NetGainNBA

This project is a full-stack NBA playoff analytics and simulation tool which uncovers team and player playstyles and analyzes which archetypes are most important to a team's postseason success. It combines data collection, cleaning, clustering, deep learning, and monte carlo simulations to provide actionable roster construction strategies for front office management.

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Demo

Click here to watch the demo video

ML Workflow

1. Data Collection & Cleaning

   • Fetched team, player, and matchup data from the NBA API.
   • Joined and cleaned datasets to create structured inputs for modeling.

2. Player & Team Clustering

   • Clustered players into playstyles using K-Means clustering.
   • Transformed player clusters into team-level playstyle proportions.

3. Regression Modeling

   • Built Random Forest and XGBoost models to forecast the number of playoff wins for a team.

4. Classification Modeling

   • Shifted to a classification approach using TabNet neural networks.
   • Used temporal sliding cross-validation to predict actual playoff matchups.
   • Achieved 88% playoff prediction accuracy (2020–2024).

5. Simulation & Monte Carlo Analysis

   • Introduced controlled randomness via Monte Carlo simulations.
   • Predicted playoff winners and displayed round-by-round probabilities for each team.

Application Insight:

• Front office management can analyze which playstyles are most impactful for playoff performance.
• Users can simulate any playoff bracket and visualize the effect of different team archetypes.

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Getting Started

To run this project (FOR NOW):

NOTE: NEED TO MAKE A .env FILE AND PUT ALL THE AUTHENTICATION KEYS FOR THIS TO WORK

1. Navigate to the frontend folder:

   cd frontend
   

2. Install dependencies:

   npm install
   

3. Start the development server:

   npm run dev
   

Backend Setup

To set up the backend environment (you can skip directly to the pip install stuff if you don't want to set up a virtual enviorment):

macOS

1. Navigate to the backend directory:

   cd backend

2. Create a virtual environment:

   python3 -m venv .venv

3. Activate the virtual environment:

   source .venv/bin/activate

4. Install the required dependencies:

   pip install Flask Flask-CORS pandas

5. Run the backend server:

   python main.py

Windows

1. Navigate to the backend directory:

   cd backend

2. Create a virtual environment:

   python -m venv .venv

3. Activate the virtual environment:

   .venv\Scripts\activate

4. Install the required dependencies:

   pip install Flask Flask-CORS pandas

5. Run the backend server:

   python main.py