WNBA Manager 2026

WNBA Manager 2026 is a simulation-focused basketball management prototype built around a day-based league loop and a deep-vs-lite simulation model.

The project combines:

• a browser-facing game shell (index.html + src/ui)
• a deep game engine prototype (src/sim/deep/GameEngine.js)
• data pipelines and transformation scripts (tools/, scripts/)
• generated datasets for player and lineup analytics (derived/, raw_data/)

Project Goals

• Simulate basketball decisions (not just random outcomes)
• Keep game logic separated from UI/presentation
• Support one deeply simulated game at a time while other games run in lite mode
• Build toward a watchable management + broadcast experience

For architecture details, see docs/architecture.md.

Repository Structure

• src/core/ – day flow and simulation orchestration
• src/sim/deep/ – deep simulation engine components
• src/ui/ – browser-side setup and main scene logic
• data/ – core runtime JSON data (players, teams, schedule, badges, tendencies)
• tools/ – ETL/build scripts for ingesting and deriving basketball datasets
• scripts/ – utility scripts for enrichment and scraping
• raw_data/ – source/intermediate data files
• derived/ – generated analytics and feature tables
• docs/ – architecture and design documentation

Quick Start

1) Run a sample deep simulation in Node

node run_sim.js

This runs a sample matchup (Aces vs Fever) using players_with_badges.json and prints a simple box score summary.

2) Open the browser prototype

Serve the repo root with any static file server, then open index.html in your browser.

Example (Python):

python -m http.server 8000

Then browse to:

http://localhost:8000

Data + Pipeline Notes

• Most heavy data processing workflows live in tools/.
• Generated outputs are written into derived/.
• Runtime game data currently lives in data/ JSON files.

If you are extending data ingestion or feature engineering, start with tools/README_tools.md.

Current Status

This repository appears to be an active prototype with simulation and data tooling evolving in parallel. Expect iteration on data schemas and engine behavior as systems are integrated.