This project is to build a statistical model to predict who will win 2017 NBA Most Valuable Player (MVP) Award in the regular season. Team has collected three 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 in each player statistics category in order to remove any mean and standard deviation effect. This Z transformation can eliminate any statistics bias or domination from any particular category. The Z scale will also analyze each player's performance as compared to the other top NBA players in the same season. The “MVP Index” has been derived from combining each player's Z statistics with equal weight as a “Uniform” model. To evaluate the model accuracy, team has derived another “Accuracy Index” of predicting the top five MVP players recognized annually. The first “Uniform” model can predict the top five winners at 47% accuracy. Team has further 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 weight factor will reflect which player statistics categories are more critically contributed to the MVP Award selection process. The “Weighted” model has further improved the Accuracy Index from 47% to 52%. To further optimize the prediction accuracy, authors have added the “Team Winning” factor. Most historical MVP winners were from the teams with best or better regular season records. Authors have assessed the team winning factor based on the “Power” model from power= 0 (equivalent to the Weighted Model), 1, 2, 3, 4, 5, 6 to power= infinity (MVP from the best Team). The MVP Index will be multiplied by the power of the team winning% in the Power model. Power transformation will improve the model accuracy but which may also create any over-fit concern when power level is getting higher. Based on the Power Model, team can improve the Accuracy Index to 70% at Power=3. There is little benefit but more over-fit risk to further increase the power level beyond 3. Authors also used Data Mining Discriminant analysis to rank players into clusters. The Discriminant model accuracy is 55% not better than Power Model. Team will use the Power=3 model to predict 2017 MVP on the first day of each month starting from Dec.1, 2016 until the season end in April, 2017. The final model prediction will be available around middle April, 2017. The same modeling technique can be applied to predict the MVP winner for Olympic event as well as for the other professional sports such as Football, Basketball, and Soccer.