Degree Name

MS (Master of Science)


Computer and Information Sciences

Date of Award


Committee Chair or Co-Chairs

Ghaith Husari

Committee Members

Brian Bennett, Mathew Harrison, Ferdaus Kawsar


Recently, strategies of National Basketball Association teams have evolved with the skillsets of players and the emergence of advanced analytics. One of the most effective actions in dynamic offensive strategies in basketball is the dribble hand-off (DHO). This thesis proposes an architecture for a classification pipeline for detecting DHOs in an accurate and automated manner. This pipeline consists of a combination of player tracking data and event labels, a rule set to identify candidate actions, manually reviewing game recordings to label the candidates, and embedding player trajectories into hexbin cell paths before passing the completed training set to the classification models. This resulting training set is examined using the information gain from extracted and engineered features and the effectiveness of various machine learning algorithms. Finally, we provide a comprehensive accuracy evaluation of the classification models to compare various machine learning algorithms and highlight their subtle differences in this problem domain.

Document Type

Dissertation - embargo


Copyright 2021 by Dembé Koi Stephanos

Available for download on Thursday, June 15, 2023