Youtube Most Replayed Scraper

Youtube Most Replayed Scraper

Youtube Most Replayed Scraper is an Apify actor that extracts structured “Most Replayed” overlay and heatmap data from YouTube watch pages, alongside key video metadata. It solves the problem of manually hunting for high-engagement moments by turning the YouTube heatmap into JSON you can analyze, filter, and export. Built for marketers, developers, data analysts, and researchers, this YouTube most replayed data scraper acts as a reliable YouTube heatmap most replayed extractor and YouTube video heatmap scraper so you can identify key moments at scale and power downstream analytics.

What data / output can you get?

Below are the exact fields the actor pushes to the Apify dataset. Values come from YouTube’s ytInitialData/ytInitialPlayerResponse and the Most Replayed/heatmap overlays.

Data field	Description	Example value
videoId	Unique YouTube video identifier	dQw4w9WgXcQ
title	Video title	Rick Astley - Never Gonna Give You Up (Official Video) (4K Remaster)
channelOwner	Channel/author name	Rick Astley
viewCount	View count string or number string	1694624700
likes	Like count string (if available)	1.8M
comments	Comment count context (if available)	2.4M
dateText	Published date (simple text)	Oct 24, 2009
relativeDate	Relative published date	15 years ago
mostReplayed.visibleTimeRangeStartMillis	Start of a most-replayed decoration window (ms)	15000
mostReplayed.visibleTimeRangeEndMillis	End of a most-replayed decoration window (ms)	30000
mostReplayed.decorationTimeMillis	Marker timestamp for the decoration (ms)	22000
heatSeek.startMillis	Heatmap segment start (ms)	120000
heatSeek.durationMillis	Heatmap segment duration (ms)	10000
heatSeek.intensityScoreNormalized	Normalized replay intensity (0–1)	0.83
error	Error message for failed items (only on failures)	HTTP 429: Too Many Requests

Notes:

• Records include both arrays: mostReplayed (marker decorations) and heatSeek (heatmap segments with intensity scores). If YouTube doesn’t expose the heatmap for a video, these arrays can be empty.
• Results are saved to the Apify dataset (export as JSON, CSV, Excel) and also written to the key-value store as most_replayed.json, plus a run summary at OUTPUT.

Key features

• 🔥 Most Replayed + heatmap extraction Extracts both mostReplayed decorations and heatSeek heatmap segments with intensityScoreNormalized, giving you precise, structured replay spikes and segment windows.

• 📡 Live dataset streaming Streams each processed video to the dataset in real time via Actor.pushData, so you can inspect and export results as soon as they’re available.

• 🛡️ Automatic proxy fallback Starts direct by default and automatically falls back to Apify Residential proxy on blocks/rate limits for reliable scraping of the Most Replayed overlay data.

• 📚 Bulk URL support Accepts an array of YouTube watch URLs in urls and processes them in one run—ideal for playlist audits, channel-wide scans, or batch studies.

• 🔐 No login or cookies required Works without authentication using standard HTTP requests and robust headers; a clean alternative to a Chrome extension scrape YouTube most replayed approach.

• 🧰 Developer-friendly JSON Designed as a YouTube most replayed JSON extractor and YouTube most replayed API alternative. Integrate via the Apify API from Python or Node.js pipelines.

• 💾 Extra outputs for automation In addition to the dataset, the actor writes most_replayed.json and an OUTPUT summary (totals, success/failure counts, proxy fallback flag) to the key-value store for downstream workflows.

How to use Youtube Most Replayed Scraper - step by step

1. Create or log in to your Apify account.
2. Find the actor “Youtube Most Replayed Scraper” in the Apify Store and click Try for free.
3. Add input URLs: paste one or more YouTube watch-page links (e.g., https://www.youtube.com/watch?v=…​) into urls.
4. Optional: Configure proxy. Leave blank to start direct; the actor will automatically fall back to Apify Residential proxy if blocked. You can also explicitly enable RESIDENTIAL.
5. Start the run. The actor fetches each video page, parses ytInitialData/ytInitialPlayerResponse, and extracts mostReplayed and heatSeek arrays with associated metadata.
6. Monitor progress. Results stream live into the dataset as each URL finishes. Failed items are saved with an error message.
7. Export results. Download from the dataset in JSON, CSV, or Excel. Check the key-value store for most_replayed.json and OUTPUT for run summary metadata.
8. Iterate at scale. Add more URLs or schedule runs to build a YouTube watch heatmap data extractor pipeline for ongoing analysis.

Pro tip: Use the Apify API to run this YouTube most replayed data scraper from your own stack (Python or Node.js). Combine with Zapier/Make/n8n to sync outputs to Google Sheets, dashboards, or a data warehouse.

Use cases

Use case	Description
Creators – Highlight detection	Identify the exact spikes viewers rewatch to repurpose into Shorts, teasers, or thumbnails that boost CTR and retention.
Marketers – Competitor hook analysis	Map the hooks that drive replays in competitor videos to inform your opening seconds, pacing, and creative variations.
Editors – Faster clipping	Jump straight to most-replayed timestamps and heatmap peaks to accelerate highlight reels and edits.
Researchers – Engagement analysis	Study audience psychology using normalized replay intensity across segments and videos.
SEO – Content optimization	Align chapters, intros, and CTAs around high-interest moments; measure impact over time.
Data engineering – API pipelines	Treat the actor as a YouTube most replayed API alternative, exporting structured JSON for downstream models and dashboards.

