We extract player statistics, game logs, play-by-play sequences, draft history, and advanced metrics from Pro-Football-Reference. 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 Player Profiles objects from pro-football-reference.com. All fields typed and schema-versioned.
"player_id": "MahoPa00", "name": "Patrick Mahomes", "position": "QB", "height": "6-2", "weight": 225, "college": "Texas Tech", "career_av": 112, "active_status": true
| # | player_id | name | position | height | weight | dob |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Game Logs objects from pro-football-reference.com. All fields typed and schema-versioned.
"game_id": "202402110kan", "player_id": "MahoPa00", "date": "2024-02-11", "team": "KAN", "opponent": "SFO", "result": "W 25-22", "passing_yds": 333, "touchdowns": 2
| # | game_id | player_id | date | team | opponent | result |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Play-by-Play objects from pro-football-reference.com. All fields typed and schema-versioned.
"play_id": "202402110kan_142", "game_id": "202402110kan", "quarter": 4, "time_remaining": "00:03", "down": 1, "distance": "Goal", "play_type": "Pass", "epa": 3.42
| # | play_id | game_id | quarter | time_remaining | down | distance |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Team Stats objects from pro-football-reference.com. All fields typed and schema-versioned.
"team_id": "KAN", "season": 2023, "wins": 11, "losses": 6, "ties": 0, "points_for": 371, "points_against": 294, "srs": 4.8
| # | team_id | season | wins | losses | ties | points_for |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Draft History objects from pro-football-reference.com. All fields typed and schema-versioned.
"draft_year": 2017, "round": 1, "pick": 10, "player_id": "MahoPa00", "team_id": "KAN", "position": "QB", "college": "Texas Tech", "games_played": 96
| # | draft_year | round | pick | player_id | team_id | position |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Pro-Football-Reference contains the definitive history of the NFL, but querying it programmatically requires handling strict rate limits, hidden DOM nodes, and complex multi-header tables. We manage the extraction layer.
Extract passing, rushing, receiving, and defensive metrics across regular season and playoffs. Normalised across eras.
Convert raw text logs into structured event sequences. Includes EPA, win probability added, and down-and-distance context.
Capture Approximate Value (AV), ANY/A, true completion percentage, and defensive pressure rates.
Historical draft classes mapped to combine measurements (40-yard dash, vertical, broad jump) and career outcomes.
Extract coaching tree records, coordinator histories, and executive tenures.
Weekly injury designations and positional snap percentage breakdowns per game.
Sports Reference enforces strict 20-request-per-minute limits. We distribute load across residential IPs to maintain throughput.
Resolve multi-tier headers, hidden columns, and dynamically injected JavaScript tables into flat, typed records.
Run one-off backfills for decades of NFL history, followed by delta updates every Tuesday morning.
Brief in. Clean data out.
Provide seasons, teams, or specific statistic tables required. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, request pacing, and table normalisation logic for Pro-Football-Reference.
Schema validation, null-rate checks, and data type enforcement before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Pro-Football-Reference employs aggressive rate limiting and complex DOM structures. Here is how we maintain reliable extraction.
Sports Reference bans IPs exceeding 20 requests per minute. We route traffic through rotating residential proxies and pace concurrency to avoid detection while maintaining overall pipeline throughput.
Pro-Football-Reference uses complex, multi-tiered HTML tables. We flatten these structures, resolve merged cells, and enforce strict type casting to ensure clean columnar output.
Many advanced metrics and snap count tables are commented out in the HTML and injected via client-side JavaScript. We parse the raw DOM comments directly to extract the hidden nodes without heavy browser overhead.
Statistics tracked in 1985 differ from 2023. Our parsers handle missing fields, handle nulls gracefully, and normalise schema drift across decades of NFL history.
Only fetch active players and recent games. Historical data remains cached. Deltas are pushed to your warehouse weekly following Monday Night Football.
Data scientists build predictive models for DFS platforms using historical snap counts, target shares, and red-zone usage.
Quantitative syndicates feed play-by-play data and EPA metrics into algorithms to identify inefficient betting lines.
Economists and statisticians analyse draft outcomes, coaching decisions, and player longevity trends.
Publishers automate historical comparisons and generate data-driven narratives for weekly NFL coverage.
ML teams use decades of play-by-play sequences to train outcome prediction models and fourth-down decision engines.
Developers populate independent sports applications with historical player statistics and team records.
"Pro-Football-Reference holds the definitive historical record of the NFL, but querying decades of play-by-play data requires a structured pipeline, not manual exports."
Sports Reference sites employ aggressive rate limiting and complex multi-header table structures designed to break naive parsers. DataFlirt handles the proxy rotation, request pacing, and DOM normalisation so your data science team can focus on building predictive models, not fixing broken scrapers.
Everything supported by our pro-football-reference.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 orchestration and request pacing. Custom middleware parses HTML comments to extract data without the overhead of headless browsers.
We maintain pools of residential ISP proxies to distribute request load and strictly adhere to Sports Reference rate limits without triggering blocks.
Pipelines run on AWS ECS. Airflow handles scheduling, ensuring weekly deltas run reliably after Monday Night Football concludes.
Data delivered to where your team already works — no new tooling required.
About pro-football-reference.com scraping, legality, and pipeline operations.
Ask us directly →Pro-Football-Reference restricts traffic to 20 requests per minute per IP. We distribute extraction across a large pool of US-based residential proxies and enforce strict concurrency limits in Scrapy to extract data reliably without triggering defensive blocks.
No. We only extract publicly available data from Pro-Football-Reference. We do not bypass authentication walls or extract proprietary data requiring a paid Stathead subscription.
Pro-Football-Reference optimises page load by commenting out secondary tables (like snap counts and advanced metrics) and injecting them via JavaScript. We parse the raw HTML comments directly to extract the table nodes, which is faster and more reliable than executing Playwright.
For active season pipelines, we run delta updates on Tuesday mornings (UTC) after Monday Night Football concludes, ensuring all statistics and game logs for the week are finalised.
Yes. Our parsers map historical franchise names (e.g., Houston Oilers) to their current franchise identifiers (Tennessee Titans) or maintain historical accuracy based on your schema requirements.
Yes. We extract the raw play description text and parse it into structured fields including down, distance, play type, yardage gained, and involved players.
Yes. We provide a sample run of up to 50 player profiles or 10 game logs to validate schema fit and data quality before commencing the full extraction.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full historical backfill of 100 years of NFL data or weekly delta updates for active players — we scope, build, and operate the pipeline. Tell us what you need.
