Benchmarking Athletics by Sport-Dom-Complete-Indp-Study (1)

Benchmarking Athletics by Sport A budget-friendly approach to data for athletic administrators

Dominic Esposito – Babson College – Professor George Recck

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Table of Contents Introduction ..................................................................... 3

Background ..................................................................... 4

EADA .............................................................................. 5

Objective ......................................................................... 5

Hypothesis ..................................................................... 6

Data .................................................................................. 7

Methodology .................................................................... 8

Data Source .................................................................... 8

Analysis ......................................................................... 11

Insights ........................................................................... 23

Summary ....................................................................... 24

Applications ................................................................... 25

Impact ........................................................................... 28

Next Steps ..................................................................... 29

Appendix ........................................................................ 30

Raw Data ...................................................................... 31

Exhibits .......................................................................... 32

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Introduction Background College athletics compared across divisions

EADA EADA is an unmined golden treasure for DIII athletics

Objective Using free data to provide a budget-friendly platform

Hypothesis Testing significance of traditional analysis v. new analysis

Background

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The majority of college athletics in the United States are broken into three divisions:

Division I, Division II, and Division III1. These divisions exist so that athletes of all different

skillsets and talents can compete evenly and fairly in college. By design, Division I is

meant to compete at a higher level than Division II, and Division II is meant to compete at

a higher level than Division III. However, regardless of competition level, each division

consists of the same components—sports, athletes, coaches, equipment, recruiting,

revenues, expenses, and of course winning and losing among many other variables.

With all of these variables existing across all divisions, data tracking has become

increasingly popular among administrators2. However, data is just that—data. Collecting

data is hard and understanding it is truly a challenge. In the average day of an

administrator, data analysis just can’t fit in3. Top tier athletic programs though make up

for this analysis elsewhere with full-time employees dedicated to spending thousands of

hours understanding, analyzing, and reporting on the data important to their individual

sport. However, data analysis and technology-enabled solutions are expensive for teams

of all divisions, especially Division III athletics which year-over-year brings in the least

amount of revenue per division and maintains the lowest aggregate budget4. This leaves

administrators unable to understand important data that is specific to each team. While it

is fairly common to keep track of program-wide revenues, expenses, participation, and

other related variables, it is fairly uncommon to benchmark these variables by sport and

compare them to other competing teams because of the cost associated with it.

1 The Role and Value of Intercollegiate Athletics in Universities by Myles Brand 2 Revenues and Expenses of Division III Intercollegiate Athletics Programs. Financial Trends and Relationships – 1997 by Daniel Fulks 3 Field Approaches to Institutional Change: The Evolution of the National Collegiate Athletic Association 1906–1995 by Marvin Washington 4 The Successful College Athletic Program: The New Standard. By John Gerdy

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Most athletic administrators default to comparing data sport-to-sport or program-

wide2. Typical conclusions sound like “men’s baseball wins more than men’s soccer” or

“expenses are higher in men’s soccer than compared to men’s lacrosse.” These

conclusions are very limited because they do not give the full picture. This research sets

out to prove that these traditional methods of athletic analysis result in misleading

conclusions and that new methods of athletic analysis, proposed in this research, result

in insightful conclusions by benchmarking a particular sport to its direct competitors.

Keeping in mind that most administrators don’t have the budget to perform this type of

analysis, each step in this new process needs to be affordable, quick, and accurate.

The first step to creating this new type of analysis is compiling an accurate data set.

EADA (Equity In Athletics Disclosure Act)

The accurate data set identified for this type of benchmarking analysis is the EADA

(Equity in Athletics Disclosure Act) data set. The EADA strives to achieve gender equity

in athletics. It requires co-educational institutions of postsecondary education that

participate in Title IV, Federal Student Financial Assistance Program, and have an

intercollegiate athletic program, to prepare an annual report to the Department of

Education on athletic participation, staffing, and revenues and expenses, categorized by

men's and women's teams3. With strong participation numbers in Title IV, nearly every

institution participates in EADA’s data collection forcing them to give data points on over

200 variables. With over 200 recorded variables across nearly every team in college

2 The Successful College Athletic Program: The New Standard. By John Gerdy 3 The empirical effects of collegiate athletics: an update by Johnathan Orszag

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athletics, EADA is an unmined golden treasure accessible completely free to every

administrator in the world.

Objective

With the low budgets and revenues of Division III athletics, technology-enabled

solutions and data analyses are widening the gap among the divisions. Many Division I

and Division II teams have integrated solutions that enable coaches to track, record,

measure, compare, and quantify real-time data of direct competitors by sport, yet many

DIII schools are stuck with technology from 30 years ago and therefore can only

understand their sports holistically. Thus the gap has made program budgets a

determinant of real success. Therefore, the objective of this research is to level the playing

field in college athletics by providing DIII and low-budget institutions with an easy to

replicate benchmarking analysis of data collected and accessible free by the U.S.

government under the Equity in Athletics Disclosure Act of 1994 and athletic data online.

Hypothesis

This research hypothesizes that:

1. The traditional approach to athletic analysis will not produce statistically

significant correlations:

Comparing athletic data directly sport-to-sport will provoke the following

administrator questions:

- Does spending increase winning percentage program-wide?

- Does spending increase winning percentage per sport?

- Does a pattern exist to winning more?

- Has our revenue increased from an increase in winning?

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The first hypothesis is that the results of answering these questions will test

statistically insignificant.

2. A new approach to athletics will yield statistically significant correlations:

Benchmarking a single sport compared to its direct competitors will provoke the

following administrator questions:

- Are we giving all of our sports a fair chance at competing?

- Do we need to increase spending in a particular sport to compete

with our competition?

- What are other teams doing that have affected them positively?

The second hypothesis is that the results of answering these questions will test

statistically significant.

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Data

Methodology Traditional v. New Methodology

Data Source EADA & winning %’s available freely online

Analysis Multiple tests to best understand the data

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Methodology & Data Sourcing To capture insights from the EADA data and data available freely online, this

research has created two processes to represent both the traditional analysis and the

new analysis. Both processes follow the same initial process—segment, gather,

standardize, and sort. However each process differs on the last three steps—compare,

graph, and analyze the data.

