Data Mining Analysis of Overall Team Information Based on Internet of Things

In professional basketball games, big data has been largely used in analyzing the reasons for winning or losing games and further to design relevant stratagem according to the analytic results to attain victory. Nonetheless, the High School Basketball League (HBL) in Taiwan never used big data or re...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.41822-41829
Main Authors: Lee, Yueh-Shiu, Wang, Jun-Ren, Zhan, Jun-We, Zhang, Jing-Mi
Format: Article
Language:English
Subjects:
Big Data
Cameras
Data analysis
Data mining
e-sports
Games
High school basketball
Internet of Things
Linear regression
Mathematical analysis
Regression analysis
spectator sport
Support vector machines
Training
Trends
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Summary:In professional basketball games, big data has been largely used in analyzing the reasons for winning or losing games and further to design relevant stratagem according to the analytic results to attain victory. Nonetheless, the High School Basketball League (HBL) in Taiwan never used big data or relevant research to analyze game results. The study aims to conduct big data analyses to discuss the key winning factors and trends for HBL. Using Excel and multiple linear regression to understand the importance level and trend of each variable to the winning rate. Additionally, combining with the Support Vector Machine (SVM) prediction to confirm whether the big data analytic result is applicable for implementing in realistic games. After implementing the analysis of multiple linear regression, based on the yearly trends, the significant influence factors are 2P%, 3P%, FTM, TRB, OREB, STL, and TOV. Consequently, the prediction has reached 85% after inputting these data into SVM.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2976728