High Impact Practices Student Showcase Spring 2026

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Course Code

STA

Course Number

4164

Faculty/Instructor

Professor Nathaniel Simone

Faculty/Instructor Email

nathaniel.simone@ucf.edu

About the Author

Royce Reeves is a Statistics major at the University of Central Florida and holds a Bachelor of Science in Business Administration (BSBA) in Accounting.

Thirumukelan Senthilkumar is a Data Science major and a freshman at the University of Central Florida.

Alsion Tafa is a senior Mathematics major on the Data Analysis track at the University of Central Florida.

The authors extend a special thanks to instructor Nathaniel Simone and to each of the group member for the collaboration on this work.

Abstract, Summary, or Creative Statement

This study examines the relationship between team performance metrics and win percentage in the 2023–24 NBA season. Using team level data sourced from NBA.com and Kaggle, we analyzed variables including shooting efficiency, rebounds, turnovers, and plus/minus. Exploratory data analysis was conducted to assess relationships and address potential collinearity. Multiple linear regression was used to model win percentage, followed by model selection to identify the most impactful predictors. The final model explains approximately 90% of the variation in team success and identifies plus/minus, three-point percentage, turnovers, and rebounds as significant factors. Results indicate that overall team performance three-point shooting, and limiting turnovers are main factors of winning. These findings show the value of statistical modeling and provide insight into which aspects of team performance most statistically contribute to success.

Keywords

NBA; Win Rate Prediction; 2023-2024 Season; Statistical Modeling; Stepwise Regression; Team Performance Metrics; Three-Point Percentage; Turnovers; Rebounds; Plus/Minus; Sports Analytics; Exploratory Data Analysis

THE SKILLS THAT WIN CHAMPIONSHIPS: AN NBA TEAM WINRATE PREDICTION ANALYSIS (2023-2024) ​


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