We extract coach profiles, training plan catalogues, pricing signals, and certification tiers from TrainingPeaks. 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 Coach Profiles objects from trainingpeaks.com. All fields typed and schema-versioned.
"coach_id": "TP-C-84921", "name": "Sarah Jenkins", "location": "Boulder, CO", "certifications": "['Level 2 Tri', 'Ironman Certified']", "base_price": 150.0, "premium_price": 300.0, "available_capacity": true, "rating": 4.9
| # | coach_id | name | location | certifications | sports_coached | bio |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Training Plans objects from trainingpeaks.com. All fields typed and schema-versioned.
"plan_id": "TP-P-44219", "title": "12-Week Sub-3 Marathon", "author_name": "Sarah Jenkins", "sport": "Running", "level": "Advanced", "duration_weeks": 12, "hours_per_week": "6-8", "price": 49.99
| # | plan_id | title | author_id | author_name | sport | level |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Coach Reviews objects from trainingpeaks.com. All fields typed and schema-versioned.
"review_id": "REV-99214", "coach_id": "TP-C-84921", "star_rating": 5, "review_date": "2023-11-14", "verified_athlete": true, "sport_type": "Triathlon", "review_text": "Incredible attention to detail with my TSS targets."
| # | review_id | coach_id | athlete_name | star_rating | review_date | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Plan Workouts objects from trainingpeaks.com. All fields typed and schema-versioned.
"plan_id": "TP-P-44219", "week_number": 3, "day_of_week": "Tuesday", "workout_title": "Threshold Intervals", "workout_type": "Run", "duration_minutes": 60, "tss_estimate": 75, "interval_count": 6
| # | plan_id | week_number | day_of_week | workout_title | workout_type | duration_minutes |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Directory Search objects from trainingpeaks.com. All fields typed and schema-versioned.
"keyword": "Ironman", "sport_filter": "Triathlon", "position": 1, "coach_id": "TP-C-84921", "name": "Sarah Jenkins", "certification_level": "Level 2", "scraped_at": "2023-11-15T08:30:00Z"
| # | keyword | location_filter | sport_filter | position | coach_id | name |
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Our TrainingPeaks scraper handles every layer of the platform: coach directories, training plan catalogues, pricing tiers, and certification verification.
Extract paginated coach listings, filtering by location, sport, and certification level to build comprehensive talent maps.
Capture plan duration, target events, estimated weekly hours, and TSS targets across thousands of published plans.
Monitor basic versus premium coaching tier prices, plan costs, and currency variations across global coaches.
Extract official TrainingPeaks Level 1, Level 2, and partner certifications (e.g., Ironman, USAT) for credential validation.
Compile athlete feedback, star ratings, and review dates to assess coach performance and sentiment.
Filter and categorise profiles by specific disciplines including triathlon, road cycling, trail running, and rowing.
Extract location data from coach profiles to map endurance coaching supply against regional demand.
Map individual coaches to their published training plans to analyse revenue diversification strategies.
Run continuous pipelines to detect new coach registrations, plan publications, or price modifications.
Brief in. Clean data out.
Provide search parameters, coach IDs, or plan categories. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and DOM parsing for trainingpeaks.com.
Schema validation, null-rate checks, and data normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern web applications use dynamic rendering and bot detection. Here is how we stay resilient.
We route requests through residential ISP proxies with realistic browser fingerprints to bypass basic rate limiting and IP reputation blocks.
TrainingPeaks relies heavily on client-side rendering. We run full Playwright browser sessions to ensure all asynchronous data loads before extraction.
We use fallback chains for CSS selectors and XPath to ensure that minor frontend updates by the TrainingPeaks engineering team do not break your data feed.
We maintain a hash index of last-seen values per coach and plan. Subsequent runs only push diffs, reducing downstream processing load.
Every run emits structured logs. We alert on null-rate spikes and coverage drops, responding before you notice any missing records.
Coaching platforms monitor individual coach pricing and plan costs to optimise their own fee structures.
Sports technology brands track coach certifications and specialisations to understand endurance market trends.
Aggregator platforms and elite teams identify top-rated, highly certified coaches for recruitment.
Fitness applications aggregate public metadata on training plans to analyse popular duration and TSS targets.
Analysts mine public coach reviews to determine what athletes value most in coaching relationships.
Endurance brands map coach locations to identify underserved regions for marketing campaigns.
"TrainingPeaks holds the definitive registry of endurance coaching talent and structured training plans. This data remains fragmented until you build a pipeline."
Most teams underestimate the investment required. Reliable TrainingPeaks scraping requires residential proxies, full JavaScript rendering for their modern frontend, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our trainingpeaks.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 and deduplication. Playwright handles JavaScript rendering and interaction flows for the TrainingPeaks frontend.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to prevent IP bans.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About trainingpeaks.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information, such as public coach directories and public training plan listings, is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract private athlete data or circumvent authentication walls.
We use residential ISP proxies and request timing modelled on human behaviour. We monitor for rate spikes in real time and trigger pool rotation automatically.
No. DataFlirt builds pipelines for public web data. We do not bypass authentication to scrape private fitness files, TSS scores, or CTL charts.
Pipelines can be configured for daily or weekly refreshes depending on your requirements. Most coach directories update relatively slowly, making weekly runs optimal.
Our packages typically start at defined extraction scopes, such as full directory sweeps on a weekly cadence. Contact us with your specific volume needs.
Yes. We iterate through paginated public reviews on coach profiles, capturing text, star ratings, and verified athlete flags.
Yes. We provide a sample run of up to 100 coach profiles or training plans during the scoping phase so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off coach directory export or continuous pricing updates across thousands of training plans, we scope, build, and operate the pipeline. Tell us what you need.
