Script origin (1P/3P)
First-party vs third-party scripts - counts and bytes.
At a glance the headline numbers for Script origin (1P/3P)
First-party vs third-party scripts - counts and bytes.
21.2% of scripts come from third parties.
The script origin (1P/3P) mix who uses what, and how fast each group loads
Script origin (1P/3P). On the fleet: 78.8% first party, 21.2% third party.
By count first party leads (78.8%); by bytes it is third party (56.0%). computed
Passing INP 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 INP.
Third party swings the hardest: 94% of sites pass INP with few, 85% with many. computed
Few vs many - does quantity cost INP? 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 Third party costs the most: the INP pass rate falls from 94% with few to 85% with many. computed
Why this matters for the Core Web Vitals, and where to start fixing it
First-party script is in your bundle, your build, your deploy. Third-party script arrives from someone else's server at whatever size and shape it has today. Both run on the same main thread. Only one of them is yours to fix.
A high third-party share is a ceiling on your INP work: optimize your own code all you want, the tags still run between your visitor's clicks. Past that ceiling the fix list changes. Fewer vendors, deferred loading, facades instead of always-on embeds.
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."phone".inp AS cwv_val
FROM site_metric_bags b
JOIN sites s ON s.origin = b.origin
WHERE b.path = 'scripts.origin'
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
HAVING COUNT(*) >= (SELECT COUNT(DISTINCT origin) FROM site_metric_bags WHERE path = 'scripts.origin') * 0.005
),
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 <= 200) 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;