Sources
The sources we used to supplement the primary dataset included many research articles that discussed various topics surrounding our project. Topics ranged from the gender pay gap between the WNBA and the NBA, WNBA attendance, fan preferences and entertainment, and social media engagement. These sources helped give context to the data we were seeing the trends and patterns that it revealed.
The conclusions found in the literature we read connected with the results from our data analysis. We saw the differences between salary, revenue, and percentage of revenue allocated to players in both the sources and the data which strengthened our project.
Our team decided to use data information from WNBA and NBA player comparisons; league salaries from Kaggle.com to conduct research on the wage gap disparities between WNBA and eight other male dominated athletic leagues. Our main focus was to expand our perspectives on characteristic factors that caused this variation in the financial pay gap, and further investigate whether other societal and structural factors influence overall success in these athletic leagues. Ultimately, we aim to understand how this pay gap is influenced by financial factors, societal attitudes and values, and systemic inequality.
Processing
The dataset used in this project was found on Kaggle (Kaggle.com). The “WNBA and NBA player comparisons; league salaries” data is very clean and ready to use for analysis and visualization. This made it easy to immediately begin our project. There are nine variables available to analyze. The dataset also included a model using log revenue and log salary, but for simplicity and application to our project, we did not utilize it. After acquiring the dataset, we downloaded it as a .csv file to be able to access it on different software and platforms.
We started by creating visualizations on Tableau, a data visualization software. These visualizations include scatterplots, maps, and bar charts comparing variables such as total annual revenue vs. percentage of revenue allocated to players as well as ordering the leagues by their respective average player salary. It can be said that the dataset lacks certain variables that could explain the quantitative variables included. For example, total annual revenue could be split into viewership, ticket sales, merchandise sales, partnerships, etc. Secondly, the dataset only includes data points from a certain point in time which hinders the ability to compare trends in the data from year to year. An interactive timeline of notable dates in the history of the WNBA was created using Timeline JS.
Presentation
Our project’s website uses a HumSpace domain. We were granted a domain by UCLA, and its design was constructed using WordPress. We sought to replicate the WNBA’s use of its bold orange theme throughout the website along with sharp contrasts of black and white. This theme not only acknowledges the spefic league we are analyzing and comparing to others, but it also effectively highlights the significant aspects of each page of the website for readers. On each section of the website, there is an accompanying image illustrating the dynamic and intense nature of the sport. The cohesiveness of the website is important as it creates a holistic image and sense of unity, as well as tying together each aspect of the project while readers navigate from page to page.
Meet Our Team
Our group consists of members with various skills and specialities to carry out reliable and innovative data analysis, ranging from statisticians to designers to editors.
Henry Emerson
Project Manager
Psychology 2025
Sydnie Yu
Web Designer
Cognitive Science 2026
Maclean Brown
Editor
Business Economics 2026
Jeimy Rodriguez-Solano
Content Developer
Psychology 2027
Hannah Um
Data Specialist
Statistics and Data Science 2026
Francis Chan
Data Visualization Specialist
Statistics and Data Science 2025
Acknowledgments
We’d like to give a special thank you to our TA, Julia Stoddard, for supporting us throughout the course of this project.
Mac Brown – About page, Data Critique, Home page, Map of the United States Displaying States With/Without NBA/WNBA Teams Visualization, Bar chart comparing average player salary across the 8 sports leagues chart, and Scatter plot of Total Revenue and % Revenue Going to Players scatter plot.
Henry Emerson – Contributed to the writing of the Data Critique and Narrative. Design/Layout of Narrative page. Provided technical assistance with linking our TimelineJS to WordPress. As project manager, I assisted the team by narrowing the scope of our project to our final topic, and fostered team collaboration and communication.
Hannah Um – About page, Conclusion, Narrative, Allocation of Total Revenue Across Leagues, Cartogram of Sports Leagues by Revenue, Maturation, and Player Salaries, Average Salaries of Players Across Leagues. Contributed to the formatting of the Data Critique Page.
Sydnie Yu – About page, Narrative, Sources, overall design and layout of website
Francis Chan – About page, Narrative, Revenue Per Player and Percentage of Revenue Allocated to Players Across Major Sports Leagues, created and designed TimelineJS, Layout of Website.
Jeimy Rodriguez-Solano- Contributed to the writing of Narrative and About Page, Percentage of Total Revenue Directly Given to Players in WNBA and NBA Leagues.