All charts used in this post permit interactivity of some sort. These were created using rCharts and animint. These in turn rely on a set of other libraries such as ggplot2 and polycharts JS. Thanks to Toby Dylan Hocking, @timelyportfolio and Ramnath Vaidyanathan for their prompt assistance on charting questions. Code, data, and other files relevant for a replication of this analysis are available at github.
Data on total revenues, total expenses, and subsidies for 228 athletic departments of public universities were provided for the year 2012. An overall rank was also provided to different athletic departments on their finances. The methodology and category explanations USA Today used are available here. Data were collected by USA Today in partnership with Indiana University’s National Sports Journalism Center. In the data analyzed below, Total Revenues for a department include the subsidies it received. An additional variable, Revenues less Expenses was calculated, which was nothing but the Total Revenues less Total Expenses. The complete data have been tabulated below. The columns can be sorted based on the value.
Now, let’s visualize the data using a heat map. Along the columns are different schools (you don’t see all labels but if you hover over the rectangle tiles, you should see the school identified). Values for expenses, revenues and other variables are scaled to a 0 to 1 scale, thereby permitting a visualization of all variables in a better manner. Higher the dollar amount, higher the intensity of red and the scaled value will be closer to 1. Lower the dollar value, lower the intensity of the red— or white if dollar amount is negligible. To verify, you can hover over the most intense red parts of the Total Subsidy row and you should find Nevada-Las Vegas with a scaled value of 1. It is because it gets the highest subsidy.
The heat map clearly shows that most schools receive subsidy to some degree; the row for Total Revenue indicates that that there are very few schools that generate very high amounts of revenue and as a result, the Revenue less Expenses row is generally a shade of faded red for most schools, save for a handful of those with very high values.
Relationship between Total Subsidy, Total Revenue, Total Expenses and Revenue Less Expenses
The correlation matrix given below provides this information. It tells you how related two variables are to each other. The correlation coefficient, a measure of the relationship, can vary from –1 through +1. A negative coefficient indicates indicates a negative relationship (in red or a shade of red) between the two variables, whereas a positive coefficient (in blue or a shade of blue) denotes a positive relationship (higher values of one variable are associated with higher values of the other variable). You should be able to hover over the tiles to get more information.
There’s a perfect positive correlation between Total Revenue and Total Expenses, and these variables are negatively correlated to the overall rank (higher the revenue, better the rank [with a lower number denoting better performance]). No surprises there. Total Subsidy is not positively related to any other variable.
Groups/Clusters of Athletic Departments
In order to classify different athletic departments into different groups, a statistical technique called k-means clustering was used. Based on few statistical measures, which can be ascertained from the code provided, it was decided that all 228 departments be grouped into 4 different groups. This grouping is done such that Schools with similar characteristics (with respect to total subsidy, total revenue, total expenses and revenue less expenses) are grouped together. Group 1 had 19 schools, Group 2 had 61 schools, Group 3 had 33 schools and the remaining 115 schools belonged to Group 4. Schools are tabulated based on the 4 groups below.
Characteristics of these groups/clusters. The chart below can be used to determine the profile of each of the 4 groups. You can select (box around a group number) and click to find few lines highlighted. This chart gives the mean scores on variables of interest for the 4 groups. Lower the mean score in the group, lesser the dollar amount for that group.
Group 1 (cluster of 19 schools) has the highest mean Total Revenues (approximately $111.48 million), highest mean Total Expenses (approximately $101 million), and highest Revenues less Expenses (over $10 million). It takes the least amount of subsidy (approximately $2.85 million). Contrast Group 1 with Group 4, a cluster of 115 schools, the largest among the four groups. Group 4 has the lowest mean Total Revenue, lowest mean Total Expenses, a subsidy average of approximately $8.3 million dollars, and the lowest Revenue less Expense of $17,550. Group 3 schools, a cluster of 33, taken in an average subsidy amount which is slightly less than Group 4 schools (approximately $7.9 million) but has the second highest mean levels with respect to Total Revenues (approximately 70.34 million), Total Expenses (approximately 69.3 million) , and Revenues less Expenses (little over $1 million) . Lastly, Group 2 schools, who receive the highest average subsidy (approximately $16.9 million) have total revenues of approximately $29 million, expenses of approximately $28.99 million for a Revenue less Expenses amount of about $37,000.
The following set of plots provide the potential to explore relationships between different variables and subsidies and the 4 different groups further. You should be able to hover over either the bars of the bar charts or the points of the scatter plots to get information of the school being hovered over. You should also be able to click on a point or bar to see where that school is positioned on the other charts. The points have been colored based on the membership of the corresponding school in the 4 different groups.
There clearly is a lot of money in NCAA athletics, even though the athletes themselves are not paid a salary. The analysis shows that subsidies are provided to most athletic departments of public schools. Given limited tax-payer dollars and the picture that emerges after juxtaposing other articles on the use of tax-payer funding for sports, a question that begs answers is whether tax-payer dollars are going to the right places.