August 21st, 2013 at 9:00 am
This week our featured book is Sports Analytics: A Guide for Coaches, Managers, and Other Decision Makers, by Benjamin C. Alamar, with a foreword by Dean Oliver. Today, we are featuring an article by Benjamin Alamar in All Things D on the difference between analytics and strategy.
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Analytics Is Not a Strategy
Benjamin C. Alamar
I have been working in sports analytics for nearly 10 years, and still, virtually every time I tell someone what I do, they say some variation of “Oh, you do moneyball.” While my normal response is “yes, something like that,” the truth is that there is real difference between “sports analytics” and “moneyball.” As I’ve written elsewhere, sports analytics (or just plain old analytics) is a set of tools, while “moneyball” is the term coined by author Michael Lewis in his 2003 book to describe a strategy that employs the tools of analytics. The tools of analytics have advanced significantly since Michael Lewis’ book, yet the “moneyball” strategy is unchanged.
Analytics involves the tools of data gathering, data management, statistical analysis, data visualization and information systems to deliver better information, more efficiently, to decision makers within an organization. Clearly the technology behind these tools has advanced rapidly in the last ten years with tools such as Hadoop, R, Qlikview and the like all making the utilization of the mass amounts of data that are now available to organizations possible.
In sports, the most significant leap forward in technology is in data gathering, where companies such as Stats llc and Catapult Sports have utilized advances in technology to fundamentally change the size and scope of data available from practice and competitions. Stats llc utilizes cameras and optical tracking technology to capture the position of everything that moves on a basketball court 25 times a second, while Catapult Sports utilizes GPS, accelerometers and other wearable technology to track player movements and physical characteristics such as heart rate. Both technologies have shifted the type of data available in sports from the count of specific on court actions (attempted shots, for example) to the continuous movements of every element on the field of play.
Despite this massive increase in the availability of data, Moneyball remains unchanged, because Moneyball is a strategy for utilizing analytics. Moneyball is the value investing of building a successful sports franchise. The concept is to utilize data to identify undervalued players so that teams with lower payrolls can still compete at a high level. The Oakland As — and, to some extent, the Tampa Bay Rays — have followed this strategy successfully for 10+ years. But, just as there are a multitude of investment strategies, there are countless strategies for building successful sports teams. Moneyball can be effective, but that does not make it the best use of analytics for every franchise. Analytic systems can require a significant investment in tools and personnel, so it is the strategy for employing those systems within the organization that determine how successful the organization with their analytics.
The key to successfully employing analytics is not to simply invest in analytic systems and blindly apply the strategies that have been made famous through popular books and movies. The key instead is to understand the strengths and weaknesses of your organization and seek to find the areas that can best utilize analytics as you build them. The Dallas Mavericks and the San Antonio Spurs of the National Basketball Association, for example are both highly analytic teams, but they also have approached analytics differently, applying their analytic resources strategically to areas that make the most sense for the team.
The San Antonio Spurs were one of the first NBA teams to hire a statistical analyst and an applications developer. They employed these personnel assets along with any technological investments, at least initially, on assisting with player acquisitions. The general philosophy of the organization from a personnel side has been to buy low and sell high — acquiring players who fit the style of play of the organization well — typically through the draft — and then trading them for other assets once the rest of the league has seen the value that the Spurs did. This is similar to the Moneyball strategy employed by the As, and has produced a team that is currently in the Western Conference Finals, with only one player picked in the top 10 of the draft.
The Dallas Mavericks were pioneers in analytics in the NBA as well, but employed a very different strategy for maximizing their investment in analytics. The Mavericks hired the first statistician in the NBA to function as part of the coaching staff. Instead of focusing primarily on player acquisitions like the Spurs, the Mavericks focused first on in game decisions, believing that is where analytics would be most impactful in their organization. The guiding philosophy for the Mavericks was that since there are a lot more in game decisions made during an NBA season than personnel decisions, the benefit to them would be best realized focusing on that part of winning games. The Mavericks won the NBA title with statistician on the coaching staff and were in the playoffs for two of the three seasons since the hiring.
Most businesses, like most teams, have limited financial resources to spend on analytics. This constraint makes it vital for organizations to not just invest and “do analytics,” but to create a strategy for maximizing the return on their analytic investments. While there is no one strategy that works best for all organizations, any organization can be helped to make better decisions by having better information.