A Longitudinal Exploratory Analysis of NBA Player Characteristics and Performance (1996–2023)

Rahmatia Rahmatia; Nurul  Inayah; Aulia Darnilasari

Authors

Rahmatia Rahmatia Universitas Sulawesi Barat Author
Nurul Inayah Universitas Sulawesi Barat Author
Aulia Darnilasari Universitas Kristen Indonesia Paulus Author

Keywords:

NBA, basketball analytics, player performance, exploratory data analysis, sports data mining

Abstract

The National Basketball Association (NBA) has evolved into a global sports institution and a rich source of structured data for performance analytics. This study aims to explore historical trends and player attributes in the NBA from the 1996–1997 to the 2022–2023 seasons using the publicly available NBA Historical Player Data dataset. Through an exploratory data analysis (EDA) approach implemented in Python, the research examines physical characteristics, national and collegiate origins, and player scoring efficiency based on true shooting percentage (TS%). The dataset comprises 12,844 player-season entries with 21 variables, including demographic, anthropometric, and statistical performance indicators. Descriptive statistics reveal that NBA players averaged 200.5 cm in height, 100.3 kg in weight, and 27.0 years in age. The United States remained the dominant country of origin (83.5%), while colleges such as Kentucky and Duke consistently supplied the most players. Among players scoring ≥10 points per game, Robert Williams III and Rudy Gobert emerged as the most efficient based on TS%. The results underscore shifts in player evaluation from traditional metrics toward efficiency and physical specialization, in line with modern sports analytics trends. This study contributes to the understanding of player evolution in professional basketball and highlights the analytical value of open sports datasets for longitudinal insights.

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