We extract player statistics, daily box scores, play-by-play logs, and advanced sabermetrics from Baseball-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 baseball-reference.com. All fields typed and schema-versioned.
"player_id": "troutmi01", "name": "Mike Trout", "height_inches": 74, "weight_lbs": 235, "bats": "R", "throws": "R", "primary_position": "CF", "debut_date": "2011-07-08"
| # | player_id | name | height_inches | weight_lbs | bats | throws |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Standard Batting objects from baseball-reference.com. All fields typed and schema-versioned.
"player_id": "troutmi01", "year": 2019, "team": "LAA", "games": 134, "home_runs": 45, "rbi": 104, "batting_avg": 0.291, "ops": 1.083
| # | player_id | year | team | games | plate_appearances | at_bats |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Box Scores objects from baseball-reference.com. All fields typed and schema-versioned.
"game_id": "NYA202305280", "date": "2023-05-28", "away_team": "SDP", "home_team": "NYY", "away_score": 7, "home_score": 10, "winning_pitcher": "Cole", "attendance": 46963
| # | game_id | date | away_team | home_team | away_score | home_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Play-by-Play objects from baseball-reference.com. All fields typed and schema-versioned.
"game_id": "NYA202305280", "inning": 3, "half": "bottom", "batter_id": "judgeaa01", "pitcher_id": "darviyu01", "event_type": "Home Run", "outs_before": 2, "wpa": 0.145
| # | game_id | inning | half | batter_id | pitcher_id | event_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Advanced Sabermetrics objects from baseball-reference.com. All fields typed and schema-versioned.
"player_id": "troutmi01", "year": 2016, "bWAR": 10.5, "oWAR": 9.7, "dWAR": 1.4, "ops_plus": 173, "babip": 0.371, "iso": 0.235
| # | player_id | year | bWAR | oWAR | dWAR | waa |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our pipeline handles the specific quirks of Sports Reference sites: commented HTML tables, strict rate limits, and historical ID mapping.
Extract height, weight, birth date, debut date, and primary positions for every player in MLB history.
Capture standard batting, pitching, and fielding tables across all seasons and teams.
Extract bWAR, OPS+, ERA+, FIP, and WAA directly from player profiles.
Sync daily linescores, team totals, and pitchers of record immediately after games conclude.
Parse pitch-level sequences, run expectancy changes, and Win Probability Added (WPA) for every game.
Extract platoon splits, home/away performance, and monthly aggregates for deep statistical analysis.
Access complete data from every MLB season dating back to 1871, fully normalised.
Integrate MiLB and college statistics linked directly to major league player profiles.
Run continuous pipelines at daily cadences to capture overnight updates and roster moves.
Brief in. Clean data out.
Provide player IDs, team pages, or historical seasons. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and HTML parsers specific to Sports Reference architecture.
Schema validation, null-rate checks, and historical ID mapping verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting data from Baseball-Reference requires specific parsing logic. Here is how we build resilient pipelines.
Baseball-Reference loads many statistical tables inside HTML comments to improve initial page load speed. Standard DOM parsers miss this data entirely. Our pipeline explicitly extracts and parses these comment blocks to construct the full statistical tables.
Sports Reference employs aggressive rate limiting (429 Too Many Requests) and IP bans for aggressive crawling. We manage request concurrency strictly and rotate residential IPs to maintain high throughput without triggering blocks.
We extract internal Baseball-Reference IDs and map them to standard Retrosheet, Lahman, and MLBAM IDs, allowing you to join this dataset with your existing baseball databases.
Baseball-Reference uses complex, multi-level table headers for splits and advanced metrics. We flatten and normalise these structures into clean, queryable column names suitable for relational databases.
Box scores and daily updates are published overnight. Our Airflow schedules trigger pipelines based on MLB game completion times, ensuring your warehouse has fresh data before morning analysis begins.
Syndicates and quantitative analysts feed historical splits and advanced metrics into models to project game outcomes.
Platform providers use historical performance data and minor league translations to build pre-season projections.
Economists and statisticians use decades of normalised play-by-play data to study performance trends and aging curves.
Broadcasters require specific situational splits and historical context to generate on-air graphics and talking points.
Front offices and independent analysts track WAR, WPA, and run expectancy metrics to evaluate player value.
Museums and historians maintain offline databases of complete franchise records and managerial histories.
"Baseball-Reference is the definitive archive of baseball history, but extracting decades of nested tables requires dedicated infrastructure."
Most teams underestimate the investment required: reliable Baseball-Reference scraping requires parsing commented HTML tables, handling strict rate limits, and mapping historical player IDs. DataFlirt absorbs that complexity so your analysts can focus on sabermetrics, not DOM parsing.
Everything supported by our baseball-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 crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering when necessary. Combined via scrapy-playwright middleware.
Custom lxml parsers designed specifically to extract and reconstruct the commented-out HTML tables unique to the Sports Reference network.
Pipelines run on AWS Lambda and ECS. 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 baseball-reference.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available factual statistics is generally permissible under applicable law. DataFlirt targets only public, non-authenticated statistical data. We do not extract personal data or circumvent authentication walls. Clients should review Sports Reference ToS and consult legal counsel for specific use cases.
We strictly control concurrency and use residential ISP proxies to distribute requests. Our infrastructure respects 429 response codes and implements exponential backoff to ensure pipeline stability without triggering IP bans.
Yes. Baseball-Reference loads secondary tables within HTML comments. Our custom parsers extract these comment blocks, render the HTML in memory, and extract the structured tabular data accurately.
Pipelines run overnight following the completion of MLB games. Data is typically delivered to your warehouse within hours of the final out.
No. Stathead requires a paid subscription and authenticated login. We only extract publicly available data from standard Baseball-Reference pages.
Yes. We extract the internal IDs and cross-reference them with standard baseball databases, providing mapping columns in the final delivery format.
Our packages start at defined historical extractions (e.g., all player pages) or daily syncs for the current season. Contact us with your specific data requirements for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete historical archive or a daily feed of box scores, we scope, build, and operate the pipeline. Tell us what you need.
