Google’s published patent on crawl scheduling describes a system that maintains a predicted change rate for every known URL, updated after each crawl based on whether the content had actually changed. URLs with a history of frequent, meaningful changes get scheduled for more frequent re-crawling. URLs with stable content get scheduled less frequently. This predictive model means your crawl frequency today is largely determined by your content change patterns over the past months — and it means that changing your update frequency does not immediately change your crawl frequency. The algorithm needs multiple crawl cycles to update its prediction, creating a lag that practitioners frequently misinterpret as Google ignoring their changes.
The predictive scheduling model builds a per-URL change probability function
Google’s crawl scheduling system maintains a per-URL statistical model that predicts the probability of content change since the last crawl. This model is the core mechanism that determines when each known URL gets re-crawled. The system is described across a family of related Google patents (US7725452B1, US8161033B2, US10621241B2), all titled “Scheduler for Search Engine Crawler,” invented by Keith H. Randall at Google.
The model works by maintaining a history log containing document identifiers (URLs) and associated metadata including content change frequency, page rank, and crawl timestamps. Each time Googlebot crawls a URL, the system compares the newly retrieved content against the previously stored version using checksum-based comparison. If the checksum differs, the content is recorded as changed, and the URL’s change frequency estimate is updated upward. If the checksum matches, the content is unchanged, and the change frequency estimate is adjusted downward.
The scheduling priority for each URL is computed as a composite score. The patent describes the formula as a Daily Score of the form: Daily Score = F(page_rank, change_frequency, age). One specific formulation described is (page_rank)^2 * change_frequency. URLs with high scores enter the “daily crawl” layer, receiving at least one crawl per day. URLs with the highest scores enter the “real-time” layer, receiving multiple crawls per day. Lower-scoring URLs are scheduled at longer intervals.
The probability of change increases over time since the last crawl. A URL that typically changes every 3 days has a low change probability at day 1, moderate probability at day 3, and high probability at day 5. The scheduling system uses this probability curve to determine when the expected value of discovering a change justifies the crawl cost. Google’s patent US8666964B1 (“Managing Items in Crawl Schedule”) provides additional detail: the system estimates a change period for each URL based on crawl history, and sets the crawl interval to half the estimated change period to avoid missing changes.
This means a URL that historically changes weekly gets scheduled for re-crawling approximately every 3-4 days. A URL that changes daily gets scheduled for daily re-crawling. A URL that has not changed in six months may wait weeks or months between crawls.
Historical change detection distinguishes meaningful changes from cosmetic updates
The scheduling model’s change detection uses content fingerprinting that filters out cosmetic changes to measure only substantive content modifications. Not every byte-level change to a page triggers an update to the scheduling model. Google’s patent US20130144858A1 (“Scheduling Resource Crawls”) explicitly describes monitoring for “interesting” content changes as opposed to all changes.
Changes that register as meaningful:
- Modifications to the primary content body (article text, product descriptions, data tables)
- Changes to title tags, meta descriptions, and heading structures
- Addition or removal of substantial content sections
- Changes to structured data markup
- Significant changes to internal link destinations
Changes that are filtered out:
- Timestamp updates (copyright year, “last updated” date without content change)
- Session identifiers and tracking parameters embedded in the page
- Ad rotation and dynamic advertisement content
- Randomized content elements (testimonial carousels, related product widgets)
- CSS and layout changes that do not alter textual content
The implication for SEO practitioners: artificially inflating change signals by updating lastmod timestamps in sitemaps without actual content changes does not increase crawl frequency. Google’s sitemap patent (US8037054B2) describes a system that uses sitemap change frequency as one input alongside actual observed changes. When the sitemap claims frequent changes but the crawled content shows no meaningful difference, the system learns to discount the sitemap’s signals for that site. This degrades the scheduling model’s trust in the site’s sitemap data, potentially reducing the effectiveness of legitimate lastmod signals when real changes occur.
Gary Illyes has stated that Google is looking for URLs “more likely to deserve crawling,” emphasizing that the scheduling system rewards genuine content investment over signal manipulation. The quality of changes matters: adding a paragraph of original analysis to an existing article is a meaningful change. Rearranging existing paragraphs or changing a date is not.
The scheduling lag: why changes to update frequency take weeks to affect crawl rate
The predictive model updates incrementally, not instantaneously. When a site changes its content update pattern, the scheduling model requires multiple crawl cycles to detect and adapt to the new pattern. This creates a scheduling lag that is one of the most misunderstood aspects of crawl behavior.