1. Segmenting Phase To test the hypothesis of this research, the administrator of a Division III team first

has to establish teams of relative performance that it wants to compare itself to in order

to gain valuable insights. Babson College competes in the NEWMAC conference and the

nearest competitor of the NEWMAC conference, as decided by Babson College’s director

of athletics Josh MacArthur is the NESCAC conference. Therefore institutions that

compete in similar sports as Babson College and are members of either the NEWMAC or

NESCAC conference will be selected as institutions of relative importance for this

research. Below are the resulting institutions to test the hypothesis:

Babson College (NEWMAC) Hamilton College (NESCAC)

Massachusetts Institute of Technology (NEWMAC)

Connecticut College (NESCAC)

Springfield College (NEWMAC) Middlebury (NESCAC)

Amherst College (NESCAC) Trinity College (NESCAC)

Bates College (NESCAC) Tufts College (NESCAC

Bowdoin College (NESCAC) Wesleyan University (NESCAC)

Colby College (NESCAC) Williams College (NESCAC)

2. Gathering Phase In the gathering phase, all data points for each institution need to be assembled.

There are two data sources that data needs to be gathered from. The first source is from

the EADA online website at http://ope.ed.gov/athletics/. In order to get a good handle on

Figure 1.1 Relevant Performance

Institutions

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trends and correlations, this research will analyze data over four years. Therefore, in order

to gather the data each year will be downloaded by visiting

http://ope.ed.gov/athletics/GetDownloadFile.aspx and then key data points4 are

extracted. A list of the key data points used for each team each year can be found in

Exhibit 1.1 (listed in Appendix).

The process to gather the winning % data points will differ by conference that the

data is gathered for. The process to gather this data is illustrated in Figure 1.2 below.

3. Standardizing Phase Once all of the data has been gathered separated and scrubbed into columns, the

data has to be standardized so that a win in Cross Country or Track is equivalent to a win

in a standard win/lose sport. For this research, a formula in Figure 1.3 has been employed

to standardize Track and Cross Country winning percentage. The formula is based off of

4 Key data points are the data points of the EADA data that overarch a particular sport. For

instance expenses in men’s track and field. These data points can be found in the appendix

NEWMAC TEAMS (Babson, Massachusetts Institute of

Technology, Springfield, Wheaton)

Go to http://www.newmacsports.com/landing/index Choose Sport

under menu item "sports" Choose "History" under menu items

Choose "Year by Year Summary" Select desired Year Look for

"conference record" and manually calculate percent as equation in cell or

look for "conference percent" Enter data into cell

NESCAC TEAMS (Amherst, Bates, Bowdoin, Colby, Conn College,

Middleburry, Trinity, Tufts, Weslyan, Williams)

Go to http://www.nescac.com/landing/index Select sports under

menu item "Men's Sports" or "Women's Sports" in menu items Select

Archive Find the year that you are looking for Select "Standings"

Look for conference winning % Enter data into cell

Figure 1.2 Winning % Gathering Process

http://www.newmacsports.com/landing/index

http://www.nescac.com/landing/index

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placement at the conference meet which has shown to be statistically significant with

conference winning percentage in a season.

Figure 1.3: Track & Cross Country Winning Percentage Formula

4. Sorting Phase

With standardized data in place the sorting can begin. In order to get graphical

representations of the data and compare it side by side the data has to be sorted for

computational programs to understand that it is being compared. Figure 1.4 showcases

an example of the sorting phase using just 2 colleges across 4 years of data.

Figure 1.4: Example of Overall Winning % and Avg. Salary of Assistant Coach

Institution Year Overall

Winning %

Average Salary

Assistant Coach

Babson 2010 58% $6,032

Babson 2011 58% $5,826

Babson 2012 57% $5,696

Babson 2013 56% $7,790

Massachusetts Institute of Technology 2010 72% $6,264




Winning % XC or Track = 𝑇𝑜𝑡𝑎𝑙 𝑝𝑙𝑎𝑐𝑒𝑠 𝑖𝑛 𝐶𝑜𝑛𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑀𝑒𝑒𝑡 – 𝐴𝑐𝑡𝑢𝑎𝑙 𝑃𝑙𝑎𝑐𝑒 𝑖𝑛 𝐶𝑜𝑛𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑀𝑒𝑒𝑡

𝑇𝑜𝑡𝑎𝑙 𝑃𝑙𝑎𝑐𝑒𝑠 𝑖𝑛 𝐶𝑜𝑛𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑀𝑒𝑒𝑡

Where 1st place = 0, 2nd place = 1, 3rd place =2….(Coaching Track and Field

by Mark Guthrie)

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5. Compare, Graph, and Analyze Phase

At this stage the process differs for the traditional analysis as opposed to the new

benchmarking analysis. First this research will explain the process for the traditional

analysis.

A. Traditional Athletic Analysis

Utilizing various sortings and arrangements, the data is manipulated to produce

the graph, chart, or function of choice. Figure 1.5 shows the possibilities of graphing the

data using a timeline analysis.

The below graph is an example of comparing two variables over a 4 year period.

This graph is the result of the spreadsheet in Figure 1.4. The purpose of these graphs is

to find correlations. The next step is to measure the statistical significance of the

correlation.

Figure 1.5: Example of timeline graph as a possible method of analysis

To determine if the relationship is statistically significant or not, this research

evaluates the correlation coefficient. The correlation coefficient is a measure of the

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strength and direction of the linear relationship between two variables. In spreadsheet

software that equation is CORREL(data_y, data_x). The below chart illustrates the

correlation variable of the spreadsheet from Figure 1.4

Figure 1.6: Correlation Coefficient

Institution Year Overall Winning %

Average Salary Assistant Coach

Babson 2010 58% $6,032

Babson 2011 58% $5,826

Babson 2012 57% $5,696

Babson 2013 56% $7,790

Massachusetts Institute of Technology

2010 72% $6,264


2011 71% $7,212


2012 73% $7,592

Correlation Coefficient 0.37

One-tailed Probability .01

Two-tailed Probability .02

Once the correlation coefficient is computed, the next step is to determine if it is significant

or not. To simplify this research the correlation coefficient that will be designated as

significant will be greater than or equal to .7 and less than or equal to -.7. With data

compiled over 4 years and across 14 teams a correlation coefficient of .7 takes into

account the variability of the data. Since the correlation coefficient of .37 in Figure 1.6 is

less than and not equal to .7, the correlation is statistically insignificant.

