How does the Search Console actually calculate the average position of your rich results?

What does Google mean by the "highest ranking" of a rich result?

The Search Console does not track each individual occurrence of your rich results in the SERP. Google takes the highest ranking achieved by any rich result present on a page, regardless of its type (FAQ, recipe, product, review, event).

If your page displays both a FAQ snippet in position 3 and a star rating in position 5, the Console will keep position 3 as the reference for that page. This simplifies reporting but masks the reality of each structured component.

Why do results in blocks share a common position?

Google often displays multiple rich results from the same page grouped in a single SERP block — typically expandable FAQs, suggested videos, or listed events. All these elements visually occupy a single position on the results page.

The Console therefore assigns the same position to all results in that block. If 4 FAQ questions are displayed together in position 2, each will be counted in position 2 in your stats. The result: your average position increases, but the actual number of clicks remains that of the entire block.

What impact does this have on the average ranking calculation in the Console?

The "Average position" metric for rich results can seem artificially low (and thus good) if you have multiple results in shared blocks at the top of the SERP. A site with 10 visible FAQs in position 1 will display an excellent average position, even if only 2-3 questions actually generate traffic.

Conversely, a site with a single well-ranked but isolated rich result will have a more transparent metric. This calculation logic makes comparisons between sites or between types of structured data tricky, if not misleading.

• Position retained: always that of the highest-ranked rich result on the page.
• Shared blocks: all results in a block inherit the position of the block itself.
• Artificial average: can overestimate performance if multiple results share a high position.
• Limited transparency: impossible to distinguish the performance of each rich result individually in the Console.

Does this calculation logic truly reflect user experience?

Yes and no. On one hand, a block of FAQs in position 1 does indeed occupy position 1 in the user's attention. But on the other hand, each question may have a radically different click-through rate based on its display order in the block, its wording, or its perceived relevance.

The Console aggregates this all into a single metric. [To verify] whether Google does not distinguish internal order of elements in a block at all — some observations suggest that questions displayed first in an expandable generate more clicks, but the Console does not reflect this in the reporting.

What inconsistencies can be observed with this aggregation method?

Issue 1: your average position might be excellent (1.2) while your clicks stagnate. Likely cause: you have 5 results in a shared block in position 1, but only the first one attracts traffic. The Console counts the other 4 as position 1 too, artificially inflating the metric.

Issue 2: removing a poorly performing rich result can paradoxically degrade your average position. If that result was sharing a high position with others in a block, its removal eliminates an entry listed in a favorable position, even if it generated no traffic.

Should we question the reliability of rich results metrics?

Let's be honest: the average position metric for rich results in the Search Console is a global indicator, not a granular dashboard. It gives a trend but does not allow assessment of individual performance of each structured snippet.

For precise management, it’s necessary to cross-reference with actual click data, the evolution of CTR, and third-party tools that capture SERP features in real-time. Never rely solely on this average position to guide the optimization of your structured data.

How to correctly interpret rich result data in the Console?

First rule: never isolate the position metric. Always look simultaneously at clicks, impressions, and CTR for each page displaying rich results. An average position of 1.5 with a CTR of 2% indicates a structural problem — likely a shared block with less attractive results.

Second rule: segment your analyses by type of structured data. FAQs do not behave like recipes or products. Compare apples with apples, not with oranges. If Google mixes everything into the same position metric, you have the Console filters to isolate each type.

What concrete actions can be taken to optimize despite these limitations?

Prioritize rich results with high click potential — those that appear at the top of a block or are shown standalone. FAQs placed first in an expandable, product images with visible prices, prominent star ratings. These are the ones driving traffic, even if they all share the same official position.

Test removing redundant or irrelevant rich results. If you pile 8 FAQs on a page and only 2 generate clicks, simplify. Fewer elements in a block can improve readability and focus attention on the snippets that matter. Measure the impact on CTR, not just on average position.

What additional tools should be used for precise tracking?

SERP crawlers like SEMrush Position Tracking or Ahrefs Rank Tracker can capture SERP features over time and identify which rich results actually appear, in what order, and how many times. This gives you a view that the Console cannot offer.

Tools for heat mapping and eye-tracking (like Hotjar on simulated landing pages) help understand which element in a block really attracts the eye. Combine this with A/B tests on the wording of your structured data to maximize the CTR of each snippet.

• Always cross-reference average position, clicks, and CTR in the Search Console.
• Segment your analyses by type of structured data (FAQ, Product, Recipe, etc.).
• Identify rich results at the top of blocks — those are the ones capturing attention.
• Test reducing the number of redundant snippets to enhance readability.
• Use third-party SERP tracking tools for a granular view of actual displays.
• Measure the impact of each change on CTR, not just on position.