Google does not use DA and DR; But they are related in order

Google does not use DA and DR; But they are related in order

Google does not use domain authority or domain ranking as ranking factors. But Google uses PageRank.

PageRank is an algorithm that measures the importance of a particular webpage. It takes into account the number and quality of internal and external links pointing to the page. Google admits that it still uses PageRank more than 20 years after its invention .

As long as Google isn’t fooling us, there is a causal relationship between your Google PageRank and your PageRank score.

The problem for us is that we don’t know what PageRank score Google uses.

To calculate PageRank for a specific page, we will need:

  • Know the exact syntax Google uses,
  • Get the full set of web data that Google has.

Unfortunately, both of these things are impossible.

This is why metrics such as Domain Authority or Domain Rating exist. Google may not use it, but we can use it to simulate PageRank.

How are DA and DR calculated?

We don’t know the exact formulas for DA and DR, just as we do with PageRank.

But the makers of these metrics, Moz and Ahrefs, mention the factors they use to calculate them.

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We calculate DR in a somewhat similar way to how we calculate DR PageRank. The main difference is that PageRank is calculated between pages, while DR is calculated between websites.

Technically, it looks something like this:

  • We find all domains that have at least one link back to the target domain.
  • We are looking for the number of other domains each domain is associated with.
  • We then pass some amount of “DR juice” from each domain associated with the target domain. This amount is (approximately) determined by dividing the DR of the binding domain by the number of unique domains it binds to.
Source: Ahrefs blog

So the main difference between DR and PageRank is that DR It is calculated for websites, not individual pages.

This makes technical sense. To calculate the domain rank score for individual pages, Ahrefs will need to spend more resources. It would have to fully crawl each domain in its database.

The same is true for the Domain Authority metric by Moz:

Domain Authority relies on data from our Link Explorer web index and uses dozens of factors in its calculations.

The actual Domain Authority calculation itself uses a machine learning model to predictively find a “best fit” algorithm that closely ties our correlation data to rank across the thousands of actual search results that we use as benchmarks against.

(…) Domain authority is calculated by evaluating multiple factors, including linking root domains and total number of links, in a single DA score.

Source: Moz blog

Are DA and DR good for SEO?

Since we don’t know the PageRank scores of our pages, we need something else to assess the strength of their cookies. We need metrics that we can work on and see our ratings improve.

but If these metrics are good, then they should correlate with Google’s ranking. Meaning, as your Page’s DR or DA score goes up, so should its rankings.

After all, there is a causal relationship between PageRank and Google rankings. So proxy metrics for PageRank must be related to Google rankings.

And they do – I checked. DA and DR are closely related to order.

Findings

There is a correlation between the DA and DR of a particular domain and its position in the top 10 organic search results. But the whole picture is more complicated.

For Domain Authority, the average correlation coefficient is 0.16.

When it comes to domain rating, the average correlation coefficient is 0.14.

This indicates that there is a slight relationship between these metrics and ratings. When you consider that PageRank (which DA and DR emulate) is one of hundreds or thousands of ranking factors, it is actually a significant correlation.

But when we look at the distribution of associations for different keywords and SERPs, they are very different.

Starting with domain rating, it had a slight positive correlation with most SERPs, but there were many edge cases.

A chart showing the correlation distribution between Domain Ranking and Google Rankings

With Domain Authority, the distribution of correlation coefficients looks very similar.

A chart showing the distribution of correlation between Domain Authority and Google Rankings

As you can see, the correlation is negative for many keywords, and for some, very strongly. This means that there are SERPs where the DA and DR do the opposite of what they should be doing.

And the keyword difficulty makes it more interesting. Both DA and DR are associated with medium difficulty keywords.

As keywords become more difficult, DA and DR become less predictive. Domain Authority is less predictive of medium difficulty keywords but does a little better for harder keywords.

The chart below shows how the relationship can vary between Domain classification And the domain authority Google rankings based on keyword difficulty.

A chart showing the relationship between domain authority, domain valuation, and Google rankings

But that doesn’t mean that DA and DR aren’t important for hard keywords. It may be the case that to rank for difficult keywords, you need to meet an importance threshold. Hence the other ranking factors are more decisive.

the main points

PageRank is one of many factors that affect the rankings of a particular page on Google. So an average correlation coefficient of 0.14 and 0.16 is a good score.

If that was the only thing affecting rating, I would have expected a much stronger correlation, but…it’s not. Congratulations to Moose and Ariffs!

My general conclusion from this research is that DA and DR can be beneficial for SEO.

But don’t blindly improve it. Before you decide you need to get some external links to rank higher, look at your SERPs.

With difficult keywords, you may want to focus on other factors first.

And with some keywords, DA and DR may be completely unrelated in order.

How did I do my research

I took 10 randomly selected fields from different countries and industries. Then I used Ahrefs to pick 200 keywords for which each of those domains were ranked, which gave me 2,000 keywords of varying lengths, types, and difficulty.

I exported top 10 organic SERP results for each of 2000 keywords using Ahrefs with US selected as location. Then I got DA and DR scores for all the areas I found.

Using this data, I was able to calculate a Pearson correlation coefficient between DA and DR for each of the 2,000 SERPs I collected.

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