People Skills in NBA’s Office Spaces

This summer, two noteworthy front-office changes seemed to shed light on the evolution of the NBA analytics movement.[1. Ed: The title of this post is an homage to the this classic scene, which, while played for laughs in the film, is discussing a vital skill in an organization with multiple areas requiring extreme subject matter expertise with no natural bridge or commonality between the spheres.]

First, the Los Angeles Lakers, widely regarded as data “nonbelievers,” reassigned assistant coach and advanced scout Clay Moser to a liaison position, with the stated objective of bridging the gap between the analysts and the coaches. According to reporter Bill Oram, “statistical analysis got lost in translation.” Whereas the analysts struggled to understand the “practical implications” of their work, he wrote, the coaches underestimated “the value of the stats.”

More recently, the Sacramento Kings hired Roland Beech as their vice president of analytics. The media accounts regarding this announcement stressed Beech’s experience as a Dallas Mavericks assistant coach who played, in the words of Mark Cuban, a “key part” in game planning, setting lineups, and optimizing rotations. “I give a lot of credit to Coach Carlisle for putting Roland on the bench and interfacing with him,” Cuban said.

In both cases, it’s interesting to see where the emphasis is placed. It’s not on the function of analytics, because even though the Lakers and Kings are seemingly in different stages of adoption, there’s an implicit acknowledgment that statistics have a role in today’s game. Rather, it’s on the implementation of analytics — how data-driven insights are packaged, delivered, and incorporated into decision-making processes, especially on the court.

This issue is hardly exclusive to NBA teams. In a global survey of over 3,000 business leaders, the MIT Sloan Management Review found that “data is not the biggest obstacle” for most organizations. Instead, nearly 40 percent of respondents cited “lack of understanding of how to use analytics” as the leading constraint, followed by the challenges of balancing “competing priorities” (over 33 percent). Analytics is less an analytical problem than a cultural and managerial one. Even if you have the best data, you still need to help key leaders such as head coaches to apply it in ways that align with everything else they have to do.

How do you address this problem? According to the MIT Sloan Management Review, you should “start with questions, not data.” Organizations often focus right away on the collection, cleansing, and conversion of data, and they leave themselves with too little time, energy, and resources for determining the potential application of the analytics. Instead, they should first define their organizational needs, which would give purpose and urgency to any analyses that may come down the line.

These needs should be shaped by those who are in the most critical positions to act on the data. Similarly, the way that the data is framed, processed, and presented should be fundamentally tailored to the end user. In recent business literature, perhaps the most recognizable articulation of this idea comes from the world of industrial design, where professionals have developed the practice of rigorously observing and interacting with users before they prototype tools for them. It’s a systematic process that’s rooted in empathy — not the touchy-feely sort, but the kind that strives to build something truly useful for people. It considers the unique challenges, circumstances, and environments in which the tools will be employed.

Above all, design empathy draws upon the real experiences of users so that their insights can be part of developing their tools. A classic example involves Danish marine radios. In the 1970s, before the London firm Moggridge Associates embarked on revamping these pieces of equipment, it sent a designer named John Stoddard to the coastal towns of Hull and Grimsby. He joined fishermen on a trip to Iceland, where he observed how they actually used the radios in the elements. The expedition helped him understand particular nuances of marine life that would have otherwise eluded him, and it resulted in a product that was not only more indispensable, but also easier to integrate in the fishermen’s existing work systems.

You can find many other examples of design empathy, from surgical tools to iMacs. In sports, as described by Travis Sawchik in Big Data Baseball, the Pittsburgh Pirates embedded analysts Dan Fox and Mike Fitzgerald into the clubhouse so that a “two-way conversation” can organically take place between the statistical and the coaching sides of the team. Eventually, coaches and players began to pose questions that stemmed from their unique “knowledge base,” like whether specific pitch sequences lead to more ground balls. Fox and Fitzgerald used these questions to drive their analytical work. Their findings ultimately assumed practical value, influenced the Pirates’ pitching philosophy, validated the expertise of the clubhouse staff, and institutionalized the process by which analytics would be implemented on the field.

This takes us back to the Lakers and Kings. Whether intentional or not (and I have no way of confirming it since my NBA connections are zero), these personnel moves seem to be a pivot toward the Pirates’ direction. Moser is joining the front office to infuse courtside experience and help figure out which analytical insights could be applied. Beech is bringing a wealth of knowledge about how data can be operationalized in pursuit of a championship. The two cases have their differences, but the general spirit is a basketball version of design empathy that seeks to maximize the utility of analytics.

In sum, I see three immediate takeways for those of us who appreciate what statistics can do to build NBA contenders.

First, it’s hard to overstate the amount of wisdom that frontline staff, especially head coaches, can impart upon analysts. Based on seminal works like Basketball on Paper, we already know that many of them have, for quite some time, possessed intuitions that advanced metrics have recently formalized. But coaches are also aware of the types of ideas that can be rapidly adopted, as well as the level of resistance that such ideas might meet. If nothing else, they can help analysts distinguish the potentially valuable questions from the more expendable ones.

Second, above and beyond the quality of the analytics, it’s important to consider how findings are communicated. Part of it is a matter of simply knowing your audience, so if heat maps happen to be preferred by some athletes, then appropriate visual presentations should be embraced. Likewise, if informal dialogue makes the data feel less intimidating, then accessible formats should be promoted. The overarching theme is that the delivery of information should facilitate action. The analytics should be conveyed in a way that coaches and players can expeditiously operationalize the main ideas and seamlessly incorporate them into their routines.

Lastly, a greater emphasis on data implementation issues is poised to create competitive advantages for teams. With the proliferation of advanced metrics across the league, it seems harder to gain an edge through sheer analytical capabilities alone: if everyone can readily access the data, then it’s more difficult to separate from the pack on this front. So, instead, the competitive advantages are likely to accrue to franchises that can act faster on information, and you can’t have this kind of frictionless execution unless systems are in place.

I admit that, once we set aside the business jargon, these lessons boil down to common sense; certainly, they aren’t rocket science. But their simplicity belies how incredibly challenging it is to transform an entire franchise from top to bottom.[1. Ed: Last season, football analyst Trey Causey published this excellent “Blueprint for an Analytical NFL Franchise” illustrating both how clear the path to such transformation really is, but also how onerous and intricate it will prove to be.] Ideas like embedding analysts in NBA locker rooms seem so easy that they are often taken for granted, yet the best organizations never leave them to chance. They are strategic, methodical, and deliberate about implementing them.