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Welcome, welcome to Wharton Moneyball.
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Welcome to a full hour of sports
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analytics here on the Wharton podcast
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network. This is Kate Massie hosting
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this week along with my longtime
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colleague, collaborator, co-host, and
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friend Eric Bradlo. Our two other
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co-hosts, Shane Jensen and Audi Winer,
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are out this week. They're doing Shane
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and Audie things. They will be back.
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Some combination of us are here almost
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every week of the year and have been for
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more than 11 years now. Delighted to be
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here with you. We are going to run
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through a regular schedule. We're going
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to record, we are recording on Tuesday
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afternoon. The show will go up on
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Wednesday. We're going to do about an
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hour. We're going to do a guest in the
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first half as we have been lately and
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then some open topics in the second
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half. We have back on the show for the
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eenth time and um a very happy annual
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marker of sorts. Aaron Shots is here
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with us which means football season is
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just around the corner. Delighted to see
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you Aaron Shots. Welcome back.
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>> Hey, we made it through another off
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seasonason. Football people any I I am
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aware there are other sports but
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football people made it through another
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offseason and I'm ready to go. I'm ready
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for a new season. I'm ready. training
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camps are open and I'm ready to uh
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completely get obsessed with whether
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guys throw interceptions to their
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teammates and pretend that it means
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something.
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>> Good. Good. Well, we appreciate the
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enthusiasm. That's what you're here for.
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Most of you guys know Aaron. If you
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don't, let me give you just a little bit
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of background. He is presently the chief
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analytics officer of FTN Fantasy. He
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also writes for ESPN. He's very well
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known for founding Football Outsiders.
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many years ago, more than 20 years ago,
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he was early into the game on advanced
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football analytics. He's the creator of
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DVOA, which has been doing a lot of work
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in our community for a long time. And um
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he's always a delight to talk to. I I
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always like to note also that he is the
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his his writing tree, his analysting
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tree is as impressive as it gets in
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football, if only that perhaps the two
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leading lights. He I give you credit for
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finding Bill Connley and Bill Barnwwell.
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If I think if you do word count right
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now, the leading word count producer on
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the NFL side for ESPN would be Barnwell
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and the leading word producer on the
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college football side of ESPN would be
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Connley. So Shots is the journalistic
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father of both of those guys.
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>> A lot of my writers in the past have
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turned out to be quite verbose. Uh Bill
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Connelly has a new book coming out by
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the way, so I hope you'll talk to him
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about that. It's really exciting. And uh
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yeah, my coaching tree includes Doug
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Ferrar from Athlon and Mike Taneer who
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has his own site now and Ryan Wilson
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from CBS, Michael David Smith from Pro
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Football Talk. It's a lot of people.
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>> You're forgetting FEI, I think.
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>> Yeah, Brian Frimo, who does the FBI
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ratings? Yep,
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>> exactly. Freo as well. That's a heck of
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a tree. Heck of a tree, Aaron. Okay,
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man. Let's get into it. This is uh NFL
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talk one for us and so God knows we're
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going to kill you with questions. I know
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you've got some stuff you want to talk
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about, too. I'm going to start at the
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top with a personal question. All right.
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I'm in a in a dangerous position this
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year. My favorite professional team is
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at the top of many people's list and my
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favorite college team is at the top of
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many people's list. It's uncomfortable.
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You'd rather be there than anywhere
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else, but it's a little uncomfortable. I
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need to know, Aaron. I need to know
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what's the case for the Ravens. We're
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used to them being like second, third,
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fourth on most people's list. There's
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usually somebody like the Chiefs or the
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Bills or the defending champion Eagles
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up. They're look looking up at them. But
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now everyone, it's ridiculous how many
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people are saying the Ravens are the
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best positioned in the league. Now I
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know there it's tight.
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>> You say Kate, there was a time. Was
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there not a time that the Bills were
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your favorite team? Like when I first
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met you almost 30 years ago now.
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>> Well, you spend a couple years in
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Buffalo and and you get you get soft for
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the Bills and they're still way high on
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the list for sure. But there I I
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discovered, Eric, last year, remember
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they played in the playoffs and it's
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like I didn't know what was going to
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happen internally when I went to know
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>> that's how you found out and it was
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clear to me. I'm sorry, dear Buffalo
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friends. It was clear to me that recent
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recency matters and it's the Ravens. So
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Aaron, I need to hear your thoughts on
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the Ravens for this year's NFL.
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>> Well, if anybody who follows my numbers
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knows that they have absolutely loved
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the Ravens for the last two years. Uh
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they have been our number one team for
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two straight years and number one in a
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historically great way like among the
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top 10 or 12 teams ever measured in the
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regular season and then they go to the
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playoffs and they fall on their faces.
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>> So the idea that the Ravens are the best
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team is based on the idea that falling
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on your faces in the playoffs is not a
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real thing. that history is filled with
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teams and players that couldn't do it in
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the playoffs until the year they finally
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did
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>> get it done.
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>> And certainly we know that there's no
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magic sauce that John Har uh John
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Harbaugh can't win in the playoffs
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because the man won a Super Bowl 13
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years ago.
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>> Is there some sort of magic sauce that
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Lamar Jackson can't win in the playoffs?
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I honestly
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>> don't think so. Has he struggled so far?
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Yes. Does it mean he'll struggle
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forever? Just ask Pton Manning or Jason
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Tatum or Barry Bonds or a number of
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different athletes if you struggle in
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the playoffs for your entire career or
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maybe it just happens early and then you
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kind of get with it. Now, the downside
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for the Ravens is that they are
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desperately trying to get past the
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Kansas City hump,
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except they also have to get past
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another team that is also desperately
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trying to get past the Kansas City hump,
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right?
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>> Which is the Buffalo Bills. It's a
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really interesting table we have in the
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book. Mhm.
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>> The top DVOA average of a five-year team
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that doesn't make the Super Bowl belongs
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to the last five years of the Buffalo
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Bills.
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>> The second highest average belongs to
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the last five years of the Baltimore
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Ravens. So these are what great teams
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would say historically get past a hump
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>> over what period of time are we talking
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about?
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>> This is over the uh since 1978.
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>> Oh my gosh.
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The best 5ear spans since 1978 to not
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make a Super Bowl are the Bills and
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Ravens of the last five years.
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>> Yeah. And we just place that on Andy
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Reid's doorstep and and Pat Mahomes
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doorstep. Geez. Good lord. Okay. Well,
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tell us why it's different this year.
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Why would it be different this year for
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either of those teams?
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>> I mean, it's just a question of, you
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know, when is somebody gonna get it done
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and and when is Kansas City not gonna
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have the brakes work for them? And I
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mean, I think Kansas City is a very well
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coached, very well quarterback team, but
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I don't think that they're invincible.
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They did show a couple of years ago that
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if somebody could get past them because
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Cincinnati did. And I think that they're
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not going to have the same luck they had
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last year. I think they may be better
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than they were last year, but they're
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not going to have the same luck. So,
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they're not necessarily going to be the
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number one seed in the playoffs. I think
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the most likely team to be the number
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one seed is the Buffalo Bills because
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they have the easiest schedule out of
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those three AFC top teams,
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>> right? I mean, Ravens visit Buffalo week
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one and then they go to Kansas City
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first half of the year sometime as well.
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It's
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>> that week one game is hugely important
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for playoff seating. It
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>> you also pointed out um it doesn't have
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to be this is what I've always thought.
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It doesn't have to be the Bills or the
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Ravens that defeat the Chiefs. As far as
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I know, Kansas City goes to
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San Diego or the whatever Harbaugh team.
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The other
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>> Harbaugh Chargers, LA Chargers. Yeah.
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No, I mean, we think Denver is very good
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this year. We think Cincinnati is good.
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>> Absolutely. The Bengals beat them. The
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Bengals beat them and then the Ravens
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and Bills, one of them wins. That's what
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I think is actually going to happen this
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year. Also, uh, it's guaranteed that
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some team from the AFC South will make
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the playoffs.
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>> That's Yep. That's the way the NFL
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works. Sure enough. Um, well, remind me
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what we learned from the Super Bowl. If
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I don't misreall, the Eagles beat down
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the Chiefs. I mean, not just a little
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bit. So, tell me what we learned from
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that about getting past the Chiefs. uh
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that offensive line is a weak link unit.
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>> Say what you mean more. Say more about
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what you mean by that.
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>> The idea is that your offensive line is
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generally only as good as its weakest
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lake.
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>> And therefore you need to be good at
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every position. And the Chiefs had
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Caliando at left guard and they had Tuni
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playing out of position at left tackle.
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And the Eagles pass rush destroyed them.
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And the other thing we learned, Vic
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Vangio is a very good defensive
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coordinator.
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>> That Eagles defense from week six
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onwards
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would have been one of the top 10
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defenses since 1978 by DVOA if you if
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you take out the first four games of the
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year. That's how good that defense was
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last year.
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>> All right. Well, let's use that as a
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pivot to the hometown Philadelphia
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Eagles. Are people sleeping on them this
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year? I know they're roughly a a a top,
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you know, I don't know, five team or so
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in most people's expectations, but some
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think Detroit going to clip them this
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year. What What is your What does the
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air and shots take?
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>> I'm the opposite. I actually think the
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Eagles are not quite going to be as good
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as people think they are. They are, I
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mean, a very good team, but this is
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something we talk about a lot. Offense
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is more predictable and more consistent
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than defense. The Eagles had the number
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one defense in the league. It is very
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unlikely they will have the number one
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defense in the league again, especially
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because using the equation that I use to
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look at personnel changes, they lost the
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most veteran personnel of any defense in
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the NFL this off seasonason. Miami was
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second. So, with all the players they've
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lost, they're depending on a lot of
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young guys. They're probably not going
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to be number one on defense again. Their
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offense in the regular season was not as
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good as it was in the playoffs. So,
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let's say that they have a little bit
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better offense than last year. And like
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the fourth best defense in the league,
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they also have the second hardest
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projected schedule after the Giants.
