Competitive Sports Analysis

How Technology Is Strengthening College Teams’ Recruitment Efforts

Minus a crystal ball to ensure perfect picks, a college sports team’s acquisition of new players always comes with some risk. That’s where Diane Bloodworth and her company Competitive Sports Analysis (CSA) come in. With the all-important method of predictive analysis, she helps schools choose the most promising athletes to lead them to victory.  Ms. Bloodworth sat down with Direct Interface Founder Kevin Jordan to share insights on how her company is leveraging the all-important method of predictive analysis, to ensure the highest level of accuracy when identifying the optimal student-athlete for college programs.

Tell me about your role with Competitive Sports Analysis​?

I’m the founder and CEO of Competitive Sports Analysis, and we provide predictive analytics for sports. We predict how well a recruit is going to fit into a college sports team. My background, I’m a serial entrepreneur. I started my career at IBM, so I have more than 25 years of experience in information technology. I am able to combine my passion for sports with my background in technology and data.

What is the industry opportunity for you?

The industry is a little bit in the early days of predictive analytics. I’m not sure coaches yet believe the predictive side of analytics, but they are more open to using analytics, and they’re more open to improving a recruiting process that’s not necessarily efficient or effective. When we look at the recruitment market, there is a problem in that coaches don’t always find the best recruits for their team, and there are student athletes who don’t find the school where they fit. By using analytics, we can match these two up better and improve the process to find the right recruits and for the coaches to win.

What developments are driving your market’s growth?

Several things are happening to make this market grow and really have a good market opportunity. One is that the coaches are now more open to analytics. They see it being used in professional teams, and they realize that it’s something that they’re going to need for a competitive advantage. The second thing is that the technology has really matured and evolved to help us not only develop the analytics, but to visualize it in a way that can help the coaches. The combination of this coming together of both the interest and the technology makes this a very interesting market in the next five years.

Can you share some challenges you’re observing?

The key industry challenges are really making sure that whatever we do with analytics and sports, it adds value to the coaches. Really the bottom line is winning. We’ve got to make sure and be able to demonstrate the value of this. I think that will get coaches more interested. The other thing is that coaches have to be reassured that they don’t have to be analytics experts. We’re going to allow them to use a platform that’s very customizable and easy for them. They may have some people on their staff who are more analytics-focused, but we want the coaches to know that they’re a part of this without being analytics experts.

How is CSA tackling these challenges?

We have a scout-smart platform, and we look at three key data points to determine the fit of a recruit for a team. We will get their stats, their high school stats. We look at skills analysis that includes both objective and subjective data points, and there’s a lot of math behind the skills analysis. We have been able to identify the best predictors of performance for athletes. Then we look at academics. The coach can decide what’s most important to them. Just using a slider scale, they can say, “Which of these three data points are more important?” So they get a customized output or a customized fit score for the recruits that they’re interested in.

What are some of the trends you’re following?

The key trends we’ve started to see, with the wearable technology, much more interest in the use in analytics. So I think we can take that as kind of a jumping off point to then take some of that data and say what kind of recruits we are looking for. Then we start to have more integrated analytics for both recruiting, performance and game planning that the coaches can use. I think if the coaches continue to gain more interest and we prove more value, we’re going to see a much greater use of analytics.