Benchmarking Athletics by Sport-Dom-Complete-Indp-Study (1)
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Transcript of Benchmarking Athletics by Sport-Dom-Complete-Indp-Study (1)
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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
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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
Massachusetts Institute of Technology 2011 71% $7,212
Massachusetts Institute of Technology 2012 73% $7,592
Massachusetts Institute of Technology 2013 82% $6,673
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
Massachusetts Institute of Technology
2011 71% $7,212
Massachusetts Institute of Technology
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
Category Sample Minimum Q1 Q3 Sample Maximum
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
Massachusetts Institute of Technology 2012 73% $1,509,817
Massachusetts Institute of Technology 2013 82% $1,597,298
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
Massachusetts Institute
of Technology
2010 72% $1,159,282
Massachusetts Institute
of Technology
2011 71% $1,198,878
Massachusetts Institute
of Technology
2012 73% $1,353,001
Massachusetts Institute
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:
Category Sample Minimum Q1 Q3 Sample Maximum
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.
Figure 1.12: Formula to standardize variables
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.
Figure 1.13: Formula to standardize variables
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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
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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
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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
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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
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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.
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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
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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.
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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
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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.
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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
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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
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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
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“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
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Appendix
Raw Data Link to raw data
Exhibits Key data points and EADA reference
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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
Coach Salary HDCOACH_SAL_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
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Women Average Rev /
Team IL_REV_WOMEN Men’s
Basketball Participation
PARTIC_MEN_Bskbal l
Women Average FTE
Coach Salary HDCOACH_SAL_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
(MALE) SUM_FTHDCOACH_M
ALE_Bskball
Baseball Participation
PARTIC_MEN_Baseb
all
Head Coaches FTE
(FEMALE) SUM_FTHDCOACH_F
EM_Bskball
Expense Per
Participant OPEXPPERPART_MEN
_Baseball
Head Coaches PTE
(MALE) SUM_PTHDCOACH_M
ALE_Bskball
OPEXPPERTEAM_
MEN_Baseball
OPEXPPERTEAM_ME
N_Baseball
Head Coaches PTE
(FEMALE) SUM_PTHDCOACH_F
EM_Bskball
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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
Participant OPEXPPERPART_MEN
