Connecting Expenditures to Athletic and Academic Outcomes Article posted on June 3, 2013

By: Dennis A. Kramer II

Part Four of a four-part, fiscal efficiencies series:

Rising athletic costs and increased media scrutiny of athletic expenditures has created an environment where athletic leaders are held ever more accountable for athletic operations. In addition, the pressure to succeed both athletically and academically creates a high-stakes environment where the inefficient use of funds has lasting effects.

When making strategic decisions in regard to athletic spending and resource allocation, understanding and quantifying the expected output and outcome is vital for an athletic department. While many aspects of an AD’s job are often described as more art than science, in areas of fiscal responsibility there is little room for best guesses. This article is designed to spur dialogue and provide a frame for the how athletic directors and senior staff measure investment and return—and make better decisions that help to increase revenue.

In a recent series, Winthrop Intelligence examined the financial efficiency of various sports individually (Football, Basketball, and Olympic Sports); these articles will provide a more holistic view of athletic-based experiences (success in the classroom and on athletic fields). By combining all sports, we hope to outline the trade-offs within athletic departments, where the reality of daily operation means access to finite monetary resources.

Overview of the Athletic Expenditure Comparisons:

The primary barrier for comparing athletic expenditures across sports is the differentiation associated with cost of operations and management. To overcome this, this article standardized sport-level expenditures using the traditional z-score approach.

By standardizing across sports, the new metric takes into consideration the average line-item expenditure within each sports, and the spread (standard deviation) of the various schools included. Each line-item is then put on a scale that is normally between -3 and 3. This process allows for comparison across sports when looking at sport-level outcomes. The table below provides an overview for average sport-level expenditure and the standard deviation—important when interpreting the results for this article.

Table 1: 2011 Sport-Level Total Expenditures Means and Standard Deviations

NCAA Sports Average Total Sport Expenditures Standard Deviation
FBS Football $9,228,564.00 $7,824,647.00
Men’s Basketball $2,771,286.00 $2,410,617.00
Men’s Hockey $2,207,823.00 $1,061,171.00
Women’s Basketball $1,594,849.00 $1,071,338.00
Women’s Hockey $1,383,508.00 $657,515.40
Men’s Baseball $1,045,272.00 $725,794.10
Men’s Lacrosse $832,041.60 $384,341.70
Women’s Volleyball $739,301.00 $394,943.60
Women’s Lacrosse $720,483.60 $347,119.00
Women’s Soccer $716,155.80 $390,393.00
Softball $704,399.80 $365,672.90
Women’s Field Hockey $686,304.60 $225,104.00
Men’s Soccer $621,229.90 $300,993.20
Women’s Water Polo $470,531.90 $201,404.60
Men’s Volleyball $456,555.00 $208,039.20

Athletic Spending & Winning Percentage:

Season winning percentage remains the predominant sport-level measure of athletic success. In connecting spending to athletic success, this article examines the impact of the resource allocation on the sports mention in Table 1. Limiting the sports to those mentioned above was purposeful, in that these contained annually reported winning percentages through the NCAA’s statistical database.

Table 2 breaks down the impact of total sport expenditures (standardized) on increases in winning percentage. Across all sports, a one standard deviation increase in total sport spending is predicted to increase winning percentage in a given season by almost 6%. In real terms, to increase your football winning percentage by 4.5%, it is predicted that an additional $7.8 million will need to be spent, holding constant opponent competition and other controlling factors.

In comparison, to increase men’s basketball winning percentage by 5.8%, an estimated $2.4 million more in total spending is needed per year, while a 7.1% increase in women’s basketball is estimated to cost slightly over $1 million. The results of this analysis illustrate the differentiated costs associated with increased athletic success, and begins to tie real dollars to such decisions. These metrics are important, for example, when deciding if increasing football winning percentage by 4.5% for $7.8 million is a better investment than allocating resources to the women’s volleyball team (25% of that $7.8 would produce a predicted increase in by 40%).

Table 2: Impact of Standardized Total Athletic Spending on Winning Percentage (2009 – 2011)

Sport Name Impact Coefficient Standard Error P-Value
All Sports 5.911 2.860 0.005
Men’s Football 4.540 2.005 0.036
Men’s Basketball 5.768 2.158 0.046
Women’s Basketball 7.163 1.628 0.000
Women’s Soccer 2.982 2.180 0.175
Men’s Soccer 6.794 2.268 0.005
Women’s Volleyball 7.999 1.603 0.000

Understanding the impact of the total athletic expenditures on sport-level athletic performance only provides a macro-level view of athletics. Looking at the various budgetary line-items—and their impact on sport performance—however, provides further information for athletic department decision makers.

Table 3 provides an overview of largest budgetary line-items and their impact on sport-level winning percentage. Results illustrate that contrary to popular belief Athletic Aid, Team Equipment, and Prior Year’s Recruiting expenditures do not significantly impact success across all sports for the current year. The lack of significance for recruiting was surprising; however, past research (by Kramer) documents that recruiting expenditures take full effect after two to three years of expending on the sport.

Across all sports, expenditures on coaches’ salaries had a positive impact on the winning percentage, where a one standard deviation increase in total coaches’ compensation is equated with an almost 8% increase in winning percentage. The actual amount of invested dollars differs by sport, where an 8% increase in winning percentage from total coaches’ salary would cost $2.3 million in football, $1.08 million in men’s basketball, $248,000 in men’s baseball, and $115,000 in women’s volleyball.

Assistant coaches appear to play a larger role in dictating winning percentage increases when compared any other budgetary item. In addition, team travel and game experience also plays a significant role in increasing winning percentage across all sports.

