Problem: Data Gaps in Prop Betting

Betting operators spew odds like confetti, but the raw numbers? Scattered. You need a single source that feeds dashboards, models, and alerts without playing roulette with missing fields.

Step 1 – Pinpoint the Data Sources

First, scrape the obvious: league APIs, third‑party odds feeds, and the occasional Twitter whisper. Then, hunt down the dark horses: player injury reports, minute‑by‑minute play‑by‑plays, and even ESPN’s “Player Prop” pages. By the way, you’ll want a reliable RSS feed for last‑minute line changes.

Step 2 – Draft the Schema

Keep it lean. One table for games (game_id, date, teams), another for players (player_id, name, team, position), and a third for props (prop_id, game_id, player_id, type, line, odds). Add a meta column for source_id so you can trace back any discrepancy.

Key Columns That Never Sleep

prop_type (over/under, points/rebounds), line_value (numeric), odds_decimal, timestamp_utc. Anything less is noise. And here is why: without a clean timestamp you’ll lose the race to the best odds.

Step 3 – Build the Extraction Engine

Write Python scripts that ping each endpoint every 30 seconds. Use requests with retry logic; a dropped connection is a data hole you can’t patch later. Throw in a simple queue—Redis works fine—to buffer bursts when the odds provider fires a flood.

Step 4 – Transform & Validate

Normalize team names (LA Lakers → Lakers), convert odds formats (American to decimal), and flag outliers. A prop line that jumps from 22.5 to 5 in seconds? Toss it. Validation rules are your guardrails.

Step 5 – Load Into the Store

PostgreSQL on a SSD box does the job. Partition the props table by month; queries stay swift even when you’ve amassed a year’s worth of data. Index on game_id, player_id, and timestamp_utc. Remember, a missing index is a performance nightmare.

Step 6 – Automate Refresh & Backfill

Set up a cron that runs the ETL pipeline nightly for historical games. For live games, keep the runner alive with a systemd service. Backup daily; restore test monthly. You’ll thank yourself when a server hiccup strikes.

Step 7 – Expose the Data

Build a tiny REST layer with Flask. Endpoints like /props?player=LeBron&date=2024-03-15 give instant access. Secure it with API keys; you don’t want rogue scrapers leaking your hard‑earned collection.

Step 8 – Plug Into nbaplayerbets.com

Finally, point your front‑end to the new API. The site can now display live prop odds, historical trends, and confidence bands without a single manual upload. Integration is just a POST request away.

Actionable Advice

Start with a single game, get the whole pipeline humming, then scale outward. One solid run beats a half‑baked marathon.