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NFL Week 1 Review: Fourth Down Decisions, Super Bowl Odds, and Kickoff Returns

September 19, 2025 / 01:03:57

This episode of Wharton Moneyball features discussions on NFL week one performances, analytics, and predictions with guest Brian Burke from ESPN. Topics include team evaluations, player performances, and analytics models.

Brian Burke, a sports data scientist at ESPN, shares insights on the Green Bay Packers' strong start and their Super Bowl chances. He discusses the teams with the best chances to succeed, including Baltimore, Buffalo, and Philadelphia.

Burke also analyzes the performance of the Indianapolis Colts and Cincinnati Bengals, highlighting their movement in rankings based on their week one games. The conversation touches on the importance of early season data in shaping predictions.

In addition, the hosts and Burke discuss fourth down decision-making in high-pressure situations, referencing a controversial call by the Baltimore Ravens against the Buffalo Bills.

The episode concludes with a broader discussion on NFL trends, upcoming matchups, and the impact of analytics on team strategies.

TL;DR

Brian Burke discusses NFL week one analytics, team performances, and fourth down decision-making with Wharton Moneyball hosts.

Episode

1:03:57

00:00:00

Welcome, welcome to Wharton Moneyball.

00:00:04

Welcome to a full hour of sports

00:00:06

analytics here on the Wharton podcast

00:00:09

network. This is Kate Massie hosting

00:00:11

this week with my longtime collaborators

00:00:13

and friends, co-host, colleagues, Shane

00:00:16

Jensen and Eric Bradlo. Our fourth

00:00:19

co-host, Audi Winer, not in today,

00:00:20

sadly. Audi out and about. Audi doing

00:00:22

Audi things. Audi doing things the rest

00:00:24

of us are jealous of. Audi's is not

00:00:26

gonna be with us today, but he will be

00:00:28

back. Some combination of us, y'all know

00:00:30

this. Some combination of us are here

00:00:32

almost every week of the year. 48, 49,

00:00:35

50 weeks of the year. We're here doing

00:00:37

the show. Have been for more than 11

00:00:40

years now. Delighted to be back. We're

00:00:42

recording on Tuesday afternoon as we

00:00:44

usually do. Gonna do an hour. Gonna do

00:00:47

the first half hour with guests. Going

00:00:48

to roll into open lines, open topics in

00:00:51

the second half hour. Got to start the

00:00:53

show straight away with a guest and one

00:00:55

of our regulars, one of our favorites,

00:00:58

one of our long times, a family,

00:00:59

practically families, Wharton Moneyball

00:01:01

family, Brian Burke. Brian Burke is here

00:01:03

from ESPN. Brian, thank you for making

00:01:05

time.

00:01:06

>> Yeah, thank you uh for having me. Uh

00:01:09

always enjoy being on and I feel like uh

00:01:12

like the fifth beetle kind of like uh

00:01:16

I want to be a co-host and I feel like

00:01:19

you watch like a show long enough like

00:01:21

Seinfeld, right? And you're like, I

00:01:22

could be in the part in the apartment

00:01:24

with Jerry and Elaine and George and

00:01:26

we'd all just get along. I Yeah. Here's

00:01:29

what

00:01:29

>> I think. I think Hold on. I think I

00:01:30

think you're the Eric Clapton of the

00:01:32

Beatles. I think I think that's the

00:01:33

right role for you. I'm gonna give you

00:01:35

the

00:01:35

>> I was going to say, Brian, the next time

00:01:37

I can offer for myself, the next time

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it's just me that's available, rather

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than me talking to myself for an hour on

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the radio, why don't you come join me

00:01:44

and you can co-host with me and we'll

00:01:46

we'll talk some sports for an hour.

00:01:48

>> Yes. Oh, absolutely. I want to be the

00:01:50

designated Audi. Um

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>> I don't That's tough, man. That's tough.

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>> Those are those are some big shoes to

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fill. Literally.

