I’m going to keep this as short and concise as I can because I can go on and on about how interesting this was but the main premise is that we have a coaching vacancy and I want to try to identify sitting coaches who could be successful here at UNC.
I have ten years worth of Directors Cup data and that is my starting point. For the sport in question, which programs have been more successful than us and can we pull a head or assistant coach away from one of those programs?
I also am really interested in efficiency. I want a coach that has over-performed relative to their peers. By that, I mean I want to identify coaches that have have the most success with the least resources. A return-on-investment metric. For this I use program expenses as reported on the EADA report and use that as a measure of investment. The equation is Directors Cup Points / Program Expenses = Price per DC point (i.e., return on investment).
The two screenshots above are my source data. Now is time for the real analysis. For the sport in question, I review the Directors Cup Points data set. I pivot that data into a table that shows the NCAA finish over the last ten years. This provides a high level overview of which programs have been the most successful. The example below highlights Cal as a program that as been really good in this particular sport.
I am really only concerned with recent results so I limit the Directors Cup data set to only the last five years and also and a criteria that quantifies program progression.
Now that I’ve identified the top programs, I want to further narrow the field of potential coaching candidates by filtering the field by that return-on-investment metric reference previously. Ideally looking for a coach who has over-performed given the level of investment in their program.
Now that I’ve identified which programs have been flat out better than us and which programs have been more efficient with their resources, I can effectively narrow the list of programs to a manageable list and begin researching the coaching candidates.
This below initial list of coaching candidates is a broad examination of who might be qualified to take the job. I look at who the coach is, how long they’ve been at their current institution, and try to assess how likely they are to leave for another job.
While researching the broad base of candidates I try to infer who might be open to making a career jump. I think a coach who has been somewhere 2-5 years might be open to taking a new job. I also give some preference to candidates who I’ve discovered have UNC connections. I came across two of them. I also assume that most people aren’t willing to take a pay cut to come here.
After I have a list of 15-20 coaches who are seemingly qualified and fit the characteristics of maybe being willing to make a career move, I Google them and compose a more in-depth look at who they are and what they have accomplished. Every coach at this point is a good one. I try to use my best judgement based on my experience in athletics to guess who might be the best fit for UNC. I would fast track all coaches on this list to the phone interview stage if they ended up applying.
The final candidate list breaks down three candidates in detail and I would try to recruit these individuals into the applicant pool.
To summarize, athletic departments can use data to identify potential coaching candidates. This method would works well for Olympic sports that sport administrators may not have a mental shortlist for coaching replacements because it combs the data and produces a list of individuals who have had proven success and that is a good starting point for a search at the very least.
UPDATE: Having zero prior knowledge of who applied for this specific coaching position, it turns out that I had come across about half of the actual final candidates within my research. This leads me to believe that this method of analysis can be pretty accurate for identifying coaches with the proper experience and characteristics. It can be predictive and informative.