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>> That is not a 13 uh win team.
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>> That's a 10 or 11 win team.
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>> Okay. Before we go on, I want to go back
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to the first thing you said there, which
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was that offense is more predictable
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yeartoear than is defense. This is kind
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of the can this is canon now, I suppose,
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for football analytics, but it's
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everybody may not know that because it's
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only been observed, I don't know, the
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last five or 10 years perhaps, but can
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you give us the intuition for that?
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Because I think most people might have
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the opposite intuition. Something about
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offense feels more volatile, so you
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might expect it to be less predictable.
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Why is it that season to season defense
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is less reliable, less consistent than a
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team's offense is?
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>> We don't.
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>> Is it Is it turnovers? Because that's
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one stat that they've said is very hard
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to predict from year to year. Is Is it
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that part of defense, Aaron, or is it
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some other part?
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>> Yeah, turnovers regress towards the mean
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strongly and stronger on defense than on
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offense. That is absolutely true. A big
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part of it is the quarterback position
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because so much of the offense is
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managed by one player, right? It's very
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hard to imagine that a team
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quarterbacked by Patrick Mahomes is
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going to have a below average offense.
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Yet, a defense with Miles Garrett as its
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best player can be one of the top three
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defenses in the league one year and one
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of the bottom three defenses in the
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league the next year, despite the fact
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that Miles Garrett is still awesome in
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both seasons. That quarterback having so
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much control over the offense leads to
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much more consistency.
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>> Super interesting. So, it's more about
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offense reliability as opposed to
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defensive unreliability. Super
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interesting. Okay, I want to come back
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to that. I know you you worry a lot
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about a hard thing in analytics, which
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is how do we forecast a quarterback when
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he moves positions, but let's hold that
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question for one second because we left
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the team previews one team too early. We
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were talking about Eagles and if you
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don't like them as one of the top teams,
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you might even like the Commanders
00:12:48
better in the East. They are coming and
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they've got one of the most exciting, if
00:12:52
not the the most exciting young
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quarterbacks in the league in Jaylen
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Daniels. What do you expect from them
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this year? Yeah, that's that's our big
00:13:00
surprise projection this year. We
00:13:02
project the Commanders over the Eagles
00:13:05
to win the NFC East. That is not to say
00:13:08
that the Eagles are not a great team. I
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think they'll be the number one wildcard
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team.
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>> Okay.
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>> Um the Commanders are going to not be as
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good on fourth down. They were
00:13:19
shockingly good on fourth down. That is
00:13:21
going to regress. But I think they have
00:13:24
balanced that out with some of the
00:13:26
additions they've made on the offensive
00:13:28
side of the ball. Debo Samuel and
00:13:30
especially Laramie Tonsel to improve
00:13:33
their offensive line. We think their
00:13:35
defense can be a little bit better than
00:13:37
last year. They were actually 23rd on
00:13:40
our defensive ratings last year. So, we
00:13:43
have them projected average like 17
00:13:46
>> and they have an easier schedule than
00:13:48
the Eagles because of which teams in
00:13:51
which divisions finished first and
00:13:53
second last year
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>> and therefore we do actually have them
00:13:57
projected ahead of the Eagles this year.
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>> Okay.
00:14:00
>> So, Aaron, just two quick follow-ups to
00:14:02
that. Um, a how much weight do you put?
00:14:04
Um, I was at every Eagle playoff game
00:14:06
this year. Um, the Eagles absolutely
00:14:09
destroyed the Commanders in the
00:14:11
playoffs. uh they ran through them. It I
00:14:13
forget if they won by 30 or whatever the
00:14:15
number was. How much weight does that
00:14:17
put into anything? The second thing is
00:14:20
the team that I didn't think the Eagles
00:14:21
were much better than last year and got
00:14:23
lucky to win. I was at that game too was
00:14:25
the Rams. So where do you put the Rams?
00:14:28
I mean Matt Stafford in that snowstorm,
00:14:31
he was one pass away from them going to
00:14:34
the Super Bowl. So I'm interested to
00:14:36
hear, do you put any weight on the fact
00:14:37
that the Eagles blew the Commanders out
00:14:39
in the playoffs? And what do you think
00:14:41
of the Rams?
00:14:42
>> First of all, that's only two games.
00:14:44
Let's say that there really is something
00:14:47
about the way the Eagles play that the
00:14:50
Commanders do not match up well with.
00:14:53
There's still 15 other games that
00:14:55
determine who wins the division.
00:14:57
>> So, it could be
00:14:58
>> predicting necessarily that they're
00:15:00
going to beat them in the playoffs.
00:15:01
You're talking about the division like
00:15:03
>> Yes, I'm talking about the winning the
00:15:04
division during the regular season.
00:15:06
>> That's totally different. I agree.
00:15:07
That's entirely different.
00:15:08
>> And the Rams, we like the Rams. We do
00:15:10
think their defense is going to take a
00:15:12
step back. There's a lot of questions in
00:15:14
the secondary. Um, their defense
00:15:16
improved a lot last year and there's a
00:15:18
sort of a plexiglass principle thing
00:15:20
where they may come back to the pack a
00:15:22
little bit. Uh, so we don't have the
00:15:24
Rams. That's a very strong division. So,
00:15:26
we also like the Cardinals this year and
00:15:29
we also really like the 49ers this year
00:15:31
and the Seahawks may be better than we
00:15:33
think because their defense because Mike
00:15:35
McDonald in Baltimore at least had his
00:15:38
defense improve a lot in his second year
00:15:40
there. So, that's a very strong
00:15:41
division, but the Rams are a very strong
00:15:43
team.
00:15:44
>> Okay, let's let's talk QBs. Um Jaylen
00:15:47
Daniels um after a spectacular year last
00:15:50
year, it's it's you know because
00:15:54
quarterbacks are so important to offense
00:15:55
and offense is so important to a team.
00:15:57
Understanding where a quarterback's
00:15:59
going to be is the single most important
00:16:01
component in forecasting a team. And yet
00:16:03
especially early in a guy's career, it's
00:16:05
hard to know what kind of steps he's
00:16:06
going to take. What tell talk about
00:16:08
Daniels? Like what do you see in
00:16:09
Daniels? What do you expect in Daniels
00:16:11
long term? What do you expect in Daniels
00:16:12
this year? I mean, the best thing I can
00:16:15
say about Daniels is that when a player
00:16:19
is that good as a rookie, you don't
00:16:22
expect that they'll take a step forward
00:16:23
in year two because they were already so
00:16:27
good as a rookie that they're not going
00:16:28
to necessarily get better.
00:16:30
>> Okay.
00:16:31
>> Uh Marino was like the exception, but
00:16:34
he's not necessarily going to get worse
00:16:36
either. I think I mean he's really good
00:16:38
last year. He's a great pocket passer
00:16:40
and then he's an amazing scrambler. He's
00:16:43
accurate. He's got a good clock in his
00:16:45
head to not take sacks, right? You
00:16:47
compare that to Caleb Williams. Now, the
00:16:50
quarterback projection system that we
00:16:52
use to try to project quarterbacks from
00:16:53
college to the pros liked Caleb Williams
00:16:56
better, but the fact is Caleb Williams
00:16:58
just he made too much of his own
00:17:01
pressure, took too many sacks, and
00:17:03
didn't have anywhere near the kind of
00:17:04
year that Jaden Daniels did. It's so
00:17:06
hard to predict. Erin, tell me, talk
00:17:09
about that because Caleb Williams, I
00:17:10
mean, I would say I don't, we'd have to
00:17:12
go through through the numbers, but he
00:17:14
wasn't quite forecasted as a
00:17:15
generational guy, but the consensus
00:17:17
around him
00:17:18
>> was he was number one,
00:17:19
>> was so strong. I mean, I could just the
00:17:21
consensus was as strong as it has been
00:17:23
in many years.
00:17:23
>> I agree with Aaron, by the way. I've
00:17:25
seen both quarterbacks live, including
00:17:27
JD Daniels first ever game, which was uh
00:17:30
Commanders at Buccaneers, and the Bucks
00:17:33
won the game. But I remember saying as I
00:17:35
was leaving the stadium,
00:17:38
"Thank the Lord the Bucks played them in
00:17:40
week one. This guy's clock is amazing.
00:17:44
This guy's accuracy. He's making the
00:17:46
right reads." You a lot of times, as you
00:17:48
know, Aaron, you go to games and you
00:17:49
say, "This guy's open all over the
00:17:51
field. What was the quarterback
00:17:52
thinking?" I don't remember saying that
00:17:54
once about Jaden Daniels during the
00:17:56
game. I think I think you hit it on the
00:17:58
head. I I don't know why that would
00:18:01
regress. Why would his ability to find
00:18:03
receivers and his I'll call it internal
00:18:05
clock not holding on to the ball too
00:18:07
long knowing when to run. I'm not sure
00:18:09
why that would regress even though
00:18:11
defense is quote unquote adjust.
00:18:14
>> I mean the biggest reason why listen CJ
00:18:17
Strad regressed a lot but he mostly
00:18:19
regressed because the team around him he
00:18:22
had injuries with his receivers and his
00:18:23
offensive line completely fell apart.
00:18:27
Washington has tried to make sure that
00:18:29
does not happen by going out and getting
00:18:32
the only good player from the Houston
00:18:34
offensive line to improve themselves at
00:18:37
left tackle, knocking last year's left
00:18:39
tackle, Brandon Coleman, into left
00:18:41
guard. So, they're hoping for a better
00:18:43
blocking to make sure that that Daniels
00:18:45
doesn't take the step back that Straoud
00:18:48
took back.
00:18:50
>> What What about Caleb Williams in
00:18:52
Chicago? you know, expectations. It I I
00:18:54
will say that as hard as it is to
00:18:56
forecast a QB coming out of college, it
00:18:58
seems to take us a number of years
00:19:01
before we really understand a
00:19:02
quarterback in the pros. We we get short
00:19:03
on guys that end up turning it around at
00:19:05
a second or third stop and we get long
00:19:08
on guys that end up progressing three,
00:19:10
four years into their career. What is
00:19:12
your current position on Williams? What
00:19:13
is your current forecast for having a
00:19:15
>> I think he can be better. I think we may
00:19:17
be making too much out of the new
00:19:19
offensive lineman that Chicago got.
