Bako Analytics: a localized deep learning framework for personalized basketball biometrics and team tactical analysis
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Date
2026-05-28
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Uganda Christian University
Abstract
The advancement of Artificial Intelligence (AI) in sports has largely bypassed low-resource environments in Sub-Saharan Africa due to the prohibitive cost of commercial hardware and the lack of representative datasets for African athletes. This project presents BAKO Analytics, Africa’s first localised multi-model deep learning framework designed for personalised basketball biometrics and tactical diagnostics. The system addresses the gap in accessible sports science in Uganda by implementing a decou- pled client-server architecture comprising a FastAPI-powered backend and a React/Vite web interface. The core innovation lies in a six-model AI pipeline using the YOLO (You Only Look Once) architecture for object detection and pose estimation. Specifically, the framework includes dedicated models for player and ball detection, court keypoint mapping (homography), and a custom biometric engine that decomposes basketball shooting form into four critical phases (DIP, SET, RELEASE, and FINISH) using 17-keypoint skeletal data. Validated through a case study with the UCU Cannons basketball team, BAKO Analytics enables elite-level performance analysis using nothing more than standard smartphone footage and modest computing hardware. Technical evaluation demonstrates high precision in action recognition and spatial tracking, even under the variable lighting conditions of outdoor courts
common in the Ugandan context. Beyond its technical contributions, the project provides a scalable and affordable blueprint for indigenous sports analytics, bridging the technological divide and fostering talent development through data-informed coaching.
Keywords: Basketball Analytics, Computer Vision, Deep Learning, Pose Estimation, Biometrics, Low-Resource AI, Sports Science
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Undergraduate