Abstract

NBA games generate a lot of data, thanks to tracking technologies like SportVU and Second Spectrum. This thesis will used this data to help understand player performance, team strategies, and game outcomes. We use advanced statistics and machine learning techniques to analyze player performance, like how efficiently they shoot (eFG%), their overall impact on the game (PER), and how much of the team's offense they use (USG%). We can also visualize shot charts to see where players tend to shoot from. Looking at team-level data, we can analyze offensive and defensive ratings, how well teams pass the ball, and how many rebounds they grab. We can even analyze different player combinations to see which ones work best. It's important to consider factors like pace of play, home-court advantage, and injuries when analyzing data. These insights help coaches, teams, and media professionals make better decisions and understand the game deeper.

Publication Date

6-2-2026

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Hammou Messtafa

Comments

This thesis has been embargoed. The full-text will be available on or around 4/30/2027.

Campus

RIT Dubai

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