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>> I just He asks the toughest questions

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and I actually I appreciate that more

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than anything. Even when he's asking me

00:02:05

the tough questions because you learn

00:02:07

you learn that way.

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>> Yeah, he's uh we all aspire to more

00:02:11

audience in our intellectual work. For

00:02:13

sure. Brian, as most of y'all know, is

00:02:15

at ESPN. He's a sports data scientist at

00:02:18

ESPN. He's also the founder of the

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website advanced football analytics. He

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was one of the very first to step out um

00:02:25

when football analytics started getting

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sophisticated. He was a Navy fighter

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pilot in a previous life. He moved from

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this black and white engineering world

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into the shades of gray probability big

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transformation midlife transformation

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and now he's right on the edge of all

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things all things football analytics.

00:02:43

Brian, so much to talk about. Week one

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is in the books. Um, why don't we start

00:02:48

there? We've got deeper topics for you,

00:02:50

but you know, you're a football guy.

00:02:52

You've been waiting all season for this

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to happen. Now that we've seen 16 games

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played, what's top of mind for you?

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>> Yeah. Um, a team that impresses me the

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most right now is Green Bay. Obviously,

00:03:03

they they picked up a a superstar

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defender. Um, convincing win over a top

00:03:10

uh conference and division opponent. Um,

00:03:13

and so we actually have we we're looking

00:03:15

at them with the fourth best chance to

00:03:17

make a Super Bowl. So, um, yeah.

00:03:20

>> So, talk real quickly about your fourth

00:03:22

best chance. Is that one of these flat

00:03:23

maxima situations? Because this seems

00:03:25

like a pretty open year. So, what are

00:03:26

the first three look like? And where are

00:03:28

the pack? Where's the pack?

00:03:29

>> Yeah. Yeah. So, you're you're absolutely

00:03:31

right. It it pretty much always is is

00:03:33

that way. It's a little flatter this

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year than than normal, but we have uh

00:03:38

Baltimore, Buffalo,

00:03:40

Philadelphia, and then Green Bay. Um

00:03:43

actually, here's another one I'll throw

00:03:45

out. I'm just looking at this. Uh

00:03:48

Chargers have a better shot over Kansas

00:03:51

City right now. Obviously, they have a

00:03:53

win in hand over them. Um but I'm still

00:03:56

really surprised to see that. Uh you

00:03:58

know, we'll see if that comes to pass.

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I'm always prepared. One of the last

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questions sometimes is like what are you

00:04:04

looking for, you know, in this upcoming

00:04:06

season? And I'm I'm waiting for the

00:04:07

Chiefs to kind of have have a down year.

00:04:09

It they're way overdue, but this could

00:04:12

be this could be that year.

00:04:14

>> Brian, what would you what would you

00:04:16

take as an early marker? It's easy to

00:04:18

call a down year, you know,

00:04:21

threequarters of the way through the

00:04:22

season. If you want to call it early,

00:04:24

being a good prognosticator, what would

00:04:26

be a sign of this is going to be a bad

00:04:28

year for the Chiefs?

00:04:31

Gosh. Um,

00:04:33

uh, Chris Jones not playing well.

00:04:38

Yeah, just uh early injuries. Um,

00:04:42

they've already got a suspended player.

00:04:44

Um, so yeah, just depletion early on.

00:04:48

Depletion early on and and just um, you

00:04:50

know, losing a couple games that they

00:04:51

they shouldn't. I mean, the the Brazil

00:04:54

game, it's really weird. There's just a

00:04:56

lot of travel. It's international. It's

00:04:57

the first game of the year. you know,

00:04:59

you can set that aside. But, um, that

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would be one thing to look at. They just

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they've been so good for so long. Um,

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maybe it is maybe it is time. Maybe it's

00:05:09

just me wish casting as an AFC, you

00:05:11

know, rival for him.

