Pillar · Hub
Expertise in E-E-A-T: does the content show knowledge?
Systems that assess the depth and quality of knowledge in content
The question
Can you tell from the page that whoever wrote it understands the subject?
What Google goes by
Expertise means demonstrable depth of knowledge in the content. Google's systems judge whether a page shows genuine subject-matter expertise. They do so through content effort (an estimate of how much work went into a page), the YMYL classification (topics where wrong information can harm health, money or safety) and the calibration of human raters. Raters are people who review search results, and they define what 'expert' means for each topic.
What this means for your page
Straight from the system pages: what each system rewards or punishes in practice.
Warnings with real-world consequences (severe weather, health, finance, legal) fall in the YMYL category. People make real decisions based on this content, so such pages must be accurate, current and clearly written. Errors or misleading information can not only harm users but also permanently damage the chard score.
Automatically generated pages without editorial added value are at a disadvantage compared to pages with context and explanation. A report that explains what an event means for those affected earns more contentEffort points than one that only lists raw data.
The QRG (Quality Rater Guidelines) are publicly available and should be required reading for editorial and product teams. The goal is what a rater would judge as 'high quality' per the QRG, not what tools measure as 'optimized'. The QRG distinguish strictly between 'Page Quality' (the quality of the page itself) and 'Needs Met' (does the page match the search query).
In the Wild
Not a lab: 8 brand advice pages from one industry, measured where they actually live, on September 16, 2026. Observations, not verdicts — anyone can repeat them.
None of the 8 meets all 2 — where one is present, the other is missing.
All 16, one by one
Patagonia — Names a person as author, visibly ↗ — Names a person as author, visibly: no
Visible on the page: "Autorenprofil Patagonia" with a brand biography. A person, Carlos Wassmann, appears only in meta[name="author"]; the JSON-LD names Organization: Patagonia.
2026-09-16 · Gerendertes DOM
Patagonia — That name leads to a profile stating qualification ↗ — That name leads to a profile stating qualification: no
No link from the page to a personal profile.
2026-09-16 · Gerendertes DOM
Vaude — Names a person as author, visibly ↗ — Names a person as author, visibly: no
The JSON-LD BlogPosting names a Person as author. Nothing of it is visible on the page: no byline, no author markup in the rendered DOM.
2026-09-16 · Gerendertes DOM
Vaude — That name leads to a profile stating qualification ↗ — That name leads to a profile stating qualification: no
No link from the page to a personal profile.
2026-09-16 · Gerendertes DOM
The North Face — Names a person as author, visibly ↗ — Names a person as author, visibly: no
No author markup of any kind in the rendered DOM, and no author in the JSON-LD.
2026-09-16 · Gerendertes DOM
The North Face — That name leads to a profile stating qualification ↗ — That name leads to a profile stating qualification: no
No link from the page to a personal profile.
2026-09-16 · Gerendertes DOM
Jack Wolfskin — Names a person as author, visibly ↗ — Names a person as author, visibly: no
The delivered HTML carries Organization schema only — no Article, no author, no author markup.
2026-09-16 · Ausgeliefertes HTML
Jack Wolfskin — That name leads to a profile stating qualification ↗ — That name leads to a profile stating qualification: no
No link from the page to a personal profile.
2026-09-16 · Ausgeliefertes HTML
Deuter — Names a person as author, visibly ↗ — Names a person as author, visibly: no
Schema is CollectionPage, not Article. No author in markup or text.
2026-09-16 · Ausgeliefertes HTML
Deuter — That name leads to a profile stating qualification ↗ — That name leads to a profile stating qualification: no
No link from the page to a personal profile.
2026-09-16 · Ausgeliefertes HTML
Nike — Names a person as author, visibly ↗ — Names a person as author, visibly: no
No author markup and no author named in the text.
2026-09-16 · Ausgeliefertes HTML
Nike — That name leads to a profile stating qualification ↗ — That name leads to a profile stating qualification: no
No link from the page to a personal profile.
2026-09-16 · Ausgeliefertes HTML
Garmin — Names a person as author, visibly ↗ — Names a person as author, visibly: no
No author markup and no author named in the text.
2026-09-16 · Ausgeliefertes HTML
Garmin — That name leads to a profile stating qualification ↗ — That name leads to a profile stating qualification: no
No link from the page to a personal profile.
2026-09-16 · Ausgeliefertes HTML
Puma — Names a person as author, visibly ↗ — Names a person as author, visibly: no
Schema is CollectionPage on a blog article. No author.
2026-09-16 · Ausgeliefertes HTML
Puma — That name leads to a profile stating qualification ↗ — That name leads to a profile stating qualification: no
No link from the page to a personal profile.
