Factors Driving Winning Possibilities In the Nba Playoffs – An Empirical Study With Data From the National Basketball Association

Abstract

In order to assist team owners, managers, and coaches in building a roster, this thesis explores the potential applications of data analytics in the sports entertainment industry. Specifically, it looks at the relationship between a team's offensive and defensive performances, which may be crucial in a team's quest for an NBA playoff championship. Regression and correlation analysis are two statistical analysis techniques that are used to classify teams based on similarities in offensive and defensive statistics from a comprehensive dataset covering 12 years of NBA playoffs. The study examines the relative significance of variables like offensive efficiency, defensive strength, shooting accuracy, rebounding, turnovers, and different team-level attributes using sophisticated regression techniques and correlation analysis. The study employs multiple regression analysis to identify significant predictors of NBA championship success, with offensive and defensive performance metrics serving as independent variables and playoff wins as the dependent variable. Additionally, correlation analysis is utilized to examine the strength and direction of the relationships between offensive/defensive statistics and playoff wins. Significant predictors identified through regression analysis provide insights into the factors driving NBA championship victories, with correlations shedding light on the relative importance of offensive and defensive performance metrics. This thesis adds to the current body of knowledge by giving a data-driven understanding of the intricate interplay of elements that drives postseason performance, as well as helpful insights for coaches, front office personnel, and basketball commentators.