We extract product listings, pricing signals, stock availability, launch drop windows, colourway variants, reviews, and category intelligence from Adidas. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Listings objects from adidas.com. All fields typed and schema-versioned.
"article_number": "ID5923", "title": "Samba OG Shoes", "sport_category": "Originals", "price": 100.00, "currency": "USD", "in_stock": true, "available_sizes": ["UK 6", "UK 7", "UK 8", "UK 9"], "rating": 4.7, "review_count": 9241, "colorways_count": 14, "is_collaboration": false
| # | article_number | title | subtitle | sport_category | sub_category | gender |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Availability objects from adidas.com. All fields typed and schema-versioned.
"article_number": "ID5923", "price": 100.00, "sale_price": null, "discount_pct": 0, "is_outlet": false, "size_stock_status": "UK 10 → sold_out", "restocked_at": "2026-05-09T14:00:00Z", "price_timestamp": "2026-05-12T07:45:00Z"
| # | article_number | price | sale_price | discount_pct | is_outlet | is_sale_item |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from adidas.com. All fields typed and schema-versioned.
"review_id": "rv_adi_2841093", "article_number": "ID5923", "star_rating": 5, "verified_purchase": true, "review_title": "Iconic silhouette, incredibly comfortable", "fit_feedback": "true_to_size", "comfort_rating": 5, "review_date": "2026-05-01"
| # | review_id | article_number | reviewer_name | verified_purchase | star_rating | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Drop & Launch Tracking objects from adidas.com. All fields typed and schema-versioned.
"article_number": "IG6180", "title": "Gazelle Indoor Bad Bunny", "drop_type": "CONFIRMED_LAUNCH", "launch_date": "2026-06-01", "raffle_eligible": true, "members_only": true, "collaboration_partner": "Bad Bunny", "scraped_at": "2026-05-12T09:00:00Z"
| # | article_number | title | drop_type | launch_date | launch_time | raffle_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Adidas scraper handles every layer of the platform: product catalogues, size-level stock availability, drop launch windows, outlet pricing, collaboration data, and the review corpus — with JavaScript rendering and anti-bot circumvention built in.
Title, subtitle, sport category, technologies, materials, images, colourways, and every metadata field Adidas surfaces — scraped at article level with full size and colour variant mapping.
Capture in-stock, out-of-stock, and restock events per size per article — timestamped per crawl. Essential for resell intelligence and demand modelling.
Track confirmed launch dates, raffle eligibility, members-only windows, and collaboration partner data for upcoming and recent product drops.
Full review text, star ratings, comfort scores, fit feedback, size purchased, and helpful vote counts — paginated across all review pages.
Distinguish full-price, sale, and outlet pricing per article. Track markdown depth and outlet availability over time.
Track organic position and featured placement for any sport category, keyword, or collection — with new-arrival and bestseller badge capture.
adidas.com, adidas.co.uk, adidas.de, adidas.co.in, adidas.com.au and 30+ regional storefronts — all from a unified schema with localised pricing.
Monitor all collaboration and limited-edition releases — partner name, drop type, quantity signals, and resell index history.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide article numbers, category URLs, or keyword sets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and anti-bot handling for adidas.com.
Schema validation, null-rate checks, size-stock accuracy checks, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Adidas uses aggressive bot mitigation — especially around high-demand drops. Here's how we stay resilient — and why teams choose managed infrastructure over DIY.
Adidas employs bot detection across TLS fingerprints, browser headers, and IP reputation — especially during high-demand drop events. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to blend with organic traffic.
Adidas product pages, size selectors, and drop countdown pages are heavily JavaScript-driven. We run full Playwright browser sessions with scroll simulation and dynamic element hydration — capturing size availability and drop data that HTTP clients miss entirely.
Adidas updates its frontend frequently across markets. Our selector strategy uses CSS, XPath, text-pattern matching, and structured data extraction as multi-layer fallbacks — so a regional DOM change doesn't break your data feed.
For large article catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost and storage bloat. Size-level stock changes generate targeted alerts rather than full re-dumps.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops — and respond before you notice. SLA uptime is contractual, not aspirational.
Resellers and resell platforms monitor size-level stock availability, drop timing, and restock events to optimise buying windows and pricing strategy.
Sporting goods retailers and DTC footwear brands benchmark Adidas full-price vs outlet pricing to calibrate their own assortment and markdown strategy.
Analysts track new silhouette launches, collaboration frequency, and category sell-through signals to identify whitespace and investment opportunities.
ML teams use Adidas datasets to train visual similarity models, footwear recommendation engines, and product attribute classifiers.
Supply chain and planning teams correlate size-stock depletion rates, restock frequency, and review velocity with demand forecasting models.
PE firms and analysts track collaboration cadence, average selling prices, and review growth curves to evaluate sportswear brand health.
"Adidas is one of the world's most monitored footwear catalogues — but size-level stock intelligence, drop timing data, and collaboration signals are invisible unless you build the pipeline."
Most teams underestimate the complexity: reliable Adidas scraping requires residential proxies, full Playwright rendering, drop-event burst capacity, and daily selector maintenance across 30+ regional storefronts. DataFlirt absorbs that infrastructure so your analysts focus on the insights — not the crawlers.
Everything supported by our adidas.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, size selector interactions, and dynamic drop-page content. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across UK/US/DE/IN regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About adidas.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Adidas is generally permissible under applicable law in India, the US, and the UK — consistent with the hiQ v. LinkedIn ruling and similar precedents. DataFlirt targets only public, non-authenticated product, pricing, and review data. We recommend clients review Adidas's ToS independently and consult legal counsel for specific use cases.
We use residential ISP proxies that appear as real consumer traffic, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains. We monitor for block-rate spikes in real time and trigger proxy pool rotation or solver queues automatically.
We support adidas.com, adidas.co.uk, adidas.de, adidas.co.in, adidas.com.au, adidas.com.br, adidas.fr, adidas.it, adidas.es, adidas.co.jp, adidas.com.sg, and 20+ additional regional storefronts — all from a unified schema with market-normalised pricing.
Yes. Our pipeline captures per-size availability status on every crawl. You can configure high-cadence (sub-hourly) monitoring on a defined article set to detect restock events and size depletion in near real-time — useful for resell intelligence and replenishment triggers.
Yes. Our pipeline monitors Adidas's confirmed launch calendar, raffle-eligible products, members-only launch windows, and collaboration partner data — with enough lead time to integrate into your downstream workflows before a drop goes live.
Our smallest packages start at a defined article list (typically 500–10,000 items) with daily delivery. For larger catalogues, drop monitoring, or custom schema requirements, we price based on volume and cadence. Contact us for a scoped quote.
Yes — including full pagination across all star-filter views, fit feedback labels, size purchased, comfort ratings, and reviewer-submitted images. Each review record is linked to the article number and colourway.
Absolutely. We provide a sample run of up to 500 articles as part of the pre-engagement scoping process — so you can validate schema fit, size-stock coverage, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off product catalogue snapshot or a continuous size-stock monitoring feed across your tracked article set — we scope, build, and operate the pipeline. Tell us what you need.