_Trckcomb
Team Expense OPEXPPERTEAM_ME
N_Trckcomb
Team Revenue REV_MEN_Trckcomb
Head Coaches FTE
(MALE) SUM_FTHDCOACH_M
ALE_Trckcomb
Head Coaches FTE
(FEMALE) SUM_FTHDCOACH_F
EM_Trckcomb
Head Coaches PTE
(MALE) SUM_PTHDCOACH_M
ALE_Trckcomb
Head Coaches PTE
(FEMALE) SUM_PTHDCOACH_F
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
Expense Per Participant
OPEXPPERPART_MEN
_IceHcky
Team Expense OPEXPPERTEAM_ME
N_IceHcky
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Data Point EADA Code Data Point EADA Code
Head Coaches PTE
(FEMALE)
SUM_PTHDCOACH_F
EM_Soccer
Head Coaches FTE
(FEMALE) SUM_FTHDCOACH_F
EM_Softball
Assistant Coaches
FTE (MALE)
MEN_FTASCOACH_M
ALE_Soccer
Head Coaches PTE
(FEMALE) SUM_PTHDCOACH_F
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
Participant OPEXPPERPART_MEN
_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
(MALE) SUM_FTHDCOACH_M
ALE_Bskball Head Coaches PTE
(FEMALE ) SUM_PTHDCOACH_F
EM_Tennis Head Coaches FTE
(FEMALE) SUM_FTHDCOACH_F
EM_Bskball Assistant Coaches
FTE (MALE) MEN_FTASCOACH_M
ALE_Tennis Head Coaches PTE
(MALE) SUM_PTHDCOACH_M
ALE_Bskball Assistant Coaches
FTE (FEMALE) MEN_PTASCOACH_M
ALE_Tennis Head Coaches PTE
(FEMALE) SUM_PTHDCOACH_F
EM_Bskball Assistant Coaches
PTE (MALE) MEN_PTASCOACH_M
ALE_Tennis WOMEN_FTASCOAC
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Assistant Coaches
FTE (FEMALE) MEN_PTASSTCOACH
_FEM_Tennis
Assistant Coaches
FTE (MALE) H_MALE_bskball
Assistant Coaches
FTE (FEMALE) WOMEN_FTASTCOAC
H_FEM_bskball Assistant Coaches
PTE (MALE) MEN_PTASCOACH_M
ALE_Tennis Assistant Coaches
PTE (MALE) MEN_PTASCOACH_M
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
Expense Per Participant
OPEXPPERPART_MEN
_Soccer
Team Revenue REV_WOMEN_Softba ll Team Expense OPEXPPERTEAM_WO
MEN_Trckcomb
Head Coaches FTE
(MALE) SUM_FTHDCOACH_M
ALE_Softball
Team Revenue REV_WOMEN_Trckco
mb
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Data Point EADA Code
Head Coaches FTE
(MALE) SUM_FTHDCOACH_M
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
FTE (MALE) MEN_FTASCOACH_M
ALE_Trckcomb
Assistant Coaches
FTE (FEMALE) MEN_FTASTCOACH_
FEM_Trckcomb
Assistant Coaches
PTE (MALE) MEN_PTASCOACH_MAL
E_Trckcomb
Assistant Coaches
PTE (FEMALE) WOMEN_PTASTCOAC
H_FEM_Trckcomb
Women’s
Lacrosse
Participation
PARTIC_WOMEN_Lac
rsse
OPEXPPERPART_W OMEN_Lacrsse
OPEXPPERPART_WO
MEN_Lacrsse
Team Expense OPEXPPERTEAM_WO
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
FTE (FEMALE) WOMEN_FTASTCOAC
H_FEM_Lacrsse
Assistant Coaches
PTE (MALE) WOMEN_PTASCOAC
H_MALE_Lacrsse
Data Point EADA Code
Team Expense OPEXPPERTEAM_WO
MEN_soccer
Team Revenue REV_WOMEN_soccer
Head Coaches FTE
(MALE) SUM_FTHDCOACH_M
ALE_soccer
Head Coaches FTE
(FEMALE) SUM_FTHDCOACH_F
EM_soccer
Head Coaches PTE
(MALE) SUM_PTHDCOACH_M
ALE_soccer
Head Coaches PTE
(FEMALE) SUM_PTHDCOACH_FE
M_soccer
Assistant Coaches
FTE (MALE) WOMEN_FTASCOAC
H_MALE_Soccer
Assistant Coaches
FTE (FEMALE) WOMEN_FTASTCOAC
H_FEM_Soccer
Assistant Coaches
PTE (MALE) WOMEN_PTASCOAC
H_MALE_Soccer
Assistant Coaches
PTE (FEMALE) WOMEN_PTASTCOAC
H_FEM_Soccer
Women’s
Tennis
Participation
PARTIC_WOMEN_Te
nnis
Expense Per
Participant OPEXPPERPART_WO
MEN_Tennis
Team Expense OPEXPPERTEAM_WO
MEN_Tennis
Team Revenue REV_WOMEN_Tennis
Head Coaches FTE
(MALE) SUM_FTHDCOACH_M
ALE_Tennis
Head Coaches FTE
(FEMALE) SUM_FTHDCOACH_F
EM_Tennis
Head Coaches PTE
(MALE) SUM_PTHDCOACH_M
ALE_Tennis
Head Coaches PTE
(FEMALE) SUM_PTHDCOACH_F
EM_Tennis
Assistant Coaches
FTE (MALE) WOMEN_FTASCOAC
H_MALE_Tennis
Assistant Coaches
FTE (FEMALE) WOMEN_FTASTCOAC
H_FEM_Tennis
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Assistant Coaches
PTE (FEMALE) WOMEN_PTASTCOAC
H_FEM_Lacrsse
Women’s
Soccer Participation
PARTIC_WOMEN_soc
cer
Expense Per Participant
OPEXPPERPART_WO
MEN_soccer
Assistant Coaches
PTE (MALE) WOMEN_PTASCOAC
H_MALE_Tennis
Assistant Coaches
PTE (FEMALE) WOMEN_PTASTCOAC
H_FEM_Tennis
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