb.co/HTdmmwZ/31.png" target="_blank" rel="noopener">Zoom in</a>)&nbsp;</li>
</ul>
</li>
<li>Their actions are easily observable and visible on-chain (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener">data can be found in the full proposal</a>).</li>
</ul>
Proposed solution
Given the presence of these efficient nominators, we can implement benchmarking based on their activities, continuously showcasing optimal actions and outcomes. By utilizing unbiased on-chain data in an open-source format, this method demonstrates superior strategies without necessitating centralized actions or permissions.
Benchmarking works as a self-learning tool for nominators (what the best nominators do, when they increase the stake, which validators and when they choose), as well as a feedback loop in the nomination process (the nominator can understand their effectiveness, compare it with the best, etc.).
We propose the implementation of nominator leaderboards &ndash; dashboards that visualize nominator behavior, offering a comprehensive view of actions and results within the system. The leaderboard allows showing the entire range of actions and results that exist in the system.
These leaderboards serve as a user-friendly interface over the core incentivization mechanism, aiming to prompt nominators to act before facing disincentivization, thus softening the tool's impact without compromising its efficacy.
By centering on regular accounts, this tool amplifies the decentralization effect, making it universally accessible and comprehensible, regardless of language, location, or depth of blockchain/ecosystem knowledge.
Key components of the system include:
<ol>
<li>Nominator Dashboard: A leaderboard for nominator rewards with insights and behavior analysis.</li>
<li>Validator Dashboard: A leaderboard for validator long-term rewards and real payouts with insights and trends.</li>
<li>Nomination Pool Dashboard: A leaderboard for nominator rewards and payouts with insights and trends.</li>
</ol>
Team and traction 
Our team specializes in researching user behavior in the web3 ecosystem. We have a track record with research in the blockchain industry, specifically in DeFi traders' behavior, dApp user experience, and utilizing AI&amp;ML. Our team already has relevant experience with different methods of blockchain research. We have conducted research to determine the most suitable method for this case. Astar, dYdX, Lido done and methods that were tested (<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1">Samples can be found in the Appendix.1 at the end of this proposal</a>).
Interface sketches 
The implementation of the interface will take the form of a &ldquo;desktop&rdquo; webapp, providing users with a convenient way to explore the system's capabilities. The design will follow the style of "Similarweb," known for its user-friendly and visually appealing approach. The following is a basic layout and visual example of an internal project page. Please note that the dataset provided in the<a href="https://docs.google.com/document/d/1WEhn-kQQjYxpWD8hC05Nc4VK9oPYROFl/edit?pli=1" target="_blank" rel="noopener"> full proposal</a> is illustrative and subject to change.
Benefits for Polkadot ecosystem from Motif 
The Motif platform empowers key stakeholders with valuable data, insights, and educational resources. It aligns interests, optimizes engagement, and fosters a stronger and more collaborative Polkadot community.
<ul>
<li>Benefits for Nominators:
<ul>
<li>Nominators self-education and benchmarking</li>
<li>Higher stickiness of system participants due to Confidence and Understanding of the system's operation</li>
<li>Nominating based on data-driven insights (not just the minimum stake or a known name)</li>
</ul>
</li>
<li>Benefits for Validators:
<ul>
<li>Competition Beyond Low Fee</li>
<li>Data-Driven Strategy:</li>
<li>Optimized Engagement</li>
<li>Potential reward growth due to improving the work of nominators in the system.</li>
</ul>
</li>
<li>Community-Wide Benefits
<ul>
<li>Decentralization focus</li>
<li>Onboarding newcomer nominators and Retaining Members</li>
</ul>
</li>
</ul>
Also, an open-source platform for the ecosystem. Based on this solution, additional elements or separate products can be based, which will improve the effectiveness of the feedback loop.
Thanks for support&nbsp;
We extend our heartfelt gratitude to those who have significantly assisted us during the nine months we prepared this proposal: Bill Laboon, Jonas Gehrlein, Raul Romanutti, Bryan Chen, Otar, Anaelle, Alex Promoteam, Tim, Denis Pisarev, Kirill Pimenov, Alice Faultcore, Subsquid guys and Dmitry, Jay Chrawnna, Tugy and Ra from Amforc, Tom, Ilhan, CoinStudio, Greg from Ryabina, Alexey cp287 and many more members from our community.

Propose and approve a spend of treasury funds.

- `origin`: Must be `SpendOrigin` with the `Success` value being at least `amount`.
- `amount`: The amount to be transferred from the treasury to the `beneficiary`.
- `beneficiary`: The destination account for the transfer.

NOTE: For record-keeping purposes, the proposer is deemed to be equivalent to the
beneficiary.

- Nominators play an important role in the Polkadot system by selecting the best nodes and contributing to decentralization.
- The current behavior of nominators is not optimal, and many accounts are at risk of losing rewards.
- There are some nominators who are very effective and consistently make good decisions.
- A proposed solution is to create leaderboards that show the actions and results of nominators, which can help them learn and improve.
- This tool will benefit nominators, validators, and the Polkadot community as a whole.

Requested: 0.32 undefined

#165 Polkadot Nominator Insight Hub. Spreading the Best Nominator Strategies through TOP Nomination Leaderboards