Download pdf
Analysis of NBA player salary based on multiple linear regression model
- 1 Guanghua Cambridge International School, Shanghai, 200000, China
* Author to whom correspondence should be addressed.
Abstract
This study explores the use of machine learning techniques to predict NBA player salaries. Traditional salary evaluation methods often rely on subjective expert judgment, whereas data-driven approaches can provide more objective and accurate predictions. With the advancement of data analysis techniques and machine learning algorithms, it is now feasible to predict player salaries based on performance data. This study employs advanced statistical and machine learning techniques to analyze detailed player performance data, including points scored, rebounds, assists, steals, and blocks, to establish a data- and algorithm-based salary prediction model. This model can assist team management in making more scientific decisions during contract negotiations and player acquisitions, thereby avoiding the overvaluation or undervaluation of players and achieving a more balanced and fair salary distribution. Accurate salary predictions help teams allocate their limited salary cap more effectively, optimizing budget management and enhancing overall team competitiveness. This study not only demonstrates the practical value of data analysis and machine learning methods in the sports field but also promotes the further development of data science in sports management. Additionally, the results of the prediction model can provide valuable references for fans and media, enhancing their understanding of player salaries and team management strategies. This transparency enriches the overall fan experience and media coverage of the sport, facilitating more informed discussions and debates about player value and team decisions.
Keywords
Player salary, linear regression model, predict.
[1]. Zhang M 2024 Analysis of NBA Player Salary using Linear Regression Analysis. Highlights in Science, Engineering and Technology, 88, 509-515.
[2]. Feng X, Wang Y and Xiong T 2023 NBA Player Salary Analysis based on Multivariate Regression Analysis. Highlights in Science, Engineering and Technology, 49, 157-166.
[3]. Chen X, Lin H, Pang L, et al. 2023 A Survey of Forecasting the Salary for Players in National Basketball Association. Journal of Education, Humanities and Social Sciences, 16, 1-6.
[4]. Zhao Y 2022 Model Prediction of Factors Influencing NBA Players’ Salaries Based on Multiple Linear Regression. 2022 2nd International Conference on Economic Development and Business Culture (ICEDBC 2022). Atlantis Press, 1439-1445.
[5]. Chen S 2016 Model Prediction of Factors Influencing NBA Players' Salaries Based on Multiple Linear Regression. Business, 26, 1.
[6]. Özbalta E, Yavuz M and Kaya T 2022 National Basketball Association Player Salary Prediction Using Supervised Machine Learning Methods. Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS 2021 Conference, 24-26.
[7]. Kremer P 2019 Predicting if NBA Teams Will Make the Playoffs Based on Estimated Wins Added (EWA) and Salary. Utica College.
[8]. Zhang J 2024 National Basketball Association Salary Prediction: A Data-Driven Linear Regression Analysis. Highlights in Business, Economics and Management, 24, 1059-1064.
[9]. Papadaki I and Tsagris M 2022 Are NBA Players’ Salaries in Accordance with Their Performance on Court. Advances in Econometrics, Operational Research, Data Science and Actuarial Studies: Techniques and Theories. Cham: Springer International Publishing, 405-428.
[10]. Swanson A 2015 How we provoked the wrath of some of the world's most perfect people. Washington Post, 10.
Cite this article
Xiong,A. (2024). Analysis of NBA player salary based on multiple linear regression model. Theoretical and Natural Science,51,206-213.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of CONF-MPCS 2024 Workshop: Quantum Machine Learning: Bridging Quantum Physics and Computational Simulations
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).