Other weekly network statistics posts

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**User account hashrate distributions still needed**

*I want to be able to publish estimated number of miners and mean and median miner hashrate again, but I can only do that if I can get some more mining pool data.*

If you have time, encourage your mining pool to provide a “Hall of Fame” feed. I need user account hashrates in order to estimate a number of different network statistics, and to do that I need user account hashrates averaged over at least an hour, preferably several hours or more. The data can be anonymised – it’s just the user hashrates I need.

If you have time, encourage your mining pool to provide a “Hall of Fame” feed. I need user account hashrates in order to estimate a number of different network statistics, and to do that I need user account hashrates averaged over at least an hour, preferably several hours or more. The data can be anonymised – it’s just the user hashrates I need.

**If you want to try your hand at generating your own**

**bitcoin network statistics**and you know some R then I’ve some scripts that allow you to download bitcoin block chain data from various APIs, converted to a .csv table (a format supported by spreadsheets). So far only Blockchain.info, Blocktrail.com, and Kaiko.com are supported.

- R bitcoin blockchain data API access function

**The network hashrate**

- The dashed line is the mean hashrate estimate.
- The grey shaded area is the 95% confidence interval for the mean hashrate estimate.
- The dotted line is the 95% confidence interval for daily hashrate averages, given the mean hashrate estimate, so 95% of the large grey dots (average daily hashrate) should be within the dotted line.
- The blue shaded areas are the confidence intervals for the forecast.
- Forecast confidence intervals are bootstrapped.

**Miner profitability and forecast**

“Income” is an estimate which ignores reward method and pool fee, and includes transaction fees.

- The first plot below shows the weekly miner income and cumulative miner income for the past 52 weeks.
- The second plot shows the weekly miner income for the past 26 weeks with an eight week forecast, and the cumulative miner income eight week forecast.
- Forecast confidence intervals are bootstrapped.

Transaction fees

Transaction fees

The first plot is a simple forecast that uses an ARIMA model to create a realistic forecast for monthly average transaction fees per block. Think of it as a trend line with confidence intervals. Since it model future values as an auto regressive and moving average function of previous values, it cannot account for sudden changes to the network — so use it as a guide only.

The plots following the first are self explanatory and are kernel smoothed estimates of block summary statistics.

**Transaction rates,**

**block rates and empty blocks**

Since these plots have been smoothed over a 14 day period, the cyclical nature of transaction rates (previously analysed here and here) will not be visible.

**Inequality measures**

**General inequality between block makers (facet 1)**

The Herfindahl index theoretically captures the equivalent share that would be enjoyed by equal-sized firms in the marketplace.

**Inequality between groups: smaller block makers and larger block makers**

**(facet 2)**

This index is measuring the inequality between two groups: the half of the network with the highest concentration of hashrate, and the half of the network with the lowest concentration of hashrate. It can be interpreted as:

Sh = Sblocks/(Sblocks + Lblocks)

**General inequality between block makers:**

**Gamma diversity**

The Gamma diversity with q = 2 is equal to the inverse of the Herfindahl Index, and in this case equals the equivalent number of competitive firms.

**Inequality between groups: Public mining pools and non pool block makers.**

and distribute rewards, and a pool with fewer miners has greater income variance.

**Organofcorti lives in the blockchain!**

**organofcorti.blogspot.com is a reader supported blog:**

**1QC2KE4GZ4SZ8AnpwVT483D2E97SLHTGCG**

- For help on ggplot2.
- For help on forecasting.

**ten errors.**- Errors in text repeated across multiple posts: I will only pay for the most recent errors rather every single occurrence.
- Errors in chart texts: Since I can’t fix the chart texts (since I don’t keep the data that generated them) I can’t pay for them. Still, they would be nice to know about!