Integration of Collegiate Sports Analytics Update 1

After a few weeks of rest and relaxation, it was now time to start my official work on my research project. For those unaware, the focus of my Monroe research is investigating the adoption of the various uses of sports analytics into varying NCAA athletic programs. Translation: I’ll be looking into whether coaches favor more of a data-driven statistical approach to individual and team adjustments over a more “old-school” method, favoring non-quantitative traits like athleticism and experience. If all goes well, I’ll be able to get a snapshot of specific advanced statistics tracked by programs, and whether making decisions based off of that translates into success on the field.

The first part of investigating the massive world of sports analytics involves a literature review, which so far has comprised of combing through dozens of websites to find those specifically related to NCAA athletics. Hopefully, after researching and learning as much as I can about the adoption of sports analytics across programs, I can develop a comprehensive list of interview questions I can use for the next stage of my project. After being pointed in the right direction by a few helpful William & Mary librarians, here are a few observations I have been able to make.

The first is that many analytics sites aren’t sponsored or run by specific schools, in fact, most of them are run by independent student groups whose passions and interests draw them into the analytics world. Instead of developing an internal program dedicated specifically to analytics, NCAA teams will turn to these sites to provide some statistical analysis. Some good examples are the Yale Undergraduate Sports Analytics Group, a student-run group that runs numbers from all Yale athletics, but specifically does work for the men’s basketball team.

The second thing I’ve discovered is that when these groups are used, they are often initially undervalued. A prominent example of this is Cats Stats, a group of Davidson students with the help of some dedicated faculty members, who were founded back in 2013. While initially providing analysis for the men’s basketball team on a “trial-basis”, the program quickly realized that the insights they provided were massively beneficial and began to treat Cats Stats with newfound respect. The next few years saw the program grow to over seventy students and to now advise seven campus varsity sports. Some alumni even went on to do analysis for the NBA and NFL.

While these insights help me glean more how sports analytics are used to great effect, they don’t reveal much about how they aren’t used. For this research to be fully successful, I want to find which programs don’t use analytics– and their reasons for doing so. Hopefully, you’ll be along for the ride as well.