Presenter(s)

Jonah Mergler

Comments

9:00-10:15, Kennedy Union Ballroom

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Description

Growing up in a family of devoted Flyers fans, I developed a deep appreciation for basketball, especially after watching Obi Toppin. The intensity, strategic plays, and constant innovation of the game fueled my passion. When choosing my capstone project, incorporating basketball was an obvious choice. Since college basketball was set to end before my presentation at Stander Symposium, I decided to focus on the NBA, where data is more accessible and player rosters remain stable for longer periods.This project utilizes machine learning and data analytics to predict the 2025 NBA champion by analyzing the last 20 years of team statistics alongside this season’s data. I compiled a dataset featuring key performance metrics, including wins, losses, field goal percentages, rebounds (offensive, defensive, and total), and steals, both for and against teams. Using this data, I developed predictive models to assess each team's likelihood of winning the championship.To classify potential champions, I employed supervised learning techniques such as logistic regressions, LDA, QDA, KNN, random forests, and gradient boosting. The model was trained and validated using historical NBA data spanning two decades. Additionally, I applied various feature selection techniques, including forward selection and LASSO, alongside in-depth exploratory data analysis (EDA) to determine the most significant predictors of championship success.This project aims to offer fans, analysts, and sports bettors a data-driven approach to forecasting the NBA champion. My findings highlight the power of machine learning in sports prediction, demonstrating how data analytics can uncover patterns that may not be immediately apparent through traditional analysis.

Publication Date

4-23-2025

Project Designation

Capstone Project

Primary Advisor

Gayan J. Warahena Liyanage

Primary Advisor's Department

Mathematics

Keywords

Stander Symposium, College of Arts and Sciences

Institutional Learning Goals

Practical Wisdom

Numbers Don't Lie: Forecasting the NBA Champion with Machine Learning

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