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B. New Athletic Analysis

The new athletic analysis looks at each sport as an “industry” and benchmarks a

specific sport of a team within the industry. In order to get that picture the new analysis

will use Box and Whisker Plots to illustrate how the team is performing relative to its direct

competitors in that sport.

In order to construct the box and whisker plot, values for the Sample minimum,

Quartile 1, Quartile 3, Sample Maximum, and the value of a specific sport must be

identified within a specific variable. Figure 1.7 illustrates this table layout clearly below:

Figure 1.7: Spreadsheet to Construct a Box Plot

Category Sample Minimum Q1 Q3 Sample Maximum Babson Value

Track Participation 22 47 129 165 40

Now statistical software will plot these numbers on a box and whisker plot as

shown below in Figure 1.8.

Figure 1.8: Example of plotting a single variable on a boxplot

18016014012010080604020

Track Participation

Boxplot of Track Participation

8/17/2014 5:14:27 PM

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In order to get a relative understanding of the placement of an individual team in

relation to the teams it directly competes against, the next step is to place the average

value over 4 years on the individual team on the graph. This placement can be seen in

Figure 1.9.

Figure 1.9: Individual team plotted on box and whisker plot of direct competitors

The next step is to test the significance of the location of the variable as compared

to the location of winning percentage on box and whisker plot of the same sport. The

winning percentage Box and Whisker plot with Babson individually labeled is shown in

Figure 1.10

Figure 1.10: Individual team plotted winning % box and whisker

Babson

Babson

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Although visually there seems to be a correlation between the two variables, the

next step will numerically give the individual team a placement on the boxplot. In order to

do this the graphs must have the same x-axis. To do this descriptive statistics are needed

for the variables in question—in this case the variables are track participation and winning

percentage. The descriptive statistics are in Figure 1.11 below:

Figure 1.11: Descriptive statistics of standardizing variables

Category Sample Minimum Q1 Q3 Sample Maximum

Track Participation 24 47 130 165


Track Winning % 60 .27 .82 1.00

To standardize the numbers on the same scale as winning percentage the formula

below will be used in Figure 1.12.

Figure 1.12: Formula to standardize variables

Standarize variables = 1 − |maximum value − average value

maximum value|

When the two variables are standardized the result is as follows:

Track Participation location variable (comparing location variable) = 0.22

Winning % location variable = 0.21

Now this research needs to test if the numbers are statistically significant on the same

scale that correlation coefficients are measured. Therefore the following equation will be

used in Figure 1.13. Figure 1.13: Formula to standardize variables

Coreelation Test = 1 − | winnging % location variable − comparing location variable |

With the equation above, the results are as follows:

1 - .1 = .99

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Just like the traditional test for statistical significance that accepted a correlation

coefficient greater than .7 or less than -.7 as significant this new test will accept the same

range. Since .99 (p-value = 0) is greater than .7, this test proves significant.

Alone, this significance test is virtually meaningless because it is just one variable

(participation in track) compared to another variable (winning percentage in track).

However, this research will perform the same test for all variables that could possibly

affect winning percentage such as participation, expense per participant, team expense,

and team revenues. The same test illustrated in this new analysis will be performed on

each of these variables and in aggregate all the variables will be analyzed to see if there

is a correlation to winning percentage.

Traditional Analysis

Analysis

First, this research will attempt to answer traditional administrator questions

through direct graphical analysis and test to see if there are any significant relationships.

This research hypothesizes that the results of answering these questions will prove

statistically insignificant. Each of these analyses provides a unique perspective on the

data and either agrees with the hypothesis, disagrees with the hypothesis, or proves that

enough data has not been gathered to substantiate the perspective.

Question #1: Does spending increase winning program-wide?

The first question this research will set out to answer is “does spending increase

winning program-wide?” In order to test the correlation, this research will look at average

winning percentage and average expense per team of all teams over 4 years. The goal

of this test is to see if spending more money per team is directly correlated to winning

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more. In order to do this analysis the data will first need to be sorted. Below is a snapshot

of the sorting for this analysis:

Figure 1.14: Average Expense and Average Winning % Compared

Institution Year Average

Winning %

Avg Expense per team

Babson 2010 58% $645,627

Babson 2011 58% $633,410

Babson 2012 57% $745,219

Babson 2013 56% $802,078

Massachusetts Institute of Technology 2010 72% $1329765

Massachusetts Institute of Technology 2011 71% $1,352,828



Graphing this spreadsheet gives the following result in Figure 1.15

Figure 1.15: Average Expense and Average Winning % Compared Graph

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The graph visually shows how schools like Colby continue to spend more year over year

but do not yield a greater winning percentage; however, teams like Massachusetts

Institute of Technology have spent more and increased winning percentage. In order to

test the significance of these results, this research will calculate the correlation coefficient

in Figure 1.16

Figure 1.16: Correlation Coefficient Avg. Expense compared to Avg. Winning %

Correlation Coefficient Avg. Expense compared to Avg. Winning % = .23, p-value two-tailed =.14

Since .23 < .7, the result is not significant. This first test proved that the traditional

analysis hypothesis was correct.

Question #2: Does spending increase winning % per sport?

While the program-wide analysis did not reveal a statistically significant correlation

between expenses and winning percentage, this next test will see if an individual sport

chosen at random has a correlation with winning percentage. The data for this test is

sampled below:

Figure 1.17: Baseball Expense per Participant and Average Winning % Compared

Instituti

on Year

Winning

%

Basebal

l

Expense

per

participant

Baseball Institution Year

Winning % Baseball

Expense per participant Baseball

Babson 2010 67% $1,229 MIT 2010 33% $1,636

Babson 2011 72% $1,382 MIT 2011 67% $1,424

Babson 2012 56% $1,722 MIT 2012 44% $1,324

Babson 2013 61% $1,636 MIT 2012 72% $1,871

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The graphed data is displayed below in Figure 1.18. It is clear that there is no relationship

with large sporadic changes evident over the four year period. The correlation coefficient

for this graph is .18 (p-value two-tailed= .25), proving the hypothesis correct again.

Figure 1.18: Baseball Expense per Participant and Average Winning % Graphed

Question #3: Does a pattern exist to winning more?

