On the Sloan Sports Analytics Conference and Adopting Advanced Stats

This feels a bit strange as I didn't actually attend the fifth annual Sloan Sports Analytics Conference - even though it's practically held in my backyard - but I went last year and streamed it live this time around and wanted to share my thoughts. For those that aren't aware, MIT has been hosting this forum since 2007 for the purpose of discussing the role of analytics and stats in the sports industry. It's no secret now that the field of sports analytics, particularly in basketball, has expanded tremendously in scale the past few years. Teams have implemented it, dedicating entire staffs to gathering data. Writers have used it to help guide analysis. And fans, at least those reading internet content, reference it in discussion.

If you're familiar with my writing, it's clear that I'm a proponent of using advanced stats in analyzing basketball. That has everything to do with my personality. I pay my bills being web/business analyst. I live data. This makes it incredibly easy for me to adopt sports analytics but I understand that it's difficult for others to accept as a valid form of analysis in sports. Kevin Pritchard, who was in this year's Basketball Analytics panel, said that 20 of the 30 NBA teams utilized advanced stats to some degree. I won't speculate on why the other ten teams haven't adopted it but I do want to discuss the reluctance among fans.

Acceptance is a dominant topic throughout the conference each year. Mike Zarren, Assistant Executive Director of Basketball Operations and stat evangelist for the Celtics, said that Moneyball created a dichotomy between those that believe in using numbers and those that don't. The problem, as with any belief, is that the polar extremes tend to dominate the conversation. Many stat supporters defend it staunchly with a "numbers don't lie" undertone on an already confusing topic. It turns people off. On the other extreme are those that don't trust data because it can be manipulated or believe so strongly in using their eyes that they dismiss the field altogether. From that comes this perception that advanced stat supporters don't "watch" the game enough. Stats are law, rather than complementary information. Attending Sloan gives you the proper perspective, though.

We tend to think that star players determine success. We think that general managers trusting their guts is what matters when targeting players. And we're absolutely right. Rockets GM Daryl Morey, who's widely known for running a stats driven franchise, takes a lot of flack for fielding a 31-32 team. How can he be considered a "genius" for assembling that kind of production? He addressed that in a panel about natural talent vs work ethic. He said all the stats in the world result in .500 teams without stars like Tracy McGrady and Yao Ming. Obtaining the rest of the parts is what requires using every bit of information available.

It's similar from a coaching perspective. Del Harris said he used per possession scoring stats all the way back in the 1970s. He had a better understanding of how his college team was performing because he calculated where its scoring should be per ten minute interval and would adjust the pace accordingly. Later in his career, as an assistant to Don Nelson, who he said dismissed stats, Harris used them to find two- and three-player combinations that played well together and suggested those lineups.

Stats to these successful basketball operators are a tool, another methodology. They have hunches and "see" things just as we do but they also have the prudence to validate their questions with more information. Zarren goes so far as to say that statistical analysis and video analysis need each other even though video analysis is more accurate. That's the Celtics' stat guy saying that what you see is more reliable.

As fans, it's naive to think that multi-million dollar businesses aren't exhausting every form of analysis to better their products. So, stats vs gut is not a case of either/or. It's about gaining a competitive advantage by understanding the entire picture. As Mark Cuban said, "analytics is risk management at the end of the day."

But even with the reluctance to adopt sports analytics, what I've noticed is that the influence of advanced stats is so widespread now that even those that dismiss them are aware of the vocabulary. True shooting percentage, total rebounding rate, pace, efficiency - they're common terms in today's NBA. So, why are they still dismissed?

I had the realization that regardless of resistance, advanced NBA stats are now mainstream and pedestrian, and in turn, no longer all that insightful. I can tell you that Carmelo Anthony's TS% isn't close to elite but it adds little in ways of analysis because he scores a lot and his teams win. What's important is why he wins despite that fact.

What people actually want from advanced stats is advanced segmentation because that's when we finally have context. Published material is getting much better with this. In my own work, I try to provide per quarter numbers, efficiency differentials, positional production - anything that provides clarity for a 2% change in shooting percentage. A common rebuttal is that game situations change so dramatically that averages aren't reliable, which is valid. At Sloan, Cuban, Zarren and Pritchard all said that their departments account for this and eliminate averages entirely. They're able to exclude the "noise" and identify actionable consistencies.

Writers don't have the money or bandwidth to do this, but teams are doing it every half of every game. How segmented can they get? Just have a look at the new data from STATS, LLC. So, as sports analytics continues to influence basketball discussion, think about it as just another methodology. General managers rank analytics with many other factors on their board. Why? Because they're trying to answer complex questions with information their eyes may have missed. As fans and as writers, we'll find usefulness in analytics by doing the same.

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