A few days ago, the sixth annual MIT Sloan Sports Analytics Conference was held in Boston. The officially stated goal of this conference is to “provide a forum for industry professionals (executives and leading researchers) and students to discuss the increasing role of analytics in the sports industry.” Unfortunately, I did not attend the conference, but have been following some of the highlights of it through articles and podcasts, mostly from ESPN’s website.
Sports analytics basically refers to the trend of applying statistical tools and various other metrics to the world of sports for the purposes of building teams and evaluating talent. For a long time, the professional sports leagues placed a lot of faith in hunches and subjective evaluations. The statistics that people did pay attention to, like batting average in baseball, were pretty basic and, in many people’s opinions, tended to overvalue some skills while ignoring other important ones altogether. That began to change in the 1970s and ‘80s as computers became more widely used and statisticians like Bill James started challenging the conventional wisdom. Today, sports analytics is part of the mainstream culture, as evidenced by the millions of people who participate in statistics-based fantasy sports leagues and by the success of both the book and film Moneyball.
At first glance, sports analytics might not seem to have much to do with market research (although I’m surely not the only market research person out there who spent their youth developing analytical skills by poring over newspaper box scores, studying the backs of baseball cards, and trying to figure out the optimal fantasy football lineup). But on a certain level, what the people at the MIT Sloan Conference were talking about is very much the same as what any market research client is trying to accomplish. One of the main reasons that the professional sports industry has embraced analytics is because there is now so much money involved in player contracts that getting it wrong has become more costly. Teams can’t afford to draft the wrong player or to overpay for an unproductive veteran free agent (unless you are the Mets). They turn to analytical tools to reduce their risk and to uncover hidden gems in the talent pool. That’s what users of traditional market research are trying to accomplish: using data to make smarter decisions, to find new opportunities, and to ensure that every move provides value to the organization.
Most of the people reading this will never have to make a decision about which power forward to draft or the future potential of a minor league outfielder with a knack for drawing walks. The only time most of us will employ sports analytics on the job will be when trying to fill out our NCAA Basketball Tournament office pool brackets. But anybody who makes decisions in a business will face a situation where the availability of good data and proven analytical tools will make the difference between success and failure. That’s what market research is all about.
If you want to learn more about how analytics can help your organization make data-driven decisions, email our Director of Business Development Sandy Baker at SandyB@RMSresults.com or call her at 315-635-9802. She’s a huge Syracuse basketball fan and would love to talk to you about Scoop Jardine’s assist to turnover ratio, Fab Melo’s increasingly better free-throw percentage, or C.J. Fair’s baseline jumper. The Bunker would certainly be willing to join into this discussion as well.