The next test tries to reverse engineer a correlation by identifying the teams that

have experienced more than ~100% growth in winning percentage over the four year

period and find patterns and changes in their variables. To first begin this analysis, this

research will identify the sports in each institution that have grown the most over four

years. This is a new type of analysis and will be completed by the following formula below:

Figure 1.19: Percent Change Formula

2013 Variable − 2010 Variable

2013 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒

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This formula has been used to identify the sports with over 100% growth rate per team.

The results are below in Figure 1.20:

Figure 1.20

Highest Team Growth Rate Per Sport

Institution Sport Change

Overtime

Institution Sport Change

Overtime

MIT

(NEWMAC)

Men’s

Baseball

117% Massachusetts

Institute of

Technology

(NEWMAC)

Softball 166%

Hamilton

College

(NESCAC)

Men’s

Baseball

301% Massachusetts

Institute of

Technology

(NEWMAC)

Women’s

Basketball

133%

Wesleyan

(NESCAC)

Men’s

Soccer

110% Wheaton

College

(NEWMAC)

Women’s

Cross

Country

600%

Babson

College

(NESCAC)

Men’s

Tennis

100% Hamilton

College

(NESCAC)

Women’s

Cross

Country

133%

Wesleyan

(NESCAC)

Men’s

Tennis

401% Connecticut

College

(NESCAC)

Women’s

Cross

Country

400%

Trinity College Women’s

Spring

Track

200% Wheaton

College

Women’s

Lax

200%

Bowdoin

College

Women’s

Soccer

93% Wesleyan

College

Women’s

Tennis

100%

Now to find a pattern, this approach will cross reference these excelling teams with

changes in participation, expense per participant, team expense, team revenue, and total

staff members. The result can be found in Figure 1.10

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Figure 1.21

Change overtime in growth sport (winning %) cross referenced with variables

Institution Winning

% Growth Sport

Change

Overtime Participation

Expense Per Participant

Team Expense

Team Total

Staff

Revenue

Members

Massachusetts Institute

of Technology

(NEWMAC)

Men’s

Baseball 117%

13% 14% 29% 26% 25%

Hamilton College

(NESCAC) Men’s Baseball

301% -13% 390% 328% 98% 0%

Wesleyan (NESCAC) Men’s

Soccer 110%

11% -13% -3% -9% 0%

Babson College

(NESCAC) Men’s

Tennis 100%

-61% 144% -5% -4% 0%

Wesleyan (NESCAC) Men’s

Tennis 400%

8% 23% 33% 23% 0%

Trinity College Women’s Spring Track

200%

195% -58% 25% 2% -14%

Bowdoin College Women’s

Soccer 93% -4% N/A 98% 16% 0%

Massachusetts

Institute of

Technology(NEWMAC)

Softball 166% - 24%

60% 22% 22%

- 25%

Massachusetts

Institute of

Technology(NEWMAC)

Women’s

Basketball 133%

-7% 18% 9% 13%

- 20%

Wheaton College

(NEWMAC) Women’s

Cross Country

600%

24%

- 33%

- 16%

- 26%

- 22%

Wheaton College Women’s

Lax 200%

9% -2% 7% -6% 0%

Wesleyan College Women’s

Tennis 100%

67% - 20% 34% 24% 0%

Correlation Coefficient with change

overtime 0.06 0.02 0.07 -0.08 -0.35

One-tailed Probability .42

.47

.42

.40

.13

Two-tailed Probability .85 .95 .84 .80 .26

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The data above illustrates that there is no single combination of participation,

expenses/participant, team expense, team revenue, and total staff members that is

indicative of strong growth in winning percentage. This again illustrates how the traditional

analysis does not result in insightful conclusions. Additionally, the correlation coefficient

in the bottom row illustrates how none of the variables showed a direct correlation with

their extensive increase in winning percentage over the four years. All of the correlation

coefficient were much less than .7 or much greater than -.7.

Question # 4: Has our revenue increased from an increase in winning %?

The last test will try to correlate winning and revenue. Below is the snapshot of data that

is used for this correlation test:

Figure 1.22

Revenue in relationship to Winning

Institution Year Winning % Revenue

Babson 2010 57% $551,223

Babson 2011 58% $536,516

Babson 2012 57% $619,076

Babson 2013 56% $657,696


of Technology

2010 72% $1,159,282


of Technology

2011 71% $1,198,878


of Technology

2012 73% $1,353,001


of Technology

2013 82% $1,418,311

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Using this data, this research has produced the following graph in Figure 1.23:

Figure 1.23: Revenue compared to winning %

Although it does not appear that an increase in winning percentage yields a direct

correlation with an increase in revenue, the correlation coefficient will show if the

correlation is significant or not. The correlation coefficient for this comparison is .21 (p-

value two-tailed = .18) and since .21 < .7, this test is not significant and thus the hypothesis

is proven correct again.

Holistic Analysis of Traditional Analysis

The traditional analysis (does “X” increase/decrease “Y”) has proven insignificant

in all of the tests above. These results tell an administrator that directly increasing “X” will

not directly increase “Y”. In all cases tested, “Y” was winning percentage because

administrators want to know what they need to do to increase their winning percentage.

Although none of the data proved to be significant each correlation did show a positive

correlation hinting that there may be insights in the data yet to be discovered. The next

analysis will benchmark each individual sport to discover if there is a relative correlation

between the variables using box and whisker plots.

New Analysis

Analysis

The next type of analysis will use box and whisker plots (boxplots) to give a holistic

view of an individual sport and how an individual team competes in relation to its direct

competitors. This type of analysis would usually be very costly, but because of freely

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available data online and methods pioneered in this research, anyone can now perform

this set of analyses. Just as the first set of analyses proposed questions and then tested

the results, this set of analyses will do the same. This research hypothesizes that the

results of answering these new questions will prove statistically significant.

Question #1: Are we giving each of our sports a fair chance at competing?

In order to understand if all sports have a fair chance at competing, this analysis

will choose a sport that has a low winning percentage and then compare the placement

of that winning percentage to the placement of its variables on the boxplot. Babson

College in Babson Park, MA will be used as the test institution and its team with the lowest

winning percentage, men’s track and field will be used. First, this research will plot Babson

on a Box and Whisker plot of all of its competitors in Track and Field. The descriptive

statistics are below:


Track Winning % 59 .55 .82 1.00

The Box and whisker plot that represents the descriptive statistics above is represented

below:

Figure 1.25: Boxplot of Men’s Track Field Winning %

Babson

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Now the same methodology will be completed for each of Babson’s Men’s track variables

including expense/participant, team expense, revenue, and participation.

