HLTV Match & Team Statistics Scraper

Advanced Esports Data Extraction & HLTV Scraping Automation

Modern esports analytics platforms require accurate and structured datasets for AI analysis, betting systems, coaching workflows, dashboards, and statistical research. Manually collecting this data from HLTV is slow, repetitive, and difficult to scale.

Our custom HLTV scraper automates the entire process — from extracting team overview statistics to grouping full BO series and collecting map-level player performance metrics.

What This HLTV Scraper Extracts

The scraper automatically collects:

• Team overview statistics
• Match history
• BO1 / BO3 / BO5 series structures
• Map-level match details
• Player statistics
• CT-side player stats
• T-side player stats
• Round splits
• Match veto information
• Event details
• Opponent information
• Match screenshots
• Structured JSON datasets

The output is optimized for AI workflows, esports analytics platforms, machine learning pipelines, and coaching systems.

How the HLTV Scraper Works

Step 1 — Load Team Statistics Page

The automation opens the HLTV team statistics page and extracts the complete team overview data.

Step 2 — Collect Match History

The scraper automatically opens the matches section and filters recent matches based on custom date ranges.

Step 3 — Group Matches Into BO Series

Instead of treating every map as a separate match, the scraper intelligently groups related maps into:

• BO1
• BO3
• BO5

series structures.

Step 4 — Visit Individual Match Pages

The scraper loads every map page individually to collect detailed statistics.

Step 5 — Extract Deep Match Analytics

For every map, the system extracts:

• Final score
• Event
• Opponent
• Map name
• Score breakdown
• CT rounds
• T rounds
• Player statistics
• Side-based performance
• Veto information

Step 6 — Generate Structured JSON Output

All data is exported into clean, structured, AI-friendly JSON files.

Team Overview Statistics Extracted

Team-Level Data Includes

• Team Name
• Total Maps Played
• Total Kills
• Total Deaths
• K/D Ratio
• Win/Loss Record
• Total Rounds Played
• HLTV Team URL

This creates a complete esports team profile dataset.

Map-Level Match Data Extraction

Every Map Includes

• Match URL
• Match Date & Time
• Event Name
• Opponent Team
• Map Name
• Final Score
• Match Result
• Score Breakdown
• CT Rounds
• T Rounds
• Screenshot Path
• Veto Information

The scraper creates a properly nested BO series structure:

Series
→ Maps
→ Player Statistics
→ Side Splits

This makes the dataset significantly more usable for AI analysis and advanced querying.

Player Statistics Scraper

Player Data Extracted Per Map

The system extracts detailed player performance metrics including:

• Player Name
• Kills
• Deaths
• ADR (Average Damage Per Round)
• Rating
• Overall Match Statistics
• CT-side Statistics
• T-side Statistics

This is especially valuable for:

• Coaching analysis
• Tactical breakdowns
• AI prediction systems
• Performance tracking
• Esports analytics platforms

CT Side & T Side Statistics Extraction

Side-Based Analytics

Unlike basic esports scrapers, this system separately extracts:

CT-Side Stats

• CT kills
• CT ADR
• CT ratings
• CT deaths

T-Side Stats

• T-side kills
• T-side ADR
• T-side ratings
• T-side deaths

This creates a much deeper tactical dataset for professional analysis.

HLTV Match Veto Extraction

Automated Veto Collection

The scraper can also extract map veto sequences directly from HLTV match pages.

Example:

• Team A removed Ancient
• Team B picked Mirage
• Team A picked Nuke
• Inferno was left over

This data is highly useful for:

• Tactical analysis
• Draft pattern research
• AI training systems
• Opponent preparation workflows

Automated Match Screenshot Capture

Screenshot Automation Features

The system automatically captures screenshots of:

• Match summary sections
• Scoreboards
• Map statistics
• Match information boxes

These screenshots can later be used for:

• Reporting systems
• AI image workflows
• Dashboard previews
• Manual verification

AI-Friendly JSON Output Structure

Clean Structured Data Hierarchy

The scraper exports data in a highly organized structure:

Team Overview
→ Series
→ Maps
→ Player Stats
→ CT/T Splits
→ Veto Data
→ Screenshots

This makes the data ideal for:

• Machine learning pipelines
• AI workflows
• Statistical modeling
• Betting systems
• Data warehousing
• Esports dashboards

Technologies Used

Core Technologies

• Python
• Playwright
• AsyncIO
• Browser Automation
• JSON Processing
• Regex Parsing
• Error Handling Systems
• Screenshot Automation

Key Features of the HLTV Scraper

Main Features

• Automated HLTV scraping
• Async high-speed processing
• BO series grouping logic
• Player statistics extraction
• CT/T-side analytics
• Structured JSON export
• Match screenshot automation
• Veto extraction support
• Duplicate prevention
• Error handling & retries
• Scalable architecture
• AI-ready datasets

Business & Analytics Use Cases

Perfect For

• Esports analytics platforms
• Betting research systems
• Coaching tools
• AI esports projects
• Match prediction systems
• Machine learning datasets
• Player performance tracking
• Statistical research
• Tactical analysis systems

Custom Web Scraping & Automation Services

At ArifSoft, we build:

• Custom Web Scrapers
• Browser Automation Bots
• AI Data Pipelines
• Lead Generation Systems
• Marketplace Scrapers
• Esports Data Extraction Tools
• Multi-threaded Scraping Systems
• Business Intelligence Platforms

We specialize in scalable automation solutions, browser scraping systems, structured datasets, and advanced data extraction workflows tailored to business requirements.