Data Mining in The NBA: An Applied Approach

被引：0
|
作者：
Hewko, Jordan [1 ]
Sullivan, Robert [1 ]
Reige, Shaun [1 ]
El-Hajj, Mohamad [1 ]
机构：
[1] MacEwan Univ, Dept Comp Sci, Edmonton, AB, Canada
来源：
2019 IEEE 10TH ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS & MOBILE COMMUNICATION CONFERENCE (UEMCON) | 2019年
关键词：
Sport; NBA; Data Mining; Association Rules; Naive Bayes; Decision Trees;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This work aims to analyze the last 6 years of professional basketball data using knowledge discovery tools. The goal is to outline some insight into how teams win and what separates these winning teams from the losing teams. Using Microsoft SQL Server Management Studio, Microsoft Business Intelligence, R and PowerBl, we analyzed regular season game data to find previously unknown trends between winning and losing teams. Using decision trees, naive Bayes and association rules, we will look at defensive and offensive data between teams. The following stats are considered: defensive and offensive rebounds, blocks, steals, turnovers, 2 point shot percentage and 3 point shot percentage. Results show that teams who earn more defensive rebounds win more games while teams that earn higher than average offensive rebounds lose more games. This is because teams that get more offensive rebounds are missing more of their shots.
引用
收藏
页码：426 / 432
页数：7
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