Analyzing NBA Players’ Future Data Performance with Multiple Regression Linear Models

DOI

10.2991/978-2-38476-475-4_112How to use a DOI?

Keywords

Multiple Linear Regression Model; NBA; Model Construction

Abstract

In professional sports leagues, data not only reflect players’ performance but also have particular predictive significance. In today’s NBA league, data analysis and prediction are very popular and necessary, and every team invests a lot of energy and financial resources into data analysis every year to improve the team’s performance or trade success. This thesis takes the data of all the games of NBA players in the 2016-2017 regular season as the base establishes a multiple linear regression model with the data that can directly reflect the performance of the players, such as scores and rebounds as the dependent variable, analyzes the predictive attributes of different factors on the data, and searches for the data with high influence, which can provide a reference to the tactical arrangement and time allocation of the NBA team before the game, and also provide a reference for the NBA team managers when evaluating the trade. Team managers are to provide specific data references for the evaluation during trade.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zhixuan Yu
PY  - 2025
DA  - 2025/11/11
TI  - Analyzing NBA Players’ Future Data Performance with Multiple Regression Linear Models
BT  - Proceedings of the 2025 10th International Conference on Modern Management, Education and Social Sciences (MMET 2025)
PB  - Atlantis Press
SP  - 1015
EP  - 1025
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-475-4_112
DO  - 10.2991/978-2-38476-475-4_112
ID  - Yu2025
ER  -