Predicting the NBA MVP Using Machine Learning

🏀 Predicting the NBA MVP with Machine Learning

Introduction

Every NBA season, the Most Valuable Player (MVP) award sparks intense debates among fans, analysts, and players. What truly defines an MVP? Is it raw individual talent, team success, or a combination of various statistical measures? The criteria are often subjective, but can we use data and machine learning to predict the next MVP?

In this project, I set out to answer that question by leveraging historical NBA data and machine learning models to predict the 2025 NBA MVP. Using a blend of traditional statistics and advanced analytics, my goal was to identify patterns in previous MVP selections and apply those insights to forecast the future winner.

Understanding the MVP Criteria

The MVP race is influenced by multiple factors beyond just scoring. Historical trends suggest that MVP winners usually exhibit the following characteristics:

• Elite Performance – High individual stats in scoring, efficiency, and advanced metrics.
• Team Success – MVPs are rarely from losing teams; top seeds dominate the award.
• Narrative & Media Influence – Voter perception matters, but it's difficult to quantify.
• Consistency – The ability to perform at a high level throughout the season.

Using these principles, I gathered a comprehensive dataset of past MVP winners and key performance metrics to develop a machine learning model that predicts the 2025 MVP race.

Data Collection & Feature Selection

To ensure an accurate model, I collected data from various sources, including:

• Historical NBA MVP Data – A dataset from Kaggle with past MVP winners and player performance stats.
• Advanced NBA Statistics – Key efficiency metrics such as Win Shares per 48 minutes (WS/48) and Player Efficiency Rating (PER).
• Team Success Metrics – Win-loss records and team seeding, as MVPs often come from the top-performing teams.

Through exploratory data analysis, I identified the most important features that influence MVP selection:

• Value Over Replacement Player (VORP) – A measure of a player's total contribution.
• Win Shares per 48 Minutes (WS/48) – Player impact on team wins per minute played.
• Player Efficiency Rating (PER) – A single number summarizing overall efficiency.
• Points Per Game (PTS) – A major factor in MVP candidacy.
• Team Seeding (Seed) – Higher-seeded teams have a stronger chance of producing an MVP.
• Usage Percentage (USG%) – Indicates how much of a team's offense runs through the player.
• Win-Loss Ratio – Team success plays a significant role in voting.

Model Development & Performance

To maximize prediction accuracy, I tested three different models:

📊 Linear Regression (Baseline Model)

• R²: 51.2%
• LOYOCV Accuracy: 61.9%
• Key Findings: While effective at identifying general trends, it struggled with complex interactions between variables.

🌲 Random Forest (Improved Model)

• R²: 66.8%
• LOYOCV Accuracy: 71.4%
• Key Findings: Performed significantly better, capturing nonlinear relationships and ranking feature importance effectively.

🚀 XGBoost (Best Model)

• R²: 67.3%
• LOYOCV Accuracy: 83.3%
• Key Findings: The most accurate model, outperforming both Linear Regression and Random Forest.

Predicting the 2025 NBA MVP

After training the final XGBoost model, I applied it to the 2025 season data to generate predicted MVP vote shares for the top contenders. The model’s prediction for the 2025 NBA MVP is:

🏆 Shai Gilgeous-Alexander

Conclusion

With an 83.3% accuracy rate, this model successfully identified past MVPs and projects Shai Gilgeous-Alexander as the 2025 winner. As the season unfolds, it will be exciting to see how well the predictions hold up.