00:19:21
They're gonna have better blocking, but
00:19:23
the problem is still Caleb Williams as
00:19:25
far as the pressure goes. It's not the
00:19:27
offensive line. That being said, Ben
00:19:30
Johnson is a very smart offensive
00:19:32
coordinator. Now, head coach is a
00:19:34
different job. We have no idea, none,
00:19:38
how good Ben Johnson is at being a head
00:19:41
coach, but part of his job is that he
00:19:43
will be running the offense. That we
00:19:45
know he's very good at. So, I mean, we
00:19:48
do think Chicago in our projections,
00:19:50
they come out as an above average
00:19:51
offense this year.
00:19:53
>> Okay. Okay. Well, we we've talked about
00:19:55
projecting guys coming out of college
00:19:57
and the challenge of that. Number one
00:19:59
pick last year, Cam Ward going to be
00:20:01
playing in the NFL for the first time.
00:20:03
What hess
00:20:06
in general? And then what do you see for
00:20:07
Cam Ward? We like Cam Ward better than I
00:20:10
think uh the consensus was that if Cam
00:20:13
Ward had come out a year ago, he might
00:20:15
have been the seventh quarterback taken.
00:20:18
>> Yeah. Right.
00:20:18
>> Our projections like him better than
00:20:20
that. We have him more like third or
00:20:22
fourth, like after Williams and Daniels.
00:20:25
Um it, you know, it's based on the
00:20:28
college performance. It's based on
00:20:30
mobility in college. It's based a little
00:20:31
bit on experience and a little bit on
00:20:33
age. Now, the age thing is interesting
00:20:36
because the change in how college is
00:20:39
working means that age and experience
00:20:43
may not mean the same things they've
00:20:45
meant in the past. Guys are staying
00:20:47
longer. The COVID year really
00:20:49
complicated this, but now NIL
00:20:51
complicates this. It used to be you'd
00:20:53
see a guy was like 24 in college and
00:20:55
you'd be like, "That guy is beating up
00:20:57
on 18 and 19 year olds. How does that
00:21:00
necessarily translate to the NFL?" Now,
00:21:03
that guy is 24 in college, and a lot of
00:21:05
the other players are going to be 24,
00:21:07
too, because they're staying in college
00:21:08
because of the NIL money. So, overaged
00:21:12
prospects may not be as overaged as
00:21:14
we've thought in the past. Cam Ward is
00:21:16
not, by the way. Cam Ward is still a
00:21:18
young guy, but this was an issue with
00:21:20
Daniels. This was an issue with Bo
00:21:21
Nicks. This was an issue with Michael
00:21:23
Penn, etc.
00:21:24
>> So, Aaron, let me ask you, as someone
00:21:26
that does mathemat mathematical
00:21:28
modeling, how do you then address that
00:21:31
issue? I'm sure a lot of our listeners
00:21:33
like in some sense we've seen a regime
00:21:35
change in the NFL where you know the age
00:21:38
distribution has changed but like we
00:21:41
can't just project from the past because
00:21:42
we haven't seen that age distribution
00:21:44
before how do you even think about
00:21:46
addressing it from an analytical
00:21:48
perspective or do you
00:21:51
assumption do you assume that you look
00:21:53
at the 24 year olds in the past and you
00:21:55
try to project them forward what do you
00:21:57
do
00:21:57
>> I think early on like now you just do
00:22:01
your projection questions the way you've
00:22:02
always done them and you just put words
00:22:05
next to them. I mean, that's why we have
00:22:06
words and not just tables in our book.
00:22:09
You know, we explain when we think, you
00:22:12
know, things are changing and and what
00:22:14
it's going to mean now after we've seen
00:22:17
three or four years of this. We'll have
00:22:19
to see. Wide receivers is another
00:22:21
position where this comes into play.
00:22:23
Like we've always found wide receivers
00:22:26
who come out as juniors are just better
00:22:28
in the pros than similar wide receivers
00:22:30
who come out as seniors. They just are.
00:22:33
But what if guys stay for senior years
00:22:35
because they can make $2 million and get
00:22:38
college checks? Like you know like what
00:22:44
that it's not that they're staying till
00:22:45
their senior year because they're not as
00:22:47
good. It's they're staying till their
00:22:48
senior year because they're making lots
00:22:50
of money. So, it's going to change how
00:22:52
we see things.
00:22:53
>> So, is that just kind of the art of it?
00:22:55
And yet, I know, you know, as modelers,
00:22:57
we try not to put our thumbs on the
00:22:58
scale, but you do put your thumbs on the
00:23:00
scale when you know your model's missing
00:23:01
something.
00:23:02
>> I mean, we we all right, I I've heard
00:23:05
people talk about this. As much as we
00:23:06
want to believe that our models are
00:23:08
objective, the way you build your model
00:23:10
is in its way its own way sort of
00:23:12
subjective. So, yeah, I mean, we try not
00:23:15
to put our thumb on the scale, but we
00:23:16
also want to be as true to life as
00:23:18
possible. and life has changed.
00:23:22
>> That's right. That's right. That's the
00:23:23
humility of it really. Um you recognize
00:23:25
that it's imperfect and you got to
00:23:26
sometimes bring a little subjectivity.
00:23:30
We could talk quarterbacks all day long.
00:23:31
I mean, another interesting one is JJ
00:23:33
McCarthy. So, he's not I mean, he's not
00:23:35
a rookie, but he is a rookie. And what
00:23:36
what is a year sitting on the sidelines?
00:23:38
He's not even getting snaps, right,
00:23:39
because he's injured, but maybe that
00:23:41
year of mental prep makes a difference.
00:23:43
What what and the team is now completely
00:23:45
dependent on him. What are your
00:23:47
expectations for McCarthy? rejecting him
00:23:49
like a rookie. There's never been
00:23:51
anything like this before.
00:23:53
>> Mhm.
00:23:53
>> You've never had a quarterback who
00:23:55
missed his entire first year because of
00:23:57
injury. You've had guys who sat on the
00:24:00
bench for their first year, but you
00:24:02
never had a guy who was supposed to
00:24:04
start and then got hurt and so you don't
00:24:07
know what to expect from him. I mean,
00:24:08
obviously there are guys who sat on the
00:24:10
bench for a year and Patrick Mahomes in
00:24:12
his second year, right, was insane.
00:24:15
But in general, I think we have to treat
00:24:18
him like he's a rookie. And that's in
00:24:20
our projections. We treat him like he's
00:24:21
a rookie.
00:24:22
>> Okay. But you he came in in a strong QB
00:24:24
draft. If he'd come out a year later, if
00:24:27
he'd come out in the Cam Ward 2025
00:24:28
draft,
00:24:29
>> he might have been the number one pick.
00:24:30
>> He's he's that that's how high you have
00:24:32
him coming out of their
00:24:33
>> I mean that it's more like people are
00:24:35
not as excited about Cam Ward. Remember
00:24:38
Cam Ward, and this is another modern
00:24:40
thing. He played for three different
00:24:41
schools included including Incarnate
00:24:44
Ward,
00:24:45
>> right? Like he he's kind of a late
00:24:48
bloomer.
00:24:49
>> Yeah.
00:24:49
>> So I think that a lot of scouty type
00:24:53
people would have put McCarthy ahead of
00:24:55
him.
00:24:56
>> Okay. Okay. Okay. Another of these
00:24:59
challenges from a modeling perspective
00:25:01
is when coaches change and a lot of us
00:25:03
have tried to do coaching modeling and
00:25:05
and we've seen some efforts here and
00:25:07
there.
00:25:08
for a for a long time we just kind of
00:25:10
called the residual coaching effects.
00:25:12
It's somewhere in the residual but it
00:25:13
can't be the entire residual. What are
00:25:15
you doing with coaching these days? And
00:25:17
when we see coaches move from one
00:25:18
franchise to another what what can we
00:25:21
say?
00:25:21
>> It's hard because you don't have a
00:25:24
history of except for a guy like Fangio,
00:25:27
you don't have a history of, hey, he's
00:25:30
coordinated 10 different defenses and
00:25:32
this is what's happened to them, right?
00:25:34
You're usually with a coach who's on a
00:25:36
new team, your sample size of his past
00:25:39
performance is either one or zero,
00:25:43
right? Like we've never seen Ben Johnson
00:25:45
run a team. We've never seen Aaron Glenn
00:25:48
run a team. We have seen Mike Vrabel run
00:25:51
a team, but only one team, only the
00:25:53
Tennessee Titans. So, we know something
00:25:56
about Mike Vrabel, but we don't
00:25:58
necessarily know what was those Titans
00:26:00
players and what was Vrabel. Now, Vable
00:26:04
is an interesting uh one to talk about
00:26:06
though because there's a flip of this
00:26:08
which is there's no way to model like
00:26:10
really bad coaches. Like there's no
00:26:14
numbers that say that Gerrod Mayo was
00:26:16
completely in over his head even though
00:26:18
everybody says Gerrod Mayo was
00:26:20
completely in over his head. So, it's
00:26:23
possible that we are underplaying the
00:26:25
change in coaching, not because Rael is
00:26:28
more important than we think he is, but
00:26:30
because Mayo was so bad.
00:26:32
>> What if every Aaron ju just say, let me
00:26:34
ask a question. What if every
00:26:35
statistical model, what about if all of
00:26:38
your models, all the other models,
00:26:40
Massie, Peabody, whatever it is, had the
00:26:43
Patriots last year at seven and 10 and
00:26:46
they went three and whatever they went
00:26:49
three and 14. Wouldn't that give some
00:26:51
suggestion? I I wouldn't say it's
00:26:53
perfect, but wouldn't we have some
00:26:54
information that maybe Jared Mayo
00:26:57
severely underperformed?
