Lab TBT on /en-us/sports/nba is 1,890ms, blocking main thread interactivity for nearly 2 seconds, causing taps and scrolls to freeze.
CURRENT — 1,890ms TBT
scroll ↕ — this is the live page
⚠ TBT 1,890ms on NBAThe page's Total Blocking Time (TBT) is 1,890ms, meaning the main thread is blocked for nearly two seconds during load. This prevents the browser …
RECOMMENDED — ≤200ms TBT
Reduce TBT to ≤200msOptimize script executionDefer non-critical JS
NBA Scores & Schedule
NBA
Live scores, schedules, and news for the NBA season.
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✓ Instant responsePage becomes interactive in under 200ms; taps and scrolls respond immediately.
FixReduce Total Blocking Time to under 200ms by breaking up long tasks, deferring non-critical scripts, and optimizing third-party code. Implement code splitting to load only necessary JavaScript for the initial view, and use web workers for heavy computations. This will allow the page to become interactive quickly, improving user experience and conversion rates.
Evidence
Observed on the live site Verified by measurement
✓ Flagged independently by 1 of 7 analysts
Lighthouse (mobile emulation, single synthetic run via DataForSEO): Total Blocking Time 1,890 ms against a ‘good’ threshold of 200ms. Lab data, not real-user field data — confirms the defect class, not the field percentile.
Business case
If this change wins
0 – 0
orders per 1,000 visitors
Medium
Effort — 2-5 days
Not A/B testable
At typical traffic
Detecting this needs about 124,891 visitors per variant. Ship it as a sequenced change and log the date instead.
Prioritisation
PXL score: 5 / 10
Assessed from the live page
Question
Answer
Points
Is the change above the fold?
Yes
1
Is the change noticeable in under 5 seconds?
Yes
2
Does it add or remove an element?
Yes
1
Does it run on a high-traffic page?
No
0
Verified by direct technical measurement?
Yes
1
Criteria that need your data
4 of these could not be answered from the live page.
Session recordings, survey or support themes, and analytics access would
raise the confidence of this ranking — they do not indicate a weaker
finding.
Question
Answer
Points
Discovered via user testing?
No
0
Discovered via qualitative feedback (survey, support)?
No
0
Supported by heatmaps or session recordings?
No
0
Found via digital analytics or real-user field data?
Lab TBT on /en-us/sports/nba is 1,890ms, blocking main thread interactivity for nearly 2 seconds, causing taps and scrolls to freeze.
Current state
notes: Total Blocking Time 1,890 ms (lab, mobile on /en-us/sports/nba)
Change
notes: Total Blocking Time ≤ 200ms (good)
Acceptance criteria
GIVEN a first-time visitor on the affected page
WHEN the change is live
THEN Total Blocking Time ≤ 200ms (good),
with no regression at 375px and 1280px widths.
Tracking
event: cro_tbt_1890ms_on_nba on interaction with the
changed element; verify it fires in BOTH variants before opening traffic.
Success metric
conversion rate on the affected step
QA checklist
Chrome / Safari / Firefox · 375px + 1280px ·
keyboard reachable · screen-reader name present · no CLS introduced
Effort
Medium —
2-5 days
Test plan
124,891 visitors per variant
(95% significance, 80% power);
25 days.
Rollback
Feature-flag the change; revert the flag if the primary metric
drops for 3 consecutive days.
1,000 visitors × baseline conversion × relative lift
= 3,648,000 annual visits to this page (measured organic traffic) x 2.00% conversion x 2.0-8.0% relative lift. Supply an average order value to state this in money.
The 2.0–8.0% relative-lift band is an assumption bounded by severity (Urgent), not a prediction.
The baseline conversion rate is an assumption — the client did not supply one, so an industry-typical 2.0% is used and labelled as such.
Why the range is conservative: Across 127,000 experiments, 12% produced a statistically significant improvement on the primary metric; a healthy programme win rate is 10-30%. Winning tests also overstate their true effect, so treat this as the prize if the change works — never expected revenue.
How effort is estimated
Bracket
Days
Typical scope
Low
0.5–2
copy, colour, labels, image swaps
Medium
2–5
layout shifts, form changes, new modules
High
5+
personalisation, funnel work, integrations
Assigned by matching the recommended change against these scopes; the estimate is a bracket, not a quote.
Sample size math
Two-proportion z-test, the standard pre-test sample-size calculation:
n = [z₀.₀₂₅·√(2·p̄·(1−p̄)) + z₀.₂₀·√(p₁(1−p₁)+p₂(1−p₂))]² / (p₁−p₂)²
Baseline conversion
2.0% (assumed)
Significance / power
95% / 80%
Sample per variant, 8.0% lift
124,891
Sample per variant, 2.0% lift
1,941,808
Halving the minimum detectable effect roughly quadruples the sample required, which is why the conservative figure is so much larger.
What PXL is
PXL is CXL's prioritisation framework: binary questions instead of subjective 1–10 guesses, weighted toward evidence.
Findings discovered by inspection score zero on all four evidence questions — that is the honest signal, not a defect. Findings verified by direct measurement earn the extension point and, where real-user field data is involved, the analytics point.