Our dataset comes from Kaggle.com, an online community where users can upload datasets and various models based on a variety of topics. Our dataset, “WNBA and NBA player comparisons; league salaries” consists of data on eight professional sports leagues from the United States and abroad. Although the title only says WNBA and NBA, this specific part of the dataset includes many different sports. Other tabs on this dataset strictly compare the NBA and WNBA with player statistics and performances. The specifics of this dataset include founding year, years of maturation, number of teams, players per team, total players, total annual revenue, revenue per player, average player salary, and the percent of revenue going to players. This dataset can help quantify the trends and performances of the WNBA against those of its male counterparts. It is easy to infer that the WNBA underperforms and is not compensated as exceedingly as the NFL, NBA, etc., but this dataset can help identify the deeper trends and compare other numbers outside of revenue and popularity. Being able to compare the percentage of revenue going to players, players per team, league maturation, and more can help tell a different story. 

The “WNBA and NBA player comparisons; league salaries” database is primarily sourced from the Wikipedia page “List of professional sports leagues by revenue,” officially published league-specific evaluations, media publications, and possibly data scraping from statistics platforms. To gain insight into potential biases or inaccuracies in the dataset, it is possible to investigate more about the Wikipedia page used to source the revenue data and perhaps contact the publisher of the dataset, Josh Strupp. Asking the publisher could help us gain more insight into where he sourced specific aspects of the data outside of the revenue columns. If the data was primarily derived from media publications, there could be potential bias in how the media covers women’s basketball in comparison to other sectors such as the NBA. 

It is hard to quantify many things in sports and thus, there are many things that this dataset cannot reveal about popularity, biases, and societal preferences. The dataset lacks qualitative context, as there are many external factors that affect a league’s performance and popularity, such as sexism, politics, storylines, and the likability of individual stars. For example, when it comes to women’s sports, many men refuse to watch them because they think that they are less entertaining or that their talent is inferior to that of the male equivalent. This kind of bias and utter resistance to being introduced to women’s sports heavily affects the WNBA and other female sports leagues’ revenue and thus player salaries because there is less viewership, ticket sales, and, more importantly, new viewers tuning in. 

Furthermore, when we focus on quantitative data, at times our perception and narrative of measuring the success and fairness (revenue, salaries, etc.) of a league can become skewed and may lead us to overlook other qualitative factors. Even with quantitative factors, the variable “total annual revenue” does not explain trends in attendance, viewership, or social media engagement. The Women’s National Basketball Association was founded in 1997. This makes the WNBA one of the 15 youngest professional sports leagues in the United States. When put in that context, the numbers that are available to be analyzed in this dataset become more significant and intriguing. There could be trends to be analyzed that are not available in this dataset, which we must keep in mind. This dataset only gives values at a point in time and not over several years which may help explain which leagues are trending up or down. 

In conclusion, the database “WNBA and NBA player comparisons; league salaries” offers valuable insights into the WNBA and other male professional sports leagues, allowing us to center our project around a detailed analysis of how fair these leagues are for its players and if the WNBA is on a positive trajectory in order to reach the heights that more established leagues like the National Football League and National Basketball Association are on. However, it is important to recognize the dataset’s limitations such as the absence of qualitative data or potential biases to truly draw accurate conclusions from our findings.