Figure 1.26: Boxplot of all Men’s Track and Field Variables

As opposed to the traditional analysis where little observations could be recorded, a

clear recurring observation is occurring here. Benchmarked on a boxplot of each

Babson

Babson

Babson

Babson

Babson Babson

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variable for men’s track and field, Babson falls to the left of quartile 1. Thus, the winning

percentage for Babson falls to the left of quartile 1 as well. To test the statistical

significance of this visual, a location will be assigned for each of Babson’s individual

data points on each Boxplot. Then the locations will be averaged and the result will be

compared to the location of Babson’s individual data point on the winning percentage

boxplot. To get these locations, this research will reference Figure 1.12 from the

methodology section of this research.


2013 Variable − 2010 Variable

2013 𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒

The results for each variable and the average of all the variables are shown below in

Figure 1.27

Figure 1.27: Track variables standardized

Track Team Revenue =.06 Track Team Expense = .14

Track Expense / Participant = .24

Track Participation = .24

Average location = .17

Now this research will use the same formula to standardize track winning percentage:

Figure 1.28: Track Winning % standardized

Track Winning % = .21

With these values in place the research will now reference the formula in Figure

1.13 from the methodology section.


P a g e | 28

Corelation Test = 1 − |𝑤𝑖𝑛𝑛𝑖𝑛𝑔 % 𝑙𝑜𝑐𝑎𝑡𝑖𝑜𝑛 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 − 𝑐𝑜𝑚𝑝𝑎𝑟𝑖𝑛𝑔 𝑙𝑜𝑐𝑎𝑡𝑜𝑛 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒|

After completing this formula the result of the correlation test is .96 which shows

that the location of the variables matches very closely with the location of the winning

percentage on the Boxplots. This would then lead an administrator to understand that the

variables align very closely with the resulting winning percentage. Therefore, this

particular sport has a low winning percentage because of its low correlated variables. To

contrast these results, the same analysis will now be used for a sport with a high winning

percentage from Babson College. Babson’s Men’s Hockey team will be used for this

example. This example will only use graphical analysis and interpretation. The graphs for

each of Babson’s Hockey team variables and winning percentage are below:

Figure 1.29: Boxplot of all Men’s Hockey Variables

Babson

P a g e | 29

Evidently, graphical analysis and interpretation leads to the conclusion that Babson’s

Men’s Hockey team and its variables are correlated. All of the variables are between the

median and the third quartile and the winning percentage is also between the median and

the third quartile. This analysis therefore shows through both examples of Babson’s Men’s

Hockey and Babson’s Men Track and Field that there is a correlation between the

placement of the variables on the Boxplot and the placement of winning percentage on

the boxplot. An administrator can now successfully answer the question that started this

analysis by using this methodology. To answer if all sports are given a fair chance at

Babson

Babson

Babson

Babson

Babson

Babson

Babson

P a g e | 30

competing, the administrator can look and see if the team’s low winning percentage is

due to the low variables in participation, team expense, expense per participant, and team

revenue. The administrator then has a good range of what he or she needs to do in order

to increase that team’s winning percentage. For example let’s take a look at Men’s Track

variables compared to Men’s Hockey variables.

Figure 1.30: Comparing Men’s Hockey Variables to Men’s Track Variables

Looking at just the team expense variable an administrator can understand a lot

about the sport. In track competing teams are spending an average of about $37,000

while competing Hockey teams are spending about $70,000. This is a huge difference

and illustrates the value of this analysis. Rather than the traditional analysis which treats

all spending the same, this analysis shows that each sport has different parameters and

requires different capital. No longer is it just that the team needs to spend more, it’s that

the team needs to get into a certain range of spending to yield a certain range of success.

Since these are measures over four years the data gives a good picture of the sport in

recent years.

Babson Babson

P a g e | 31

Question # 2: Do we need to increase spending in a particular sport to compete

with our competition?

In order to answer this next question, five teams will be plotted on both the winning

percentage and expense box and whisker plot to show if there is a direct correlation

between placement on the winning percent box and whisker plot and placement on the

expense box and whisker plot. The two box and whisker plots with individual points are

listed below for Women’s Basketball. The teams that will be used for this test are Babson,

Williams, Colby, Amherst, and Springfield.

Figure 1.31: Women’s Basketball Expense & Winning Percentage

Boxplot w/ Plotted Teams

Amherst

MAS

SAC

Colby

Wheaton

Babson

Babson

Amherst MAS

SAC

Colby

Wheaton

P a g e | 32

Upon interpreting the two graphs it is clear that there is a direct correlation between

winning percentage and expense in women’s basketball. Although the correlation is not

a 1:1 correlation it is evident that each of the teams remains in the relative range on the

expenses box and whisker plot as it does on the winning percentage box and whisker

plot. For example, Massachusetts Institute of Technology is on the left whisker, Wheaton

is in between quartile 1 and the median, Colby is between the median and Q3 and

Amherst and Babson are both in between quartile 3 and the end of the right whisker. An

administrator can now visually see the jump that they need to make in order to compete

with another school. For example, if Massachusetts Institute of Technology wants to

compete with Wheaton in women’s basketball, it needs enter the range on the boxplot

between quartile 1 and the median. This would mean raising expenses to $27,000 -

$32,000. The next analysis will show the results of a team actually making this jump in

range.

Question #3: What are other teams doing that have affected them positively?

In order to understand the answer to this question and test its statistical

significance, this research will take the teams with an exceptional growth rate from 2010

to 2013 identified in Figure 1.121 and look at the variable that changed the most in their

growth year and how it affected winning percentage. The team that will be used for this

analysis is Wesleyan’s Women’s Tennis Team for its large change over time (100%) and

the variable tested for significance is participation because it is its largest growth variable.

First this research will show the boxplot of Wesleyan’s Tennis team winning percentage

and team expense in 2010 and then compare that with its boxplots in 2014.

