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Random Forest Prediction of NBA Regular Season MVP Winners Based on Metrics Optimization

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DOI: 10.23977/infkm.2023.040409 | Downloads: 41 | Views: 509


Jiuxi Han 1, Zhipeng Yu 2,3


1 Beijing Huiwen Middle School (Peking Academy), Beijing, 100061, China
2 The Open University of China, Jinan, 250014, China
3 Hanting Administration for Market Regulation, Weifang, 261100, China

Corresponding Author

Zhipeng Yu


In the National Basketball Association (NBA), there are various individual awards, but one of them, the Most Valuable Player Award, is considered crucial. This award represents a player's outstanding performance in a team sport, and its recipient not only plays an important role on the court, but also attracts widespread attention and discussion in society and the sports world. An effective MVP prediction model can synthesize a player's statistical performance and team success. This study proposes a random forest algorithm to predict seasonal MVPs. In this study, seasonal statistics of 300 players were analyzed using nearly 50 years of NBA seasonal data with an era split: early NBA vs. small ball era. Correlation analysis was used to eliminate interdependent criteria. As a result, the number of criteria was reduced from 20 to 7, which were defined as decision factors: (i) Points Per Game (PPG), (ii) Field Goal Percentage (FG%), (iii) Three-Point Percentage (3P%), (iv) Rebounds Per Game (RPG), (v) Assists Per Game (APG), and (vi) Turnovers Per Game (TOV). After correlation analysis, the Random Forest algorithm was applied in order to predict MVP for the years 2021-2023.The results of the study clearly show that the use of the Random Forest algorithm to predict MVP is a highly feasible method with excellent adaptability and foresight. The high feasibility of this approach, which was able to provide accurate predictions across seasons and tournament environments, further validates its potential for application in the NBA. The excellent adaptability of the Random Forest algorithm means that it is able to effectively deal with a wide range of decision factors and data characteristics, including in the context of data in the underdog era, which can change frequently. This allows us to better cope with evolving sports environments and data challenges and provide reliable support to decision makers. As basketball and sports data continue to grow and become richer, and as machine learning techniques continue to evolve, the Random Forest algorithm will hopefully further improve its performance and range of applications. It can be a powerful tool for decision makers and team managers to help them make more informed decisions, identify the players with the highest potential to win MVP awards, and contribute to team success.


Indicator Optimization, Correlation Coefficient, Random Forest, Data Mining, MVP


Jiuxi Han, Zhipeng Yu, Random Forest Prediction of NBA Regular Season MVP Winners Based on Metrics Optimization. Information and Knowledge Management (2023) Vol. 4: 53-62. DOI:


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