Q1: What play_types do users hold most

What makes a Top Shots moment valuable? (Part I)

(Flow - 7/22/2022)

Introduction:

NBA Top Shot is an NFT marketplace where sports fans can buy, sell and trade basketball video clips. Launched in 2020, NBA Top Shot is a partnership between the NBA and Dapper Labs, the creators of CryptoKitties.

This dashboard will investigate into this marketplace to create an analysis on NBA Top Shots moments and attempt to uncover any correlations between a specific category and sales volume.

Methodology:

To do this analysis I used

• Flipside Crypto database
• flow.core.dim_topshot_metadata table
• flow.core.fact_nft_sales table
• flow.core.fact_prices table

So we wanna find a relationship between NBA TOP SHOTS sales volume and its different properties.

In this bounty we wanna focus onPlay_types. In this marketplace, every NFT has its own exclusive properties; One of them is play_type.

At the first step lets see what play_types we have;

As you see, there are nine different play_types including:

3pointer, Assist, Block, Rim, Dunk/Layup, Handles, 2pointer and Mid-Range.

(These play_types are subsets of someplay_category)

Ok…Lets start our journey:

Now that you know sub types, its time to understand how many is held?

To reach this goal, first I should avoid double counting buyer and seller and besides, calculate number of held NFTs, so I tried to give every NFT a ‘1’ number and finally sum all of these numbers to find the total NFT held by that particular user. (How to delete seller from our computation? Just by giving it ‘-1’ instead on ‘1’)

Anyway, As you see after visualization pot query we see this beautiful chart:

• Most held play_type is Rim with more than 70k (about 40 percent of all of NFTs) and least held is 2Pointer with only 8.
• Rim play_type has the most trading volume too (more than 3 million since April 20)
• Totally there are more than 178k NFTs been held by users
• Play_types trading volume and play_types total holding number, both have similar orders.

O2: How much volume in sales do these play_types generate?

Now we wanna find the volume. First step is to know what tokens we have for trading. By finding unique currencies on flow.core.fact_nft_sales table we see there are two tokens:

Flow token and Dapper Utility Coin

So here I wanna find all of the trading volumes made by these two token based of play_types of every NFT :

• Since April 20, we had several spikes in NBA Top Shots trading volume.
• Almost all of trades have done by Dapper Utility Coin and Flow token trades are so few that can be ignored easily and since the Dapper Utility coin is a Stablecoin we can easily read these numbers as dollor.
• Most daily volume is for May 6, when we had more than 470k trade volume.
• Most of days, Rim has highest daily volume (among play_types)
• Recently trading volume has decreased so much (more than 99.7%).

Lets see is there any relationship between every play_types ratio (among all play_types) and total sale volume or not?

In general view, there is no relationship between these two factor. But if we see closer we may find something. For example in 21 to 24 July period, we see a significant increase in total sale volume; now if we check play_types ratios, we see:

• “Rim”, “Assist”, “Mid_range“, “Steal”, “Dunk_layup” play_types have inverse relationship

• “3 pointer”, “block”, “Handles” have direct relationship

• In significant total sale increases, people tend to buy “3 pointer” and “handles” more than other play_types (with a increase of about 340 percent)

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• Other time periods show different results so we conclude there is no significant correlation between total sale and Play-types ratio

• Rim has the lowest fluctuation among play types

Flow price has no significant effect on play_types

> This analysis was created by MRFTI (Flipside, mrfti#7948 on Discord) on 10 July 2022 for Flipside Crypto's What makes a Top Shots moment valuable? (Part I) Bounty".