← Legibility Index 2026

Dick's Sporting Goods

Sporting Goods & Outdoor · #27 by 2025 U.S. retail sales ($20.20B)

Measured at www.dickssportinggoods.com on 2026-09-01

51
Mixed
53 platform-adjusted
rank 78 of 111

Pillar breakdown

Each pillar shows the points earned against the maximum that was actually observable. Where a signal could not be observed it is removed from both sides rather than scored as a failure.

Product Data Depth

8.4 / 25

Buyer Answerability

2.5 / 6

14 pts not observable

Commerce Transparency

9.8 / 20

Machine Readability

13.1 / 20

Agent Interface

10 / 15

Every signal

Product Data Depth

Identity
3.3 / 6
Identifiers
3 / 12
Attribute depth
2.1 / 7

Buyer Answerability

Descriptive contentnot observable
Imagerynot observable
Ratings and reviews
2.5 / 6
Catalog consistencynot observable

Commerce Transparency

Public pricing
3.8 / 5
Stock visibility
3 / 4
Shipping in markup
0 / 4
Return policy in markup
0 / 4
Un-gated access
3 / 3

Machine Readability

Product markup
6 / 6
Required-field completeness
4 / 5
Recommended-field depth
1.6 / 5
Variant expression
1.5 / 4

Agent Interface

Crawler access to products
4 / 4
AI crawler stance
3 / 4
Product sitemap
3 / 4
Agent surfaceplatform
0 / 3

What the site-level probe found

robots.txt readableyes
AI crawler stanceno AI user-agents named (an absent rule permits the crawler)
Sitemapyes, including a product sitemap
UCP profilenone at /.well-known/ucp — this is not evidence against adoption
llms.txtnone

The pages we sampled

Drawn by seeded random selection from your own product sitemap — 24,998 candidate product URLs, seed "anglera-legibility-2026". Same seed, same pages, every time. Open any of them and check our arithmetic.

What would move this number

  • Publish a valid GTIN on every page. 0% of your sampled pages carried one that validates. It is the field an agent uses to match your product to the same product at three competitors.
  • Fill in the recommended Product fields. Your markup is present but shallow — shipping, returns, condition and price validity are the ones most often missing.
  • Express variants as a ProductGroup. Size and colour are the retail catalog, and without hasVariant an agent sees unrelated products.
  • Put ratings in the markup, not just the pixels. Reviews are the single biggest factor shoppers cite, and only 38% of your sampled pages expose them where a machine can read them.

What this score is not

It is not an audit and not a judgement of the business — it is a 8-page sample taken on a stated date, scored on a published rubric with no model judgement in it. Nothing here predicts whether any assistant will recommend you; for most answer engines that is decided by a private merchant feed we cannot see. If a number here is wrong, tell us and it goes in the public corrections log.

Go deeper than eight pages

This scorecard is a sample — eight product pages, drawn at random from a published seed. It tells you where Dick's Sporting Goods stands, not which SKUs are costing you. We’ll run a real sample of the whole catalog through the same extractor and send back the fields that are missing, by product.

Everything above stays free either way — the rubric, the sampled pages, the dataset, and every other company’s scorecard.