P a g e | 33

Figure 1.30: Women’s Tennis Winning and Expense 2010 Compared to 2013

Here it is clear that Wesleyan’s women tennis team’s winning percentage has

jumped from being between the left whisker and quartile 1 in 2010 to being between

quartile 1 and the median in 2013. This change looks to be in part influenced by

Wesleyan’s tennis team’s increase in participation entering the upper quartile range. The

surrounding teams substantiate Wesleyan’s jump as other schools such as

Massachusetts Institute of Technology and Tufts experienced similar conditions.

Massachusetts Institute of Technology jumped from the upper quartile to the right whisker

and saw the same movement in participation. On the other hand, Tufts decreased its

participation to the lower quartile and saw similar movement in winning percentage.

Finally, Babson’s lack of movement illustrates an even more important point on how this

Wesleyan

Wesle y an

Colby MIT

Tufts Babson

Babson

Colby Tufts

MIT

Wesleyan

Wesleyan

Colby MIT Tufts

Babson

Babson Colby

Tufts

MIT

P a g e | 34

analysis is truly relative. Traditional analysis would say that Babson’s increase in winning

from 2010 to 2013 and decrease in participation from 2010 to 2013 is counterintuitive and

forces the result to not show significant correlation. However, a closer look at the Boxplot

shows that the competition in women’s basketball has become less competitive as the

whole box and whisker plot has shifted to the right; yet Babson still remains in almost the

same location as it did 3 years ago when the competition was much more competitive.

Therefore, Babson has remained only a little better than the median over the four year

period whereas teams that have grown their participation, like MIT, have exceled way

ahead.

P a g e | 35

Insights

Summary The new analysis is very promising for athletics

Applications Benchmark, Forecast, & Understand Positioning

Impact EADA, Division III Decision makers, data-analytics

Next Steps Get more data & test accuracy with sample

P a g e | 36

Summary

This research project’s mission is to put data analytics in the hands of DIII

administrators at almost no cost at all, relying on the hypothesis that analyzing EADA

and winning percentage calculations could produce useful insights for decision makers.

The research set out to prove that typical data analysis (will x increase y) is not

statistically significant and that a new approach is needed to understand data in athletics.

The traditional methods were trialed first showing no statistical significance and thus

proving the first hypothesis and its sub-questions correct. It showed how looking for

trends in athletics directly was simply not possible due to the variability in the data. This

analysis contrasted against the new benchmarking analysis which used boxplots to show

a relative measure of performance for each team and their sports. Relying on the five

number summary backed by data accumulated over 4 years, the boxplot gives a picture

for each variable that puts the data in perspective.

Utilizing Boxplots this research was able to correlate the location of individual

points on a series of Boxplots backed by EADA data to the location of an individual point

of a Boxplot backed by winning percentage data. This correlation showed that the

location of an individual team’s winning percentage could be located by understanding

the location its associated variables on a Boxplot. For Babson Hockey that meant proving

that each variable (participation, expense, etc.). Ultimately this correlation showcases

that there is a possibility for decision makers to use the EADA data and winning

percentage to gain valuable insights, to benchmark their performance yearly, and to take

action.

P a g e | 37

Applications

Administrator Dashboard

The Boxplots illustrate the performance of a team in comparison to its most relative

competitors. Therefore it makes a great benchmarking tool for DIII decision makers. For

example consider software that relies on EADA data and automatically graphs the data

into boxplots. The resulting administrator dashboard below could be the result.

This dashboard would clearly illustrate to an administrator which sports are performing

out of their relative norms. For instance in the above dashboard it is clear that Babson’s

Spring track team falls below the lower quartile. This could have two meanings: 1) The

team is underperforming or 2) The team cannot compete fairly against its competition.

The administrator could then check the expanded graphs to get the answer. Figure 1.25

in the data analysis section illustrates those expanded variable plots which clearly show

that all variables are below the lower quartile.

Figure 1.31

Babson

Babson

Babson

Babson

Babson

P a g e | 38

This kind of approach can help administrators talk with their coaches and put things in

perspective. What does success mean for a team like Babson’s Spring Track team?

Success for Babson’s Spring Track team should definitely be different than Babson’s

Hockey Team based on Figure 1.25 and Figure 1.26. These figures showcase that

Babson’s Hockey Team is comfortably within the lower quartile and upper quartile of all

its variables. Therefore, success to Babson’s Hockey team should mean getting closer to

the upper quartile and success for Babson’s Spring track team should mean getting past

the first quartile. Because this data is so easily passed by, many administrators view their

team on an evening playing field5. The fact is that all sports are different and that the

competition within each sport is also different. Therefore, the sports have to be treated on

an individual basis.

Forecasts & Predicting

Because the two Boxplot charts (winning % and the EADA variables) show a relationship

identified in Figure 1.25, it is possible to reverse engineer the EADA variables to predict

winning % placement. In the two examples this proved true. For Babson’s Spring Track

team all of the EADA variables fell below the lower quartile and thus so did their winning

percentage. Likewise, all of Babson’s Hockey EADA variables fell within the lower quartile

and the upper quartile and so did their winning percentage. Therefore this application can

be used by DIII administrators to understand, within certain accuracy, the ability of their

team to perform given the results of their variables.

If a sport’s variables all fall within the lower and upper quartile, its winning % should as

well, if the sport is below the lower quartile, the winning % should be as well. This will only

5 The Successful College Athletic Program: The New Standard. By John Gerdy

P a g e | 39

work to a certain degree given that there will be some variability. Nevertheless, averaging

the locations of these variables on the boxplot to predict winning percentage on the

boxplot has proven to result in the most accurate predictions.

Dynamic Modeling

If the concepts illustrated in this research were made into an online tool, where

administrators could set their own EADA variables for the next year by simply sliding up

or down on the Boxplot, they could understand how their resulting winning percentage

may change. Imagine the Babson administrator sliding up Babson’s Spring Track

variables and seeing how their winning % is affected. This makes the data actionable so

that administrators can set goal for future years

Comparative Representation

All administrators have to apply for a budget and this tool can help present actionable info

to the budget committee. For instance, a sport may not get money because they were not

performing and it is unlikely that they will be able to compete with bigger juggernauts in

the sport; however, looking at it from the angle of giving the sport a fair chance of

competing by changing its location on the box and whisker plot shows the budget

committee the opportunity for the individual sport.

