Date of Award
5-2022
Document Type
Thesis open access
Department
Computer Science
First Advisor
Matthew Hibbs
Second Advisor
Yu Zhang
Abstract
Sports prediction has always been an interesting problem in the entertainment industry. Many data scientists have come out different methods on this problem. We hope to see how well a neural network model can predict an individual game outcome and the final ranking on NBA data. We examined the possibility of different unbiased deep learning models can perform as well as other mathematics methods. We were also looking for what types of data are more influential for the models. Then, we can make some assumptions on our models and the other sports prediction methods.
Recommended Citation
Lee, Fan, "Deep Learning in Sports Prediction" (2022). Computer Science Honors Theses. 65.
https://digitalcommons.trinity.edu/compsci_honors/65
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.