Author(s)

Jiajun Wu

School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China.

In basketball, each player’s skill level is the key to a team’s success or failure, the skill level is affected by many personal and environmental factors. A physics-informed AI statistics has become extremely important. In this article, a complex non-linear process is considered by taking into account the average points per game of each player, playing time, shooting percentage, and others. This physics-informed statistics is to construct a multiple linear regression model with physics-informed neural networks. Based on the official data provided by the American Basketball League, and combined with specific methods of R program analysis, the regression model affecting the player’s average points per game is verified, and the key factors affecting the player’s average points per game are finally elucidated. The paper provides a novel window for coaches to make meaningful in-game adjustments to team members.

Physics-Informed Statistics, Multiple Linear Regression, Average Score per Game, R Program Analysis

Wu, J. (2024) Statistical Approach to Basketball Players’ Skill Level. Journal of Applied Mathematics and Physics, 12, 1352-1363. doi: 10.4236/jamp.2024.124083.