basketball-analytics
Here are 41 public repositories matching this topic...
Kinetic Adaptation via Wasserstein Heuristics and Identity
-
Updated
Mar 15, 2026 - Python
Competition-winning Rebound Prediction Notebook
-
Updated
Sep 6, 2023 - Jupyter Notebook
Coaching Experience's Effect on Winning
-
Updated
Aug 30, 2023 - Jupyter Notebook
Dunk Vision is a desktop basketball shot tracker and data capture tool built using Python and tkinter. Designed for youth coaches, players, and parents, it lets users track, visualize, and analyze shot data to improve individual and team performance as well as direct training efforts. For courtside users, data can be exported for later analysis.
-
Updated
Sep 26, 2025 - Python
Automated NBA shot chart and heat map generator built with Python, R, and the NBA API.
-
Updated
Jul 30, 2026 - Python
-
Updated
May 29, 2025
Multi-scenario analysis of how NBA trash talk and conflicts affect player performance
-
Updated
Dec 8, 2025 - Jupyter Notebook
A full CV basketball analytics system powered by YOLOv12, ByteTrack, SuperPoint/LightGlue, FastAPI, MongoDB, minIO, Prometheus and Grafana to turn raw basketball broadcast footage into rich spatial analytics and visualized insights.
-
Updated
Dec 13, 2025 - Jupyter Notebook
A professional NBA scouting application that combines live player data from the official NBA Stats API with a two-stage AI agent architecture. The system researches player tendencies, identifies comparable players, fetches their live stats, and synthesizes everything into an executive scouting memo.
-
Updated
Jun 1, 2026 - Python
Files used in code to generate findings, code, and associated visuals and research report
-
Updated
Jan 17, 2025 - Python
Aplicación para recomendar pares de jugadores NBA complementarios para la temporada 2020-2021, utilizando datos de 2015 a 2020.
-
Updated
Nov 12, 2024 - TypeScript
Objective basketball shot recognition using wearable sensors and biomechanical machine learning. Classifies court zones (Paint, FT, 3-Point) with 94.5% accuracy using quaternion-based earth-frame transformations and optimized SVC.
-
Updated
Jun 23, 2026 - Jupyter Notebook
AI-Powered Basketball Analytics Platform using Computer Vision, FIBA LiveStats, and Artificial Intelligence.
-
Updated
Jul 1, 2026 - TypeScript
Public case study for a private NBA fantasy analytics platform
-
Updated
Jun 17, 2026
Study NBA player positions through classification models
-
Updated
Jul 9, 2026 - Jupyter Notebook
Interactive Shiny dashboard for NBA Draft analytics, rookie success prediction, player comparison, team draft grades, and front office decision support.
-
Updated
Jul 7, 2026 - R
In-game momentum quantification — run detection and swing-state analysis in NBA games, 2023-24.
-
Updated
Mar 7, 2026
A curated marketplace of high-quality community sports plugins for Claude Code, Codex, and AI coding assistants. Covers analytics, live scores, fantasy sports, fitness tracking, and more.
-
Updated
Jul 20, 2026 - TypeScript
Measures the real "possessions per free throw" constant behind NBA True Shooting% from play-by-play data, instead of assuming the standard 0.44, and checks whether the gap is big enough to matter for player comparisons.
-
Updated
Jul 5, 2026 - Python
Improve this page
Add a description, image, and links to the basketball-analytics topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the basketball-analytics topic, visit your repo's landing page and select "manage topics."