📺 Youtube Most Replayed Scraper

📺 Youtube Most Replayed Scraper

📺 Youtube Most Replayed Scraper is a focused YouTube heatmap data extractor that pulls the “Most Replayed” engagement graph and replay segments directly from public YouTube watch pages. It solves the problem of guessing viewer interest by programmatically capturing the exact watch-time peaks and most replayed segments—ideal for marketers, developers, data analysts, and researchers. At scale, this YouTube most replayed scraper helps you scrape YouTube most replayed timestamps across many videos and build datasets that surface highlights and popular moments automatically.

What is 📺 Youtube Most Replayed Scraper?

This tool is a specialized YouTube engagement heatmap scraper that extracts two core datasets from YouTube watch pages: most replayed segments and the per-interval intensity heatmap. It eliminates manual analysis by turning the “Most Replayed” graph into structured data. Teams like growth marketers, content strategists, editors, and data scientists use it to extract YouTube highlights timestamps, identify watch time peaks, and compare engagement patterns across videos. Once deployed, it enables bulk discovery of YouTube popular moments for optimization, benchmarking, and reporting.

What data / output can you get?

Below are the exact fields streamed into the Apify dataset from each processed URL. Fields mirror the actor’s output schema and the code path that pushes data.

Field	Description	Example
videoId	Unique video ID parsed from the player response	“dQw4w9WgXcQ”
title	Video title (from player/initial data)	“Never Gonna Give You Up (Official Video)”
channelOwner	Channel/author name	“Rick Astley”
viewCount	View count (string as seen on page or raw count)	“1694624700”
likes	Like count string (when available)	“18,549,049”
comments	Comment count string (when available)	“2.4M”
dateText	Published date text	“Oct 24, 2009”
relativeDate	Relative date text	“15 years ago”
mostReplayed	Array of most-replayed decorations with visible time ranges	[{ "visibleTimeRangeStartMillis": 12345, "visibleTimeRangeEndMillis": 23456, "decorationTimeMillis": 18000 }]
heatSeek	Array of heatmap points with start/duration and normalized intensity	[{ "startMillis": 120000, "durationMillis": 1000, "intensityScoreNormalized": 0.92 }]
error	Error message (only present on failed items)	“HTTP 429: Too Many Requests”

Notes:

• Results are live-streamed to the Apify dataset as each URL completes.
• In addition to the dataset, the actor saves a Key-Value Store item “most_replayed.json” with all results and a run “OUTPUT” summary (totals, successes, failures, proxy fallback flag).
• You can download results from the Apify dataset (API and console export options such as JSON/CSV/Excel).

Key features

• 🧠 Most replayed segments extraction Captures the most-replayed portions of each video as visible time ranges, letting you pinpoint the highlights without manual scrubbing.

• 📈 YouTube engagement heatmap scraper Extracts per-interval intensity values (“heatSeek”) so you can quantify where watch time peaks occur and compare engagement patterns.

• 🔄 Live dataset streaming Each processed URL is pushed immediately to the Apify dataset, enabling near real-time pipelines and incremental consumption.

• 🛡️ Automatic residential proxy fallback Starts direct by default and intelligently falls back to Apify Residential proxy when blocked, then sticks to it for reliable throughput.

• 🧱 Resilient retry logic Implements backoff and retries on retryable HTTP statuses and network errors to improve completion rates for large batches.

• 🧺 Batch URL support Paste multiple YouTube watch URLs and process them in one run—perfect for channel-level analysis or competitive benchmarking.

• 🧩 Developer-friendly JSON output Structured arrays for mostReplayed and heatSeek make this a clean YouTube most replayed segments tool for downstream analysis in Python or via the Apify API.

• 💾 Dataset + KV artifacts Stores per-item records in the dataset and writes an all-results JSON plus a run summary to the Key-Value Store for auditing and reuse.

How to use 📺 Youtube Most Replayed Scraper - step by step

1. Sign in to Apify
   Create a free account or log in to your Apify workspace.

2. Open the actor
   Find “📺 Youtube Most Replayed Scraper” in the Apify Store and click Try for free.

3. Add your input URLs
   In the input form, paste one or more YouTube watch URLs (one per line) into the urls field.

4. (Optional) Configure proxy
   Leave proxy empty to start direct. If access is blocked, the actor automatically falls back to Apify Residential proxy. You can also supply a proxy configuration.

5. Start the run
   Click Start. The actor fetches each video page, extracts the Most Replayed graph and heatmap arrays, and pushes results live to the dataset.

6. Monitor progress
   Watch the console logs for progress. Each processed URL is saved immediately—even if some fail, you still keep completed results.

7. Download your data
   Open the Run dataset to view results. Export via the Apify console or API in your preferred format. You’ll also find “most_replayed.json” and a run “OUTPUT” summary in the Key-Value Store.

Pro Tip: Chain this with your analytics pipeline to scrape YouTube most replayed timestamps at scale, then enrich with your internal metrics for deeper insights into highlights and watch time peaks.

Use cases

Use case	Description
Content creators — highlight finder	Identify replay spikes to repurpose into Shorts, teasers, or thumbnails—optimize for the moments viewers rewatch the most.
Marketers — competitive hooks	Compare competitor videos’ most replayed peaks to learn which intros, CTAs, or segments drive engagement.
Editors — fast timestamping	Jump straight to high-interest segments to accelerate highlight reels and save editing time.
Researchers — engagement analysis	Quantify audience attention by analyzing normalized heatseek intensity across multiple videos.
Data analysts — trend detection	Aggregate heatmaps across a playlist or channel to surface patterns in watch-time peaks over time.
API pipelines — automated ingestion	Use the Apify dataset and KV artifacts to feed dashboards or ML features with mostReplayed and heatSeek arrays.

