Other weekly block maker statistics posts
If you want to try your hand at generating your own block maker 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 Blockchain.info, Blocktrail.com and Kaiko.com are supported.
- R bitcoin blockchain data API access function
Solved block statistics table. This table lists all statistics that can be derived from the number of blocks a hashrate contributor has solved for the past week. Block attributions are either from primary sources such as those claimed by a particular pool website, or secondary sources such as coinbase signatures, or known generation addresses. When dependent on secondary sources only, data may be inaccurate and miss some blocks if a particular block-solver has gone to some trouble to hide solved blocks. This will result in an underestimate of the block-solver hashrate.
“Dot pool” and “Day pool” are block makers that are unknown, but that have enduring coinbase signatures, address clustering, or generation address reuse similarities. However, since they are unknown and unclaimed we can’t be sure if these block makers are actually part of another known block maker.
|Unknown recurring generation address||Blocks solved this week||Percentage of network||Percentage of unknown||Estimate of hashrate||Blocks solved ever|
|18EPLvrs2UE11kWBB3ABS7Crwj5tTBYPoa||7||0.68 %||43.75 %||13 Phps||102|
|18HEMWFXM9UGPVZHUMdBPD3CMFWYn2NPRX||6||0.58 %||37.50 %||11 Phps||6|
|12znnESiJ3bgCLftwwrg9wzQKN8fJtoBDa||3||0.29 %||18.75 %||5 Phps||3|
Diachronic hashrate distribution: Stacked histogram percentage of network blocks over the past week.
Rather than a simple “pie chart” of blocks solved over the past week, this plot represents an estimate of the hashrate intensity function at any given time. This means that variance is reduced, so if you want to see intra-week changes in network ownership this chart is much more than a simple 24 hour or four day pie chart. If there are very sudden changes in hashrate – for example a 50% or greater change over several hours – the smoothing method will not be able to distinguish this from variance.
Hashrate distribution: Heatmap of historical percentage of network blocks attributable to block makers.
Because the data is a daily summary, the kernel smoothing shows quite clearly the variance in hashrate distribution that occurs in block making. It will also show the intra-week hashrate movements which were previously unavailable.
- For help on ggplot2.
- 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!