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Welcome to Wharton Money Ball. Welcome to a full hour of sports analytics here on the Wharton Podcast Network.
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This is Cade Massey hosting this week with the whole crew. We have Shane Jensen in here.
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We have Audie Wynder in here. Eric Bradlow, who was not scheduled to be, but slid in here pleasantly at the last
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minute. So we're gonna be fully staffed for this show. We've got a double guest show this week.
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We usually run guests for half the show and open lines the other half, but we wanted to hit the NFL draft with two
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barrels this week, and we had the chance to get two great guests, and so we jumped on it.
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First up, Richard Thaler. Richard Thaler is a professor at the University of Chicago.
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He also is a Nobel Prize winner. Was it 2017, 2018? I should know that, Dick, and I don't.
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2017 or 18? He- 17. So we've been doing this show for 12 years, guys, and we finally have a Nobel
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laureate on the show. This is the first. Dick, thank you for making time. We'd talk with you anytime.
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We're happy to have time with you. That doesn't mean it's the first time you
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invited me. Well, that's true. That's true, and I don't know why that
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is, honestly. We would take you on a lot of different topics, but we decided to grab you
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on the NFL draft. We've just come through the draft. We're gonna have two guests.
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We can kind of debrief the draft, but we thought we'd chat with you first, Dick,
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and I'm gonna take the perspective, I think the easiest way to think about it, I'm
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gonna take the perspective of your wife, the honorable professor, former professor, Franz LeClaire, who has
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not really loved the project we've been working on all these years. She doesn't really understand why we waste our
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time with the NFL, and so I wanna ask you that question, Dick. Why is a Nobel laureate messing around studying
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the NFL draft? I mean, why is that worthy of your time? Yeah, well, as you know, I have one
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criterion for anything I choose to do, which is it has to be fun, and so studying sports is fun.
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Now, of course, that is not a satisfactory answer to Madame LeClaire, but my serious answer
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to that question is that we don't have that many opportunities to study business decision-makers
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making decisions, so there's a big merger going on, and there's competing bidders, and we don't
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know what they're thinking and how they came up with the numbers, how the process evolved.
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In sports, we get to watch, so some team continues to punt on fourth down, fourth
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and one everywhere on the field. We get to see that, and most of my life, we would just say, what are
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they doing? But many years ago, you and I got the idea that the NFL draft would be
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an interesting institution to study because it involves so many different things, so I mean, the
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institution is interesting. Well, why do they have a draft at all? You could ask, like in European soccer, there's
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nothing like a draft, but they have it, and there's this odd chart, and our history of this went back to learning about
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this chart and actually calling up the guy who made it, and that's an interesting story.
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The guy was a partner of Jerry Jones at the Dallas Cowboys, an engineer, and the
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coach at the time, Jimmy Johnson, asked him a question. He said, look, we get offers to trade
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the fourth pick for some other picks, and we have no idea what the right price should be.
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Can you figure it out? And this guy's an engineer, so he's a numbers guy, and he got all the trades
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that had happened up to that point, and my impression, maybe your recollection of this conversation
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is better than mine, but my recollection is he said, kind of wrote them all down,
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and then kind of drew a line on a piece of graph paper, and he made the first pick worth 3,000, and the
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last pick worth one, and there's a smooth curve you can draw through that, and so
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for a while, that was proprietary with the Cowboys, but then assistant coaches move around.
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Now, Google NFL draft chart, and it will pop up. It hasn't changed in 30 years, and teams
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use that as like a price list. I think of it as like the blue book for used cars.
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You can look up what your 10-year -old Honda Civic should sell for, and so the paper we wrote a long time ago
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that my wife mocks us about asks the question of is that price list the right one?
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So you can trade the first pick for the eighth and ninth picks, according to that
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chart, or for half a dozen second-round picks, and that was the origin of our research paper, and what we found is that
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price list is very wrong. It's much too steep at the beginning that you'd much rather have the eighth and ninth
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picks than the first pick, and we've now had a re-dive into the idea of this asking, since we published a paper about
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this 15 years ago, has anything changed? And the short answer is no, so it's
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been nice talking to you, Cade. Well, let's take it one step further, and then Adi wants to jump in with a
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question, but I would say what did you learn? What do you think we have learned?
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We came back to it. God knows, I hope we learned something from coming back to this.
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It was bad enough that we spent so many years working on it the first time, and now we're gonna do it again.
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What have we learned this second time around, Dick? In what way do you think about this
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differently because of what we've done the last couple years? Well, so I think there were two big
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questions we started with this time. One is, do they still use this Jimmy Johnson chart?
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And the second is, have they gotten any better at picking players? Now, the reason why that chart is wrong
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is that, you know, I said the first pick is worth the same as eighth and ninth. That suggests the first player has to be
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twice as good, excuse me, or twice as valuable, or more precisely, how much he contributes
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minus what you have to pay him. The surplus has to be twice as much for the first one as to any pair
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you could trade for. And for that to be true, you'd have to be really good at predicting.
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Mm-hmm. And our favorite stat from the original paper was one we called the better than the
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next guy stat, which is take any two players who play the same position who are drafted one after the other.
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So the third running back compared to the fourth and so forth. And we say, what's the probability that the
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earlier player is better than the next one? And in the original paper, the answer to
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that question was 52%. Now, if they're perfect, it's 100. If they're random, it's 50.
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It's a little better. And it's a little higher in the first round. And so that was the first question is,
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well, now we've got stats and the combine and AI and who knows, maybe they've gotten
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a lot better at this. And the answer is they haven't. That number is now 53%, first round 58%.
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So they've not all of a sudden mastered the art of predicting who's gonna be good.
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And the second one is, do they still use that chart? And the answer is yes, kind of ridiculously
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so. So there was one trade early in the draft, the Browns traded down from six to
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nine or 10. And on the chart, that was worth 1 ,600 points. And they paid 1,602.
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And so- Okay, those prices are like floors. So they're the asking price. If the buyer's really keen, they'll sometimes pay
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more than that. But we don't see too many sales. Well, and that finding, I think was-
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I can't hear you. Let me know if you can't hear this, but the observation is that we thought they
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might learn away from that curve. We thought the prices might change. I mean, they've had more than a decade
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of learning. And in fact, it went the other way. The curve is the price. And it's just unbelievable that that curve was
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created based on 1980s trades. It's been in existence for 35 years now, and it hasn't changed.
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Free agency has come, salary cap has come, and the curve hasn't changed. In fact, if you look at the coherence
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to the curve, it's gotten tighter over time. So it's one of the most surprising things.
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Let's take a couple of questions. Adi was first and then Shane. Yeah, so I've actually had the opportunity to
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dig into this a bit and even published a paper on it, which I think Cade is certainly aware of.