Scenario: Increasing update frequency. A blog post that was static for two years begins receiving weekly updates. The scheduling model’s current prediction for this URL is “very low change probability” based on two years of no-change crawl results. On the next scheduled crawl (which may be weeks away due to the low prediction), Googlebot discovers a change. The model adjusts upward slightly. On the following crawl, if another change is detected, the model adjusts upward again. This process continues over multiple crawl cycles.
The patent’s formulation uses rolling averages to smooth out noise. A single observed change after years of stability produces a small adjustment. Multiple consecutive changes produce larger adjustments. Based on the patent’s described mechanics, significant schedule changes require approximately 3-5 consecutive crawl cycles where the observed state (changed or unchanged) differs from the prediction. For a URL previously crawled monthly, this means 3-5 months before the scheduling model fully adapts to a new weekly update cadence.
Scenario: Decreasing update frequency. A news article that was updated daily for a month stops receiving updates. The model still predicts high change probability based on the recent daily change history. Googlebot continues crawling frequently, discovers no change each time, and gradually adjusts the prediction downward. The deceleration lag is typically shorter than the acceleration lag because the model can observe “no change” on every visit, accumulating evidence faster.
Practical implication: Sites launching a new content update strategy should not expect crawl frequency increases for 4-8 weeks after establishing the new pattern. The lag is a feature of the system, not a bug — it prevents the scheduler from overreacting to temporary content changes and ensures that crawl resources are allocated based on sustained patterns rather than short-term spikes.
Per-section scheduling patterns emerge from URL-level predictions
While the predictive model operates at the URL level, Google’s crawling infrastructure optimizes efficiency by aggregating URL-level predictions into section-level scheduling patterns. This aggregation is a practical engineering optimization that reduces scheduling overhead for sites with millions of URLs.
Google’s crawl budget documentation describes how crawl demand operates at the site level, influenced by popularity and staleness across URL segments. When a site section (e.g., /blog/, /products/, /news/) has consistently high change rates across many URLs, the section receives elevated crawl scheduling as a group. Individual URLs within the section benefit from the section’s aggregate change rate even if their individual change rates are lower.
This produces observable behavior in server logs. A /blog/ section with 10 actively updated posts and 90 static posts will show elevated crawl frequency across all 100 posts, not just the 10 that change. The actively updated posts raise the section-level change rate prediction, which lifts the scheduling priority for the entire section.
The reverse is also true. A /products/ section where 95% of pages are static and only 5% receive regular updates has a low aggregate change rate. Even the 5% of pages with genuine updates receive slower scheduling than they would if the section’s overall change rate were higher.
Strategic implication: Concentrating content updates within specific URL sections builds section-level scheduling momentum more effectively than distributing updates evenly across all sections. If a site publishes 10 updates per week, placing all 10 in the /blog/ section produces a higher section-level change rate for /blog/ than spreading 2 updates each across 5 different sections. The concentrated approach results in faster re-crawling of the entire target section.
Botify’s research confirms that refresh crawling (re-crawling known pages) typically consumes 75-95% of total crawl budget. The section-level scheduling mechanism determines how that refresh budget is distributed across site sections, making it one of the most impactful levers for controlling where Google allocates crawl attention.
Influencing the scheduling model through deliberate content management patterns
Understanding the predictive model enables deliberate strategies that train the scheduler to allocate crawl resources according to business priorities.
Establish consistent update cadences. The model responds most favorably to regular, predictable change patterns. A blog section updated every Tuesday and Thursday trains the model to expect changes at those intervals. Irregular updates (five posts one week, none for three weeks) produce an averaged change rate that underestimates the peak and overestimates the troughs, resulting in suboptimal scheduling.
Concentrate updates in priority sections. As described in the section-level scheduling discussion, directing content updates to specific URL segments builds scheduling momentum in those segments. For sites where blog content is the primary organic traffic driver, concentrating update activity in the blog section ensures that section receives the highest crawl frequency.
Avoid cosmetic change anti-patterns. The following practices degrade model accuracy rather than improving scheduling:
- Updating lastmod timestamps without content changes
- Changing copyright years or “last reviewed” dates site-wide
- Rotating boilerplate elements (testimonials, sidebar content)
- Republishing unchanged content with new publication dates
Google’s December 2025 Core Update specifically refined how freshness signals are evaluated, further penalizing artificial freshness signals that lack corresponding content substance.
Use sitemap lastmod accurately. When lastmod values consistently match actual content change dates, the sitemap becomes a trusted signal that reinforces the scheduling model. Accurate lastmod values give Google advance notice of which URLs have changed, allowing the scheduler to prioritize those URLs in the next crawl cycle.