Table 3: Impact of Standardized Line-Item Expenditure on Winning Percentage (All Sports: 2009 – 2011)

Sport Name Impact Coefficient Standard Error P-Value
Total Athletic Expenditures 5.911 2.860 0.005
Athletic Aid 3.048 2.047 0.137
Total Coaches’ Salary 7.930 1.516 0.000
Head Coaches’ Salary 5.980 1.230 0.000
Assistant Coaches’ Salary 7.880 1.959 0.000
Team Travel 6.076 1.178 0.000
Equipment 0.820 1.315 0.533
Game Expenses 5.238 0.763 0.000
Recruiting (Prior Year) -0.647 1.452 0.656

Athletic Spending & Academic Success:

While athletic success is a key component of athletic efficiency, the academic excellence of student athletes is at the core of the athletic department mission. In taking the same approach as looking at the impact on winning percentage, we examined resource allocation and the impact on a team’s APR score.

In analyzing the impact of total sport expenditures on APR scores, one standardized unit increase in total athletic expenditures positively impacted APR scores across all sports by 2.64 points (only Men’s Soccer did not have a significant relationship of the eight sports highlighted in Table 4). In particular, men’s basketball has a predicted 11.3 APR point increase, and men’s football has an 8.6 APR point increase for every additional standardized unit of fiscal support. Examples of the impact on various sports can be found in Table 4 below.

Table 4: Impact of Standardized Total Athletic Spending on APR (2009 – 2011)

Sport Name Impact Coefficient Standard Error P-Value
All Sports 2.644 0.911 0.004
Men’s Football 8.588 1.453 0.000
Men’s Basketball 11.314 1.941 0.000
Women’s Basketball 7.502 1.265 0.000
Women’s Soccer 5.271 1.596 0.001
Men’s Soccer 3.890 2.336 0.099
Baseball 6.822 2.423 0.005
Softball 6.272 1.744 0.000
Women’s Volleyball 6.113 1.531 0.000

As with winning percentage, understanding total expenditures provides a nice overview; however, understanding budgetary allocations provides targeted assistance for athletic decision-makers. Unlike winning percentage, where a number of the budgetary items played a significant role in increasing athletic outcomes, increasing APR scores appears to be only directly linked to expenditures in terms of head coaches.

Similar to winning percentage, the investment amount differs by sport; however, a $1.25 million increase in head coaches salary is predicted to increase APR scores by 3.3 points in football, $884,410 in men’s basketball, $164,000 in men’s baseball, and $62,000 in women’s volleyball. With APR playing a prominent role in access to men’s and women’s NCAA championships, the investment (while in some cases large) could yield substantial net revenues, as access to championships is directly related to the increases in revenue.

Table 5: Impact of Standardized Line-Item Expenditure on APR (All Sports: 2009 – 2011)

Sport Name Impact Coefficient Standard Error P-Value
Total Athletic Expenditures 2.644 0.911 0.004
Athletic Aid 0.664 1.206 0.582
Total Coaches’ Salary 3.345 0.873 0.000
Head Coaches’ Salary 2.049 0.911 0.025
Assistant Coaches’ Salary 0.251 1.073 0.815
Team Travel 1.507 0.898 0.094
Equipment 1.406 0.859 0.102
Game Expenses 0.918 0.781 0.240
Recruiting (Prior Year) -0.752 0.874 0.389

Sport-Level Efficiency:

Results presented in Table 6 provide the sport-level average technical efficiency as it relates to academic success. While the actual technical efficiency output is of less importance, the relative rank of sports provides a good overview (across three years) of team allocating resources efficiently in order to increase their academic success.

Four men’s sports fall below the all-sports average (soccer, baseball, football, and basketball), with men’s basketball being the less efficient sport in terms of athletic success. Women’s Lacrosse and Field Hockey produced the most technically efficient allocations, relative to academic success. Table 6 provides a more detailed ranking of sports by technical efficiency.

Table 6: Sport Level APR Technical Efficiency (2009-2011)

Sport Name Sport-Level Technical Efficiency
Women’s Lacrosse 0.9893
Field Hockey 0.9864
Men’s Volleyball 0.9844
Men’s Hockey 0.9835
Women’s Volleyball 0.9784
Women’s Soccer 0.9768
Softball 0.9740
Men’s Lacrosse 0.9718
Women’s Basketball 0.9667
All Sports 0.9646
Men’s Soccer 0.9629
Baseball 0.9579
Football (FBS) 0.9455
Men’s Basketball 0.9416

Summary of Concluding Thoughts:

Analyzing sport-level expenditure patterns to maximize both athletic and academic success is a key opportunity for athletic decision-makers. Understanding the trade-offs that exists between revenue generation and athletic wins—and the costs to obtain those wins—will allow athletic departments to allocate their limited resources in a more efficient manner.

Finally, having access to analyses that isolate the impact of various budgetary items will assist athletic decision-makers in producing the largest returns on their investments. In an environment where athletic expenditures are highly scrutinized, it is imperative that athletic leaders have access to tools that allow them evaluate options that effectively and efficiently allocate resources to produce the largest gains.

Methodological Notes: The analyses conducted within Tables 2 – 5 utilized a non-linear quadratic regression based approached with impeded school level fixed effects. Both academic and athletic covariates were included to ensure appropriate estimation parameters. Covariates were included based on current academic literature on factors impact athletic and academic success.

Estimates found within Table 6 were produced using a trans-logged stochastic frontier analysis. Through this analysis both cost and production functions were estimated and technical efficiency derived through both models. The utilization of both approaches allows for triangulation of the results. Ultimately, the cost approach was presented within this table.

For a complete methodological explanation, please contact the author.