DraftKings

DraftKings odds data provides moneyline and spread information for upcoming NBA games, scraped through Covers and stored for downstream analytics.

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Source System

DraftKings is a digital sports entertainment and gaming company that offers daily fantasy sports as well as a sportsbook platform.

• Their gambling odds are made available on a public site called https://covers.com, which is where this data is scraped for this project
• Live odds, parlays, and total point over / under thresholds are not scraped in this project
• Moneyline odds and the spread for each home and away team are scraped and stored

Note: The time of day in which the data is scraped is important, as during the games their content changes significantly and the odds turn into live odds

For the NBA Project, data is scraped from this website once a day at 12 pm UTC, after gambling data has been updated and made available for that night’s upcoming games.

Data Ingestion Process

Web Scraping is used to extract data from the following pages:

1. Daily Odds -> https://www.covers.com/sport/basketball/nba/odds

After the data has been pulled, it’s stored into Pandas DataFrames and upserted into Postgres in the bronze Schema.

Source Tables

• bronze Schema
  1. draftkings_game_odds

Data Quality Considerations

1. Since data is only pulled once per day, there can be minor inconsistencies between when odds are collected and when games are played (typically 10–12 hours later).
   • In most cases, these differences are negligible.
   • However, significant shifts can occur if key players are ruled out after the odds are pulled (e.g., a star player downgraded from day-to-day to out), which can impact line movement.
   • I explored alternatives like using a Lambda function to scrape odds data hourly, but determined the added complexity, especially around orchestration and downstream dbt + dashboard updates, was not justified at this stage.