2026-09-16 · Ausgeliefertes HTML
SChecked on the page: a dated measurement of a live page. It can change tomorrow; the date says when it held.
What you can do
Google’s own Helpful Content best practices, filtered to this dimension.
Original information, own research or analysis; substantial, complete coverage of the topic that goes beyond the obvious — not mere summaries or rewrites.
Helpful Content: Content & Quality · Original content systems · Helpful content system (part of Core Ranking) · Panda (integrated into Core Ranking)
Content for people, not primarily for search engines; no mass or automated production without added value; no content that exists only to capture traffic.
Helpful Content: People-first / Avoid search-engine-first · Helpful content system · Spam detection systems (incl. SpamBrain)
Recognizable expertise in the topic area; the site/author has a traceable background; others recommend or cite the source.
Helpful Content: Expertise + E-E-A-T · Reliable information systems · Link analysis systems & PageRank
Which practice belongs to which dimension is our assessment [C]. Both ends, the practice and the named systems, are documented by Google [O]. All eight, with the systems behind them →
The systems
Primary (3)
Systems where Expertise is the primary E-E-A-T dimension.
chard rates the content quality of individual pages — calibrated against the judgments of human quality raters that Google trains according to strict guidelines (QRG). A variance score shows how confident the model is in its verdict. chard is especially sharp on YMYL content (Your Money or Your Life — topics with real life consequences such as health, safety, severe weather) and on suspected misinformation.
A language model estimates how much editorial effort and depth went into a page. The system distinguishes between shallow, machine-generated content and pages with recognizable research, structure and substance. The value is not a hard measurement but a model estimate, one that increasingly reliably separates thin AI content from genuine editorial engagement.
Not a live signal used daily in ranking — but the foundation on which all quality models are calibrated. The IS-Score (Information Satisfaction) summarizes how well a page meets a user's information need, rated by trained human raters. The QRG (Quality Rater Guidelines) are the public rulebook by which these raters judge.
Flanking (9)
Systems that support Expertise indirectly.
Deep Dives (15)
NavBoost: How Google Uses Click Behavior to Re-Rank Search Results
From DOJ testimony to leaked API documentation — the most important ranking signal you can't directly manipulate
Panda: The Site-Wide Quality System Named After Its Inventor
How Google's war on content farms produced a patent trail that leads directly to the update's name — and what the API leak reveals about its current form
Q* and P*: The Two Pillars of Google's Ranking Architecture
The DOJ trial revealed that Google's ranking ultimately reduces to two top-level signals — Quality (Q*) and Popularity (P*). This is what the evidence shows about how they work.
IS-Calibration: The Human Rater System That Trains Google's Quality Models
The hardest evidence in the entire matrix — DOJ/sworn material confirms the rater system that calibrates every quality signal Google uses
chard: Google's Page-Level Content Quality Model
The leak field that rates individual page quality — calibrated by human raters, feeding into NSR, and why YMYL pages face stricter standards
OriginalContentScore: How Google Measures Content Originality
The leak field that measures how much original material a document contains — information that is only found there and that an AI couldn't reconstruct from other sources
contentEffort: How Google Estimates Editorial Effort
The AI model that estimates how much editorial work went into a page — and why 'effort' is becoming a ranking signal
Topic-Embeddings: How Google Understands What Your Page Is About
The focus vectors and radius that determine topical relevance — and why a weather site needs a narrow focus to rank for weather queries
Glue: How Google Measures Interaction with Special Search Features
The NavBoost counterpart for carousels, 'People also ask' boxes, and news features — measuring whether users interact with special elements, not just blue links
RankBrain and DeepRank: How AI Changed Google's Understanding of Queries
From keyword matching to semantic understanding — the AI systems that interpret what users mean, not what they type
tofu, keto, Rhubarb: The Subchunk Refinement Signals
Codenames for refinement signals at the most granular level, the individual subsections of a page. They are the weakest evidence in the matrix.
Site Reputation Abuse: What Google Suspended in Europe — and What It Did Not
What Google suspended in the EEA is the manual action — the replacement removes exactly what parasite SEO pays for
Google Update History: What a Timeline of Named Updates Can and Cannot Show
Google names roughly ten updates a year and ships thousands of changes. The timeline is real, dated and official — and it is a filtered excerpt of Google's own communication decisions
What Good Content Is: contentEffort and Google's Soft Metrics
One leaked field, one rater definition, twelve official questions, and a checklist that marks which parts are evidence and which are our reading.
Links That Count: Five Link Tactics Against the Leak and the Patents
A popular link guide reads PageRank buckets, trusted anchors and context hashes out of Google's leak. The fields are real. The trusted sources are news sites, the bucket counts onsite anchors, and the cap is 200, not zero.