Nylon Calculus: Previewing the 2017 MIT Sloan Sports Analytics Conference

Mar 1, 2017; Atlanta, GA, USA; Dallas Mavericks head coach Rick Carlisle talks with forward Nerlens Noel (3) in the second quarter of their game against the Atlanta Hawks at Philips Arena. The Hawks won 100 - 95. Mandatory Credit: Jason Getz-USA TODAY Sports
Mar 1, 2017; Atlanta, GA, USA; Dallas Mavericks head coach Rick Carlisle talks with forward Nerlens Noel (3) in the second quarter of their game against the Atlanta Hawks at Philips Arena. The Hawks won 100 - 95. Mandatory Credit: Jason Getz-USA TODAY Sports /

Like the Christmas Day games and the All-Star break, the 2017 MIT Sloan Sports Analytics Conference affords us an opportunity to gauge how the NBA has been progressing. But it encourages a different type of reflection than what these standard milestones typically prompt. Rather than focusing on seasonal dynamics — how recent games and specific matchups affect playoff seedings and lottery odds — SSAC ideally facilitates macro-level examination, with an eye towards leveraging nascent analytical insights for long-term competitive advantage.

This year is no different. If we spend a few minutes on the SSAC agenda, we’re led to contemplate some of the recent developments that are poised to shape the league in the foreseeable future. It’s not to suggest that the conference organizers necessarily seek to make these connections, nor does it imply that the speakers and panelists intend to draw upon major analytics trends in the same manner. It’s simply to say that, as basketball enthusiasts, we can spot the timeliness and relevance of various SSAC presentations amid broader NBA discussions.

Start with “Beyond the Foundation: Building a Team around a Superstar.” Moderated by ESPN analyst Jackie MacMullan and headlined by general managers David Griffin, Bob Myers and Masai Ujiri, this panel explores “some of the challenges that come with building a team around a particular player’s gifts.” It’s a noteworthy topic in a season when exceptional individual performances captivate fans on a fairly regular basis.

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For example, Russell Westbrook paces the NBA with a usage rate over 40 percent, playing a crucial role for the Oklahoma City Thunder even on the defensive glass. Mike D’Antoni’s offensive system is entirely predicated on James Harden, forging tight alignment between the front office, the coaching staff and player personnel. The Denver Nuggets now appear to be fully committed to making roster decisions through the Nikola Jokic “point center” prism. The Cleveland Cavaliers’ efforts to fortify their rotation are motivated in no small part by LeBron James’ immense workload and the risks that come with such dependence on the franchise cornerstone. The Boston Celtics continue to weigh the optimal time to cash in their assets for a bonafide difference-maker. Yes, the NBA has always been a star-driven league, but armed with new data, teams seem to be addressing this reality in evolving ways.

More broadly, “Beyond the Foundation” comes at the heels of the new collective bargaining agreement. We know that, in response to Kevin Durant’s departure from Oklahoma City, the league contemplated various measures to help teams (especially those in small markets) retain their stars. We’ve already seen unintended consequences emerge, with DeMarcus Cousins traded to New Orleans just as Sacramento faced a looming decision on a lucrative contract extension. For their part, front offices must quickly adapt their strategies, including their use of analytics in talent evaluation, to this new governing system. It’s no surprise salary cap expert Larry Coon has a separate talk dedicated exclusively to the CBA, which is also likely to come up during the “Silver Asks Silver” conversation between Nate Silver and Adam Silver.

In some respects, this new agreement marks the institutionalization of analytics, as it features an entire section on wearable devices. It defines the types of equipment that can be classified as “wearable,” establishes an advisory committee to approve devices for player use, and outlines parameters for data collection, security and access. In particular, it stipulates that players may decline to use a wearable device at any time. Player data from these technologies “may not be considered” for contract negotiations, and unless the league and the Players Association first come to an agreement, they are off limits for public consumption and commercial purposes. As Rian Watt observes, despite leaving some big questions unanswered, these clauses establish “a presumption that players own all data about themselves.”

Although wearable devices are specifically called out, it’s safe to say that other technologies for improved performance are subject to similar treatment, even if the pro-player predisposition is not. In “Sports Science: The Next Frontier,” panelists are asked to ponder the “rapid growth” of their field, as well as the “barriers to increased adoption.” “Sustaining Greatness” creates a forum for both athletes and their “teams of specialists” to discuss how they work together on nutrition, training and recovery. In “The Science of Sleep,” Meeta Singh of the Henry Ford Health System offers “strategies to combat the deleterious effects of jet lag.” These panels promise to shed light on the competitive edge that rigorous techniques may provide, but given the voluminous and potentially invasive nature of the data, perhaps they’ll also touch on the importance of privacy safeguards and other necessary constraints. Indeed, to the extent that athletes feel comfortable with the extra steps taken to protect their personal information, they might participate more readily in sports-science initiatives, have greater fidelity to training regimens and ultimately generate more value for their teams.

Beyond performance metrics and systems, SSAC has its usual suite of sessions that strive to build upon preexisting literature and advance the analytics dialogue. Numerous research papers employ machine learning and other sophisticated methods to examine everything from the structure of offensive possessions to the attributes of shooting styles to the automation of “ghosting” player decision-making to the ever-elusive quantification of injury risk. Some sessions cover the challenges of overcoming front office cognitive bias, the potential for data-driven in-game adjustments and the “truths and myths of the 3-point revolution” (the latter featuring our dear friend Seth Partnow).

But perhaps the most intriguing set of presentations revolves around the impact of basketball analytics outside the sport itself. In “A Step in the Right Direction: The Positive Effects a Sports Team Can Have on Public School Education,” John Borders, David Sparks and Mike Zarren share their experiences with and evaluation of the Step Your Game Up program, which provides Celtics-related incentives for at-risk youth to improve school attendance and academic grades. Similarly, in “From Sports to Science: Using Basketball Analytics to Broaden the Appeal of Math and Science among Youth,” John Drazan, Amy Loya, Benjamin Horne and Ron Eglash discuss how they developed an open source shot chart program, organized shooting clinics, helped youth participants generate individualized heat maps and determined whether the experience had an effect on STEM interest.

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Both projects aim to capitalize on the allure of basketball to promote social good. In light of the presidential election and rising NBA player activism, they represent what might very well be the most constructive type of response to the “stick to sports” complaint.