Impact

This research has an impact on EADA data, data analytics in athletics, and all Division III

decision makers. First, this data proves that there are useful applications for the EADA

data other than their intentions which are to provide gender equity in athletics. Moreover,

this study proves how data analytics can be used in athletics with almost no cost

associated. It is common though that it will take millions in order to implement a smart

P a g e | 40

“big-data solution” for athletics6. However, this research also failed to prove that a

correlation exists between changing one EADA variable and winning more overtime.

Finally, Division III decision makers now have the ability to benchmark their teams and

understand their positioning as compared to the competition like never before with the

methodology, analysis, and interpretations pioneered in this research.

Next Steps

The next steps of this research would be to test at least 30 independent cases of a team

and a sport for significance in the Boxplot results. How many teams correlate to the results

found with Babson’s Spring Track and Hockey teams? How significant is the actual

correlation? Once these measures are known weights may needed to be placed for

different sports and different variables. What’s important in one sport may be different

than another and thus the Boxplots may need to be adjusted. For example in track,

perhaps the participant variable needs a stronger weight than the team expense variable

whereas in Baseball it’s the opposite. Finding and understanding those weights could

lead to more accurate correlations. Finally, the boxplots would need to be completed year

by year and hopefully expand the range of years. Currently, this research is only using

the average of 4 years and could benefit from additional years.

6 Field Approaches to Institutional Change: The Evolution of the National Collegiate Athletic

Association 1906–1995 by Marvin Washington

P a g e | 41

Appendix

Raw Data Link to raw data

Exhibits Key data points and EADA reference

P a g e | 42

Raw Data

The raw data can be found freely online at

https://docs.google.com/spreadsheets/d/1kYp9sxF5_ZVjeTcVyRtCxH2JUxC46uWr0jt02

6-2Z3A/edit?usp=sharing

Exhibit 1.1

Data Point EADA Code Data Point EADA Code

Men Average

Participation IL_PARTIC_MEN / #

Sports Competing

Team Expense OPEXPPERTEAM_ME

N_Baseball

Team Revenue SUM_FTHDCOACH_M

ALE_Baseball Men Average Expense

Per Participant IL_OPEXPPERPART_

MEN Head Coaches FTE

(MALE) SUM_FTHDCOACH_M

ALE_Baseball Men Average Rev /

Team IL_REV_MEN

Men Average FTE

Coach Salary HDCOACH_SAL_FTE

_MEN Men Average FTE


_MEN Head Coaches FTE

(FEMALE) SUM_FTHDCOACH_F

EM_Baseball Men Average Head

Coach Salary HDCOACH_SALARY_

MEN Head Coaches PTE

(MALE) SUM_PTHDCOACH_MA

LE_Baseball Men Average Asst.

Coach Salary ASCOACH_SALARY_

MEN Head Coaches PTE

(FEMALE) SUM_PTHDCOACH_F

EM_Baseball Men Average Recruiting Exp. /

Team

RECRUITEXP_MEN

Assistant Coaches

FTE (MALE) MEN_FTASCOACH_M

ALE_Baseball Women Average

Participation IL_PARTICWOMEN / #

Sports Competing Assistant Coaches

FTE (FEMALE) MEN_FTASSTCOACH

_FEM_Baseball Women Average Expense Per

Participant

IL_OPEXPPERPART_

WOMEN Assistant Coaches

PTE (MALE) MEN_PTASCOACH_M

ALE_Baseball

Assistant Coaches

PTE (FEMALE) MEN_PTASSTCOACH

_FEM_Baseball Women Average Exp /

Team IL_EXP_WOMEN

P a g e | 43

Women Average Rev /

Team IL_REV_WOMEN Men’s

Basketball Participation

PARTIC_MEN_Bskbal l

Women Average FTE


_WOMN Expense Per

Participant OPEXPPERPART_MEN

_Bskball Women Average Head

Coach Salary HDCOACH_SALARY_

WOMEN Team Expense OPEXPPERTEAM_ME

N_Bskball Women Average Asst.

Coach Salary ASCOACH_SALARY_

WOMEN Team Revenue SUM_FTHDCOACH_M

ALE_Bskball Women Average Recruiting Exp. /

Team

RECRUITEXP_WOME

N Head Coaches FTE


ALE_Bskball

Baseball Participation

PARTIC_MEN_Baseb

all

Head Coaches FTE


EM_Bskball

Expense Per


_Baseball

Head Coaches PTE

(MALE) SUM_PTHDCOACH_M

ALE_Bskball

OPEXPPERTEAM_

MEN_Baseball

OPEXPPERTEAM_ME

N_Baseball

Head Coaches PTE


EM_Bskball

P a g e | 44

Data Point EADA Code

Assistant Coaches FTE (MALE)

MEN_FTASCOACH_M

ALE_Bskball

Assistant Coaches FTE (FEMALE)

MEN_FTASSTCOACH

_FEM_Bskball

Assistant Coaches PTE (MALE)

MEN_PTASCOACH_M

ALE_Bskball

Assistant Coaches PTE (FEMALE)

MEN_PTASSTCOACH

_FEM_Bskball

Men’s All

Track

Combined

Participation

PARTIC_MEN_Trckco

mb

Head Coaches PTE

(MALE) SUM_PTHDCOACH_MA

LE_Baseball

Expense Per


_Trckcomb


N_Trckcomb

Team Revenue REV_MEN_Trckcomb

Head Coaches FTE


ALE_Trckcomb

Head Coaches FTE


EM_Trckcomb

Head Coaches PTE


ALE_Trckcomb

Head Coaches PTE


EM_Trckcomb

Assistant Coaches FTE

(MALE) MEN_FTASCOACH_M

ALE_Trckcomb

Assistant Coaches FTE

(FEMALE) MEN_FTASSTCOACH

_FEM_Trckcomb

Assistant Coaches PTE

(MALE) MEN_PTASCOACH_M

ALE_Trckcomb

Assistant Coaches PTE

(FEMALE) MEN_PTASSTCOACH

_FEM_Trckcomb

Ice Hockey Participation

PARTIC_MEN_IceHck

y


OPEXPPERPART_MEN

_IceHcky


N_IceHcky

P a g e | 45

Data Point EADA Code Data Point EADA Code

Head Coaches PTE

(FEMALE)

SUM_PTHDCOACH_F

EM_Soccer

Head Coaches FTE


EM_Softball

Assistant Coaches

FTE (MALE)

