Analysis of Different Personnel Factors to Develop A Predictive Index to Make Optimal Decisions by Basketball Teams

International Journal of Economics and Management Studies

© 2021 by SSRG - IJEMS Journal

Volume 8 Issue 10

Year of Publication : 2021

Authors : Tanush Soni

10.14445/23939125/IJEMS-V8I10P111

How to Cite?

Tanush Soni, "Analysis of Different Personnel Factors to Develop A Predictive Index to Make Optimal Decisions by Basketball Teams," SSRG International Journal of Economics and Management Studies, vol. 8,  no. 10, pp. 82-87, 2021. Crossref, https://doi.org/10.14445/23939125/IJEMS-V8I10P111

Abstract:

The goal of teams in the National Basketball Association (NBA) is to win as much as possible. Several studies have been conducted to analyze the factors that go into making a sports team successful. Studies have mainly looked at a team’s playbook strategy—the coach’s strategy. The next step is to analyze personnel decisions— the strategy of the general manager. Through a mixed-method research approach, the research study analyzes data with respect to 10 different ‘personnel’ factors to develop a predictive index that could help basketball teams make optimal personnel decisions, particularly with regards to the coaching staff and player composition. Under the quantitative approach, a multiple regression analysis was used to identify the factors that exerted an impact on the performance of the NBA team in the 2019-2020 season and measure the extent of their impact on the performance of the team. After analyzing the factors, the strongest indicators of a team’s success seem to relate to the star players on the team. A basketball team looking to reach the top, according to this model, would benefit from acquiring the best players available and playing them frequently in order to win, even at the cost of overall team composition.

Keywords:

NBA, Basketball, Factors contributing to success, winning formula, predictive index.

References:

[1] Al-Amine, R., Quantifying the Contribution of NBA Coaches using Fixed Effects, Towards Data Science, https://towardsdatascience.com/quantifying-the-contribution-of-nba-coaches-using-fixed-effects-56f77f22153a, (2020, September 29).
[2] Bell, D. J., Nolan, J., &Agard, D. (n.d.), Estimating NBA Playoff Success Probabilities [Infographic], Northern Kentucky University. https://www.nku.edu/~nolanj1/SIS/Poster_Bell.pdf.
[3] Grossmann, M., Dietl, H., & Lang, M. (n.d.), Competitive Balance and Revenue Sharing in Sports Leagues with Utility-Maximizing Teams, Journal of Sports Economics 12(3) (2009) 284-308.
[4] Hatcher, T., &Seeborg, M., What is the Superstar Effect for an NBA
Franchise?,26th Annual JWP Conference, Illinois Wesleyan University, Digital Commons @ IWU (2015, April 18).
[5] Kubatko, J., Oliver, D., Pelton, K., & Rosenbaum, D. T., A starting point for analyzing basketball statistics, Journal of Quantitative Analysis in Sports, 3(3) (2007).
[6] Oliver, D., Basketball on paper: Rules and tools for performance analysis, Potomac Books (2011).
[7] Prinz, A. L., Indirect evolution and aggregate-taking behavior in a football league: Utility maximization, profit maximization, and success, Games, 10(2) (2019) 22.
[8] Silva, Rocha Da J. V., & Rodrigues, P. C., The three eras of the NBA regular seasons: Historical trend and success factors, Journal of Sports Analytics, Pre-press, (2021) 1-13.