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And I was really surprised when he told me, Cade told me that it hadn't changed
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at all. Because we know a lot of people in sports and they're pretty smart. I mean, not everyone is smart.
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I mean, I'm gonna be perfectly honest. But there's a lot of smart people, including
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some of our former students and very close friends now are pretty high up in lots
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of places. And when I explained what you guys were finding that it hasn't changed, the remark that
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came back to me was, well, we don't maximize expected value with our trades, which is
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where your wrongness comes in. The utility is expected value. We actually look for tail distribution probabilities.
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And so I went back and I looked at that and I found that the trade curve actually does match a utility function that
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weights with much, much higher probability superstars. So they've cut off, it's hard to measure
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superstars. It's a second contract. So, and taking a lead from Cade, what we did was with size of the payroll,
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essentially the cap that you're devoting to one player. So if you call superstar something like 15
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% of the cap, then, and you wanna make a trade that balances the expected number
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of superstars, then it actually does match. And that seemed to be a very highly,
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at least it puts a gloss on it. I mean, for many trades are really dumb because they aren't trading.
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They think they're doing that, they're not. They really should be trading expected value.
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But for some teams, I think the smarter ones, when they do trade up for a higher pick, they have that in mind.
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So we've done the following analysis to look at that. We've looked at every two for one trade
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that has happened. And then we've also looked at every feasible two for one trade.
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So I mentioned you could trade the first pick for eight and nine. You could trade it for seven and 12
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and six and so forth. And what we find is when you trade down, you get the same number, you get
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twice as many games started from the two players and the same number of pro ball
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appearances. So you don't, if we're settling for all -star appearances, you don't do better.
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You break even on that and you get - That's not enough. All-star period, that doesn't quite do it.
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You need superstar status, not just all-star. Yeah, well, if you heard of, there's a
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guy, kind of a tall guy, Tom Brady, I think 199. I rest my case. Hold on, I'm gonna- Not an anecdote
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on the one extreme? For real? Adi, what you need- Well, maybe I can jump in with- No, no, let
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me jump in. Shane, Shane, let me jump in first on this point. And we should continue the conversation in other
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places, Adi, but what you need for that curve to be correct is that the team places zero value on any level of performance
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other than extreme superstar. That's right. You need zero value on anything other than
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superstar. And there's no evidence from any labor market in professional sports that suggests that that's the
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value they place on performance less than superstar. I don't doubt, Dick and I said in
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presentations in the 2010s, in the 2000s, we said, look at this, the Pro Bowl curve
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comes pretty close to the Johnson curve. The Pro Bowl curve, I think you're right.
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You said this thing a minute ago, you said, this is what they have in mind when they're making the trade.
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Yes, it's something they have in mind. It's the psychology of the trade up, but
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it is far from a rational psychology. They care about getting starts, even out of
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first round picks. They pay a great deal for mere starters in every free agent market.
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The psychology might be a heavyweight on superstars, but there's no way that that rationalizes the
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trade curve, given anything else that we observe. Two quick comments. First, you don't have to put zero.
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You can have an S-shaped curve and you'll get the same thing. Secondly. I'm gonna have to understand that better.
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I don't accept that as a rationalization. You can have a logistic function that approximates
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it. Oh my God, this is gonna be the most extreme indicator function. Are you kidding me, Artie?
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Come on. It doesn't go that quickly. The second thing is, and I think this is sort of relevant, when you talk about...
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Well, you know what? I'm just gonna pass. All right, so I'll just say the following.
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In the formal analysis of the paper, which I think is gonna be out anytime this decade, the way we value players is by
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what percentage of the salary cap they get in their first free agent contract. Yes.
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That's what I do. That'll include superstars. And we'll have this conversation again when the
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paper is done, and otherwise we're gonna just go into the weeds. Shane. Yeah, and I hope I'm not just asking
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the same kind of question from a different direction, but earlier when you were talking about
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what they could have learned and how you kind of expect things to be updated, you
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mostly talked about that you would have gotten at least over time a more accurate evaluation
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of the actual talent. But that's only, I think... I just wanna clarify that that's only kind
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of half of the story in terms of like whether a one is worth a seven and an eight or whatever.
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It's not just that accurate, like the accurate evaluation of their various talent.
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It's actually also a big part of the story is how the talent distribution, or I
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guess how the value distribution falls off away from one, right? I mean, if you had sort of, I
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guess, an extreme enough distribution of actual value, the number one pick could be worth seven
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or eight. It's just in actual football, it doesn't seem to drop off like that.
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I guess it's maybe the empirical part of this. You know, if you look at the first
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round picks over the last couple of decades, there are a lot of busts. Just look at the quarterbacks drafted by the
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Chicago Bears until the recent one. And they're, you know, it's not the case
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that they hit very often. And, you know, as Mr. Brady shows, or for God's sakes, Mr. Irrelevant, Brock Purdy, it's
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not the case that the best players are all in the top five. They're not. It is also the case that there are
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just more busts at the top than we remember. We think of those guys, because they are
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the highest likelihood of superstar, we think that the likelihood of superstar is higher than it
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actually is. Eric was gonna jump in with a question. Yeah, so Professor Thiel, I wanted to ask
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you, so you also studied learning, right? And so I would think, I'm a Bayesian,
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but that doesn't mean just in Bayesian computation. I do think about how people update beliefs,
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and I do a lot of work on that. So if the chart is mispriced in some way, why aren't teams learning this over this
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long period of time, especially given, in my view, and your guys' empirical study, the overwhelming
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evidence that they do? Yeah, so I think most, so I think almost every team has somebody on it who
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has read our paper most have somebody who's been able to update it. There are many other updates around the professional
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sports world. So, and I think a lot of teams have another chart they think reflects actual value.
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But my impression from talking to people in the league is that the Jimmy Johnson chart
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is the list price. And if you're selling, that's your asking price. And you'll get a lot of nos.
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You'll hear from teams, yeah, we wanted to trade out, God knows what the Rams were
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thinking, but you'll hear, oh, they wanted to trade out, okay, then what price were they
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willing to take? And we don't see very many trades below the chart. So smart teams would have to be willing
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to sell at a discount. Right. So, and there's an old story, well, remember, right when the first version of our paper
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came out, the 49ers had the first pick and they needed a quarterback. And for months, they were debating between two
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hot prospects and they couldn't make up their mind. And a reporter, a local reporter called me
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and said, what advice would you give to the Niners? And I said, I'd announce a sale on
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the first pick, 20% off. And that's not what they did. And they used the first pick to draft
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Alex Smith. And there's another guy who was playing on the other side of the bay, who had
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a pretty good career and he went 24th. So, no one seems to, my advice is so worthless.
