Integration of Collegiate Sports Analytics: Update 4

As we roll into the last month of summer before school starts, I am still working on developing the survey and writing out my literature review. Ideally, I would like these questions to be as fine-tuned as possible before sending them out, just to make sure there is no bias in the wording or structure. I’ve asked my dad– a career market researcher– for help with this, and he has been hugely beneficial to editing any potential questions I send his way. Since there are just four weeks left until the school year (and the conclusion of the research) is upon us, I will have to commit to decently strict update schedule in order to fulfill the requirement.

In the last few weeks, I’ve been working on most likely the most tedious part of the entire project, and that is choosing the schools pulling names of the various NCAA personnel I wish to send the survey out to. To do this, I had several options, most of which involved manually choosing the schools and teams that were to be selected. I chose a different way, not just in the interest of time, but also in that it would likely skew the results since the teams were not being randomly selected. Thus, I looked for a way to generate a list of teams completely randomly. After some searching, I managed to find a diamond in the rough: an online dataset that listed every team in the NCAA, by sport, with the grades and overall academic trends for each. After downloading said dataset and deleting some of it– I didn’t need any of the academic data– I had a dynamic table in Excel that I could easily filter out select teams with. I wrote a quick formula that allowed me to quickly generate a list of random teams, organized by sport, so that I could then go online manually and find an email address or telephone number that would allow me to contact them over the coming weeks. I’m pretty proud of the formula, as it allowed me to use something from my major in a research setting.

For the survey, I elected to narrow the field down to the 4 most popular men’s NCAA sports (Football, basketball, baseball, and soccer) as well as the most popular women’s NCAA sport, basketball. While it is a shame to not weigh men’s and women’s sports equally, my research says that there are huge lapses in data for women’s collegiate sports as a whole. If I am able to generate enough responses from the existing potential respondents, ideally I could go back and use the same Excel sheet to find more potential respondents from more women’s sports. Additionally, ideally I would like to get analytics use data from a wider variety of sports, but the scope and timing of the overall project need to remain feasible.

I’m excited for the next few weeks, as I will attain actual data on a topic I have only been able to research as of now.

Here are the first 15 colleges that I will contact from each sport:

Football:

California State University, Fresno
Stanford University
Washington State University
Texas Christian University
University of Nebraska, Lincoln
University of Cincinnati
Southern Utah University
University of South Dakota
University of Tennessee at Chattanooga
Furman University
Florida Atlantic University
Wake Forest University
University of Illinois, Champaign
Abilene Christian University
Monmouth University

Basketball:

Missouri State University
Stetson University
Fordham University
Illinois State University
Louisiana Tech University
Stanford University
University of Arkansas, Little Rock
The Ohio State University
University of Denver
Wagner College
Boston College
Drexel University
Ball State University
University of Notre Dame
Eastern Illinois University

Soccer:

Columbia University-Barnard College
Miami University (Ohio)
University of Louisville
Northern Kentucky University
University of Pittsburgh
Georgetown University
South Dakota State University
Siena College
Elon University
University of Mississippi
Furman University
Loyola University Maryland
Southern Utah University
Murray State University
North Carolina State University

Baseball:

Rider University
Monmouth University
Marist College
New Jersey Institute of Technology
Eastern Illinois University
Columbia University-Barnard College
University of Minnesota, Twin Cities
Towson University
The University of Southern Mississippi
Coppin State University
Indiana State University
University of Connecticut
Eastern Kentucky University
Gardner-Webb University
University of California, Berkeley

Women’s Basketball:

Indiana University-Purdue University, Fort Wayne
Mount St. Mary’s University
North Carolina Central University
San Diego State University
Fordham University
University of Virginia
University of Rhode Island
Harvard University
Bryant University
Savannah State University
Youngstown State University
University of Memphis
University of Wisconsin-Green Bay
Marshall University
Western Carolina University

Comments

  1. Sounds like a cool project! Any chance of running a pilot survey to test for the bias you mentioned you were concerned about or to develop a scheme for coding open-ended responses? Piloting the survey could also help with refining multiple choice option answers. With the tight timeline you mentioned in this post, however, I can see how adding a pilot stage may not be suited to this study.

  2. The open-ended responses are mostly being used for a more qualitative analysis; as several questions are being used for organizational purposes (School, conference) or for general initial observations, i.e. their own definition of analytics usage. I’ve gone through several drafts of the survey, as well as enlisted the help of a market research professional in order to pre-emptively deal with any bias in the questions.