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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 #322414 View Presentation
Title: Predict NBA 2016-2017 Regular Season MVP Winner
Author(s): Mason Chen*
Companies: Mason Chen Consulting
Keywords: Sports Analytics ; Modeling ; Minitab ; Z Score ; Power ; Accuracy

Build a statistical model to predict who will win 2016-17 NBA Most Valuable Player Award. Team has collected 3 raw data from public Sports domain: (1) player statistics, (2) team win%, and (3) historical MVP winners. Before building the proto model, the player statistics have been standardized to the Z scale to remove any mean & standard deviation effect. The "MVP Index" has been derived from combining each player's Z statistics equally as a "Uniform" model. To evaluate the model accuracy, team has derived another "Accuracy Index" of predicting the top five MVP players. The "Uniform" model can predict the top five winners at 47% accuracy. Team has also derived the "Weighted" model by adding the weight factor which was calculated based on the dispersion between the top two MVP winners and the remaining players. The "Weighted" model has improved the Accuracy Index to 52%. To further optimize the prediction accuracy, authors have added the "Team Winning" factor. Authors have assessed the team winning factor based on the "Power" model from power= 0, 1, to infinity. Based on the Power=2 Model, team can improve the Accuracy to beyond 67%. Westbrook and Harden are top two candidates

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

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