Hey All,

This post is in continuation with my Yesterdays post - Hive Reputation Explained: Step-by-Step Conversion Guide - with an EXAMPLE..., where we saw Hive users who had a reputation close to my Hive reputation which is 79.3. This data was not sufficient to conclude to anything and hence this part2 post where we fill further deep dive into looking these users which are close to my reputation what their #HIVE Power aka HP is and at the same time we will see #HIVE users closest to my Hive Power and their Reputation. I know it may sound some what confusing but trust me when you see that data and table it would be intresting to analyze, compare, and truly understand ONEs position among others. Ok here is the the DBeaver UI where I had the query working. And then we have the output of the query in table form.

Top 10 Hive users closest to my Reputation & their Hive Power

I leveraged Python script using Pandas and Matplotlib to visualize ythe above table. You can clearly see that my reputation score which is (79.28) places me right in the middle of other only one top accounts with a Hive Power of stayoutoftherz (449K HP). At the same time there are 5 users whoes reputation and Hive Power is almost close to each other. One One extreme case where creativecrypto has almost no Hive Power (0 HP) but still a comparable rep score of (79.20). Overall we can say that users can have similar reputation but widely different influence in terms of HP.

And here is the working Query to retriev records users closest to your reputation and the users Hive Power.

WITH globals AS (
    SELECT 
        CAST(total_vesting_shares AS FLOAT) AS total_vests,
        CAST(total_vesting_fund_hive AS FLOAT) AS total_hive_fund
    FROM DynamicGlobalProperties
),
base AS (
    SELECT 
        a.name,
        a.reputation,
        CASE 
            WHEN a.reputation = 0 THEN 25
            ELSE 
                (LOG10(CAST(ABS(a.reputation) AS FLOAT)) - 9) 
                * (CASE WHEN a.reputation > 0 THEN 1 ELSE -1 END) * 9 + 25
        END AS rep_score,
        a.vesting_shares,
        g.total_vests,
        g.total_hive_fund
    FROM Accounts a
    CROSS JOIN globals g
)
SELECT TOP 11 
    name,
    reputation,
    rep_score,
    vesting_shares AS hive_power_vests,
    (vesting_shares / total_vests) * total_hive_fund AS hive_power_hp
FROM base
ORDER BY ABS(rep_score - (SELECT rep_score FROM base WHERE name = 'gungunkrishu'));

Next I didnt stop here and went on to query records with a slight change. I wanted to see users reputation when compared to my Hive Power. Interesting, isn’t it?.

Top 10 Hive users closest to my Hive Power and their Reputation....

This is what I get when I plot the above table information using Phython. I got only one user whoes Hive Power and reputation score is close to me. Rest users have a scatterd Hive power and reputation score. Two users having greater HP and score than me and rest had lesser HP and score.

Working Query to retriev records users closest to your Hive Power and their reputation

WITH globals AS (
    SELECT 
        CAST(total_vesting_shares AS FLOAT) AS total_vests,
        CAST(total_vesting_fund_hive AS FLOAT) AS total_hive_fund
    FROM DynamicGlobalProperties
),
base AS (
    SELECT 
        a.name,
        a.vesting_shares,
        a.reputation,
        CASE 
            WHEN a.reputation = 0 THEN 25
            ELSE 
                (LOG10(CAST(ABS(a.reputation) AS FLOAT)) - 9) 
                * (CASE WHEN a.reputation > 0 THEN 1 ELSE -1 END) * 9 + 25
        END AS rep_score,
        g.total_vests,
        g.total_hive_fund
    FROM Accounts a
    CROSS JOIN globals g
),
closest AS (
    SELECT TOP 10 
        name,
        vesting_shares AS hive_power_vests,
        (vesting_shares / total_vests) * total_hive_fund AS hive_power_hp,
        reputation AS raw_reputation,
        rep_score AS readable_reputation
    FROM base
    ORDER BY ABS(vesting_shares - (SELECT vesting_shares FROM base WHERE name = 'gungunkrishu'))
)
SELECT *
FROM closest
ORDER BY hive_power_hp DESC;

Overall, it was a great learning experience to pull records and explore Hive users with Reputation Scores and Hive Power similar to mine. It’s always insightful to see how these two metrics work together in reflecting an account’s influence and growth on Hive.

That’s all for today’s post on “Hive Reputation Explained: Part 2 – Hive Power & Reputation Analysis.” I hope I was able to explain the concept clearly. If you have any follow-up questions or doubts, please feel free to share them in the comments below.Happy Learning with HiveSQL....Cheers...

Hive Reputation Explained: Part2 - HIVE POWER & HIVE REPUTATION - Analysis...

Hive #Blockchain #Crypto #HiveSQL #Reputation #HiveCommunity #Web3 #Decentralization #HiveBlog

Best Regards Paras

Image Courtesy:: pro canva license, hiveblocks, DBeaver

PS:- None of the above is a FINANCIAL Advice. Please DYOR; Do your own research. I've have an interest in BlockChain & Cryptos and have been investing in many emerging projects.