Why choose 📺 Youtube Most Replayed Scraper?

This tool is built for precision and reliability: it extracts exactly the most replayed segments and heatmap intensity values you need, and keeps running even when targets get defensive.

• 🎯 Accurate extraction of YouTube heatmap signals (mostReplayed + heatSeek)
• 🚀 Scales to batches of URLs with live dataset streaming
• 🛡️ Automatic fallback to Apify Residential proxy when blocked
• ⚙️ Developer-ready JSON output for analytics and pipelines
• 🔁 Resilient retries with backoff on transient errors
• 💼 Works without login or cookies on public watch pages
• 🔗 Easy to integrate via Apify dataset/API in your existing workflows

Compared with browser extensions and brittle scrapers, this production-ready YouTube most replayed peaks extractor delivers consistent, structured data for automation-driven teams.

Is it legal / ethical to use 📺 Youtube Most Replayed Scraper?

Yes—when used responsibly. This actor extracts publicly available information from YouTube watch pages, such as replay heatmaps, timestamps, and visible metadata. It does not access private profiles or authenticated data.

Guidelines for compliant use:

• Only scrape public watch pages and publicly visible fields.
• Avoid collecting personal or private user data.
• Be mindful of YouTube’s Terms of Service and local regulations.
• Use results for analysis, research, or optimization—never for spam or misuse.
• For edge cases, confirm compliance with your legal team.

Input parameters & output format

Example input

{
  "urls": [
    "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    "https://www.youtube.com/watch?v=oHg5SJYRHA0"
  ],
  "proxy": {
    "useApifyProxy": true
  }
}

Parameters

• urls (array, required)
  Description: Paste one or more YouTube watch URLs. One per line. At least one URL is required.
  Default: ["https://www.youtube.com/watch?v=dQw4w9WgXcQ"]

• proxy (object, optional)
  Description: Proxy editor field. Proxy is off by default. If the site blocks the request, the actor turns on Apify Residential proxy and stays on it. Configure here to customize.
  Default: {}

Example dataset output

[
  {
    "channelOwner": "Rick Astley",
    "title": "Never Gonna Give You Up (Official Video)",
    "videoId": "dQw4w9WgXcQ",
    "viewCount": "1694624700",
    "likes": "18,549,049",
    "comments": "2.4M",
    "dateText": "Oct 24, 2009",
    "relativeDate": "15 years ago",
    "mostReplayed": [
      {
        "visibleTimeRangeStartMillis": 15000,
        "visibleTimeRangeEndMillis": 30000,
        "decorationTimeMillis": 22000
      },
      {
        "visibleTimeRangeStartMillis": 120000,
        "visibleTimeRangeEndMillis": 135000,
        "decorationTimeMillis": 128000
      }
    ],
    "heatSeek": [
      {
        "startMillis": 0,
        "durationMillis": 1000,
        "intensityScoreNormalized": 0.12
      },
      {
        "startMillis": 60000,
        "durationMillis": 1000,
        "intensityScoreNormalized": 0.91
      },
      {
        "startMillis": 121000,
        "durationMillis": 1000,
        "intensityScoreNormalized": 0.88
      }
    ]
  }
]

Notes:

• On failures, an item with empty metadata/arrays is still pushed with an error field containing the message.
• The actor also writes all results to the Key-Value Store under “most_replayed.json” and saves a run “OUTPUT” summary (total, succeeded, failed, residentialFallbackUsed, timestamp).

FAQ

Do I need to log in to use this YouTube most replayed scraper?

No. The actor works on public YouTube watch pages without login or cookies. It fetches the page HTML and parses public scripts for the Most Replayed and heatmap data.

Can it scrape YouTube most replayed timestamps for multiple videos at once?

Yes. Add multiple watch URLs to the urls field and the actor will process them sequentially, streaming each result live to the dataset for batch workflows.

Does it include YouTube heatmap intensity values?

Yes. The heatSeek array contains startMillis, durationMillis, and intensityScoreNormalized values that quantify per-interval engagement.

What happens if YouTube blocks my requests?

The run starts direct by default. If requests are blocked, the actor automatically falls back to Apify Residential proxy and sticks with it for the remainder of the run.

What data formats can I download?

Results are saved to the Apify dataset, which you can access programmatically or export from the console in common formats. The actor also stores an all-results JSON (“most_replayed.json”) in the Key-Value Store.

Does the output include likes, comments, and dates?

Yes. Besides the mostReplayed and heatSeek arrays, the actor extracts metadata such as title, channelOwner, viewCount, likes, comments, dateText, and relativeDate.

Is there a way to integrate this with my Python scripts or BI tools?

Yes. Consume the Apify dataset (and the “most_replayed.json” artifact) via the Apify API from Python or your data stack to build automated pipelines for analysis and reporting.

What if a URL fails to process?

The actor retries failed requests with backoff. If a URL ultimately fails, it still pushes an item with empty fields and an error message so you have a full audit of attempted inputs.

Final thoughts

📺 Youtube Most Replayed Scraper is built to extract YouTube’s Most Replayed segments and engagement heatmap with reliability and scale. With structured arrays for mostReplayed and heatSeek plus rich metadata, it helps creators, marketers, analysts, and researchers turn public watch-time peaks into actionable insights. Developers can consume the Apify dataset and KV artifacts via API to automate pipelines. Start extracting smarter highlights and watch-time peaks today—at scale and with confidence.