Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
12-2019
Committee Chair or Co-Chairs
Jeff Knisley
Committee Members
Nicole Lewis, Michele Joyner
Abstract
Recent advancements in sports information and technology systems have ushered in a new age of applications of both supervised and unsupervised analytical techniques in the sports domain. These automated systems capture large volumes of data points about competitors during live competition. As a result, multi-relational analyses are gaining popularity in the field of Sports Analytics. We review two case studies of dimensionality reduction with Principal Component Analysis and latent factor analysis with Non-Negative Matrix Factorization applied in sports. Also, we provide a review of a framework for extending these techniques for higher order data structures. The primary scope of this thesis is to further extend the concept of tensor decomposition through the use of function spaces. In doing so, we address the limitations of PCA to vector and matrix representations and the CP-Decomposition to tensor representations. Lastly, we provide an application in the context of professional stock car racing.
Document Type
Thesis - unrestricted
Recommended Citation
Reising, Justin, "Function Space Tensor Decomposition and its Application in Sports Analytics" (2019). Electronic Theses and Dissertations. Paper 3676. https://dc.etsu.edu/etd/3676
Copyright
Copyright by Justin Reising
Included in
Applied Statistics Commons, Multivariate Analysis Commons, Other Applied Mathematics Commons