NBA MVP Prediction Model

Tongan Wu

The NBA Most Valuable Player (MVP) award has been awarded since the 1955-56 NBA season, with one player with the most perfect behaviors each regular season. The determination of MVP shifted from being voted by NBA players to being decided by sportswriters and reporters of the United States and Canada in 1981. For decades, the final choice of MVP has been one of the most popular discussion topics in the NBA league as well as among all the sportswriters, media outlets, and basketball fans. This interest has also evoked many predictions toward the determination of the Most Valuable Player.

Based on previous investigation and articles, there are many factors affecting who is awarded the MVP award, including average points per game, true shooting percentage, win shares, win shares/48 min, assists, rebounds, fouls, turnovers, blocks, adjusted production, free throw percentages, and much more.

In this research paper, I investigate the question: which factors predict who is awarded the NBA MVP., To do so, I conducted background research, developed graphs of different variables and how they relate to the MVP award, created a stepwise logistic regression model to predict the Most Valuable Player in the NBA based on the players’ data of each regular season, and tested my model based on previous data and MVP results. I find that the factors such as the PER (Player Efficiency Ratings), the TS% (True Shooting Percentage), the TRB% (Total Rebounds Percentage ), the AST% (Assists Percentage Percentage), USG% (Usage Percentage), the WS (Win Share), and the Adjusted Production have positive relationship with MVP, while the ORB% (Offensive Rebounds Percentage), the DRB% (Defensive Rebounds Percentage), and the TOV% (Turnover Percentage) are in opposite.

Finally, some unexpected results are discussed after the prediction and the testing process, as well as some limitations and future plans.

Key Words: NBA, MVP prediction, logistic regression model, possibilities, variables, code