Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Rigdon, Steve

Editorial Board Member: Glickman, PhD, Mark

4 Issues per year

CiteScore 2016: 0.44

SCImago Journal Rank (SJR) 2015: 0.288
Source Normalized Impact per Paper (SNIP) 2015: 0.358

See all formats and pricing
More options …

Are the "Four Factors" Indicators of One Factor? An Application of Structural Equation Modeling Methodology to NBA Data in Prediction of Winning Percentage

Tarek Baghal
Published Online: 2012-03-12 | DOI: https://doi.org/10.1515/1559-0410.1355

Significant work has gone into the development of team and individual statistics in the NBA; for example, the team measures of the “Four Factors.” Less work has been conducted using multivariate analyses of these metrics, including identifying possible new statistical techniques to analyze these data. In particular, this research examines the feasibility of using structural equation modeling (SEM) for multivariate analyses of NBA Four Factors data. SEM consists of both confirmatory factor analysis (CFA) and path modeling. Before SEM is employed, this research first examines the effects of offensive and defensive Four Factors in a linear regression model, expanding previous research and providing a baseline for the SEM. In doing so, the data show the importance of effective field goal percentage. Next, structural equation modeling is employed. The CFA finds that offensive Four Factors are indicators of a single latent factor, labeled “offensive quality.” The “defensive quality” latent factor is estimable when replacing opposing teams’ free throw rate with steals per possession. The SEM is extended to regress winning percentage on latent offensive and defensive quality as well as salary. Salary is an important and often overlooked part of multivariate models examining team statistics, but it is easily incorporated in SEM. The explained variance for the regression in the SEM is similar to that of the linear regression model and indicates the importance of both offensive and defensive quality, with offensive quality having a larger effect. Team salaries are related to offensive quality, but not defensive quality or winning. As such, a second structural equation model is proposed where the effect of salary on winning is mediated by its relationship with offensive and defensive quality. Since salary is related to offensive quality but not defensive quality, and offensive quality is more important to winning percentage, this suggests that money spent is done so for offensive performance and affects winning through the performance paid for. These results suggest potential team strategies, as well as the applicability of SEM to sports analytics, not only to NBA data, but to other sports data as well.

Keywords: NBA; Four Factors; salary; structural equation modeling

About the article

Published Online: 2012-03-12

Citation Information: Journal of Quantitative Analysis in Sports, ISSN (Online) 1559-0410, DOI: https://doi.org/10.1515/1559-0410.1355.

Export Citation

©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston. Copyright Clearance Center

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Matthew van Bommel and Luke Bornn
Data Mining and Knowledge Discovery, 2017

Comments (0)

Please log in or register to comment.
Log in