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No team has yet adopted the strategy of not knowing what to do with the first pick and announcing a sale, because it makes
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them look bad. Well, this is the thing that I want to emphasize because it's a very fair question.
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It seems to be an off equilibrium place we're at. The price that is so rigorously adhered to
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doesn't seem to match the value of the assets that come from those picks. And so the question is, why does it
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persist? Every team in the league has a chart and this is what happens guys. The trades come up and they've got Jimmy
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Johnson chart and they've got their own charts. And they compare it on all these charts
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and they know that the price is basically gonna be Jimmy Johnson. And then they decide how badly they want
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it by looking at their own charts. And so I asked a guy just last week, we were texting just before the draft.
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This is a guy who has run the trades for multiple teams. And I said, why would you say some
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teams are not willing to accept less than Jimmy Johnson, basically fail or sale, even if
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their other charts really like the trade at a lower price? So this is key. Why does Jimmy Johnson pricing persist even when
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lots of teams have sharper charts that diverge from Jimmy? Yeah, so this is the guys I got
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permission, I'm not gonna tell you who it is or use any team names, but he gave me permission to use his text.
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Why is he selling down? He says, this is the answer. He gives me the answer. He says, perception.
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This is quote from our text last week to my question. He says, perception, how another team was looked
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at, for example, when they got way too little in that general manager's first trade.
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You never wanna look like the sucker. And the safest way not to is to not deviate from what has gotten that deal
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done before, which is Jimmy Johnson. And that's the story. Taylor talked about the interview with the guy
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who created the chart, Mike McCoy. He told us that in 2006. He says, this way, no one gets skinned.
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He says, it's CYA, CYA, because these guys, they don't wanna look bad. They don't wanna look bad in the press.
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They don't wanna look bad to their peers. And that's enough to keep them.
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Now, one last wrinkle's needed. The guys who wanna buy at lower prices can't access it.
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They can't short. There's no shorting in this market. So the only guys that could move it
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are the guys that would sell at lower prices. But there are these basically punitive measures if
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you sell below Jimmy Johnson. It's hard to buy that they won't do that, that you're essentially saying that they're letting
00:25:25
something that's been known for 20 years get in the way of, I mean, listen, look
00:25:29
at fourth down decision-making. It took forever, but now they all do it. It's done, right?
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It took forever. They don't all do it. So they get it like half right. We didn't know how this was gonna come
00:25:48
out. What I'm telling you is it doesn't look like they've learned anything.
00:25:53
Well, let me ask you another question. How do you know how to build a Super Bowl-winning football team?
00:25:59
What's the secret sauce? Maybe there's something, I mean, why would we in the outside know how to do that?
00:26:04
Because implicitly you're saying that you're not doing it right because we know how to build
00:26:07
the team and you don't. How do we know that? No, I think, look, no one would hire
00:26:15
anybody that's on this call to do such a job, but look, right? I'm sitting in the office of a money
00:26:25
management firm that has my name on it. And we don't think we know how to run companies, but we think that we can
00:26:36
put together a portfolio of stocks that outperforms its benchmark. And that's by being right 53, 54%
00:26:48
of the time, right? So I think teams, the smarter teams are doing better, but they basically are, that's because
00:27:03
they trade down more often and never trade up. But they don't wanna look bad and they
00:27:16
run a big risk of getting fired. Look, 10 of the 32 coaches got fired last year.
00:27:22
I don't know the data on GMs, but you can't afford to look dumb. I actually look forward to hearing about that
00:27:33
study that shows that the teams that don't ever trade up and don't trade or trade
00:27:40
down more often, they do well. I don't know if it's false or not, but is that true?
00:27:45
It's something we looked at. We used to call this the golden, what the Holy Grail.
00:27:54
The Holy Grail, yeah, right. There's so much else. Some teams are better at fourth downs.
00:28:03
Do they do better? The Steelers were terrible at fourth downs and somehow still managed to win.
00:28:13
So there's blocking and tackling and the stuff we geeks care about. Well, Adi, the last thing we do in
00:28:24
our first paper is to run an analysis of the accumulation of draft capital in one
00:28:30
year and the downstream consequences on the playing field. So it's wins. What we care about is wins, right?
00:28:36
And what we find is the accumulation of draft capital doing the trade down, managing their
00:28:42
draft capital in the way we would recommend is positively correlated with downstream wins.
00:28:47
Now, is it direct or does it just reflect the kind of sophisticated management? We don't know, but we find a reliable
00:28:53
relationship between exactly what you just said. It's the last test in the published paper.
00:28:58
Trading down, accumulating draft capital is positively related with downstream wins.
00:29:03
Eric has a question. Yeah, I want to ask Professor Thaler another question. Here's another way to rationalize, and I wonder
00:29:08
if you guys have looked at this. Suppose, since as you know, utility functions also
00:29:13
sometimes have uncertainty bought into them. In other words, we take the mean utility,
00:29:17
we might add an uncertainty term. Is it also possible that the whole mechanism is due to the top-end picks are
00:29:25
seen as much more certain with lower standard error? Later picks is seen as much more variable.
00:29:31
Therefore, a rational person would like, even it seems overvalued if you're maximizing expected utility, but
00:29:38
if it's a risk-adjusted expected utility, it's not. Because that could also rationalize it.
00:29:44
People overestimate the certainty with which the top picks. So it's a variance story, not just a
00:29:51
mean. Well, trading down even past the first round works like two-thirds of the time.
00:30:06
And it's just not the case. After the first pick, it's better than the next guy's 52%.
00:30:18
And you can't predict the tails. So I can't think of any rational basis by which you wouldn't prefer twice as many
00:30:36
starts and the same number of pro bowls. And to be specific about the variance, the
00:30:42
variance actually goes down later in the draft. It doesn't go up, especially if you're looking
00:30:46
at surplus. Surplus, which is after you've paid them, you've got this very high volatile left tail at
00:30:54
the top of the draft. And later in the draft, yes, you've got outliers, but mostly you've got a bunch of
00:30:59
low performances and standard deviations are coming down. Last comment. The big variance comes from Tom Brady and
00:31:08
Brock Purdy. Guys, we're going to need to cut Professor Thaler loose. Any last questions for him?
00:31:23
What questions remain for you, Dick? Give us one last question you have about the draft.
00:31:28
Now that you've looked at it, come back to it, looked at it some more. You've got decades now of looking at this.
00:31:34
What questions remain for you about it? Well, I think the, within the team dynamics and the role of the owners, I
00:31:54
think would be really interesting to study. And let's just say they don't always help.
00:32:05
Okay, why don't we wrap it there? Dick, thanks so much for making time for us.