00:26:59
>> Yeah, I think so. But here's an
00:27:01
interesting thing. Um, so I have an
00:27:02
equation called the postgame win
00:27:05
expectancy.
00:27:06
And what it does is it looks at the
00:27:08
stats of a game and says based on the in
00:27:10
the stats of this game, which team would
00:27:12
we have expected to win?
00:27:14
>> I love And there are certain coaches who
00:27:15
have a history of outperforming what
00:27:17
that equation says, like Mike Tomlin and
00:27:20
Andy Reid. There's also a coach who has
00:27:23
a really amazing history of underplaying
00:27:28
what the postgame win expectancy would
00:27:30
say, and he's considered one of the best
00:27:32
coaches in the game. And that's Kyle
00:27:35
Shanahan.
00:27:36
>> Oh, I was going to guess Shawn McVey,
00:27:38
but okay.
00:27:40
>> No, it's Kyle Shanahan. So, what if does
00:27:43
that mean Kyle Shanahan's not a good co?
00:27:45
Right? Like there's a lot of
00:27:47
measurements that you try to put into
00:27:48
coaching, but I do think that if you
00:27:51
have a consistent record of winning
00:27:53
close games and winning more games than
00:27:55
your underlying stats would suggest,
00:27:57
it's probably a sign you're a good
00:27:59
coach. And the opposite is also probably
00:28:01
true. But that's not true of the
00:28:03
Patriots last year. The Patriots did not
00:28:05
tremendously underperform the underlying
00:28:08
stats. The underlying stats were bad.
00:28:11
Well, that's the thing. The coach gets
00:28:13
some responsibility for that
00:28:14
fundamental,
00:28:16
>> right? I mean, when you hear that like
00:28:17
Gerrod Mayo was not even aware of like
00:28:19
which meetings he should be holding on
00:28:21
which days, like you have to expect that
00:28:23
that impacted the underlying stats.
00:28:26
>> Yeah. Yeah, that's right. But that is an
00:28:28
interesting that is one glimpse into
00:28:31
coaching effects. It's not the whole
00:28:32
picture and it's not perfect as you
00:28:34
said, but as you say, as soon as you get
00:28:36
a somewhat sufficient sample size, it
00:28:39
says something. It says something about
00:28:41
the coaching.
00:28:43
>> Let me ask you, Aaron at all because I
00:28:44
find this whole topic fascinating. Just
00:28:46
one quick followup to that.
00:28:48
>> Do you find that the same stats help you
00:28:51
predict, let's call it expected outcome
00:28:54
at different parts of the distribution?
00:28:56
Like in other words, can I just have one
00:28:58
set of stats that would predict a really
00:29:00
bad team's outcome, a good team's
00:29:02
outcome, and a great team's outcome or
00:29:05
>> Yeah, I think it does. I mean I think
00:29:07
using the same stats it uses you know
00:29:10
DVOA it looks at penalties it looks at
00:29:12
how many plays you run right like DVOA
00:29:16
is more predictive which is an
00:29:17
efficiency metric uh than how many plays
00:29:20
you run but in a single game how many
00:29:23
plays you run is important right like
00:29:25
you want to run more plays that's not
00:29:27
necessarily predictive for future
00:29:29
performance but it helps you win that
00:29:31
game but I think those underlying stats
00:29:34
for like determining single games are
00:29:36
basically the same whether you're a
00:29:37
really good team or a bad team.
00:29:39
>> Let me ask you one other follow-up
00:29:40
question just for because we may have a
00:29:42
big machine learning audience, blackbox
00:29:44
audience out there. Um, when you think
00:29:47
about these models or even you and your
00:29:49
disciples, it's it was great to hear
00:29:51
from you guys early on what your
00:29:52
disciple tree is. Um, would you be happy
00:29:56
today just building a machine learning
00:29:58
black box with, you know, kind of throw
00:30:00
away whatever you know about the NFL and
00:30:02
whatever does best out of sample
00:30:03
prediction, you're just going to jam it
00:30:04
into some black box or do you see it as
00:30:06
a blend of the two? Like what do you
00:30:08
feel most comfortable with?
00:30:10
>> I would say a blend. I will say that the
00:30:14
uh the big data bowl a couple years ago
00:30:17
when they did uh trying to predict the
00:30:20
rushing yards on a play based on where
00:30:22
the players were, the winners were two
00:30:24
dudes from Europe who knew nothing about
00:30:26
football and just put everything into a
00:30:28
black box. So that was kind of
00:30:30
interesting. I'm also I mean going to
00:30:31
just be really honest here, which is I
00:30:33
am not a trained data scientist. Even
00:30:35
though I've been doing this for over 20
00:30:37
years, I'm very self-taught. I have an
00:30:39
economics degree. There was no such
00:30:41
thing as a data science degree in the
00:30:43
mid1 1990s. The people who have come
00:30:46
after me are much more adept at teaching
00:30:50
machine learning to a computer than I
00:30:51
would be. But I love the answer though
00:30:54
um because it highlights that there are
00:30:57
different kinds of problems in sports,
00:30:59
never mind different kind of problem.
00:31:00
There are even different kinds of
00:31:02
problems within the NFL. So the example
00:31:05
you gave was a really cool year where
00:31:07
they were predicting the yards from from
00:31:09
at any given moment on a rush rushing
00:31:11
play like stop the action and say what's
00:31:13
the expected yards from here. That's
00:31:15
very different from saying are the 49ers
00:31:17
going to be any good in 2025 with the
00:31:19
rushing yardage forecast. You've got not
00:31:24
infinite data but you've got a lot of
00:31:25
data. You've got micros secondsonds as
00:31:28
your unit of of analysis and you've got
00:31:31
as much videotape from history as you
00:31:32
can cram in essentially because there
00:31:34
hasn't been that much change. That's a
00:31:36
good environment for blackbox models.
00:31:38
When it comes to are the 49ers going to
00:31:40
be any good in 2025, the unit of
00:31:43
analysis is essentially a season, a a
00:31:46
team season and that's really chunky and
00:31:49
really coarse and we don't have much
00:31:50
data. That's not great for blackbox
00:31:52
models.
00:31:54
>> Yeah. I mean, I think there's a lot of
00:31:55
subjective things when you look at
00:31:57
whether teams are going to be better or
00:31:58
worse from year to year and things like
00:32:01
trying to figure out which personnel
00:32:03
changes count and how to count them.
00:32:06
>> Yeah. Right. Huge. And that's why I
00:32:08
think that's why we're going to be
00:32:09
having these kinds of conversations for
00:32:10
the rest of our lives anyway. We're not
00:32:13
going to have these things solved
00:32:14
anytime soon. Okay, Erin, that's we've
00:32:16
already taken more time than we should.
00:32:17
Let's ask one last question. Give us one
00:32:19
more storyline from 2025 NFL. one more
00:32:22
storyline that you think is gonna be
00:32:24
interesting, you're gonna have your eye
00:32:25
on as the season unfolds that you think
00:32:27
we should pay attention to, too.
00:32:29
>> And Kate, I'm gonna get one more
00:32:31
question in here that I really want to
00:32:32
know the answer to, too.
00:32:33
>> Okay.
00:32:34
>> I'll mention, by the way, that all of
00:32:35
these projections and stuff, we do the
00:32:37
preseason book we've done for 21 years.
00:32:39
So, I do want to point out to people the
00:32:41
FTN football almanac 2025 is available
00:32:45
on Amazon or a PDF version which costs a
00:32:47
little less. You can get at
00:32:49
ftnfantasy.com/almanac.
00:32:53
Uh, one of my interesting picks for this
00:32:55
year is that we like Jacksonville as our
00:32:58
favorites in the AFC South. Partly for
00:33:02
the same reason that we like Washington
00:33:04
in the NFC East, which is the Eagles
00:33:07
were the number one defense in the
00:33:09
league last year. It's very unlikely
00:33:11
they'll do that again. The Houston
00:33:13
Texans were the number two defense in
00:33:15
the league last year. Okay,
00:33:16
>> it's very unlikely we'll do that again.
00:33:19
We like the Jaguars defense to improve a
00:33:21
little bit. We like the Jaguars offense
00:33:23
to improve a little bit and we may be
00:33:25
underelling what Liam can Cohen can do
00:33:28
with that offense if he's a very good
00:33:30
head coach.
00:33:32
But the big variable here is Travis
00:33:36
Hunter playing both ways, which is the
00:33:38
most interesting story of the year as
00:33:40
far as I'm concerned because it's it's
00:33:43
only been done a couple of times in the
00:33:45
last 30 years. Roy Green had a year
00:33:48
where he played both wide receiver and
00:33:50
safety. And Deion Sanders had a year
00:33:52
where he played both wide receiver and
00:33:54
cornerback. Nobody's ever tried to do it
00:33:56
to the extent that Travis Hunter has
00:33:58
talked about doing it for multiple
00:34:00
years. It's fascinating.
00:34:02
>> Okay. You did the whole Jack
00:34:03
Jacksonville bit and didn't mention
00:34:04
Trevor Lawrence. That's amazing.
00:34:06
>> Well, that's the thing is the question.
00:34:08
Can Liam Cohen unlock Trevor Lawrence
00:34:10
like he unlocked Baker Mayfield?
00:34:12
>> Okay, that's cool. That's good. That's
00:34:14
great. All right, Eric, you last
00:34:15
question here.
00:34:16
>> Yeah, I just wanted to ask him. So,
00:34:17
these are yes, no questions. We always
00:34:20
say half the NFL teams that made the
00:34:22
playoffs last year typically don't make
00:34:24
it this year. So, if I'll just list the
00:34:26
quickly, you just tell me yes, no. Do
00:34:28
they make the playoffs? The Chiefs?
00:34:30
>> Yes.
00:34:31
>> The Bills?
00:34:32
>> Yes.
00:34:33
>> The Ravens?
00:34:34
>> Yes.
