Basketball Github Oi Jun 2026

"A Deep Learning Pipeline for Segmentation and Classification of Basketball Players"

# Basketball Analytics Project ## Overview This project is dedicated to analyzing basketball data to gain insights into player performance, team strategies, and game outcomes. Utilizing data science techniques, we aim to provide a comprehensive analysis that could be useful for coaches, players, and fans alike. basketball github oi

The rise of public basketball data (e.g., SportVU, NBA play-by-play) has enabled algorithmic analysis of player movements and decision-making. However, many existing models are computationally heavy. This paper explores how style algorithms — optimized for time and memory constraints — can be adapted to basketball analytics. We curate a set of GitHub repositories containing implementations of spatial indexing (KD-trees), dynamic programming for trajectory simplification, and graph-based play clustering. Using a dataset of 500 NBA possessions, we compare OI-inspired methods against baseline machine learning models. Results show a 40% reduction in runtime with comparable accuracy (92% vs 94%) for play-type classification. We release a Python package on GitHub linking OI modules to basketball data pipelines. However, many existing models are computationally heavy