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Freakonomics podcast: Can Backgammon Save Us from Ourselves?
Posted By: ah_clem In Response To: Freakonomics podcast: Can Backgammon Save Us from Ourselves? (Bob Koca)
Date: Sunday, 14 June 2026, at 1:51 p.m.
Yes, that analysis is not unique to backgammon.
But if you read or listen to the podcast, it was Frank Frigo and someone named Chuck Bower who worked with NFL teams and applied the same kind of analysis that they have applied in backgammon. Apparently, this wasn't used for football prior to that.
From the transcript:
FRIGO: We built proprietary models for a variety of applications in sports, in commodity trading, in healthcare, in education. The idea was to take a game-based approach to solving difficult problems. There’s a lot of data out there, but there’s not a lot of good ways to take that data and steer it towards a particular objective. So in commodity trading, it might be R.O.I. In education, it might be how to reduce freshman attrition rates. In healthcare, it was how to reduce hospital readmissions for cardiac patients. And then, of course, in sports, one of the big, big breakthroughs that we applied was, we took a lot of idea from the modeling of games, and applied it to professional football.DUBNER: When you say you applied it to football, what kinds of scenarios, or with what kind of goals in mind?
FRIGO: Originally, when I was just watching the game as a fan and I was a pretty seasoned backgammon player, I would look at what appeared to be the inherent risk-aversion bias of head football coaches. They would get to fourth-down decisions and they would sort of pucker up and relinquish possession.
DUBNER: You’re saying they’re very risk-averse.
FRIGO: At least they used to be, before we came onto the scene. It struck me that those kinds of decisions are very, very similar to backgammon decisions, and decisions that were modeled. One of the great breakthroughs in the modeling of games, and backgammon in particular, was that when you focus on the proper objective, which in backgammon is giving yourself the highest probability of winning the match, and in football, if you apply the same metric, which is how do I make a decision that gives me the highest expected outcome of a win — not maximizing yardage, not assuring I get some amount of points on a particular drive — when you shift that objective and that metric, it really opens things up and you start to see that these risk-averse decisions are actually quite wrong. We set out to build a model that could actually measure it. We did this by building a fully customizable simulation model that was built on a lot of N.F.L. empirical data. My partner, Chuck Bower, who is also an experimental physicist, did a lot of research at Indiana University and worked for N.A.S.A. He and I put our heads together to build a model that could simulate N.F.L. games from start to finish, running clock, penalties, the whole thing. We had a hypothesis that coaches were giving up quite a bit. It turned out after we started studying it more deeply that they were worse at it than we even thought they were. They were giving up even more. Then we got excited. We’re like, it’s time to start knocking on some doors.
DUBNER: And so how much is your model or versions of your model used in the N.F.L. today?
FRIGO: I would say it influenced probably just about every team, some more directly, some indirectly. We licensed to about 12 N.F.L. teams. Most famously, we worked with the Philadelphia Eagles during their Super Bowl run. They used it religiously
Anyway, worth a listen or read to hear Bob Watchel, Mochi, Frank Frido, Marc Olsen, etc. in the mainstream press.
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