00:34:35
>> The Texans?
00:34:37
>> No.
00:34:38
>> The Chargers?
00:34:39
>> No.
00:34:40
>> The Steelers?
00:34:42
>> No. The Broncos,
00:34:44
>> yes.
00:34:45
>> The Lions,
00:34:46
>> yes.
00:34:47
>> Eagles,
00:34:48
>> yes.
00:34:49
>> Rams,
00:34:52
>> yes.
00:34:54
>> Buccaneers,
00:34:56
>> yes.
00:34:57
>> Vikings,
00:34:58
>> no.
00:34:59
>> Commanders,
00:35:00
>> yes.
00:35:01
>> Packers,
00:35:03
>> yes.
00:35:05
>> That's
00:35:05
>> That's the whole field. That's the whole
00:35:07
field, man.
00:35:08
>> That's great.
00:35:09
>> All right, Eric, I just realized you're
00:35:10
wearing your Bucks colors. This you may
00:35:12
intentionally not
00:35:13
>> no this is an R this is not
00:35:14
>> Tampa Bay
00:35:16
>> Mike Tir's chapter talks about Tampa Bay
00:35:19
deserves more respect as a really good
00:35:21
team and not just as a team that gets to
00:35:23
go because somebody from that division
00:35:25
gets to go.
00:35:27
>> Okay, I'll believe that when I see it.
00:35:28
But okay.
00:35:29
>> And we love Baker Mayfield, let me tell
00:35:31
you.
00:35:31
>> All right.
00:35:32
>> We love Levante David. We love Levante
00:35:34
David.
00:35:36
>> Erin Shots, great to talk to you, man.
00:35:38
Great preview. Wish you the best. Fun
00:35:40
time of year. Have fun for the next
00:35:42
month as we ramp up.
00:35:43
>> Absolutely, man. I hope everybody enjoys
00:35:45
the NFL season. ftnfantasy.com/almanac
00:35:49
to get the book.
00:35:50
>> There you go. That has been Aaron Shots,
00:35:52
formerly football outsiders, one of the
00:35:55
real pioneers in football analytics.
00:35:58
Welcome back to Wharton Moneyball.
00:36:00
Welcome to the second half of this
00:36:02
week's show. Kade Massie here, hosting
00:36:03
with my longtime colleague, co-host Eric
00:36:06
Bradlo. We are gonna do the second half
00:36:09
of the show as an open topics half
00:36:12
number of things to talk about but we
00:36:14
just got off the line with Aaron Shots.
00:36:17
Aaron is a frequent contributor here at
00:36:19
Wharton Moneyball has been for all of
00:36:21
our 11 years I'd say and is good sign
00:36:24
that NFL is upon us. Football is upon
00:36:26
us. And he does a great preview. I think
00:36:27
Aaron does a fantastic preview. Super
00:36:29
helpful, super insightful. Eric, any
00:36:31
observations coming out or or let's say
00:36:33
anything left to say about the NFL here
00:36:35
about a month out? No, I I like I loved
00:36:37
what he said. I mean, he said that
00:36:39
offense is more persistent. Um,
00:36:42
partially he said defense is less
00:36:44
persistent because of turnovers. We've
00:36:46
talked about that. There's actually
00:36:48
reversion, mean reversion that typically
00:36:50
happens in turnovers. Um, offense is
00:36:52
more persistent because quarterbacks
00:36:54
don't bo and weave that much. And so, I
00:36:58
thought that was a very good insight.
00:37:00
And so what he basically said was is
00:37:03
that if you have a team that was average
00:37:06
offensively but great defensively and
00:37:09
like for example the Eagles and that's
00:37:11
what made them a great team. You'd have
00:37:14
to forecast some sort of regression
00:37:17
towards that. And I think that's the
00:37:19
thing I like about it too is, you know,
00:37:21
it's in all the complex models and
00:37:23
worlds we have now, that's something you
00:37:26
can explain to somebody and it it
00:37:28
doesn't it doesn't it's not just
00:37:29
empirically true, but it makes sense.
00:37:33
>> Yep. Um I found his team by team
00:37:37
breakdowns helpful. It's interesting
00:37:39
that they're a little short on the
00:37:40
Eagles. If you go to the markets, our
00:37:42
friend Neil Payne sometime the last two
00:37:44
weeks summarized the market forecast
00:37:47
like the probability of a team winning a
00:37:48
Super Bowl. He got market forecasts from
00:37:50
two places, FanDuel and Poly Market.
00:37:52
This interesting new market, Poly
00:37:54
Market, the for the numbers are very
00:37:56
highly correlated. And you can look at
00:38:00
what it says going into this year. We've
00:38:01
got five teams at like 10% or higher.
00:38:05
No, four teams. Four teams 10% or
00:38:07
higher. And that's more than we've had
00:38:09
in a few years. It's definitely more
00:38:11
bunchy at the top. And despite Aaron's
00:38:13
take, the Eagles are the highest, at
00:38:16
least on the poly market side, at about
00:38:18
12% chance of winning the Super Bowl.
00:38:21
>> Yeah. So, I love this bunchiness um that
00:38:24
there's more teams at the top. Um I
00:38:26
could debate just because I was there
00:38:28
and you know, again, this is a bias that
00:38:30
I have. I think people are underelling
00:38:33
the Rams. Um, that was a damn good
00:38:35
football team that came into
00:38:37
Philadelphia and was one play away from
00:38:40
going to the Super Bowl. Um, they're not
00:38:43
that low, you know, they're like the
00:38:44
seventh or eighth best team.
00:38:45
>> Well, let's say what that is, but but as
00:38:47
a as a fraction, it is so much smaller.
00:38:50
It's like 4% or something. Yeah. Yeah.
00:38:52
Yeah.
00:38:52
>> Yeah. The other part that's interesting
00:38:53
is a lot of that bunchiness, this is
00:38:55
what we talked about with Aaron, is the
00:38:57
Chiefs, Bills, and the Ravens, and only
00:38:59
one of them Super Bowl.
00:39:01
>> That's right. That's why the Eagles
00:39:02
That's why the Eagles are popped up
00:39:04
above them because that market poly
00:39:05
market understands that and they're they
00:39:08
just have less competition in their
00:39:09
competition.
00:39:09
>> Actually, you brought up a really
00:39:10
important point because not a novice,
00:39:12
but someone that's not thinking about it
00:39:13
as carefully might say, "Well, of course
00:39:15
that means the Eagles are the strongest
00:39:17
team." No, the Eagles are in a weaker
00:39:19
division. They have really one serious
00:39:22
contender. Not that they don't have
00:39:23
others, but the Lions I think people
00:39:26
would consider in that top tier.
00:39:28
>> Yeah. in the AFC. I mean, there's a lot
00:39:30
of teams. There's a lot of teams that
00:39:33
could be competitive and certainly the
00:39:34
Ravens, Bills, and Chiefs. That big
00:39:36
three,
00:39:37
>> you know, three versus two tier, three
00:39:39
versus two top tier teams changes your
00:39:42
probability of going to the Super Bowl
00:39:44
and winning dramatically. If you have
00:39:47
one other top tier team, you have to
00:39:49
beat one. If you have three, depending
00:39:51
on your break and the playoff uh
00:39:53
seating, you may have to beat two. And
00:39:55
that's a big difference. Well, that that
00:39:58
concentration is so high as well. So,
00:40:00
three AFC teams above 10% to win the
00:40:03
whole thing and then nobody down until
00:40:06
somewhere below four. So, it's just
00:40:08
heavily heavily weighted to the top. And
00:40:10
and Aaron, maybe the most stunning thing
00:40:12
Aaron said was that since 1978, so
00:40:14
almost 50 years worth of data, the the
00:40:18
two highest five-year runs of DVOA,
00:40:21
which is his advanced analytics measure
00:40:23
for quality of a football team, the two
00:40:25
highest five-year runs without appearing
00:40:27
in a Super Bowl belong to the current
00:40:30
Bills and Ravens in the last 47 years.
00:40:33
It's absolutely stunning. That's because
00:40:35
Chiefs
00:40:35
>> the last two years, you you might have a
00:40:37
different opinion. I think it's hard to
00:40:38
argue. I'm not arguing with the outcome.
00:40:40
The outcome was the outcome.
00:40:41
>> I think it's hard to argue the Chiefs
00:40:43
were a better football team than the
00:40:45
Ravens or Bills the last two years. I
00:40:47
don't I'm happy to go back three, four,
00:40:49
five years and say that, but not the
00:40:51
last two. And I don't expect I do not
00:40:53
expect the Chiefs to be a better
00:40:55
football team. And let's even talk about
00:40:57
Aaron also talked about postgame matchup
00:41:01
beating expectations.
00:41:03
I think the Chiefs are going to excel at
00:41:05
that. You even mentioned they got Andy
00:41:07
Reid. I mean, come on. That's worth a
00:41:10
lot. I'm not saying John Harbaugh is not
00:41:12
a very good coach. I'm not saying um
00:41:14
Sean uh why can't I think of his last
00:41:16
name? The Bills coach.
00:41:17
>> Yeah, I never can do names either. Sean.
00:41:20
>> Yeah. He's obviously a very good He's
00:41:23
obviously a very good coach, but he's
00:41:25
not Andy Reid. And so I think the Chiefs
00:41:28
may be the third best team in the in the
00:41:30
AFC. And I think right now I would put
00:41:33
them as again since they still have Pat
00:41:35
Mahomes, they're still the favorite to
00:41:36
make the Super Bowl. Why not?
00:41:38
>> All right, so we got we got we started
00:41:40
our football previews a little bit early
00:41:42
this year. We're going to we're going to
00:41:43
slow walk them in. We're going to stage
00:41:44
them in over the next few weeks. We've
00:41:45
got a five weeks or so before we launch
00:41:47
NFL. We've got some college football
00:41:49
previews coming up in future weeks.
