NBAStats

TODO

Scraping & Data

Scraping using the basketball_reference_web_scraper Python package or creating separate scraping functions.

1. Create relational database schema: (Done)

   • Design the database schema (e.g., schedule table, game table, player stats table, etc.) using dbdiagram.io.

2. Get every season's schedule: (Done)

   • Fetch and store schedules from 1985 to the present in a database or JSON files.

3. Scrape every game: (In progress)

   • Develop a robust scraping method, potentially by creating a dedicated class for this purpose.
   • Collect as much data as possible.

4. Clean the database: (In progress)

   • Ensure data quality and consistency by cleaning the collected data.

ML

1. Predict NBA champion/play-in tournament teams:

   • Identify the best machine learning model to predict which team will win the NBA championship or participate in the play-in tournament.

2. Predict game outcomes and player stats:

   • Develop a model to predict the outcome of games and player statistics, possibly using data from the current and previous seasons.
   • Optimize the models for maximum efficiency.

App

• Create a Streamlit app to make all the data and predictions accessible.

Automation

• Automate the scraping and data cleaning:

  • Use Airflow or Snowflake to automate the scraping process and database cleaning based on the schedule. Scrape the website after every game.

• Batch process the ML models:

  • Update the machine learning models with new processed data.

• Update the Streamlit app automatically:

  • Ensure the app is updated in real-time with the latest data and predictions.