Measuring Predictors of Winning in NCAA Basketball

Stella D. Tomasi (), Banghee So () and Austin Raymond
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Stella D. Tomasi: Towson University
Banghee So: Towson University
Austin Raymond: Deloitte Consulting LLP

A chapter in Handbook of Visual, Experimental and Computational Mathematics, 2026, pp 1301-1317 from Springer

Abstract: Abstract Among basketball fans, the question of whether a great offense or great defense is more important for winning has been an age-old debate. This debate also begs the follow-up question of which of these offensive and defensive metrics are the most important for winning basketball games. The aim of this research project is to determine which metrics are the most important for winning games, and whether better offensive or defensive metrics are a greater overall indicator of winning games, as well as championships, in modern college basketball. The sample consisted of data from the 2013–2021 NCAA Division I men’s college basketball seasons. The metrics measured to predict winning percentage included eight offensive and seven defensive variables. A series of linear regression analyses were performed for each variable in relation to team winning percentage. The results confirmed that a strong offense is the most predictive indicator for team success. Stepwise multiple regression analysis was performed in relation to games won to assess which variables were the best subset for winning games. The results confirmed that the most viable subset consisted of mainly offensive variables. Stepwise logistic regression analysis was performed in relation to championship status to assess which variables were the best subset for winning championships. These results suggest that both offensive and defensive efficiency metrics play critical roles in determining championship outcomes. However, offensive metrics appear to have a slightly greater influence on championship success.

Keywords: Regression analysis; Winning percentage; Games won; Championship status (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-16368-4_45

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