00:41:51
Eric, I know you're just back from your
00:41:54
annual pilgrimage with your cousin and
00:41:56
the boys to the Hall of Fame inductions
00:42:00
for baseball. How'd it go? What stood
00:42:02
out to you?
00:42:04
>> Well, couple interesting things that I
00:42:06
heard. First, um, Billy, there's only
00:42:09
eight, what was it, eight relievers that
00:42:13
are in the Hall of Fame, full-time
00:42:15
relievers. Billy Wagner's one of them.
00:42:18
Billy Wagner's right-handed. He broke
00:42:21
his right arm as a kid, so learned to
00:42:23
pitch left-handed. So, he's a
00:42:25
left-handed pitcher that made the Hall
00:42:26
of Fame who's not left-handed. So, that
00:42:29
was one thing I thought was interesting.
00:42:31
>> I'm sorry. I don't understand what
00:42:32
you're saying. You lost.
00:42:33
>> He's right-handed.
00:42:35
>> You mean pitches lefty?
00:42:37
>> He's right-handed outside of baseball,
00:42:40
but he pitches he pitches with his left
00:42:41
hand. Okay, got it.
00:42:42
>> That is correct. That's because he broke
00:42:44
his arm as a youngster and couldn't
00:42:45
pitch with his right arm.
00:42:47
>> Okay. Okay. That was one thing. The
00:42:50
second thing is that um
00:42:53
Ichiro
00:42:54
besides giving a hilarious speech
00:42:57
um he had no hits until age 27.
00:43:03
>> No major league baseball hits.
00:43:05
>> Correct.
00:43:05
>> Yeah. Yeah. Yeah. Yeah.
00:43:07
>> And they actually published a list. They
00:43:09
showed it actually up on the screen.
00:43:10
I'll make it up. Like Tai Cobb had like
00:43:12
1,600.
00:43:14
You know, there were 11 players that had
00:43:16
a thousand of their 3,000 hits by the
00:43:18
time they hit that age six. Wow.
00:43:20
>> And Icho had zero.
00:43:23
>> And of course, his first 10 seasons
00:43:26
record, obviously, all 200 hit seasons,
00:43:28
including his fourth year, we had set
00:43:30
the all-time record of 262 hits in a
00:43:33
season. Um, he averaged 236 hits per
00:43:37
season in his first 10 seasons. So,
00:43:40
that's a lot. Let me just real real
00:43:41
quickly. 236
00:43:44
is is pushing one and a half a game. And
00:43:48
how many at bats do these guys expect to
00:43:50
get? Not that many more than three. So,
00:43:52
it's almost half of his at bats he's
00:43:53
getting hits. Maybe maybe they get four
00:43:56
at bats. Maybe it's
00:43:58
>> maybe
00:44:00
it would have to be. And actually, I
00:44:02
have to look at his batting average
00:44:04
after his first 10 seasons. I think his
00:44:06
career batting average was 331. I'm just
00:44:08
doing it by recollection. I'm not sure,
00:44:10
but it could very well have been between
00:44:12
340 and 350, which would be exactly your
00:44:15
math. If he got, you know, four at bats
00:44:18
a game, 640 a season, and he got one and
00:44:21
a half hits per game, that's hitting 350
00:44:24
out of four. Okay. So, that could very
00:44:26
well be. He may have had a 350 lifetime
00:44:29
batting average after 10 seasons.
00:44:30
>> What do you think his longest hitless
00:44:32
streak was, but in games or or at bats?
00:44:38
Well,
00:44:40
I mean, if he's basically hitting
00:44:41
onethird of the time, then the
00:44:43
probability of not getting a hit in a
00:44:46
game is uh well, he'd have to it's 2/3
00:44:52
to the fourth. So, 8 over 8 over 9 8
00:44:56
over 8110th probability. He doesn't get
00:44:59
a hit in a game. So if you call that a
00:45:01
geometric distribution, his the expect I
00:45:04
don't know 10 I don't know uh games
00:45:06
without a hit. I was going to say 10,
00:45:09
but that seems way too high.
00:45:10
>> That seems too high, right? The way you
00:45:12
just went through it, that seems too
00:45:13
high to be more than 50%.
00:45:15
>> It does seem way too high because
00:45:16
there's a 10% chance roughly that he
00:45:20
won't, you know, 2/3* 2/3* 2/3 times
00:45:23
2/3, which is not hit, not hit, not hit,
00:45:25
not hit. That's eight out of roughly
00:45:28
that's one10enth. If you put a geometric
00:45:30
distribution on that, like I'm flipping
00:45:32
a one out of 10 coin. Um, oh, the
00:45:36
waiting time. Yeah, the waiting. I I
00:45:38
don't know. Maybe I'm computing the
00:45:40
distribution of the maximum wrong.
00:45:41
That's probably what I'm doing. I'm just
00:45:43
probably thinking about the distribution
00:45:45
of the maximum wrong. I don't know what
00:45:47
it is.
00:45:47
>> Okay, let's stop. Let's stop the math
00:45:49
now and give an intuition. Like six
00:45:50
games or so.
00:45:51
>> Yeah, I'm going to say five or six
00:45:52
games.
00:45:53
>> Yeah, something like that. That's that's
00:45:54
extraordinary. Okay. Um, but I
00:45:57
interrupted your Hall of Fame thing.
00:45:59
>> No, no, no. The only other thing, the
00:46:01
only other thing I would say was that I
00:46:03
also had forgotten when I looked at his
00:46:05
stats how great a player Dave Parker
00:46:07
was. You know, he was close. Not I don't
00:46:10
know. He had 20 over 2700 career hits. I
00:46:14
assumed his stats were much worse. And
00:46:17
so I now have to wonder why he wasn't
00:46:20
potentially in the Hall of Fame earlier.
00:46:22
Um because just because I maybe it was a
00:46:24
different era when people were voting
00:46:25
and everything else like that, but I
00:46:27
compare him to a lot of the players that
00:46:29
have gotten in recently and you know to
00:46:31
me he was as good I saw them both. He
00:46:33
was as good as Andre Dawson. He was as
00:46:35
good as Andre Dawson and you know he was
00:46:38
as good as a lot of people that are
00:46:40
currently in the Hall of Fame. So that
00:46:42
was the other thing that struck me.
00:46:44
>> Eric, remind me remind me what was so
00:46:47
special about those Pirates teams back
00:46:49
then with Willie Starel and Dave Parker
00:46:51
like that. There's something that like
00:46:52
the fish that ate Pittsburgh or whatever
00:46:54
the what was so crazy about what was
00:46:55
going on with those guys. I forget what
00:46:56
was going on with those guys.
00:46:58
>> Well, I think a bunch of it was just
00:46:59
they were very interesting big time
00:47:02
personalities. I think that was part of
00:47:04
it. Also, they just had an amazing, you
00:47:07
know, an amazing pitching staff, amazing
00:47:09
hitting team, and it was also just, as
00:47:11
you remember, Pittsburgh was title town
00:47:14
back then, right? That was the Steelers
00:47:16
uh era.
00:47:17
>> Don't remind me. Don't remind me. No,
00:47:19
I'm just saying that was a big era for
00:47:21
the city of Pittsburgh. Um, and they
00:47:24
were just I think um Pittsburgh again
00:47:27
Dave Parker obviously just passed away
00:47:30
two months ago. His son spoke. It was he
00:47:32
was just saying it was a team with a lot
00:47:34
of personality.
00:47:34
>> Yeah. Fun. Fun. Okay. All right. Let's
00:47:37
do a little bit of contemporary baseball
00:47:38
because your boy Judge just hit the IR
00:47:41
or whatever they call it and DL
00:47:42
whichever one it is. And meantime that
00:47:46
pesky catcher out of Seattle dinged a
00:47:48
couple more. I think I'm liking my I
00:47:49
think I'm liking my position on the on
00:47:52
the Raleigh versus Judge bet.
00:47:54
>> Well, now all of a sudden you're looking
00:47:56
good.
00:47:56
>> Hey, who's that? You could you had to
00:47:59
factor in injuries. Judge is not exactly
00:48:01
injury robust.
00:48:02
>> Yeah, of course Judge's injury was from
00:48:04
throwing the ball. And so the
00:48:06
interesting part from the Yankees
00:48:08
perspective, and you I forget if we
00:48:09
talked about this last week or not,
00:48:11
>> if Judge can't throw, then Judge can't
00:48:13
play the outfield, which means Judge has
00:48:15
to DH, which means you now put Stanton
00:48:18
in the outfield because you you got to
00:48:20
play Stanton. He's the second or third
00:48:22
best hitter on the Yankees, maybe
00:48:23
fourth. So you got to play him. And so
00:48:26
now you put him in the outfield and then
00:48:28
you get defensively much worse. So the
00:48:31
they've got a lot of trouble. Um, I now
00:48:34
put it at at best. I think Raleigh has
00:48:37
40. I think Judge has 37. I may be
00:48:40
wrong, but I think he has 37. Judge is
00:48:42
on the 10day injured list. If he comes
00:48:44
back, I still like Judge, but not by
00:48:47
much. He might be five behind by the
00:48:50
time it happens. And there'll be only
00:48:51
55. Actually, I I don't know how I can
00:48:54
say I like Judge. If he's five behind
00:48:56
with 55 to play, his rate, well, it
00:49:00
would have to be 0.1 more per game,
00:49:03
which means over 162, I'd have to think
00:49:05
he's a 16 better home run hitter than
00:49:09
Raleigh, and that seems unlikely.
00:49:11
>> It's a little strong, but on the
00:49:14
>> on the on the other hand, these things
00:49:15
do seem to be chunky. So, there's a lot
00:49:18
of chance there, just you might not
00:49:20
expect it. Um, but I think Rley got to
00:49:23
40. I may have this wrong. I thought he
00:49:24
got two in a row relatively quickly. I
00:49:26
thought he was at 41, but perhaps
00:49:28
>> Oh, he could be. I just the last time I
00:49:29
heard he was at 40.
00:49:31
>> Okay, Eric, I want to give you some bits
00:49:34
and bobs from around sports.