00:32:10
Appreciate it. Good luck with everything you have rolling out there. Just off the line with Professor Richard Thaler
00:32:18
dialing in from California. He spends most of his time, half his time out there talking NFL draft.
00:32:25
We are gonna spend the next half hour with a one-on-one fight between me and Adi about our papers.
00:32:30
We'll suspend Ben for next week. We have to work some things out, okay? Is that really happening?
00:32:38
No. Alternatively, we'll fight- Shane and I could moderate that discussion if you'd like.
00:32:46
Okay, I'm ready. I am ready, but we're gonna- I've been to an econ seminar, and those are
00:32:50
pretty bloody, so. I've never been to one, and I've started to wade into that territory and realizing it.
00:32:58
Yeah, Adi decided to turn the first half of the show into an econ seminar, so we'll take it up.
00:33:02
We'll take it up elsewhere. We've got instead Ben Robinson. We are an all-in-one show.
00:33:09
All-NFL draft show this week. We're gonna return to previously scheduled topics like
00:33:13
baseball. Adi has some primary research, some new primary research he wants to share.
00:33:18
We'll talk about it. But in the meantime, we still are processing what happened over the weekend.
00:33:22
NFL draft had its thang. Ben Robinson, for those of you who don't know, created and runs Grinding the Mox.
00:33:31
Ben has been on our show many times over the years. He was, and I think maybe it's safe
00:33:37
to say, the first aggregator of Mox out there. He was an early, at least an early
00:33:44
aggregator of Mox. And he has, I would say he's grinded over the years and built a little bit
00:33:53
of an empire, an influencer. He's got a partnership now with the NFL. He consults to teams.
00:34:02
More and more, the conversation around the draft is around things like this, like the consensus
00:34:07
and big boards and how teams perform versus those measures. And so in many, in a very real
00:34:13
way, Ben's work has become central to the conversation around the NFL draft.
00:34:19
It's been fun to see. We felt like we caught him pretty early. He's making, I think he's really making the
00:34:24
conversation smarter out there. Even the analytics of it, like how do you aggregate these things is become super interesting.
00:34:31
And so the methodologies are interesting. So Ben, welcome back. Thanks for making time for us.
00:34:36
Glad to have you. Oh, it's an honor to be here. And in terms of being an early Mox
00:34:42
draft aggregator, I was not the first. I initially thought I was the first. Okay.
00:34:46
But would you guess Brian Burke was the first? Of course he was. Of course he was.
00:34:52
Tough to be first on anything ahead of Brian Burke, turns out. But you've certainly invested more over the years.
00:35:01
Give us a sense. Give us an update of state of the union of Grinding the Mox through the 2026
00:35:07
draft. What's important? What are the high points? How should we think about your enterprise at
00:35:11
this stage? Well, yeah. Like I said, it's an honor to be on. So yeah, no, Grinding the Mox started off
00:35:16
as kind of a humble side project and now we're a data and insights company. And so, you know, this year I have
00:35:26
two team clients that I've worked with and I, this year added five more. So we're working with about a quarter of
00:35:32
the league is using our data in some shape or form to inform their draft preparation
00:35:37
and draft planning and draft strategy as well. All the internal tools that teams have at
00:35:43
their disposal to think about the draft and how they want to maneuver and plan to
00:35:47
get either the players they want or the kind of positions they wanted to target in
00:35:51
the draft. But overall, we're trying to, you know, do what we've always done, which is, you know,
00:35:56
using mock drafts, the wisdom of the crowds and machine learning to predict the draft.
00:36:01
And so we have a really consistent hit rate of getting about 80 to 85% of the top 100 players matched.
00:36:10
This year was a little different, but then in the first round, we're usually getting between
00:36:14
28 and 29 of the 32 in our top 32. And so that type of consistency is really hard to repeat by most experts, because as
00:36:22
we know, in the wisdom of crowds, these things go up and down and up and down. So the data can help a lot in
00:36:28
setting those expectations accurately. And like you said, making people smarter consumers
00:36:33
and fans of the draft. So two clarifying questions, just to stay with this for a moment, more background.
00:36:40
You said you have seven NFL class. Now, how do they use your data? So there's a lot of firewalls that exist
00:36:47
between me and the teams that I work with. So I don't necessarily get to go into
00:36:52
the draft rooms and hear, this is what we did, this is how we used your stuff. My general sense of just the state of
00:36:58
the art and knowing what I know about the industry is that lately teams have been
00:37:03
developing internal simulators. So the types of simulators you see online, the teams are developing that internally with a
00:37:11
combination of public data, their internal grades, maybe you might pay for some betting data too
00:37:17
and throw that in there. And the decision-makers are using that in the lead up to the draft.
00:37:23
You know, when we talked a lot in the past about fourth down decision-making, we
00:37:26
were often saying, you know who's really good at making fourth down decisions?
00:37:29
People who get a lot of experience with that. In game, it just doesn't happen very often.
00:37:34
And so we were saying, hey, they should play more Madden. That is like more realistic.
00:37:40
You'll get more of these scenarios where you'll get to say, okay, this is the risk,
00:37:42
this is the reward. And it's the same for the draft. Having these like internal tools, these simulators that
00:37:48
can give more GMs more bites at the apple. You only get a certain number of picks
00:37:53
every year, a very small number. And so being able to thread the needle, think about your strategy.
00:37:58
If this thing happens, how does my strategy change? Having more bites at the apple is a
00:38:03
big piece of internal draft planning. And so my data feeds into some of those products.
00:38:09
I think some teams are also real time in the draft using it to better understand
00:38:13
what's likely to happen in the next few picks. So for example, if a team gets a
00:38:18
call and is offered a trade and they have to ask, if I trade back, what's the chance my guy is still gonna be
00:38:25
there? And they need a good sim to answer that question. And of course it has to be a
00:38:29
sim that's updated continually as people make picks. And so I can imagine your data being
00:38:33
super helpful for that process. Last clarifying question before we go to Eric, how have you gotten better over time?
00:38:40
I'm guessing that these are more accurate than they used to be. What is the frontier for you as you
00:38:45
improve your model over time? I think the main thing is about data innovation. Methodologically speaking, teams are largely using models that
00:38:55
have been around for a while. You'll hear about like the, my model is the model stuff that I sell to teams
00:39:02
is like a Bayesian hierarchical model. It's not actually super complicated. Those types of models have been around for
00:39:07
a while. You'll hear about like other types of methodologies as well. But for me, it's a lot about data.
00:39:14
It's less about necessarily, hey, let's try to like methodologically fix this issue.
