At a glance the headline numbers for Third-party categories
Those third parties grouped by what they do.
12.6% of sites carry analytics. 4.5% embed video players.
The third-party categories mix who uses what, and how stable each group is
Third-party categories. On the fleet: 20.6% cdn, 18.1% framework, 12.6% analytics. 100.0% of sites use at least one cdn.
Passing CLS per bucket every category and count level at once - color is the pass rate
Each row is a category, each column its own count bucket (few on the left, many on the right); the cell is the share of those sites passing CLS.
Rum swings the hardest: 84% of sites pass CLS with few, 65% with many. computed
Few vs many - does quantity cost CLS? the pass rate with few vs many of each category
Per category: the pass rate among pages with FEW of it (hollow ring) against pages with MANY (solid dot), worst trend first. Thin buckets are excluded from the endpoints.
More Rum costs the most: the CLS pass rate falls from 84% with few to 65% with many. computed
Why this matters for the Core Web Vitals, and where to start fixing it
Third parties grouped by what they do, and the category predicts the damage. Ads and chat inject visible UI, so they shift layout (CLS). Analytics and tag managers run code, so they block interactions (INP). Font and CDN services sit on the render path, so they delay paint (LCP).
Budget per category, not per tag. Most stacks need at most one of each: one analytics tool, one tag manager, one chat widget. The duplicates are where the easy wins live.
Chrome field data from 189,915 sites, representing millions of real page loads. How we measured.
Live queries (2) — admin only
SELECT 1 AS ok FROM site_metric_bags LIMIT 0;WITH flat AS (
SELECT b.cat, b.n, b.size, s.crux."all".cls AS cwv_val
FROM site_metric_bags b
JOIN sites s ON s.origin = b.origin
WHERE b.path = 'stack.third_party_categories'
AND b.cat IS NOT NULL
),
totals AS (
SELECT cat,
SUM(n) AS fleet_n,
SUM(size) AS fleet_size,
COUNT(*) FILTER (WHERE n IS NOT NULL) AS sample,
COUNT(*) FILTER (WHERE n > 0) AS with_any,
quantile_disc(n, 0.95) AS p95,
COUNT(*) AS n_rows
FROM flat
GROUP BY cat
),
widths AS (
SELECT *,
GREATEST(1, CAST(round(COALESCE(p95, 0)) AS INTEGER)) AS cap,
LEAST(GREATEST(1, CAST(round(COALESCE(p95, 0)) AS INTEGER)), 12 - 1) AS regular_buckets
FROM totals
),
widths2 AS (
SELECT *,
GREATEST(1, CAST(ceil(cap * 1.0 / regular_buckets) AS INTEGER)) AS width
FROM widths
),
binned AS (
SELECT f.cat, f.cwv_val, w.fleet_n, w.fleet_size, w.sample, w.with_any, w.width, w.regular_buckets,
CASE WHEN f.n IS NULL THEN NULL
ELSE LEAST(CAST(FLOOR(f.n * 1.0 / w.width) AS INTEGER), w.regular_buckets)
END AS bucket_idx
FROM flat f
JOIN widths2 w ON f.cat = w.cat
)
SELECT cat, fleet_n, fleet_size, sample, with_any, width, regular_buckets,
bucket_idx,
COUNT(*) AS n,
quantile_disc(cwv_val, 0.5) AS median,
COUNT(*) FILTER (WHERE cwv_val IS NOT NULL) AS total_cwv,
COUNT(*) FILTER (WHERE cwv_val IS NOT NULL AND cwv_val <= 0.1) AS good
FROM binned
WHERE bucket_idx IS NOT NULL
GROUP BY cat, fleet_n, fleet_size, sample, with_any, width, regular_buckets, bucket_idx
ORDER BY cat, bucket_idx;