00:05:12

>> But could be the thing to

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>> Exactly, Brian. I I put in my I lit my

00:05:17

candles last week on this show, said my

00:05:19

prayers, put put it out there as a

00:05:21

long-suffering a not just one AFC team,

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but two AFC teams beating at the door

00:05:26

behind the Chiefs. I'm ready for them to

00:05:27

step aside. Eric's trying to jump in

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here. Speaking of uh Yeah, a little a

00:05:31

little AFC ascia. It's a little bit.

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>> No, no, I'm staying with I'm staying

00:05:34

with my question is more

00:05:36

>> your answer to Kate's question. So, when

00:05:39

Kate asked you like what caught your eye

00:05:41

in week one, I could imagine a lot of

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things. One could be which teams are at

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the top. Two is which teams surprised

00:05:48

you the most? Three, it could be which

00:05:50

teams foot FBI move the most. um which

00:05:55

teams exceeded the line the most which

00:05:57

could be different than which teams m

00:05:59

move the most. Um how did you think

00:06:01

about answering Kate's question and what

00:06:04

what was surprising to you? And I'm

00:06:06

leaving surprising broadly defined here.

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>> I mean that's a good way to organize all

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those all those things. Um the Colts

00:06:14

moved the most uh up um just because

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they the size of their victory was so

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big. Um, and the way FBI works is

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basically um, EPA uh, per play. Uh, so

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if you have big chunk EPA gains um, and

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your defense plays really well on top of

00:06:33

that, then you're you're going to you're

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going to move up. And the priors are

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fairly weak um, on the NFL side. Uh, so

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it's easy for teams to move. They they

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bumped up almost three points on a per

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game basis. So, if we thought that they

00:06:47

were, let's say, a three-point underdog

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against the average team on a neutral

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site, now we think, you know, they're

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they're probably just about average is

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where we are.

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>> This is great because you've answered my

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question because I was going to say

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you're not focused purely on the

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difference in their rank because they

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could be very bunched up and so they

00:07:04

moved a little bit, but they jumped.

00:07:06

You're actually focused on their

00:07:08

underlying latent strength parameter,

00:07:10

which is something that's continuous,

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which seems to me to be a better measure

00:07:13

of strength.

00:07:15

Uh yeah. Yeah. I mean you're you're

00:07:17

you're absolutely correct. So in the NFL

00:07:19

in particular, the the middle so like

00:07:22

the middle 20 teams, let's say, um are

00:07:25

bunched up together almost

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indistinguishable in in a certain way.

00:07:28

Like yeah, we might have, you know, the

00:07:30

the number 12 best team ranked as like a

00:07:33

plus one over the average team, one

00:07:35

point per game over the average team.

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And you know, the the 20th or 22nd best

00:07:40

team might be, you know, minus one or

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1.5 or something like that. But then

00:07:44

there's this uncertainty around all of

00:07:45

them and they're just kind of in this

00:07:46

this muddied middle. Um so you can shoot

00:07:49

up a bunch of rank point, you know, rank

00:07:52

numbers pretty easily, especially early

00:07:54

in the year. Uh the Bengals are one,

00:07:56

they struggled against the Browns. Uh so

00:07:58

they're they got moved down. So they're

00:08:00

the second biggest mover. Um looks like

00:08:03

Houston as well. They were they they

00:08:04

dropped a few point few uh ranked

00:08:06

points, but yeah, the Bengals just did

00:08:08

not did not impress.

00:08:10

Brian, can you talk a little bit more

00:08:12

about FBI uh in general? How much do

00:08:15

y'all revise that year-over-year? My

00:08:17

sense is that some years it's minor

00:08:18

tweaks, some years it's major tweaks.

00:08:20

Where are we in the cycle? And what has

00:08:22

been your role? How confident do you

00:08:24

feel about that power ranking system?