00:39:21
Because there's a lot of uncertainty in the draft. And so to me, finding the ways to
00:39:26
inject new data sources is what's gonna make your model, I think, better. The better data you'll usually weigh out over
00:39:32
the better method in this case. There is like some amount of evidence to be made that if you wanna get a
00:39:40
very accurate simulation in some ways projection, though a lot of the mock drafts are kind
00:39:45
of copying each other in very like mimicky ways. And so you still, but you still need
00:39:51
to be able to find those needles in the haystack that matter. And so you need to have access to
00:39:55
as much of the data as you possibly can so that you can find the people that are giving you the appropriate signal that
00:40:01
have the track record of being accurate. And then don't just mimic what everyone else
00:40:06
is saying. It actually has something that brings value to the conversation. So I think you still need data and
00:40:11
that's the thing. So I've integrated big boards as well, just like straight up player rankings into my data
00:40:16
as well over the years. And that's been a meaningful addition to help provide priors.
00:40:21
But to me, it's more about just the data you can bring. Like I mentioned before, the mock drafts, the
00:40:24
big boards, some type of, whether it's like gambling odds or maybe even prediction market odds,
00:40:30
depending on those, that can add a lot to a value and not necessarily the modeling
00:40:35
approach. That can be fairly straightforward. Eric. I don't know if this has ever happened
00:40:41
on our 12 years of Moneyball where the guest has literally answered every single point of
00:40:46
my question. I was gonna ask him, would you rather have better data or better models?
00:40:51
I was gonna ask him how he deals with the correlated nature of the drafts. Like I just copy what Cade Massey puts
00:40:58
up there and now you got seemingly two observations that are convergent, but I've actually added
00:41:02
no information. But I will ask one question then. But first of all, thank you for the
00:41:07
clarification. In a world where everyone seems to be using neural nets and more complicated nonlinear types
00:41:17
of models today, despite I'm a Bayesian and I'm Mr. Bayesian hierarchical model, I love fitting
00:41:22
them. Why not jam a massive neural net against your data where you can find all possible
00:41:30
combinations of things that help you predict outcomes or like why in some sense be more
00:41:36
statistician and probabilistic-like and why not jam the old black box computer science model at
00:41:42
it? I think a lot of it has to do with the uncertainty in the data that you're having.
00:41:48
So the mock draft world of data is a censored world of data. Most mock drafts are only 32 picks long.
00:41:55
The first round is what gets the most eyes, the most attention. And so you're dealing with data that just
00:42:00
has a bunch of built-in issues I think that statistical models do a better job of addressing.
00:42:07
I think you can throw the machine learning methodology at it too and you probably would
00:42:13
get pretty decent results. But I do think that the uncertainty, like being able to account for the uncertainty and
00:42:19
actually understanding the generation, like how your data is generated, what it represents, you wanna try
00:42:25
to match that to the problem. But then I think the other thing too is just- You score yourself, Ben, beyond
00:42:28
just I'll call it the pure hit rate. Like in other words, being off by one is very different than being off by 15.
00:42:35
And in some sense, you could imagine a black box model always in general underestimates uncertainty.
00:42:42
And so you wanna- And you feel better about that in that way. Yeah. So I mean, you could come up with
00:42:47
a model that could be 95% right, but then the confidence intervals are huge and it just has no meaningful interpretation.
00:42:54
But to me, there's just a lot of uncertainty with the draft. And so you just kind of have to
00:42:57
be, get used to being wrong a lot. And so, and then there's also these correlated
00:43:03
errors you mentioned. So in the first round of the draft this year, my mean absolute error, just like
00:43:10
very basic metric of prediction accuracy was very average. The first round, there might've been a couple
00:43:17
big picks, but for the most part, the players went right around where we thought they
00:43:21
would go. Especially if we value higher picks more than later picks. But then if you move into the top
00:43:28
100 and that's the first three rounds, this year's draft, in terms of like my average
00:43:35
mean absolute error, just without adjusting for any pick value, it was seven picks higher on
00:43:40
average. So that means in the top 100 this year, there was just a lot more uncertainty.
00:43:46
And that's just with my static pre-draft model. I don't have like a live model that
00:43:49
I use. The teams obviously have these tools to be able to make those assessments in draft.
00:43:55
But yeah, there was a lot more uncertainty. And I think some of that was because
00:43:58
of these correlated errors that you could pick up. So one of the things that was the
00:44:03
story of round two and three of the draft this year was the rise of the, what I would call the overpriced blocking tight
00:44:09
end. The teams, a lot of NFL teams are following the money around what is innovation in
00:44:16
the NFL. And the Rams ran a lot of three tight end sets where you need a blocking
00:44:21
tight end to have the threat of the run game and teams want to be able to run those.
00:44:25
And so they were like, well, if that's the new scheme innovation, we want to be
00:44:31
able to get that. It bumped up the price in the draft of the blocking tight ends.
00:44:37
Seth Walter from ESPN asked a bunch of team sources about a bunch of players who
00:44:40
went earlier than expected and asked them, is that really your reach? And for the blocking tight ends, he said,
00:44:46
actually, I think they were just a lot very undervalued by the draft community this year
00:44:50
and overvalued or maybe more valued correctly, right? By the teams. So these, you could pick these up in
00:44:57
a model, but if you just jammed everything into a machine learning model, would miss that
00:45:01
all the time. Real quick point about that dynamic. This is a classic case of non-stationarity.
00:45:07
The world is changing. So we don't know who's got the valuation correct, but we know if everyone's playing offense
00:45:13
or a lot of teams are playing offense differently now, however we valued positions in the
00:45:18
past will be less relevant. And so it's an open question on where the tight end value is going, but it's
00:45:24
just this perennial challenge of modeling when the world is moving. Adi. So actually we did a little impromptu seminar
00:45:33
on draft valuations, but we weren't thinking at all about kind of what you're thinking about.
00:45:38
Just to, so I want to clarify, you're trying to predict where the players will actually
00:45:42
go in the actual draft. Yes. As opposed to figuring out, well, who's the good player that's going to go lower or
00:45:49
higher? And you're trying to model future value, which is what we were talking in the first
00:45:54
half hour of our segment. So my, so the question is, is that something that teams want to know from you?
00:46:00
Or, I mean, are they just interested in knowing the person they want, where is it
00:46:05
likely to go and how do they have to trade or position their order so that they make sure they get their people?
00:46:11
So to answer your first question, so I'm, yeah, I'm trying to predict where players will
00:46:16
get drafted. So my friend Arif Hasan creates every year since like 2015, what he calls a consensus
00:46:23
big board, where he gets hundreds of player rankings and aggregates them together.
00:46:28
Recently, he did a little analysis on his sub stack, which is called Wide Left. And he analyzed how good different publicly available
00:46:37
metrics were at predicting where players are going to get drafted. And also how good some of these metrics
00:46:43
were at predicting future value based on the pro football reference, approximate value stat, which is
00:46:48
probably one of the best stats we have, you know, for looking at value. And my data predicts the draft the best.
