Online Program Home
  My Program

Abstract Details

Activity Number: 159 - SPEED: Sports and Business
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Sports
Abstract #322437 View Presentation
Title: Predict NBA 2016-2017 Regular Season Team Winning%
Author(s): Timothy Liu* and Mason Chen
Companies: and Mason Chen Consulting
Keywords: Sports Analytics ; Modeling ; SPSS ; Z Score

Build a statistical model to predict the team winning percent based on the offensive, defensive, and differential statistics during 2015-16 NBA regular season. The team statistics have been standardized to Z score in each category to remove any mean and standard deviation effect. We used a multiple linear regression model to predict the team winning record. After trimmed the insignificant input variables based on the significance P-Values, the predictive model can predict team winning percent with R-Square > 0.95. The regression model has indicated that the importance of 3-point Percentage, Turn Over, and Point Per Game are critical to the offensive efficiency. In defense, how to defend the rebound and opponent's field goal percentage are most critical. Warriors' team record has been identified as an outlier since their winning percent and team statistics are separated from the other teams. We have considered the 2nd-order Interaction Term. Defense Field Gold% * Defense Point Per Game is the most significant interaction term. This model may also provide NBA coaches how to build a better team to win more games.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

Copyright © American Statistical Association