00:49:37
>> 41.
00:49:37
>> All right. So, he did sneak out there a
00:49:38
little bit. Um, the things that have
00:49:41
caught my eye are a little unusual.
00:49:43
Maybe it's because it's the time of
00:49:44
year, but we're a little bit in a in a
00:49:46
in a Eb in major sports, but some of the
00:49:49
interesting bits have been a little bit
00:49:51
more businessy. We don't do a lot of
00:49:52
sports business here, but some of these
00:49:53
are businesses. Some of them are stuff
00:49:55
from women's professional leagues. So,
00:49:57
for example, I just saw, you know, the
00:49:59
the the Euro the women's Euro finals was
00:50:02
this past weekend, and England won. We
00:50:04
saw some headlines around that, but I
00:50:06
just saw the TV numbers for that. And
00:50:09
the the the US numbers were the were a
00:50:13
record for a women's soccer game.
00:50:14
There's something like I'm missing the
00:50:16
exact same like 1.4 four 1.5 million
00:50:19
viewers, which is a 50% increase
00:50:21
apparently over the 2022 Women's World
00:50:24
Cup. And even the the the whole
00:50:26
tournament, not just the final, but the
00:50:28
whole tournament was averaging 458,000
00:50:30
or something like that, which is a 100%
00:50:32
increase over 2022. And people are
00:50:35
thinking that this is because the NWSL
00:50:38
has become so popular around here. And
00:50:40
then those players go overseas and play
00:50:42
in some of these um national tournaments
00:50:45
and their interest goes with them. But
00:50:48
so how when you just like raw numbers,
00:50:50
this is one thing we ought to do more of
00:50:52
Eric and I and I think we've been trying
00:50:53
some in recent years is just to be
00:50:55
calibrated on the number of eyeballs on
00:50:57
various sporting events because that's
00:50:59
what drives revenue in sports. And so we
00:51:01
need to have some sense of what's a big
00:51:02
event, what's not a big event. When you
00:51:04
hear 1.4 4 1.5 viewers and it peaked at
00:51:07
like 1.9 for the women's Euro final in
00:51:10
the US. How does that strike you?
00:51:15
>> First of all, I love women's soccer. I
00:51:18
love watching it. I love the game. Um,
00:51:21
in some ways I can um not associate, but
00:51:24
I can like watch the game and you know
00:51:27
identify more with it than the men's
00:51:29
game which I can't identify with at all.
00:51:31
Um, the women's game I think is a
00:51:33
beautiful game to watch.
00:51:34
>> So, hold on. Let me Rick, let me jump in
00:51:36
on that. Um, my advisor
00:51:39
used to say, he's a golfer. He used to
00:51:41
say, I think we ought to all watch more
00:51:43
women's golf instead of men's golf
00:51:45
because we can't really expect to do
00:51:47
what the men do, but our swings, if we
00:51:49
if we tried to mimic the women's swing,
00:51:51
we'd be better off.
00:51:52
>> I completely agree with that. And I I
00:51:54
played golf this weekend. I need to
00:51:55
mimic something other than my own swing.
00:51:57
But um you know I'm I'm surprised at
00:52:01
that number. I'm not surprised at 1.35
00:52:03
million. I'm surprised that it's a
00:52:05
record for women's soccer because you
00:52:07
know if we think that there's I don't
00:52:09
knowund 100 million US households that's
00:52:12
still only a and you know I don't know
00:52:14
how many TV viewing households there
00:52:16
are. you know, I don't like I think the
00:52:19
average NHL game gets about that number
00:52:22
and so and so it's not large compared to
00:52:26
other sports and premier games from
00:52:30
other sports, but I'm glad to see that
00:52:32
it's growing. And you're right, we are a
00:52:34
business show as well and the business
00:52:37
of sports is generated by TV revenue.
00:52:40
That's the business of sports. The
00:52:41
reason the NFL players get paid well,
00:52:44
MLB players, etc. are the TV deals and
00:52:47
packages. That's the primary source of
00:52:50
revenue. And I hope this trend
00:52:53
continues. If you would asked me to
00:52:54
guess what the highest ever
00:52:57
this game, I would have probably guessed
00:52:59
the number around five to six million.
00:53:02
>> Right. I definitely would have been
00:53:03
three or four. And you make a very good
00:53:04
point. I mean, can you imagine that in
00:53:06
the, you know, the US women's
00:53:07
championship in what was that 2000, the
00:53:09
the the World Cup, 2000 against China,
00:53:13
we had fewer than one and a half million
00:53:15
Americans watching that match. That's
00:53:17
really surprising, but apparently
00:53:19
apparently true. Um, but the increases
00:53:23
also the increase from the last Euro um
00:53:26
championship are notable that that that
00:53:29
we're jumping that high essentially
00:53:31
year-over-year or tournament over
00:53:32
tournament.
00:53:33
>> Yeah. Just to let you know I this is the
00:53:35
wonderful thing about large language
00:53:36
models. So the average Major League
00:53:40
Baseball viewership per game on Fox,
00:53:43
ESPN, TBS, the national networks is 1.84
00:53:47
million viewers.
00:53:49
>> 1.84. So the average MLB
00:53:52
that's the average tele like all in a
00:53:55
given night like all the games they're
00:53:57
all they're a on a random Wednesday
00:53:59
night or whatever they're averaging 1.8.
00:54:01
That's probably better than I would have
00:54:02
expected.
00:54:03
>> And NFL, by the way, what's your guess
00:54:05
for the NFL?
00:54:06
>> Oh, it's an entirely different thing.
00:54:07
So, I know I just saw the stat. You see
00:54:10
variations on it, but like 72 of the
00:54:13
highest 100 programs in 2024 were NFL
00:54:16
games.
00:54:17
>> Yeah, it's 17.5 million
00:54:20
>> for the average NFL game. Yeah, exactly.
00:54:22
Okay, Eric, what I think we're doing and
00:54:24
we ought to do more of is basically the
00:54:26
analytics of sports business. So, we
00:54:28
like analytics, right? I don't care that
00:54:30
much about business, but if we can make
00:54:31
it more interesting analytically, but
00:54:34
this is just fundamental calibration.
00:54:35
This is this is what drives dollars. It
00:54:38
drives signings. It drives everything.
00:54:39
So, just be calibrated. We we can keep
00:54:41
on trying to to be calibrated all. So,
00:54:44
let me give you I don't have the numbers
00:54:45
on this. Actually, did while I'm asking
00:54:46
you this question, Eric, do the research
00:54:49
on the average um TV audience for the
00:54:52
WNBA game. Give us that. And then let me
00:54:56
make the following observation. I just
00:54:57
learned this. So, we know that the new
00:55:00
TV deal is coming for NBA, WNBA, and
00:55:04
they're expecting a whopping um
00:55:05
increase. So, I think they're expecting
00:55:07
something at two or $300 million. And
00:55:09
that's more than a two-fold increase.
00:55:12
It's I'm I'm getting the numbers wrong.
00:55:14
It's a massive increase this coming
00:55:16
season. And here's the interesting bit,
00:55:19
Eric, and this is what I just learned in
00:55:20
the past week. Because players and
00:55:23
agents have known that that's when the
00:55:25
new TV contract was going to come up.
00:55:26
And because they've anticipated the
00:55:28
numbers going much higher, no, everybody
00:55:31
has set their contracts to expire this
00:55:34
off season. So essentially, unless
00:55:36
you're on a rookie deal,
00:55:38
>> the entire the entire veteran crop of
00:55:41
WNBA players is going to be up for free
00:55:44
agency this off season. So, you're we're
00:55:47
going to see something in the WNBA we've
00:55:49
never seen before, which is a I don't
00:55:52
know if it I think it's the majority of
00:55:54
players are going to be up for signing
00:55:56
with whatever team. It's going to be a
00:55:58
super super interesting offseason.
00:55:59
>> So, so if I had asked you to guess the
00:56:01
ratio of NBA to WNBA average viewer,
00:56:06
what would be your guess on the ratio?
00:56:07
I'm not even asking you for the number,
00:56:08
the ratio.
00:56:10
>> Well, I'm not I don't walk around with
00:56:13
high expectations for NBA audiences. My
00:56:16
sense is that they're down a fair bit
00:56:18
and they keep on coming down. So I think
00:56:20
they may be as low as like 2 million or
00:56:22
something. Um and so and I'm working to
00:56:25
the ratio, Eric. I have to do it this
00:56:26
way with basketball, the women's
00:56:30
basketball, I'm going to go in the high
00:56:33
six digits. And so I'm going to put it
00:56:35
at about 3 to one.
00:56:37
>> You're very well calibrated. You're not
00:56:39
off by much. You're you're off, but not
00:56:41
by much. Men's is at 2.68 million.
00:56:44
higher than I thought.
00:56:45
>> Women's is at 1.32. So it's two
00:56:47
>> also higher than I thought.
00:56:48
>> But also but I'm saying though I mean if
00:56:50
you would asked me I would have guessed
00:56:52
five or seven to one not just the NB
00:56:55
WNBA. I would way overestimated the NBA.
00:56:58
>> Yeah. Yeah. That No, I've been that's a
00:57:00
story over the last few years that just
00:57:02
keeps on coming down and people are
00:57:03
amazed at the valuations keep on going
00:57:05
up. No, but your point is is that you
00:57:09
know the shirts that you and I both
00:57:11
support that the women's shirts were pay
00:57:13
us the damn money we deserve. If the
00:57:15
ratio is 2 to1 the salaries might be 100
00:57:18
to one, 50 to1, 25.
00:57:21
>> Yeah. Yeah. So that's that that's a
00:57:23
that's a big topic and one we probably
00:57:25
ought to tackle. Whenever they get into
00:57:26
those formal negotiations, we should
00:57:27
talk about it because it's a really
00:57:29
interesting question. The biggest issue
00:57:30
is the val the club franchise values are
00:57:34
really going up in the WNBA. That's
00:57:36
where the owners are doing great on
00:57:37
franchise values. Revenue is is moving
00:57:40
and it's about to move. It's about to
00:57:42
move again this offseason, but it's not
00:57:44
anything like the changes in franchise
00:57:46
values.
