We extract live scores, play-by-play events, fantasy projections, player stats, and betting odds from CBS Sports. 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 Scores & Games objects from cbssports.com. All fields typed and schema-versioned.
"game_id": "NFL_20231022_KC@LAC", "home_team": "Los Angeles Chargers", "away_team": "Kansas City Chiefs", "status": "In Progress", "quarter": "3", "clock": "04:12", "home_score": 17, "away_score": 24
| # | game_id | home_team | away_team | status | quarter | clock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Player Profiles objects from cbssports.com. All fields typed and schema-versioned.
"player_id": "224987", "name": "Patrick Mahomes", "team": "Kansas City Chiefs", "position": "QB", "height": "6-2", "weight": "225", "college": "Texas Tech", "status": "Active"
| # | player_id | name | team | position | height | weight |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fantasy Projections objects from cbssports.com. All fields typed and schema-versioned.
"player_id": "224987", "week": "7", "opponent": "LAC", "projected_points": 24.5, "ownership_pct": 99.8, "start_pct": 95.2, "position_rank": 1
| # | player_id | week | opponent | projected_points | ownership_pct | start_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Play-by-Play objects from cbssports.com. All fields typed and schema-versioned.
"event_id": "evt_84921", "game_id": "NFL_20231022_KC@LAC", "quarter": "3", "clock": "04:12", "team": "KC", "action_type": "Pass", "description": "P.Mahomes pass short right to T.Kelce for 14 yards to the LAC 32.", "down": "1st and 10"
| # | event_id | game_id | quarter | clock | team | player |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Betting Odds objects from cbssports.com. All fields typed and schema-versioned.
"game_id": "NFL_20231022_KC@LAC", "sportsbook": "Caesars", "spread_home": 5.5, "spread_away": -5.5, "moneyline_home": 210, "moneyline_away": -260, "over_under": 48.5, "timestamp": "2023-10-22T14:30:00Z"
| # | game_id | sportsbook | spread_home | spread_away | moneyline_home | moneyline_away |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our CBS Sports scraper handles every layer of the platform: live game trackers, fantasy projections, player statistics, and betting odds — with JavaScript rendering, session management, and anti-bot circumvention built in.
Extract real-time scores, clock status, and quarter data across NFL, NBA, MLB, NHL, and NCAA games.
Capture granular event data, down-and-distance, and textual descriptions for every play in a match.
Scrape CBS Sports' proprietary fantasy point projections, ownership percentages, and start rates per week.
Extract career stats, current season splits, physical attributes, and draft history for every active player.
Capture spreads, moneylines, and over/unders from integrated sportsbooks with timestamped line movements.
Monitor player injury status, practice participation, and return timelines across all major leagues.
Extract division standings, conference rankings, win/loss records, and upcoming match schedules.
Scrape prospect rankings, mock drafts, and scouting reports for NFL and NBA drafts.
Run one-off historical exports or configure continuous pipelines for live game days with sub-minute latency.
Brief in. Clean data out.
Provide league names, team IDs, or specific data types like fantasy projections. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and anti-bot handling for cbssports.com.
Schema validation, null-rate checks, latency testing, and event sequencing before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Sports data requires low latency and high reliability. Here's how we stay resilient — and why teams choose managed infrastructure over DIY.
Live sports require aggressive polling without triggering rate limits. We distribute requests across a vast pool of US residential IPs, ensuring play-by-play events and score updates hit your webhook within seconds of the CBS GameTracker updating.
CBS Sports heavily relies on React and asynchronous API calls for its live gamecasts and fantasy dashboards. We run full Playwright browser sessions to hydrate these widgets, capturing data that headless HTTP clients miss entirely.
Sports sites frequently alter DOM structures during major events or playoffs. Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, and internal JSON state extraction — so a layout change doesn't break your data pipeline.
For historical stats and player profiles, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost, storage bloat, and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing events, schema drift, and coverage drops — and respond before you notice. SLA uptime is contractual, not aspirational.
Daily fantasy operators aggregate CBS projections and ownership data to calibrate their own pricing models and contest structures.
Quantitative bettors feed live play-by-play data and injury reports into in-play betting models to identify mispriced lines.
Sports publishers use automated score feeds and standings data to populate their own digital properties and broadcast graphics.
Data scientists train machine learning models on historical player stats and weather conditions to predict game outcomes.
Mobile app developers integrate live scores and news feeds to keep users engaged during game days.
Agencies and front offices track advanced metrics and draft rankings to assess player market value and contract negotiations.
"CBS Sports holds some of the most accurate fantasy projections and live play-by-play data on the internet — but none of it is queryable unless you build the pipeline."
Most teams underestimate the investment required: reliable sports scraping requires low-latency residential proxies, full JavaScript rendering for live gamecast widgets, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our cbssports.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, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US 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 cbssports.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available sports data, scores, and statistics is generally permissible. DataFlirt targets only public, non-authenticated data such as live scores, public player profiles, and general fantasy projections. We do not extract private fantasy league data or circumvent authentication walls. Clients should review terms of service and consult legal counsel for specific use cases.
For live games, we configure high-frequency polling pipelines that can deliver play-by-play events and score updates via Webhook within 15 to 30 seconds of the event appearing on the CBS GameTracker.
Yes. We can run backfill jobs to extract historical career statistics, past season splits, and historical draft data across all major leagues supported by CBS Sports.
Yes. We extract the integrated sportsbook lines, including spreads, moneylines, and over/under totals, complete with timestamps to track line movement leading up to kickoff.
Sports sites often change their UI for major events like the Super Bowl or March Madness. Our selector strategy uses multi-layer fallback chains and targets underlying JSON data where possible, ensuring layout changes do not break the pipeline.
Absolutely. We provide a sample run of up to 50 games or 500 player profiles as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, 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 historical stats dump or a continuous live-score feed — we scope, build, and operate the pipeline. Tell us what you need.
