My research paper examines the game-related statistics that distinguish winning and losing teams in
Region X, a men's college basketball league in Atlanta that includes Division I, II, and III schools. While
previous research has analyzed NCAA, NBA, and youth basketball, Region X remains an underexplored
area in basketball analytics. My paper aims to fill that gap by identifying key statistical factors that
contribute to winning. Using descriptive statistics (means and standard deviations), I will summarize
performance trends. Then, through comparative analysis (independent t-tests or one-way ANOVA), I will
determine which statistics show significant differences between winning and losing teams. Finally, I will
apply discriminant analysis to identify the most influential performance indicators. If needed, a
classification and regression tree (CRT) model may serve as an alternative approach. The findings of my
research paper could benefit coaches and players in Region X and other college leagues by highlighting
which statistical factors—such as field goal percentage, rebounds, or assists—are most predictive of
success. My research contributes to the broader field of basketball analytics by offering insights into how
different competitive levels within a single league impact performance. Potential challenges include
sample size limitations and multicollinearity among variables, but the study aims to provide meaningful
takeaways for improving team strategies and player development at various collegiate levels.