MEN_FTASCOACH_M

ALE_Soccer

Head Coaches PTE


EM_Softball

Assistant Coaches

FTE (FEMALE)

MEN_FTASSTCOACH

_FEM_Soccer

Assistant Coaches

FTE (MALE) WOMEN_FTASTCOAC

H_FEM_Softball

Assistant Coaches

PTE (MALE)

MEN_PTASCOACH_M

ALE_Soccer

Assistant Coaches

FTE (FEMALE) WOMEN_FTASTCOAC

H_FEM_Softball

Assistant Coaches

PTE (FEMALE)

MEN_PTASSTCOACH

_FEM_Soccer

Assistant Coaches

PTE (MALE) WOMEN_PTASCOAC

H_MALE_Softball

Men’s Tennis

Participation

OPEXPPERPART_MEN_

Tennis Assistant Coaches

PTE (FEMALE) WOMEN_PTASTCOACH

_FEM_Softball

Women’s

Basketball Participation

PARTIC_WOMEN_Bs

kball Expense Per


_Tennis

Team Expense

OPEXPPERTEAM_ME

N_Tennis Expense Per

Participant OPEXPPERTEAM_ME

N_Lacrsse Team Revenue

SUM_FTHDCOACH_M

ALE_Tennis Team Expense OPEXPPERTEAM_ME

N_Lacrsse Head Coaches FTE

(MALE ) SUM_FTHDCOACH_M

ALE_Tennis Team Revenue REV_WOMEN_Bskbal l

Head Coaches PTE

(MALE ) SUM_PTHDCOACH_M

ALE_Tennis Head Coaches FTE


ALE_Bskball Head Coaches PTE

(FEMALE ) SUM_PTHDCOACH_F

EM_Tennis Head Coaches FTE


EM_Bskball Assistant Coaches


ALE_Tennis Head Coaches PTE


ALE_Bskball Assistant Coaches

FTE (FEMALE) MEN_PTASCOACH_M

ALE_Tennis Head Coaches PTE


EM_Bskball Assistant Coaches


ALE_Tennis WOMEN_FTASCOAC

P a g e | 46

Assistant Coaches

FTE (FEMALE) MEN_PTASSTCOACH

_FEM_Tennis

Assistant Coaches

FTE (MALE) H_MALE_bskball

Assistant Coaches


H_FEM_bskball Assistant Coaches


ALE_Tennis Assistant Coaches


ALE_ bskball Assistant Coaches

PTE (FEMALE) MEN_PTASSTCOACH

_FEM_Tennis Assistant Coaches

PTE (FEMALE) WOMEN_PTASTCOAC

H_FEM_bskball Softball Participation

PARTIC_WOMEN_Sof

tball Women’s All Track Comb. Participation

PARTIC_WOMEN_Trc

kcomb Expense Per

Participant OPEXPPERPART_WO

MEN_Softball

Team Expense OPEXPPERTEAM_WO

MEN_Softball


OPEXPPERPART_MEN

_Soccer

Team Revenue REV_WOMEN_Softba ll Team Expense OPEXPPERTEAM_WO

MEN_Trckcomb

Head Coaches FTE


ALE_Softball

Team Revenue REV_WOMEN_Trckco

mb

P a g e | 47


Head Coaches FTE


ALE_Trckcomb

Head Coaches PTE

(FEMALE ) SUM_FTHDCOACH_F

EM_Trckcomb

Head Coaches PTE

(MALE)

SUM_PTHDCOACH_F

EM_Trckcomb

Head Coaches PTE

(FEMALE ) SUM_PTHDCOACH_F

EM_Trckcomb

Assistant Coaches


ALE_Trckcomb

Assistant Coaches

FTE (FEMALE) MEN_FTASTCOACH_

FEM_Trckcomb

Assistant Coaches

PTE (MALE) MEN_PTASCOACH_MAL

E_Trckcomb

Assistant Coaches


H_FEM_Trckcomb

Women’s

Lacrosse

Participation

PARTIC_WOMEN_Lac

rsse

OPEXPPERPART_W OMEN_Lacrsse

OPEXPPERPART_WO

MEN_Lacrsse


MEN_Lacrsse

Team Revenue REV_WOMEN_Lacrss

e

Head Coaches FTE (MALE)

SUM_FTHDCOACH_M

ALE_Lacrsse

Head Coaches PTE (FEMALE)

SUM_FTHDCOACH_F

EM_Lacrsse

Head Coaches PTE (MALE)

SUM_PTHDCOACH_M

ALE_Lacrsse

SUM_PTHDCOACH_ FEM_Lacrsse

SUM_PTHDCOACH_F

EM_Lacrsse

Assistant Coaches FTE (MALE)

WOMEN_FTASCOAC

H_MALE_Lacrsse

Assistant Coaches


H_FEM_Lacrsse

Assistant Coaches


H_MALE_Lacrsse



MEN_soccer

Team Revenue REV_WOMEN_soccer

Head Coaches FTE


ALE_soccer

Head Coaches FTE


EM_soccer

Head Coaches PTE


ALE_soccer

Head Coaches PTE

(FEMALE) SUM_PTHDCOACH_FE

M_soccer

Assistant Coaches

FTE (MALE) WOMEN_FTASCOAC

H_MALE_Soccer

Assistant Coaches


H_FEM_Soccer

Assistant Coaches


H_MALE_Soccer

Assistant Coaches


H_FEM_Soccer

Women’s

Tennis

Participation

PARTIC_WOMEN_Te

nnis

Expense Per

Participant OPEXPPERPART_WO

MEN_Tennis


MEN_Tennis

Team Revenue REV_WOMEN_Tennis

Head Coaches FTE


ALE_Tennis

Head Coaches FTE


EM_Tennis

Head Coaches PTE


ALE_Tennis

Head Coaches PTE


EM_Tennis

Assistant Coaches

FTE (MALE) WOMEN_FTASCOAC

H_MALE_Tennis

Assistant Coaches


H_FEM_Tennis

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Assistant Coaches


H_FEM_Lacrsse

Women’s

Soccer Participation

PARTIC_WOMEN_soc

cer


OPEXPPERPART_WO

MEN_soccer

Assistant Coaches


H_MALE_Tennis

Assistant Coaches


H_FEM_Tennis

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Benchmarking Athletics by Sport-Dom-Complete-Indp-Study (1)

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