00:46:55
I'm very proud of that. I think I do a nice job. It's good to hear. My data predicts actual NFL success of those
00:47:04
publicly available metrics, the second best. The thing that actually predicted NFL valued the
00:47:10
best was actually his board of player rankings. But by me being closer to the actual
00:47:16
draft in the top 100, you end up getting a lot more of those right than wrong. I still think he needs to do like
00:47:24
an additional analysis where he compares it to the actual draft. But by being closer to where these top
00:47:29
100 players should be selected, we do a better job of predicting the draft than even
00:47:33
some of like the other publicly available aggregated boards out there. So sometimes you do want to try to
00:47:40
be as close to the market as possible. And I know that Timo Rieske from, I don't know if he's still at pro football
00:47:46
focus or not, but had done this analysis in the past about, you know, do you wanna be closer or further away from the
00:47:53
consensus? Where does it matter? When does it matter? And so it does have some impact, whether
00:48:00
they do the teams wanna use my data to predict where players are gonna be drafted
00:48:03
or where, yes. Did they wanna use it to predict how good players are gonna be? Player evaluation, no.
00:48:09
And so I think the vanguard of player evaluation is gonna be trying to beat the draft, not trying to approximate the draft.
00:48:16
But I do think it's something you should consider. It might make your model a little smarter
00:48:19
pre-draft. But yeah, I think teams are doing a lot more different types of things on player
00:48:26
evaluation on the data side. And I'm still not a hundred percent sure that it's making its way into the decision
00:48:34
makers. Like, I don't know how much it's infiltrating decision-making. There's a lot of alignment that has to
00:48:38
happen organizationally for insights to get from the draft, from the analyst room into the draft
00:48:44
room. I remember reading Future Value, which is a great book about, you know, prospecting in baseball.
00:48:52
And there were teams that were drafting entirely from models. I don't think we're anywhere near that in
00:48:58
the NFL yet. Scouts still dominate the conversation. And I think we've also learned in other
00:49:03
sports that scouts have really important data that they bring to the table about players.
00:49:08
But yeah, teams are using this to try to kind of figure out how the draft might play so that they can find those
00:49:14
specific players that they want. And so what I find is that the teams that are reaching the most in my
00:49:21
data for players are usually just, they just don't consider that. They just have thought before the draft.
00:49:31
And we might use the analytics department to make sure we're in a position to get
00:49:34
the guy we want in that area that we want. But we're not using them to figure out
00:49:39
which players to pick. We're super scouts. We're the smartest guys in the room.
00:49:43
We're gonna pick the guys we want based on the coaches we have to maximize the
00:49:47
opportunity to still have our jobs next year. Can I ask a quick follow-up question?
00:49:52
Just like a real, because if you imagine, if you look back at the draft years later and you rank the players by quality
00:49:59
and then you rank them by their draft, how often does the top five players in the draft by quality get drafted in the
00:50:08
top five? Is that? Yeah, just by the way, as an extension to the question I was gonna ask you,
00:50:13
Ben, by the way, was a related one to Adi's, which is how much of the variation does, if someone just did pure, forget
00:50:21
what position I need, forget any of that. I just rank according to perception of quality
00:50:26
and I correlate that with grinding the mops. Adi wanted the top five. I wanted, in general, for the top hundred.
00:50:35
How much variation is sucked up just by the pure quality rankings versus, let's call it,
00:50:40
the context of I need a tight end, I need a running back, et cetera? So we have a student who did this
00:50:45
for the NBA. I know the number. I'm curious to know what the number is for the NFL.
00:50:50
This was exactly, I have another question for Ben, but this is my question. So the number for the NBA is one
00:50:55
and a half. So of the top five players in a draft class in the NBA, one and a half are drafted in the top five.
00:51:02
Interesting. I wish you hadn't given that number yet. I was gonna ask everybody to guess.
00:51:07
Yeah. Well, I would have guessed the answer to my question was about a quarter to a
00:51:11
third of the variation in where people are drafted, it would have been based on pure
00:51:18
quality. Like if all Ben did was use a consensus not of where people are gonna be
00:51:24
drafted, but of the rank order of quality, that that might suck up 25% to 30% at most of where people are
00:51:33
actually drafted. But Ben, maybe you know the answer to this. I don't really know the answer.
00:51:37
I think it's my data is very correlated to the draft. So for the most part, my data might
00:51:44
be saying, hey, like this guy is expected to go here and he goes there. There's like differentials between where players were expected
00:51:51
to go and where they go. That's kind of what we know is that what Timo Riske has brought into this conversation,
00:51:56
which is that draft reaches are real and steals don't necessarily exist. My bet is that it would probably be
00:52:04
more than one and a half. So there's the classic, this is the classic draft curve literature and actually our friend, Ron
00:52:13
Yurko at Carnegie Mellon, they did a little bit of an update on the draft curve.
00:52:19
How do you, do you optimize your draft curve for just value? Do you optimize it for superstar?
00:52:24
And they were doing it with Madden rating data. And so that was a new analysis that
00:52:30
they released in recently that probably would do a better job of telling you those sorts
00:52:33
of things. I don't necessarily think we have the public data to tell us necessarily who is that
00:52:40
or not. I think the Madden data might be some of the best data we actually have publicly
00:52:43
to do that across positions. But I would like to think, I think it would be higher than one and a
00:52:49
half, but I don't know if I would, I would say probably maybe two. I don't know if I would say anything
00:52:53
more than that. I can answer the question this evening if we just consider the second contract value, which
00:52:59
is I think a reasonable way to go at it. And I'd be curious to get everybody's number
00:53:03
out of five on average, how many of the top five contracts, once they reach free
00:53:09
agency, how many of those top five in a draft class were drafted in the top five?
00:53:15
I'm going under the one and a half number that Adi just said. Under one and a half.
00:53:19
That's strong. That is strong. It's an average. So you can pick a non-integer.
00:53:26
2.5. 1.2. Yeah. Under. I'm going under also. Ben, let me ask you. Not around 1.2. I like your number,
00:53:38
Eric. You anchored me. God damn it. Let me ask you a question. My feeling is that usually in a draft
00:53:44
class, you usually have a couple of quarterbacks that go really high. That's the thing.
00:53:49
The quarterbacks are gonna get you. This year's class might be biasing you to
00:53:53
think that this is not an average quarterback class, but I do think that, yeah, like
00:53:58
in a more normal quarterback class, there will be two quarterbacks that go in the top
00:54:02
five and then they have a decent chance of being in those numbers or an edge rusher or a tackle or, you know.
