With Page Authority, we decided to go with a different

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This model's advantage was its simplicity, but it left much to be desired. Previous Page Authority models trained against SERPs, trying to predict whether one URL would rank over another, based on a set of link metrics calculated from the Link Explorer backlink index. A key issue with this type of model was that it couldn’t meaningfully address the maximum strength of a particular set of link metrics. For example, imagine the most powerful URLs on the Internet in terms of links: the homepages of Google, Youtube, Facebook, or the share URLs of followed social network buttons.


There are no SERPs that pit these URLs against one another. Instead, these extremely gambling data brazil powerful URLs often rank #1 followed by pages with dramatically lower metrics. Imagine if Michael Jordan, Kobe Bryant, and Lebron James each scrimaged one-on-one against high school players. Each would win every time. But we would have great difficulty extrapolating from those results whether Michael Jordan, Kobe Bryant, or Lebron James would win in one-on-one contests against each other. When tasked with revisiting Domain Authority, we ultimately chose a model with which we had a great deal of experience: the original SERPs training method (although with a number of tweaks).


training method altogether by predicting which page would have more total organic traffic. This model presented several promising qualities like being able to compare URLs that don’t occur on the same SERP, but also presented other difficulties, like a page having high link equity but simply being in an infrequently-searched topic area. We addressed many of these concerns, such as enhancing the training set, to account for competitiveness using a non-link metric. Measuring the quality of the new Page Authority The results were — and are — very promising.