Our team used a publicly available, scraped CSV dataset found on GitHub through a Google search. The dataset, titled raw_player_data.csv, includes information on NBA players from 1990 to 2024, such as name, country of origin, team, all-star appearances, and salary. While the dataset is not officially sourced from the NBA, it provided a strong foundation for analyzing patterns of representation, recognition, and equity for international players over time.
This project examines how barriers and inequalities for international NBA players have changed, and whether performance is fairly recognized regardless of nationality. Rather than just documenting the globalization of the NBA, we focus on when shifts happened, why they happened, and how international players are treated once they make it to the league.
We guided our analysis with the following questions:
1. What are the historical trends of international player representation in the NBA?
2. What factors have been most influential in addressing international barriers?
3. How well do international players perform compared to U.S.-born players?
4. Are international players recognized and compensated fairly (performance-driven) compared to U.S.-born players?
A big part of our research was not just about answering these questions but also about exploring how these aspects of inequality have changed over time and the historical events that have impacted them.
To supplement the dataset, we incorporated secondary sources such as The Journal of Sports Economics, ESPN, and The Athletic, which helped contextualize NBA recruitment, global expansion strategies, and shifts in media representation. We also drew from Data Feminism (Klein & D’Ignazio) to interrogate how data collection and visibility reflect power dynamics, including who gets counted, who gets seen, and whose success gets recognized.
We began by downloading the dataset as a CSV and processing it using Python. In this phase, we filtered for years from 1990 onward, standardized player and team names, and grouped players by region (e.g., Europe, Africa, Latin America). We created calculated fields for whether a player was “International” (i.e., birth country not equal to the U.S.), grouped players by the decade in which they entered the league, and computed metrics such as All-Star selection percentage and career scoring averages. These data transformations allowed us to trace performance, visibility, and entry trends over time.
We exported the cleaned dataset to Excel for formatting and then built our visualizations using Tableau. This included bar charts, scatter plots, and choropleth maps. The visualizations were built with careful consideration of what each chart should communicate and how to reduce noise while maintaining clarity.
For instance, the bar chart showing the number and percentage of international players by decade reveals a steep upward trajectory in both metrics, particularly in the 2000s and 2010s. These trends align with NBA globalization efforts, such as Commissioner David Stern’s global expansion campaigns, Olympic basketball dominance, and cultural exports like Michael Jordan. This growth signals how
NBA-driven initiatives helped open the door to international players.
Our stacked scatter plot comparing career performance and All-Star recognition shows that, by the 2001–2020 period, international players are performing on par with U.S. players and achieving nearly identical All-Star selection rates. In some earlier decades, international players even surpassed U.S. players in All-Star recognition, revealing that recognition is increasingly tied to performance, not
nationality.
Finally, our series of choropleth maps shows the geographical expansion of international NBA players over time. By mapping international player counts by country for the eras 1945–1980, 1981–2000, and 2001–2020, we documented a clear increase in both the number of represented countries and the volume of players from each. The earliest map featured only 14 countries, while by 2001–2020, the number had grown to 71, and 85 total by 2024.
We layered historical context into our map analysis to support our narrative. For example:
- Canada leads in international representation due to proximity and basketball’s popularity there.
- South America’s growth aligns with the post-2016 Olympic boom and increased exposure to NBA culture and talent.
- Africa’s growth corresponds with NBA development investments (e.g., Basketball Without Borders, BAL).
- China’s rise in representation is linked to the Yao Ming era and the NBA’s expanding media and commercial reach in Asia.
By removing the U.S. from the map, we highlighted these international developments without distortion, allowing a clearer visualization of how access, opportunity, and visibility have expanded globally
