The #1 result in Google has an average of 3.8x more backlinks than positions #2 through #10, according to Backlinko's analysis of 11.8 million search results conducted with Ahrefs data. That's one of the most-cited correlation findings in SEO. It's also, on its own, a genuinely incomplete picture — because Google's own Gary Illyes has stated publicly that links aren't even in the algorithm's top three ranking factors. Both facts are true simultaneously, and reconciling them is the actual point of this post.
The Backlink Correlation, and What It Actually Shows
Backlinko's study, one of the largest independent correlation analyses available, found a strong relationship between a site's overall link authority (measured via Ahrefs Domain Rating) and search ranking position, along with the 3.8x backlink gap between position #1 and the rest of the first page. A separate, smaller-scale study in the personal injury legal sector found a related but distinct pattern: the number of referring domains per individual page didn't predict that page's ranking well, but the total number of referring domains to the domain overall was a strong indicator of the site's traffic. That distinction — page-level links versus domain-level authority — matters more than either study's headline number in isolation.
Why "Correlates With" Isn't "Causes"
This is the central nuance most SEO content skips. Google's Gary Illyes stated in 2023 that links are "not in the top three" ranking factors — a direct statement from inside the company running the algorithm, which sits in real tension with a correlation study showing the #1 result has 3.8x more links than the rest of the page. Both can be true because correlation studies measure what ranks well tends to also have, not what causes ranking. Pages that earn strong backlinks tend to also have strong content, better user experience, more brand recognition, and more marketing investment generally — any of which could be doing more of the actual causal work than the links themselves. Google has made the same point explicitly about a related metric: social shares correlate with rankings too, but Google has stated this is because popular content earns both social shares and backlinks simultaneously — the backlinks drive rankings, not the shares, even though both show up correlated with position.
Word Count Is the Clearest Example of This Trap
Backlinko's own data found the average first-page Google result runs about 1,447 words — a widely repeated statistic that gets misread constantly as "longer content ranks better." No evidence supports word count as a direct ranking factor, and this has been directly addressed by Google's own Matt Cutts in the past. The actual relationship: longer content tends to be more thorough, and thorough content tends to earn more backlinks and cover a topic well enough to satisfy more search intents — word count is a downstream symptom of a well-built page, not an input the algorithm is scoring. Padding content to hit 1,447 words without the underlying comprehensiveness doesn't reproduce the correlation.
What a Named Ranking-Factor Model Actually Weighs
One commonly cited framework, from First Page Sage, attributes roughly 75% of ranking weight to five factors: content quality (23%), keyword presence in the title tag (14%), backlinks (13%), demonstrated niche expertise (13%), and searcher engagement (12%). It's worth being precise about what this is: one firm's estimated model based on their own analysis, not a confirmed weighting published by Google, since Google does not disclose its actual factor weights. Treat frameworks like this as a reasonable prioritization guide, not a verified formula — the same caution that applies to any third-party correlation study of a system whose exact mechanics aren't public.
What's Changed: Link Quality Over Volume
Across multiple current sources, one shift shows up consistently: backlink evaluation has moved from rewarding volume toward rewarding relevance and trustworthiness. One frequently cited framing puts it directly — a single backlink from a genuinely relevant, authoritative site in your niche is now treated as worth more than 100 links from unrelated, low-authority domains. This tracks with the broader direction of Google's public statements: links embedded within genuinely relevant, high-quality content from topically-aligned sources carry more weight than high link volume achieved through exchanges, mass guest posting, or other manufactured link-building tactics.
How to Use Correlation Studies Without Being Misled by Them
- Treat every correlation stat as "associated with," not "caused by," until there's a stated mechanism or a company statement explaining the actual causal relationship.
- Distinguish page-level correlations from domain-level ones. The legal-sector study above found these behave differently — a factor that predicts overall domain traffic isn't automatically the same factor that predicts a specific page's rank.
- Weight direct statements from Google above third-party correlation studies when the two conflict, as with backlinks and the "not top three" statement — Google has the actual mechanism; correlation studies are inferring it from the outside.
- Don't optimize for the correlated symptom instead of the underlying cause. Padding word count instead of building comprehensive content is the clearest version of this mistake, but it applies to backlink volume, keyword density, and most other individually-cited "factors" too.
- Prioritize content quality and topical authority as the foundation, since every major framework reviewed here, despite disagreeing on exact weights, agrees this sits at or near the top.
The honest summary: backlinks correlate strongly with rankings in every large-scale study available, and Google itself has said links aren't a top-three factor. Both statements are accurate. The resolution isn't picking one and discarding the other — it's understanding that correlation studies measure what successful pages have in common, not necessarily what made them successful in the first place. The same caution applies to AI citation studies, which is exactly why an AI Content & GEO strategy has to be built on more than a single correlated metric.