Estimate your Lighthouse performance score from FCP, SI, LCP, TBT, and CLS metrics using official scoring weights for version 10+.
Google Lighthouse calculates a performance score from 0 to 100 using a weighted combination of five metrics: First Contentful Paint (FCP), Speed Index (SI), Largest Contentful Paint (LCP), Total Blocking Time (TBT), and Cumulative Layout Shift (CLS). Understanding the weights helps you prioritize which metrics to improve for maximum score impact.
This calculator converts your raw metric values into sub-scores using log-normal distributions (matching Lighthouse's scoring methodology) and combines them using official weights: FCP (10%), SI (10%), LCP (25%), TBT (30%), and CLS (25%). TBT and LCP together account for 55% of the total score.
Use this estimator to predict how metric improvements will affect your overall Lighthouse score, helping prioritize optimization work for maximum impact.
Integrating this calculation into monitoring and reporting workflows ensures that engineering decisions are grounded in real data rather than assumptions about system behavior. Precise measurement of this value supports informed infrastructure decisions and helps engineering teams optimize system architecture for both performance and cost efficiency.
Lighthouse scores influence both SEO rankings and stakeholder perception. Understanding how individual metrics contribute to the overall score helps you prioritize the improvements that will have the greatest impact on your score. Data-driven tracking enables evidence-based infrastructure decisions, reducing the risk of over-provisioning costs or under-provisioning that leads to performance bottlenecks.
Score = FCP sub-score × 0.10 + SI sub-score × 0.10 + LCP sub-score × 0.25 + TBT sub-score × 0.30 + CLS sub-score × 0.25. Sub-scores use log-normal distribution curves (simplified to linear approximation in this estimator).
Result: Estimated score: ~75
FCP 1.8s scores well (high sub-score), SI 3.4s is medium, LCP 2.5s is borderline Good, TBT 200ms is moderate (major weight), and CLS 0.1 is Good threshold. The estimated overall score is ~75. Reducing TBT (30% weight) would have the largest impact.
Lighthouse converts raw metric values into 0–100 sub-scores using log-normal distribution curves. Each metric has a median value (score = 50) and a point-of-diminishing-returns (score ≈ 90). Values better than the target curve receive higher sub-scores.
The scoring weights reflect the relative importance of each metric for user experience. TBT at 30% reflects the critical importance of interactivity. LCP and CLS at 25% each capture loading and stability. FCP and SI at 10% each capture early loading signals.
Lighthouse scores: 0–49 = Poor (red), 50–89 = Needs Improvement (orange), 90–100 = Good (green). The scoring is intentionally difficult — reaching 90+ requires systematic optimization across all metrics.
Start by identifying your lowest-scoring metrics. Use the weight distribution to calculate which improvement will yield the most score points. For example, reducing TBT from 600ms to 200ms (30% weight improvement) will typically yield more score points than reducing FCP by the same relative amount (10% weight).
No. Lighthouse scores are estimates based on simulated conditions. They vary between runs due to network variability, CPU load, and other factors. This calculator provides an approximation to help with prioritization, not an exact prediction.
Lighthouse 10 updated scoring weights. The current weights are: FCP 10%, SI 10%, LCP 25%, TBT 30%, CLS 25%. Previous versions had different weights. TBT and LCP are the most impactful metrics in the current version.
TBT measures the total time between FCP and Time to Interactive (TTI) where the main thread was blocked for more than 50ms. It is a proxy for interactivity — lower TBT means the page responds faster to user input.
While SI has low weight, it captures perceived visual loading progress. Optimizing SI often correlates with improving other metrics because it reflects overall loading efficiency. It measures how quickly content is visually populated.
Target TBT first (30% weight): reduce JavaScript, code-split, defer non-critical scripts. Then LCP (25%): optimize hero images, preload key resources. Then CLS (25%): set image dimensions, avoid late-loading elements.
Yes, but only for very simple pages. Complex applications with third-party scripts, dynamic content, and rich interactivity typically score 60–90. A score of 90+ is considered excellent. Focus on user experience, not chasing a perfect score.