This project is to build a statistical model to predict who will win 2017 NBA MVP Award. Team collected three raw data from public domain: (1) player statistics, (2) team win%, and (3) historical MVP winners. The player statistics have been standardized to Z score in each category to remove any mean and standard deviation effect. The MVP Index has been derived from combining each player’s Z statistics as "Uniform" model. To evaluate the model accuracy, team has derived Accuracy Index of predicting the top five MVP players recognized annually. Team has also derived the "Weighted" model by adding the weight factor which was calculated based on the dispersion/separation between the top two MVP winners and the remaining players not in top 5.  The “Weighted” model has improved the Accuracy Index from 47% to 52%.  To further improve the prediction accuracy, we have added the "Team Winning" factor.  We have assessed the team winning factor based on power model from power=0, 1, 2, 3, 4, 5, 6 to power=infinity.  Team can improve the Accuracy Index to 70% at Power=3.  There is no benefit to further increase the power level. We will use the Power=3 model to predict 2017 MVP in the season end.