Degree Name
MS (Master of Science)
Program
Computer and Information Sciences
Date of Award
5-2021
Committee Chair or Co-Chairs
Ghaith Husari
Committee Members
Brian Bennett, Mathew Harrison, Ferdaus Kawsar
Abstract
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
Recommended Citation
Stephanos, Dembe, "Machine Learning Approaches to Dribble Hand-off Action Classification with SportVU NBA Player Coordinate Data" (2021). Electronic Theses and Dissertations. Paper 3908. https://dc.etsu.edu/etd/3908
Copyright
Copyright 2021 by Dembé Koi Stephanos
Included in
Artificial Intelligence and Robotics Commons, Databases and Information Systems Commons, Data Science Commons, Software Engineering Commons, Theory and Algorithms Commons