Why choose Youtube Most Replayed Scraper?

• 🎯 Precision from native structures: Parses ytInitialData and ytInitialPlayerResponse to accurately extract replay overlays and metadata.
• 🧱 Resilient by design: Automatic fallback to Apify Residential proxies when blocks occur—no manual intervention required.
• 📈 Built for scale: Feed multiple URLs and stream results live; ideal for channel-wide audits and longitudinal studies.
• 👨‍💻 Developer access: Clean JSON output for pipelines; integrate via Apify API with Python or Node.js.
• 🔒 Safe and public-only: Targets publicly available watch pages and overlays—no login, no cookies, no scraping of private data.
• 🔁 Platform-grade reliability: Retries with backoff and clear error reporting; failed items are still stored for auditing.
• 🧰 Versatile exports: Pull datasets as JSON, CSV, or Excel to power analytics and BI tools.

In short, this YouTube most replayed overlay data scraper delivers structured, reliable results without browser automation or brittle extensions.

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

Yes—when done responsibly. This actor extracts public information from YouTube watch pages, including the Most Replayed heatmap and visible metadata.

Guidelines for compliant use:

• Only collect public data from watch pages and overlays; avoid private or gated content.
• Do not scrape personal data or attempt to bypass access controls.
• Respect YouTube’s terms and applicable laws (e.g., GDPR/CCPA). Analyze engagement, don’t harvest identities.
• Use proxies ethically; Apify Residential fallback is designed for reliability, not abuse.
• Consult your legal team if you operate in regulated contexts or have edge cases.

Input parameters & output format

Example JSON input

{
  "urls": [
    "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    "https://www.youtube.com/watch?v=9bZkp7q19f0"
  ],
  "proxy": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

Parameter	Type	Required	Default	Description
urls	array	Yes	["https://www.youtube.com/watch?v=dQw4w9WgXcQ"]	Paste one or more YouTube watch URLs. One per line. At least one URL is required.
proxy	object	No	{}	Optional Apify proxy configuration. By default, the run starts direct and automatically falls back to Apify Residential if blocked. Configure here to customize.

Notes:

• Results are pushed to the dataset as each URL is processed.
• The key-value store includes most_replayed.json (array of all results) and OUTPUT (run summary).

Example JSON output (single record)

{
  "channelOwner": "Rick Astley",
  "title": "Rick Astley - Never Gonna Give You Up (Official Video) (4K Remaster)",
  "videoId": "dQw4w9WgXcQ",
  "viewCount": "1694624700",
  "likes": "1.8M",
  "comments": "2.4M",
  "dateText": "Oct 24, 2009",
  "relativeDate": "15 years ago",
  "mostReplayed": [
    {
      "visibleTimeRangeStartMillis": 15000,
      "visibleTimeRangeEndMillis": 30000,
      "decorationTimeMillis": 22000
    },
    {
      "visibleTimeRangeStartMillis": 54000,
      "visibleTimeRangeEndMillis": 68000,
      "decorationTimeMillis": 61000
    }
  ],
  "heatSeek": [
    {
      "startMillis": 120000,
      "durationMillis": 10000,
      "intensityScoreNormalized": 0.83
    },
    {
      "startMillis": 130000,
      "durationMillis": 10000,
      "intensityScoreNormalized": 0.76
    }
  ]
}

Field notes:

• error appears only on failed items and contains an error message. On success, it is omitted.
• If YouTube does not expose a heatmap for a given video, mostReplayed and heatSeek may be empty arrays.

FAQ

Is there a free trial?

Yes. This actor listing includes trial minutes on Apify so you can test extraction before subscribing. You’ll see the allowance on the actor’s pricing panel in the Apify Store.

Do I need to log in to YouTube or use cookies?

No. The scraper works without YouTube login or cookies. It fetches public watch pages and parses the Most Replayed overlay and heatmap data directly.

What if a video doesn’t show the Most Replayed heatmap?

The actor will still return the metadata. mostReplayed and heatSeek arrays may be empty when YouTube doesn’t expose heatmap data for that video.

Can I use this with Python or Node.js?

Yes. Treat it as a YouTube most replayed JSON extractor and YouTube most replayed API alternative. Trigger runs and fetch datasets via the Apify API from Python or Node.js.

How many URLs can I process in one run?

You can pass multiple watch URLs in urls. The actor processes each URL and streams results to the dataset. For very large lists, consider splitting into multiple runs or scheduling.

Does it extract chapters or “Key moments” labels?

This tool focuses on the Most Replayed overlay and heatmap segments. It does not parse chapter metadata; if you need chapters, combine this with a YouTube key moments scraper in your pipeline.

How do I export the results?

Open the run’s dataset and export to JSON, CSV, or Excel. You can also download most_replayed.json from the key-value store and query results programmatically via the Apify API.

How does it handle rate limits or blocks?

It starts direct by default and automatically falls back to Apify Residential proxy when needed. It also retries with backoff and saves failed items with an error message for auditing.

Closing CTA / Final thoughts

Youtube Most Replayed Scraper is built to transform YouTube’s Most Replayed overlay into structured, analytics-ready data. With precise heatmap extraction, bulk URL support, and automatic proxy fallback, it helps creators, marketers, analysts, and researchers uncover highlight moments and optimize content strategy. Developers can orchestrate it via the Apify API to power Python or Node.js workflows and keep datasets fresh on schedule. Start extracting smarter YouTube engagement insights today and turn replay spikes into actionable decisions.