Ben Alamar is a U of M graduate and a star in the field of sports analytics who has helped many professional teams use data to their advantage. His book, "Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers" is due out in August.

Full disclosure: Ben is also a friend we have known for 20 years, and we were the best man in his wedding with wife Amy, also a good friend from our U of M days.

Despite his association with us, he is an interesting guy with an interesting book. So we wanted to share a few things with you. Find him here on Twitter and find a link to buy his book here.


Q First off, when you started in the field of sports analytics, it was relatively new – or at least relatively obscure. What tempted or caused you to push in that direction?

A I grew up with a passion for sports. I played in high school and college and was a rabid fan on the Washington Redskins and Washington Bullets. I also had a real interest in the application of math and statistics to practical problems, which is why I studied economics at the U and then again in graduate school. When I discovered that there was a real possibility of making a career by combining these two interests, I did not spend too much time trying to decide whether I would pursue it, but rather how I would pursue it.

Q I saw an interview in which you described how the OKC Thunder nearly took Brook Lopez instead of Russell Westbrook, instead choosing Westbrook after digging deeper into advanced metrics. Can you take me through that process?

A The basic issue that the Thunder faced in that situation was that they liked Westbrook a lot as a person but the team had a desperate need at the time for a PG (essentially the quarterback of the offense) and Westbrook had played mostly off the ball in college. They needed to feel confident that he could make the transition. We charted every shot that UCLA took that season (Westbrook's team) and analyzed how effective he was as a passer - finding players in good position to make shots, relative to other PGs. When the analysis of Westbrook's passing efficiency was compared to that of other high level PGs, it was another piece of evidence that he could be a high level passer. This process provided management with more information and thus less uncertainty about how he would transition in the NBA.

Q You’ve worked as a consultant for several teams. Aside from the Westbrook example, what are some other concrete examples of decisions made with sports analytics in mind that proved beneficial?

A The first decision my analysis was ever a part of was for the Portland Trailblazers during the 2005 NBA draft. I was working for a startup fantasy sports company at the time, and we were contacted by Kevin Pritchard (then the Trailblazers' GM) to develop a statistical model that would help predict performance of college players in the NBA. We had Jarret Jack rated highly, and the Blazers ended up trading up from the 27th spot in the draft to take Jack at 22. Similar to the Westbrook decision, the analysis was not the sole reason for the decision, but provided additional evidence to support the decision, reducing the risk of the decision for Pritchard. Jack has gone on to have a good career in the NBA - getting votes for 6th man of the year this season with the Golden State Warriors.

Q Which teams are at the forefront of using the types of metrics you’ve studied, advocated for and developed?

A The top teams in the NBA are really the Rockets, Spurs and Mavericks, but most teams in the NBA now utilized analytics to some extent. In the NFL, it is still growing, but teams like the Ravens, 49ers, and Patriots are all utilizing analytics in the decision making process, and the Cleveland Browns just hired the Cowboys analytics director to build up their work in the area.

Q Your book -- Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers – is due out in August. At what point did you feel as though you had enough material or research to put into a book.

A Really the motivation for the book came from a conversation at a sports analytics conference. I was talking to someone from an NBA front office who had been tasked with developing his team's analytics group. He confessed that while he was really interested in it, he had no real knowledge on how to move forward. I gave him some advice but quickly realized that, given the complexity of the subject and financial commitment necessary, there was a limit on how much I could assist. The most effective anlaytics groups are designed to complement and support the organizations strategy. Having seen first hand how analytics can be effective in numerous environments, I thought that developing a handbook for non-technical people, on how to grow and manage analytics would be a useful tool.

Q Fast-forward a defined number of years – say, 20. Will we all be sitting around wondering why some sports organizations were so slow to adapt to some of these models?

A I think the reasons for slow adoption are fairly understandable. Top decision makers in sports (or any business for that matter) have gotten there typically because they have been wildly successful in their sport or business at a variety of levels. These highly successful people are then presented with a new set of information that they have been successful without and do not have a strong understanding of why this information should be used, how much confidence to have in it, or how to assess the skills of the people they have hired. Adopting a new technology like analytics, is to some extent risky - so unless the top decision maker is motivated to invest both financial resources and their own time in learning about, they simply won't change their style of decision making. Helping to reduce the cost in terms of time for decision makers is the goal of my book, and as analytic teams like the Spurs and Ravens continue to win, the motivation to try will continue to emerge.

Q What is the next frontier in this field?

A The amount of data in sports is exploding. We are getting access to data that tracks heart rates, movements, player locations... As the data grows and becomes more complex, the challenge for analysts will be to open their imaginations and find novel and effective uses for the data. While it is not hard to come up with ideas for this type of data, it is a challenge to identify the high value opportunities with it. Which tools can be developed that will actually get used by coaches and general managers, and not just be fun projects for the analysts. There are so many possibilities, analysts must step back and find the best uses for their skills with the goal of helping their team win more games.

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