00:54:10
So like when you get to drafts and you see like are these non-premium positions going in the top five a lot?
00:54:17
Sometimes. This year it did. And so, but yeah, overall, I think it's more than one and a half, but yeah,
00:54:25
maybe I'm just over-indexing on my mental model. I'm gonna go under. I'm gonna go under as well.
00:54:32
What is your thought? I mean, I can't even tell you the dismay of my son, one of my three
00:54:37
sons who does a lot of work in sports analytics, trained by Adi. When Jeremiah Love was drafted third, he was
00:54:44
apoplectic. He just could not believe it. So did you have him going that high in the draft?
00:54:52
Forget whether he's the right, it's the right choice. No, I understand that's not what you look
00:54:55
at. Did you have Jeremiah Love going like a third to the Cardinals and was that what
00:55:01
the consensus said? So in the grindingthemocs.com public expected draft position board, he was my fourth ranked player.
00:55:10
In the Bayesian model, he actually was ranked third. So, but the idea, I think the vast,
00:55:17
that was kind of his ceiling was gonna be pick three. And he went there. It definitely changed the complexion of the top
00:55:23
five of the draft. I think it did a lot of other teams some really strong favors because it definitely
00:55:31
shook up. I think that if Jeremiah Love hadn't been drafted third overall, he would have gone fourth
00:55:36
overall. I think that would have been a big disservice to Cam Ward who the Titans drafted
00:55:41
first overall last year to draft a running back that doesn't really help him at all
00:55:45
or change the offense in a really meaningful way for him and like the evaluation of
00:55:50
him as a quarterback. But yeah, I had him going that high. It's very regularly known that we'll have these
00:55:56
conversations around positional value of players especially at the top of the player rankings.
00:56:02
They just tend to go for the most part at the top of the player rankings. The one position that we see year in
00:56:09
year out where if a player is ranked high, and we saw that this year with Ohio State safety, Caleb Downs, safeties get depressed.
00:56:17
Their value, they always are going a good amount later than we thought in the draft.
00:56:22
So teams are willing to draft running backs really high. They're willing to draft linebackers really high.
00:56:28
Safeties for whatever reason, they get pushed down relative to expectation. So Caleb Downs was one of my three
00:56:35
biggest fallers in the draft. He was my seventh ranked player and he ended up going 11th.
00:56:41
And so that's sort of the one thing that is the exception to the rule of highly ranked players go high in the draft
00:56:49
as safeties. And I don't have a great answer for it, but we saw this year the Seahawks
00:56:56
win the Super Bowl, not drafting a safety in the first round, although I did have
00:57:01
Nick M. Unwary from South Carolina in last year's draft as a late first round value in my
00:57:07
numbers. But that safety position, if you can find that perfect blend of someone who can play
00:57:15
the run in the box and then cover tight ends and receivers up the seam, that's
00:57:19
become a lot more valuable. But for some reason we're spending high draft picks on blocking tight ends and not as
00:57:26
much on safeties that can make a difference in the defense. Ben, you mentioned that Downs was one of
00:57:32
your big three fallers. Can you give us the others and then can you give us those players that went
00:57:38
most outperformed? They did better than expected. Who were the surprises for you in that
00:57:43
direction? So the three biggest surprises on day one of the draft, number one was Alabama quarterback
00:57:52
Ty Simpson. So that's like a whole podcast in and of itself around what the implications are of
00:58:00
drafting a quarterback in the first round who I did not think would go in the first round at pick 13 overall.
00:58:10
But yeah, so he went at pick 13. There were very few people who projected him. I had this metric weighted mock drafts where
00:58:18
basically not every mock draft is created equal. Some mock drafts are created closer to the
00:58:22
draft. Some mock drafts are made by smarter predictors and he only showed up in 4%
00:58:29
of mocks to the Rams that were in his weighted mocks. There was a lot of noise of him
00:58:34
going to be traded up for by the Arizona Cardinals, but that didn't happen. So that was the biggest shock of the
00:58:41
first round was Ty Simpson. But the Minnesota Vikings, they drafted defensive tackle
00:58:46
Caleb Banks from Florida who I also thought was a second round selection. Coming out of the senior bowl, he was
00:58:54
one of my biggest risers in my data, had a really lovely senior bowl. He's a big man and he has been
00:59:01
dealing with some injuries his entire career at Florida. And he re-aggravated an injury at the
00:59:07
Combine and he was already kind of a inconsistent player, but you can definitely watch a
00:59:12
couple of games and see like there are some real flashes of brilliance there. In terms of some of the biggest fallers
00:59:20
in my data, players that fell the most kind of in the draft, at least on night one, one of the biggest values in
00:59:26
the draft this year was Ohio State linebacker slash edge rusher, Arvel Reese. He was in the running to be the
00:59:36
second overall pick. And I think most people believe that if he wasn't the second overall pick to the
00:59:40
Jets, that he would be the third overall pick. Didn't he go five? He went fifth.
00:59:44
And so that was really surprising. I think that that was one of the biggest surprises of the draft.
00:59:50
And I think that if Jeremiah Love, like I said, like that was, I think that the Giants were probably thinking about some of
00:59:57
the other guys that would be available and were completely taken aback. I wouldn't have surprised me if they had
01:00:02
maybe had Reese number one on their board at the beginning of the draft. So they got a ton of value.
01:00:08
I think they had a great draft. They also drafted a tackle at pick 10 and Francis Maui Noah from Miami.
01:00:14
So I thought that was really great value. I also thought the Eagles got some good
01:00:20
value from this Makai Lemon draft selection. I thought that was like a little surprise
01:00:26
that he fell a little bit. Ruben Bain from Miami. He fell a little bit in the draft.
01:00:32
So the draft is full of these like little surprises. So far I'm liking this.
01:00:35
It's the Eagles and the Buccaneers. I'm liking this so far. Don't interrupt him, Cade.
01:00:39
Let Ben keep talking. As a Pats fan, I'm also happy the Eagles selected a wide receiver.
01:00:43
Yeah, yeah. But yeah, there's all these great stories. The other guy that was the surprise really
01:00:52
was Omar Cooper Jr., the wide receiver from Indiana. Before the combine, he was thought of as
01:00:59
a second round pick and then people really looked into him more and liked his profile
01:01:03
a lot. And the Jets saved him the indignity of having to be a day two pick and took him via a trade up at the
01:01:12
end of round one. So that was a big surprise. I had him as my 20th ranked player
01:01:15
and he ended up going 30. Ben, that reminds me of a feature of your work that is notable, especially before the
01:01:24
draft. You plot the trajectory of these players' projections over time. So you show guys not rising or falling
01:01:32
on draft day, but like in the lead up to the draft, in the weeks up to the draft, guys move up and down
01:01:37
the mock board. What have you learned over time about characteristics that lead guys to go up and down
01:01:43
ahead of time? I think a lot of the time, in the preseason, there's a lot of variance.
