Validators process transactions and receive fees from users (supply-side fees).

Algorand is a smart contract platform for decentralized applications (a blockchain). Algorand was founded by Silvio Micali and launched in 2018.

ALGO holders own and govern the protocol. They currently take a cut of the transaction fees paid by users (revenue). Algorand's investors include Union Square Ventures, Multicoin Capital, Pillar, etc.

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Algorand

# Algorand Block time Methodology

## 1. Overview

Algorand Block time is calculated as the average duration between the production of successive blocks on the network. This metric serves as a key indicator of network latency and the speed at which transaction finality is achieved. It represents the efficiency of the underlying consensus mechanism in maintaining state consistency and processing throughput.

## 2. Data sources

- **Blockchain events**: Block metadata and timestamps from the Algorand blockchain

## 3. Step-by-step calculation

### Component A: Block interval derivation

- **Purpose**: Determine the elapsed time between consecutive blocks.
- **Logic**: Block numbers and their associated timestamps are retrieved from the blockchain ledger. For every block, the timestamp of the sequential preceding block is identified. The time difference between the current block timestamp and the previous block timestamp is calculated in milliseconds to establish the duration of each individual block production cycle.
- **Key fields**: `block_timestamp` and `block_number`.

### Component B: Daily aggregation and averaging

- **Purpose**: Generate a representative daily average for network performance.
- **Logic**: Individual block intervals are grouped by day based on UTC timestamps. An arithmetic mean is applied to all intervals recorded within each 24-hour period to determine the average block time for that day.
- **Key fields**: `block_time`.

### Component C: Normalization and finalization

- **Purpose**: Ensure data consistency and continuity.
- **Logic**: The daily average values are cast to floating-point numbers to maintain precision. A zero-filling strategy is applied to the time series to ensure that any days without recorded activity are accounted for in the final dataset. The data is filtered to ensure it aligns with established chain availability and freshness parameters.

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

avg(block_timestamp - previous_block_timestamp)

AlgorandALGO

Block time

Block time

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