Improve server response time. Google’s crawl capacity limit — the maximum requests per second Googlebot will make — increases when the server responds quickly. Faster TTFB allows more crawl requests within the same time window, which means the scheduling model’s predictions can be fulfilled faster. Gary Illyes has emphasized that the scheduler listens to signals from search indexing: when quality signals improve, crawl demand turns up accordingly.
The interaction between scheduling predictions and external demand signals
The predictive scheduling model is one component of Google’s overall crawl demand calculation. The other component is external demand: signals from outside the scheduling model that override or supplement predictions.
Google’s crawl budget documentation defines crawl demand through two factors: popularity (URLs that are more popular on the internet tend to be crawled more often) and staleness prevention (Google’s systems attempt to prevent URLs from becoming stale in the index). The scheduling model primarily addresses staleness prevention. Popularity-driven demand operates through a different mechanism.
External demand triggers that override scheduling predictions:
New backlinks. When a URL acquires new external links from authoritative sources, its popularity signal increases. The crawl scheduler increases the URL’s crawl priority independently of the change-rate prediction. This is why a page that has been static for months may suddenly receive increased crawl attention after gaining new backlinks — the popularity signal overrides the low change-rate prediction.
Trending topic association. When a topic related to a URL’s content begins trending in search demand, Google’s systems increase crawl demand for relevant URLs. This burst crawl attention provides fresh data for the search results and simultaneously updates the scheduling model with new crawl observations.
Sitemap submission with trusted lastmod. Submitting or updating a sitemap with accurate lastmod timestamps creates external demand for the modified URLs. If the site has established trust for its lastmod signals (through historical accuracy), the sitemap submission can trigger priority re-crawling that overrides the scheduling model’s lower prediction.
Search quality feedback. Illyes explained that scheduling is dynamic: as signals from search indexing indicate that content quality has increased across a set of URLs, the system increases crawl demand for those URLs. This creates a positive feedback loop where quality improvements lead to more crawling, which leads to faster indexation of further improvements.
The interaction between prediction-based and demand-based scheduling means that the predictive model alone does not fully determine crawl frequency. A URL can receive crawl attention above its predicted rate when external demand signals are strong. Conversely, a URL with a high predicted change rate may receive reduced crawl attention if its popularity signals decline. The crawl budget allocation framework describes how these inputs combine into the overall crawl budget that determines realized crawl behavior.
Does changing from infrequent content updates to daily publishing immediately increase Googlebot’s crawl frequency?
The transition from infrequent to frequent updates is not immediate. Google’s scheduling model adjusts incrementally based on observed change patterns over multiple crawl cycles. If a URL that historically changed monthly begins changing daily, the scheduling model starts increasing predicted change probability after several successful change detections. The ramp-up typically takes two to four weeks of consistent daily updates before crawl frequency stabilizes at a higher level. Inconsistent updates during this calibration period slow the adjustment.
Does Google’s crawl scheduling model account for the time of day a URL typically changes?
Google’s scheduling system predicts when a URL is likely to change based on historical patterns, which can include time-of-day patterns. A page that consistently updates at 9am local time may develop a higher crawl probability around that window. However, this level of temporal precision is most observable on high-frequency sites like news publishers. For sites updating daily or less frequently, the scheduling model operates on a coarser time scale, prioritizing the right day or week rather than the right hour.
Does a URL that stops receiving updates for several months eventually drop out of Google’s active crawl schedule?
A URL that stops changing sees its predicted change probability decrease with each crawl that detects no update. Over months of no changes, crawl intervals extend from days to weeks to months. The URL does not drop out of the schedule entirely; Google periodically re-checks even dormant URLs to verify they still exist and have not changed. The recrawl interval for long-dormant URLs may extend to every two to three months, but complete removal from the crawl schedule does not occur unless the URL returns a 404 or 410.
Sources
- Google Patents. “Scheduler for Search Engine Crawler.” US7725452B1, US8161033B2, US10621241B2. https://patents.google.com/patent/US7725452B1/en
- Google Patents. “Managing Items in Crawl Schedule.” US8666964B1. https://patents.google.com/patent/US8666964B1/en
- Google Developers. “What Crawl Budget Means for Googlebot.” https://developers.google.com/search/blog/2017/01/what-crawl-budget-means-for-googlebot
- Search Engine Journal. “Google’s Crawling Priorities: Insights from Analyst Gary Illyes.” https://www.searchenginejournal.com/googles-crawling-priorities-insights-from-analyst-gary-illyes/512876/
- Search Engine Roundtable. “Google Explains Crawl Rate and Crawl Demand Make Up Crawl Budget.” https://www.seroundtable.com/google-crawl-budget-23265.html
