Introduction

NEAR Protocol is a layer-1 blockchain that uses Nightshade, a unique sharding technology, to achieve scalability. It was launched in 2020 as a decentralized cloud infrastructure to host decentralized applications (DApps).

NEAR offers cross-chain interoperability through the Rainbow Bridge and a layer-2 solution called Aurora. Users can bridge ERC-20 tokens and assets from the Ethereum blockchain to the NEAR Protocol network, which gives them access to higher throughput and lower transaction fees.

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In this dashboard we are going to take a look into NEAR balances on this blockchain.

$NEAR Distribution in Buckets

In the next chart we are putting wallets in special buckets, each bucket alludes a range of balance, please note that to have better visibility both axis of this table are in logarithmic scale.

It can be confirmed that a vast majority of users have small balances, almost 11M users have less than 10 NEAR in their wallets. as buckets depth increases, less users are present in each bucket.

Top Wallets

The following chart depicts the balance of the top 9 wallets and all other wallets, it can be seen that:

• The top 9 wallets have balances that are almost as large as other users' balances.
• The Binance cold wallet 3 contains over $50 million NEAR, which is 18.6% of all available NEAR.

Approach

Tables

To conduct our study, we use the Flipside Crypto database. They offer one of the best and most efficient blockchain data in the industry. From said data source, we are using following tables:

• near.core.fact_transfers → This table is used to access token transfer transactions, these tokens include NEAR.
• near.core.ez_dex_swaps → This table provides data about swaps done on NEAR protocol, we can use token_in and token_out columns to filter out the transactions where NEAR is either swapped to or from.
• near.core.fact_transactions → This table contains all the transactions done on NEAR network.
• near.core.fact_actions_events_function_call → This table is used to get the details about the transaction function call, we can easily filter the desired functions using the METHOD_NAME column.

Limitations

In this study, we face some limitations, first of all, the NEAR chain data in Flipside Crypto is not completely backfilled; So our study cannot be very accurate.

A second limitation is that there is no reliable way to calculate the most accurate balance value. The fact_transfers table doesn’t contain every transaction, which makes calculating balance even harder, we will use some workarounds to find the most accurate balance for each wallet.

In the next section we’ll take a look at some of this limitations and find methods to solve them.

Methodology

As already stated, we are using fact_transfers table to access NEAR transfer transactions, but this table doesn’t contain every transfer transaction. We are also using the same table to calculate transaction fee paid by each account, paid fees also account for outflow volume.

The other important data that cannot be found in said table is the swapping activity, that’s why we are using near.core.ez_dex_swaps table, using this table and specifying NEAR token as token_in or token_out we can find balance changes from swapping,

Staking and unstaking are also important activities on a blockchain, staking means locking NEAR, which in turn means taking out that amount from the balance, in a similar manner unstaking means claiming NEAR tokens which increase its balance amount. To access this data we need to use FunctionCall table by filtering methods that contains names like stake and unstake.

Multisend is also another function that is used frequently, It is used to send an specific token to multiple users, 'multisend_from_balance' and 'multisend_from_balance_unsafe' are the method names related to this activities.

Study Period

This study was conducted using available data and during its full period.

$NEAR Distribution

This chart show the distribution of NEAR of wallets. All the charts are interactive and you can exclude/include data by clicking on its legend. It can be seen that:

• More than 97% of the wallets contain less than 0.1 NEAR.
• By excluding the 0-0.1 range, we can see that more than 40% have less than 1 NEAR. 25% have less than 10 NEAR and 20% of the remaining wallets have less than 100 NEAR.

Summary

Let’s summarize our findings:

• We have faced some limitations, such as dataset not being fully backfilled and lack of a balance table, even the table of transfers does not include all transfers.
• Large whales hold almost half of the NEAR’s supply.
• Majority of wallets contain less than 0.1 NEAR.