The 2017 MIT Sloan Sports Analytics Conference is a gathering of some of the smartest and most engaging basketball minds on the planet. While we’re here Nylon Calculus is going to try and pull a few of these brilliant minds aside for some short Q&As. Also, check out our running blog for Day 1 of the conference.
Nylon Calculus: What have been some of your favorite parts of the conference so far?
Paine: I thought the presentations of the research was unusually good this year. I walked past every single one of those poster boards and every single one I stopped at and looked at. I felt like there wasn’t a dud in the whole bunch. In the past there’s been a relatively high batting average, but I don’t think I’ve ever seen it be this consistent. To me, I haven’t gone to a whole of panels today, but this was just something that stood out to me — being really excited about the research.
Nylon Calculus: You have been coming to the conference for a long time. I’m wondering what you think about the evolution of the conference and how well it reflects the evolution of analytics for content producers, people like 538, and people who are consuming analytics. Does it feel like an accurate reflection? Or does it feel like it’s own animal to some degree?
Paine: It actually, surprisingly, is an accurate reflection at this point. There was a trend a few years ago where it seemed like it was more about people figuring out what people could market as analytics without people actually being a part of the analytics community. But now I’m starting to see a huge uptick in the number of academics who are bringing really cutting-edge research methods to problems, and now that we have all this data, especially from player tracking, being present in all these sports we’re starting to see a weird upswing.
The old take that everyone had, maybe like four years ago at the Sloan Conference was like, it’s passé, no one is talking about anything interesting because everyone has been snapped up by teams, it’s just boring and a waste of time. And I don’t think that’s true. Maybe it was four years ago, more so, but now I think there’s an influx of new ideas. There are so many people who have data capabilities and research capabilities that are trying to get their work out there. Going back to the research presentations — super interesting stuff that’s cutting-edge and exciting.
Nylon Calculus: Is it weird to morph from an attendee to now you’re moderating a panel and the Hot Takedown podcast is now an event in front of an audience?
Paine: I like being in the audience more than being on stage, for sure, because it’s less work. But I don’t think it’s that different thought. It’s all part of the same conversation whether you’re hearing other people talk about it. I’m sure you’ve felt that way plenty of times when you’re sitting at a panel as an audience member and you would hear a conversation happening and you would want to be like, “hey, hey, I can say something about that. You’re wrong!”
Everybody feels that way I’m sure, so to be up on stage now, there’s probably somebody in the audience that thinks that I’m wrong, and they’re probably right.
Nylon Calculus: Can you talk about the Hot Takedown podcast and some of the challenges of incorporating analytics into an audio medium?
Paine: That’s the biggest hurdle that we face each week is trying to figure out a way to talk around numbers and use numbers, but not delve so deep into it that someone who is listening to it in their car or while they’re working out doesn’t know what the hell we’re talking about. I think that’s why the team that we have with Chad [Matlin] and Kate [Fagan] and myself, we each bring different aspects of it. I’m the data guy and they have to reel me in and make sure I don’t get bogged down too much in the numbers. That kind of balancing act is important because when you don’t have the advantage of being able to visualize something like a concept or a stat or something, it really ups the difficulty level.
Nylon Calculus: You wrote a piece recently about the NCAA Selection Committee incorporating metrics into their process and I think you drew a really interesting line at the end, that illustrated the idea of metrics that illustrate how good a team is that are separate from winning the game. And sometimes those things line up and sometimes they don’t. How do you balance that, or is it something that’s inevitably out of balance?
Paine: It’s such a tough balance. Everyone that I talked to, even the stat people that were at that meeting, they were always careful to be like, “We think the scales should be more tilted towards the backwards-looking, ‘how accomplished are you as a team?’ rather than the, ‘your point differential is great but your record is bad because you’ve had bad luck in close games.'”
Look, this is mostly reward for the best teams and 90 percent of the time those two things are going to line up no matter what but we’re really just talking about edge cases where a team has maybe underperformed relative to their talent, and you can make an argument that they should be seeded higher based on that. I think that is the direction that you might see a little bit more of when they start using some advanced metrics. But I think more often than not you’ll see the inverse that where a team has a good record and maybe not the best advanced metrics and they’ll still get the benefit of the doubt from the seeding. Just because you have to reward teams for winning games first and foremost, and then you can play around at the edges with the advanced metrics that are there too.