We extract live scores, historical box scores, player stats, injury reports, and betting odds from ESPN. 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 Live Matches & Scores objects from espn.com. All fields typed and schema-versioned.
"match_id": "401581023", "league": "NBA", "home_team": "Los Angeles Lakers", "away_team": "Denver Nuggets", "status": "IN_PROGRESS", "clock": "04:12", "home_score": 102, "away_score": 98, "period": 4
| # | match_id | league | home_team | away_team | status | clock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Play-by-Play objects from espn.com. All fields typed and schema-versioned.
"event_id": "10492811", "match_id": "401581023", "period": 4, "clock": "04:12", "team": "LAL", "player_id": "3975", "play_type": "3PT_JUMP_SHOT", "description": "LeBron James makes 26-foot three point jumper", "scoring_play": true, "home_score": 102, "away_score": 98
| # | event_id | match_id | period | clock | team | player_id |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Player Statistics objects from espn.com. All fields typed and schema-versioned.
"player_id": "3975", "match_id": "401581023", "player_name": "LeBron James", "position": "SF", "minutes_played": "34:12", "points": 28, "rebounds": 8, "assists": 11, "field_goals_made": 10, "field_goals_attempted": 18
| # | player_id | match_id | team_id | player_name | position | minutes_played |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Team Rosters objects from espn.com. All fields typed and schema-versioned.
"team_id": "13", "team_name": "Los Angeles Lakers", "player_id": "3975", "player_name": "LeBron James", "position": "SF", "jersey_number": "23", "injury_status": "Day-to-Day", "injury_date": "2023-11-15", "expected_return": "2023-11-18"
| # | team_id | team_name | player_id | player_name | position | jersey_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fantasy Projections objects from espn.com. All fields typed and schema-versioned.
"player_id": "3975", "player_name": "LeBron James", "sport": "NBA", "position": "SF", "upcoming_opponent": "PHX", "projected_points": 26.5, "projected_rebounds": 7.8, "projected_assists": 8.2, "roster_percentage": 99.8
| # | player_id | player_name | sport | position | upcoming_opponent | projected_points |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our ESPN scraper handles live scoring feeds, complex box score structures, and historical archives. We manage the rate limits and internal API interception required for high-frequency sports data.
Sub-minute latency for live match events across NFL, NBA, MLB, NHL, and global soccer leagues.
Chronological event logs, including player attribution, shot coordinates, and penalty flags.
Full statistical breakdowns for all active players, parsed instantly post-match.
Extract ESPN's proprietary fantasy projections, roster percentages, and positional rankings.
Capture moneyline, spread, and over/under data from ESPN's integrated sportsbook feeds.
Daily synchronisation of team depth charts, injury statuses, and transaction logs.
Backfill decades of match results, standings, and statistical records across major leagues.
NCAA football and basketball data, including AP Top 25 rankings and recruiting databases.
Extract Basketball Power Index, FPI, and other proprietary predictive models.
Brief in. Clean data out.
Provide target leagues, teams, or historical date ranges. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, handle ESPN API rate limits, and map complex box score structures.
Schema validation, null-rate checks, and statistical anomaly detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Sports data requires high frequency and complex parsing. Here is how we maintain reliable feeds when traffic spikes during live games.
ESPN throttles aggressive polling during live games. We distribute requests across rotating proxy pools to maintain sub-minute latency without triggering rate limits.
ESPN's frontend relies heavily on React and GraphQL. We intercept raw JSON payloads from internal API endpoints rather than parsing fragile HTML.
Sports event logs are highly unstructured text. We use regex and NLP to normalise raw strings into structured schema fields for player IDs, coordinates, and play types.
League structures change yearly. Our pipelines handle playoff bracket formatting, pre-season vs regular-season flags, and shifting API endpoints automatically.
Every run emits structured logs. We alert on missing box scores, delayed live feeds, and schema drift, ensuring downstream models never ingest stale data.
Quants ingest historical box scores and play-by-play data to train predictive models and identify line discrepancies.
Developers build lineup optimisers and draft assistants using ESPN's roster percentages and injury updates.
Publishers automate match recaps and stat graphics by piping real-time box scores into their CMS.
Coaching staffs analyse opponent tendencies using historical play-by-play shot coordinates and lineup efficiencies.
Daily Fantasy Sports operators monitor player performance trends to adjust salary cap pricing dynamically.
Mobile app developers integrate live scores and news feeds to keep users updated on their favourite teams.
"Sports data decays in seconds. Extracting historical box scores is trivial, but maintaining a sub-minute pipeline for live play-by-play requires serious infrastructure."
Most teams underestimate the complexity of sports data. Reliable ESPN scraping requires handling aggressive rate limits during prime-time games, parsing complex internal GraphQL queries, and normalising unstructured play-by-play text. DataFlirt absorbs that complexity so your quants can focus on the models, not the infrastructure.
Everything supported by our espn.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.
ESPN's frontend heavily utilises internal APIs. We intercept GraphQL and REST payloads directly using Playwright network monitoring, bypassing fragile DOM parsing.
Live sports require sub-minute latency. We distribute requests across massive residential IP pools to poll live endpoints aggressively without triggering WAF blocks.
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 espn.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available sports data, such as box scores and play-by-play, is generally permissible. DataFlirt targets only public, non-authenticated data. We do not circumvent ESPN+ paywalls or extract private fantasy league data.
For live matches, our pipelines can poll and deliver state changes via Webhook in under 30 seconds, depending on the sport and API endpoint structure.
Yes. We use custom parsers to extract player IDs, action types, shot coordinates, and scoring flags from raw play-by-play text logs.
We distribute request load across thousands of residential ISP proxies, ensuring high-frequency polling without triggering IP bans or throttling.
Yes. We can backfill historical box scores, standings, and player statistics for multiple decades across major leagues like the NFL, NBA, and MLB.
Yes. We extract data for NCAA football, basketball, and other collegiate sports, including AP Top 25 rankings and team rosters.
Our pipelines are monitored 24/7. When ESPN updates its internal APIs or a league changes its playoff structure, we detect schema drift and patch the extractors immediately to maintain SLA.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off historical backfill of NBA box scores or a continuous live feed for NFL play-by-play, we scope, build, and operate the pipeline. Tell us what you need.