01:01:52
Over the cycle this year, there's usually a lot more fallers than risers. So the risers usually just come out of
01:01:59
nowhere, like you didn't know about them or they were very low on your radar preseason.
01:02:05
This year, there were at least three quarterbacks who I thought were gonna potentially have the
01:02:10
profile of being players that would be undrafted. Penn State's quarterback, Drew Aller, Clemson's Cade Klubnick,
01:02:17
and LSU's Garrett Nesmeyer. Coming into the preseason, all were considered potential
01:02:22
top quarterback prospects. And along with some other prospects deciding not to enter the draft this year, that made
01:02:28
the quarterback class really kind of unexciting. But Fernando Mendoza, you also, it takes time
01:02:35
to kind of, for people to update their priors. So Fernando Mendoza has a couple of good
01:02:40
games. You didn't think much of him coming into the season, although I think some people did
01:02:44
think that he was potentially on the radar for prospects. But as he starts to really rack up
01:02:50
these things and you get the sense that it's not just Indiana beating down on Ball
01:02:55
State or whoever, and they start to play well against Penn State and Oregon, you kind
01:03:00
of begin to update your priors and then you kind of, it takes a while for those things to change in a meaningful way
01:03:07
for the crowd. So the players that do come into the consciousness, they come into it in a big
01:03:12
way. So the big risers this year were Arvel Reiss and Fernando Mendoza. But Arvel Reiss is probably one of the
01:03:17
biggest risers in the draft this year, just because in the preseason, they thought he was
01:03:22
just like a linebacker who was gonna be playing linebacker. No one really cared about him.
01:03:26
But then he started showing up in all these games and you begin to kind of consider him as potentially the top prospect in
01:03:33
the draft, somebody who you would pick at second overall, despite not really having played Ed
01:03:38
Drescher at all as a full-time position. All right, we're gonna have to cut you
01:03:42
loose, but Eric's got a last question for you. It's gonna be a yes, no answer.
01:03:46
Are there team effects in here? Indiana wins- I was gonna ask, oh, that team effect, okay.
01:03:50
No, no, team effects, like Indiana wins- The college team, college team. And now everybody, it relates to Cade's question
01:03:55
about risers. Like Indiana wins the national title, then all of a sudden wide receivers in Indiana are
01:04:01
going higher than expected and all. How does that work? I think a little bit. I think you'd like to think that that's
01:04:08
correlated, right? Like if Indiana's winning the title, they should have a lot of draftable players.
01:04:14
There's so much attribution there. Like coaching makes a big difference. Obviously playing, you're playing at different positions means
01:04:21
different things. Luck can mean different things. But yeah, to me, yeah, there's definitely a
01:04:26
lot more of a microscope on players on good teams. And coming into the playoffs, I thought Fernando
01:04:34
Mendoza had done everything he needed to do to prove that he was the number one overall pick.
01:04:38
I thought he had done everything he needed to do. But then there were other players who even
01:04:41
produced well and people were still saying that they needed to do more. And so for example, that was kind of
01:04:45
like Ty Simpson. Coming into the playoffs with Alabama, he had been pretty up and down.
01:04:52
He had gotten hurt. He had played well against some like low level competition, struggled against some others.
01:04:57
And to be honest, I really didn't think he did a lot more. I was surprised that he went as high
01:05:02
as he did. But yeah, in terms of team effects on the college side, I think it's kind of
01:05:07
just baked in in the revealed preferences. On the team side, you were mentioning like,
01:05:12
are there team effects in my model? My model is team agnostic. I do think that there's not enough historical
01:05:19
track record for most general managers for us to be able to say anything meaningful.
01:05:23
And so I choose to believe that you want to inject less certainty into your model.
01:05:28
And so I don't mean to throw any shade, but ESPN has this draft predictor or
01:05:32
draft day predictor model. And one of the things, and I'm going to write about it this summer.
01:05:37
I've been collecting data from the draft day predictor for a long time, like ever since
01:05:41
it's been live. And if you do, I'm going to do some type of like Breyer score analysis of
01:05:46
looking at what your prediction said around the time when the player was going to go
01:05:51
and then the actual, you can make that comparison. And my guess is that when you do
01:05:55
tend to inject, like the teams are only considering these players based on scouts, inks, needs,
01:06:00
or the teams are only going to consider this player at this pick because of that.
01:06:05
You're just putting your finger on the scale when you need to be letting it ride,
01:06:08
letting the randomness, the flatness of some of these priors instead of trying to over-priorize
01:06:13
your model. Super interesting. You're full on flirting with us when you're
01:06:17
talking Breyer scores. I mean, come on. Just directly flirting. Why not log loss?
01:06:23
Yeah, yeah, exactly. I think we'll do both. We'll do both. Just an example.
01:06:28
Do it both ways. Exactly, do it both ways. All right, Ben, we should let you go,
01:06:33
man. Thank you for making time. Congrats on making it through another draft season.
01:06:36
Good job. And we'll look forward to seeing what you do next year. Yeah, thank you guys so much.
01:06:40
And like you mentioned, my data is everywhere, but this past year, grindingthemocs.com, my grinding
01:06:46
themocs substack, I'm writing there pretty regularly. And then I also had my data in
01:06:52
NFL IQ, which is NFL Next Gen Stats off-season dashboard, which was pretty cool and
01:06:57
exciting. And yeah, no, this year was great. Getting to work with about a quarter of
01:07:01
the teams in the league has been something that I've been working towards for many years.
01:07:05
So it's a long time coming. It's great to be back on with you. Great fun, good work.
01:07:10
You've been grinding. You have literally been grinding and it's good to see a payoff.
01:07:13
That has been Robinson, founder and CEO of Grinding the Mocs. And that has been a full NFL Draft
01:07:19
Show. We seem to be themed these days. About two weeks ago, we did a full golf show.
01:07:23
That one was unintentional. This one was planned. We'll get back to a little more open
01:07:27
-minded, broad-minded, including baseball. And let me point out to some of you guys, they're playing playoff hockey right now.
01:07:33
It's the best thing going. It's great. Enjoy it. For the whole team here, for Eric Bradlow,
01:07:39
Shane Jensen, Adi Weiner. For our guests, Ben Robinson and Richard Thaler. For our production team, Dion Simpkins, making it
01:07:47
always happen. Marissa Patel, I mean, Marissa Reyna. And Dee Patel, the boss lady, Dee Patel.
01:07:53
Thank you guys for everything. Thank you guys for listening. Come back and join us next time.
01:07:56
Between now and then, enjoy your sports.