Sports Illustrated writer Luke Winn has wrote about the rise of the "Up-Transfer"
. A low-major player transferring to a mid-major or a mid-major player transferring to a high-major are examples of up-transfers.
As the sample size for these transfers increases each year, we can use historical stats to learn more about these potential transfers. In other words, how do players from Conference A perform after transferring to Conference B? Every player is different of course, but gathering all possible information is crucial to making recruiting decisions.
The grad transfer may be the scenario most answerable via analytics. Grad transfers play right away, eliminating the uncertainty of projecting how much a transfer will improve in his redshirt year. They also usually have three years of D1 statistics under their belt.
This off-season in particular featured a few grad transfers that became highly coveted targets despite coming from the lowly conferences. To give a very brief (and admittedly incomplete) example of how data can be a tool for recruiting decisions in your program, take a look at the table below of past MEAC grad transfers:
The sample size here is tiny and there are certainly better ways of evaluating players than the selected statistics. However, you can see that MEAC grad transfers have not performed well in the past.
Truthfully, this example was left incomplete on purpose. We can only give so much information away without losing the competitve advantage that data gives us at New Mexico State. That's the real takeaway: Anywhere there is data, there is an opportunity to get a leg up on the competition - scouting, recruiting, player development, and much more.