00:57:46
>> No, but Eric, hold on, hold on, hold on.
00:57:49
This is the key point. Historically,
00:57:51
>> professional athletes haven't
00:57:52
participated in franchise increases.
00:57:55
They participate through revenue. And
00:57:57
it's a whole it's a whole question of
00:57:59
whether they can get a part of franchise
00:58:00
and that you that now we're talking
00:58:01
about are we talking about labor versus
00:58:03
capital. So it's this interesting
00:58:05
philosophical question.
00:58:06
>> No, no, I just loved your previous I I I
00:58:09
appreciate it now even more than 30
00:58:11
seconds ago or two minutes ago when you
00:58:12
said it like you'd be a fool to sign a
00:58:15
long-term WNBA contract right now. Why
00:58:18
would you sign it now? I want my
00:58:20
contract to be expiring. No, they that's
00:58:23
and everyone saw it and so it's just
00:58:24
going to be this amazing offseason
00:58:26
moment where it's it's like be a
00:58:28
free-for-all essentially resigning all
00:58:30
these veteran players. All right, last
00:58:32
one for you, Eric. And this is instead
00:58:34
of a a business question, I'm go back to
00:58:36
statistics, but I'm going to stay in the
00:58:37
women's professional world. It's a
00:58:39
little detail I saw in the bottom of
00:58:41
this article. So, there's a young rookie
00:58:44
who just made her debut, a PGA debut at
00:58:47
the Scottish Open this past week, Lahi
00:58:48
Wde. Lahi W. She's done real well as an
00:58:50
amateur. She's done ridiculously well as
00:58:53
an amateur, but this was her first um
00:58:55
professional tournament and it counts as
00:58:56
a PGA tournament even though it's the
00:58:58
Scottish Open. She wins the dang thing.
00:59:00
All right. So, that's great. We should
00:59:02
all keep an eye on Lahi W. And by the
00:59:04
way, the the women's I think it's the
00:59:07
women's is it the US Open is this
00:59:09
weekend? I think it's No, no, no. We've
00:59:10
already had the US Open. Is it the
00:59:12
British Open? Essentially, the Europe
00:59:13
there's a major this weekend and I'm
00:59:15
shame on me for not knowing exactly. I
00:59:17
think that's coming up this weekend. But
00:59:18
here's the question.
00:59:19
>> She just won Scottish. It's probably the
00:59:20
British because they probably do what
00:59:22
the men do. Scottish than British,
00:59:24
>> right? Okay. Here's my question for you.
00:59:26
I get your I want to hear you talk this
00:59:27
one out loud.
00:59:30
What do you think in the 75 years of the
00:59:32
LPGA?
00:59:34
What's the longest streak of a new
00:59:38
winner?
00:59:40
What's the longest streak of new of
00:59:42
between a a winner repeating anybody
00:59:46
repeating? What's the longest streak of
00:59:48
um not not first- time winners? I mean,
00:59:50
just the longest streak of not having a
00:59:52
repeat winner in 75 years of LPGA. And
00:59:56
I'm tell
00:59:57
>> you're talking about a given person the
00:59:58
distance between their first win and
01:00:00
second win.
01:00:01
>> No.
01:00:02
>> What are you referring to?
01:00:03
>> I'm saying for anybody to repeat. So if
01:00:07
you know, so Lahi W. If she if she won
01:00:11
five weeks from now, that would be a
01:00:13
four-week streak of No. Well,
01:00:17
let's say four new winners and then Lahi
01:00:18
Wins again. That would be a four-week
01:00:20
streak of there not being a repeat
01:00:22
winner.
01:00:23
>> Oh. Oh. What's the longest streak of
01:00:25
there not being a repeat winner?
01:00:28
>> Yes. Yes. Yes.
01:00:29
>> Oh. In the LPGA.
01:00:30
>> In the LPGA. This is a harder problem
01:00:32
than I usually give you guys. So, let's
01:00:34
just talk about how what's your
01:00:36
expectation. I'm going to tell you that
01:00:38
they just hit their record mark in 75
01:00:41
years. and I would have had no idea how
01:00:43
to think about how to calculate. I'll
01:00:45
give you the answer, but I first want to
01:00:46
hear you talk about estimating.
01:00:48
>> Don't give me the answer first. Let me
01:00:49
think tell you how I think about it. So,
01:00:51
um I don't I would imagine this may be
01:00:55
this may already end me and doom me for
01:00:57
too low a number. Um I doubt there's
01:01:00
been any year on the LPGA tour where
01:01:04
there hasn't been someone that's won
01:01:06
twice. Now, if that's true, if that's
01:01:09
true, I'm not saying it's true.
01:01:11
If that's true, then the number has to
01:01:13
be less than the number of tournaments
01:01:15
in a year on the LPA LPGA tour. So what
01:01:19
I'm computing the stat is the maximum
01:01:22
number of wins by a player in a given
01:01:25
season. And if that number is two
01:01:30
for every season, the maximum of your
01:01:33
statistic can't be more than about 25 or
01:01:36
27 or however number of tournaments
01:01:38
there are in a given year. So that's why
01:01:41
I'm going to guess somewhere in the high
01:01:43
20s because I don't think there's been
01:01:45
an LPGA season whether it's Nelly Corda,
01:01:47
Anukica Soren Stam, Nancy Lopez, Ro Jang
01:01:51
or whoever the hottest golfer was in a
01:01:54
given year. Somebody's won twice in that
01:01:57
year and therefore that's how I'm going
01:02:00
to bound the maximum from above.
01:02:02
>> Well, that was my first thing I was
01:02:03
going to say. You you didn't come up
01:02:04
with a real answer. You just bound it.
01:02:06
So you're saying less less than that
01:02:07
>> down from there a little But but it's
01:02:09
interesting to me how top down you went
01:02:10
because usually we build things very
01:02:11
very bottom up and you you just kind of
01:02:13
went cluji in the middle which is fine.
01:02:15
It gave you a bound.
01:02:16
>> I could do the other thing too.
01:02:17
>> Yeah. Let's try to go bottom up to come
01:02:19
what? Let's give us a more precise
01:02:20
number underneath that bound.
01:02:21
>> Well, if I went from the bottom up,
01:02:24
let's just say there's someone let's say
01:02:26
a very good golfer has a 5% chance to
01:02:28
win a given tournament. Right? So the
01:02:30
expected weight time until the next win
01:02:34
is geometric with probability 0.05. So
01:02:37
the expected number is 20 20 weeks 20
01:02:40
events it would take that's not the
01:02:43
distribution of the maximum number of
01:02:44
weeks but I also have a large number of
01:02:47
players I mean I could simulate this in
01:02:49
about two seconds and see what the
01:02:52
answer is. Um
01:02:54
>> but that's hold on let's say because
01:02:56
what you just did is really interesting.
01:02:57
You said for a good player your
01:03:00
expectation is that they might win every
01:03:02
20 tournaments or so, but then so
01:03:07
but then we're talking about records,
01:03:08
right? So we're like how what's the most
01:03:10
extreme?
01:03:11
>> I know.
01:03:11
>> And and we're also talking about
01:03:12
multiple players and so it kind of those
01:03:15
go in different directions.
01:03:16
>> Exactly.
01:03:16
>> Multiple players are going to shorten
01:03:17
that but the maximum is going to
01:03:19
lengthen it.
01:03:19
>> Tell me the answer is something like 60
01:03:21
or something like that.
01:03:22
>> No, no, no, no. Like look what you just
01:03:24
did. For example,
01:03:24
>> I'm staying with my number. I'm staying
01:03:26
my bottom up and top down seem to have
01:03:28
some sort of convergent validity and I'm
01:03:31
going to stay with a number in the 20s
01:03:33
somewhere.
01:03:34
>> Well, I want to I want to say again that
01:03:35
you're you you you did a real simple
01:03:37
thing. You said one player, decent good
01:03:40
player, 5% chance. I would expect that
01:03:42
player to win every 20 tournaments. And
01:03:44
then I said, well, there are two other
01:03:45
factors. One, we're not looking for the
01:03:47
expected. We're looking for that how
01:03:48
what's the extreme version of that? The
01:03:50
max of that that pushes it longer. On
01:03:52
the other hand, we've got more than one
01:03:54
player and so somebody's going to do it
01:03:56
below their expectations. So that factor
01:03:58
goes the other way. You might say just
01:03:59
kind of heristically since I've got
01:04:01
factors going both directions, I'm going
01:04:03
to stand on 20. And had you done that,
01:04:06
you'd have been shockingly close to the
01:04:08
answer. Their record as of this week, it
01:04:10
may go longer. It's not done. It could
01:04:12
go longer. The record is 19.
01:04:15
>> Yeah. So, but I also forget whether I
01:04:17
was close or not close. I like well I
01:04:20
was I like the fact that the top down
01:04:22
and the bottom up both led to or the
01:04:25
upper bound and then the more granular
01:04:28
probabilistic led to similar answers.
01:04:31
>> Well, cool. That was it's always fun for
01:04:33
me to hear you hardcore basian
01:04:36
statistitians work through the way you
01:04:37
think of these things and sometimes it's
01:04:39
very involved and sometimes it's
01:04:40
heristic and I think both can be
01:04:41
instructive. All right, why don't we end
01:04:43
it there? Eric enjoyed the visit. Thank
01:04:46
you for being here. Thanks in absentia
01:04:48
from our colleagues Shane Jensen, Ali
01:04:51
Winer. Thank you to the big boss Dion
01:04:53
Simpkins for keeping the wheels on the
01:04:55
track. We hope all is well today as you
01:04:57
are away doing doing Deion Simpkins
01:05:00
things. Thank you guys for listening as
01:05:02
well. Come back and join us next time
01:05:04
between now and then. Enjoy your sports.
01:05:08
[Music]
