Claim
Algorithm recovery is a systematic diagnostic, not guesswork. The 170+ page Google Quality Rater Guidelines train Google's 12,000+ human raters — and Google's algorithms are trained on what those raters score. Sites that align with the QRG criteria recover; sites chasing shortcuts don't. Four-step recovery workflow: (1) timing analysis correlating drop with named updates, (2) QRG-based audit of E-E-A-T signals, (3) competitive gap analysis vs. now-ranking sites, (4) remediation plan (content pruning, author attribution, expertise demonstration, trust signals).
Mechanism
The QRG is not just rater-training; it's a public-ish proxy for what Google's models are being trained to predict. Common penalty drivers Haynes identifies: lack of named authors with verifiable credentials, thin "me too" content adding no unique value, YMYL content without professional expertise, poor reputation signals (negative reviews, BBB complaints, no industry citations), affiliate-heavy content prioritizing monetization over user value. Remediation that aligns with QRG works because algorithms are downstream of the same rubric.
Conditions
Holds when:
- The site operates in YMYL or expertise-sensitive categories where the QRG most applies.
- The team can credibly add author credentials and primary-source citations.
Fails when:
- Programmatic-content sites where author attribution feels manufactured.
- Penalties driven by technical or link-spam issues rather than quality signals.
Evidence
"Algorithm recovery is a systematic diagnostic process, not guesswork; the Google Quality Rater Guidelines are the closest thing to a public roadmap for what Google considers quality."
— Marie Haynes (synthesized from operator's published work)
Signals
- Audit artifact maps each E-E-A-T signal vs. competitors who now outrank.
- Author bios link to credentials and external proof.
- Content pruning is part of recovery plan, not just additions.
Counter-evidence
AI Overview / answer-engine search is reshaping the QRG-as-ranking-proxy assumption — passage-level extractability now matters in ways the QRG framework doesn't fully address. Some practitioners argue Google's actual algorithm signals diverge meaningfully from QRG criteria.
Cross-references
- ins_eeat-quality-rater-perspective — adjacent operator (Cyrus Shepard)