00:08:26

>> Um it's a really good question,

00:08:28

especially in light of the college

00:08:30

rankings last week. Uh we took some

00:08:33

arrows. Um we can get into that if you

00:08:35

like but uh the I I rebuilt this FBI

00:08:38

model from the ground up uh starting

00:08:42

last season. So last season was the

00:08:44

first year with a new uh completely new

00:08:47

revised model. It is it's not only

00:08:50

rebuilt every year though as far as like

00:08:52

the parameters go. It's rebuilt every

00:08:55

every time we run it. it it re um

00:08:58

relearns all the different parameters

00:09:00

and how heavy the the prior should be

00:09:03

weighted and all the other kind of

00:09:04

hyperparameters within within the model.

00:09:06

It's a basian regression. Um so

00:09:09

everything is kind of relearned. It's

00:09:10

always sort of up to date. It goes back

00:09:12

about you uses about the past 10 seasons

00:09:15

to kind of calibrate things. Um so I'm

00:09:17

I'm really confident it did like

00:09:20

embarrassingly well last year. uh so

00:09:23

well that I I know we're just due for a

00:09:26

horrible reversion to the mean uh this

00:09:29

season. But yeah, really confident. Part

00:09:31

part of the reason we're so confident is

00:09:33

the priors are very solid. We we kind of

00:09:36

cheat and we reverse engineer the Vegas

00:09:39

overunder like win totals um as kind of

00:09:42

our starting point for the prior. So

00:09:44

we're always pretty chalk in the

00:09:46

beginning of the year. Um

00:09:47

>> Okay. Well, let's stay with that for a

00:09:49

second because you you said is basian

00:09:51

and of course this is a quite a basian

00:09:53

podcast if only implicitly but the the

00:09:56

hallmark I would say is as the only you

00:09:58

know probably most outside basian person

00:10:01

on the podcast I would say the hallmark

00:10:03

is this whole idea that you have prior

00:10:06

and that you update with as new

00:10:07

information comes in that's kind of the

00:10:09

hallmark of a basian system and so trick

00:10:12

it's one thing to it's one thing to

00:10:13

backwards engineer prior from the

00:10:15

betting market that's fine lots of folks

00:10:16

can do that But now you've got to decide

00:10:19

how much weight to give those things.

00:10:21

>> Yeah.

00:10:21

>> And so can can you talk to us about what

00:10:23

you find? What's the optimal weight? And

00:10:25

we often talk about it in terms of I I

00:10:27

think I I think I got this term

00:10:29

somewhere fellas fictitious sample size.

00:10:31

Like how many games worth of information

00:10:34

is in the prior? Meaning after after how

00:10:37

many games of the season is the rating

00:10:39

that you have on the team half prior and

00:10:42

half games? Shane, I'm sorry for staying

00:10:43

in there for just a second. Yeah, and if

00:10:46

I could just add a specific kind of

00:10:47

thing, too. You you you talked earlier

00:10:49

about how, you know, these teams after

00:10:51

week one are moving quite a bit because

00:10:54

the the these carefully designed priors,

00:10:56

it sounds like you don't actually put

00:10:57

much weight on to start the season. I'm

00:10:59

a little surprised by that.

00:11:01

>> Well, we don't decide the weight of the

00:11:03

prior. The the model decides itself. So,

00:11:06

we're looking at the past 10 seasons or

00:11:08

so, and we're letting the model find the

00:11:10

the way I term it is like the least

00:11:12

unlikely. They're all unlikely. All

00:11:14

these parameters are extremely unlikely.

00:11:17

You know, we have the Ravens number one

00:11:19

right now, like plus 6.1 points per

00:11:22

game. The the the chance that they

00:11:24

actually end up that way or that they

00:11:26

are like, you know, sort of um you know,

00:11:29

ground truth truly 6.1 are is extremely

00:11:33

unlikely. It's just the least unlikely

00:11:35

of all the different uh and then the

00:11:37

weights of the prior basically the the

00:11:39

width of the the the prior distributions

00:11:43

the uncertainty around our estimates is

00:11:46

what is basically what the weight is

00:11:49

when we say weight of the priors and

00:11:51

that is left for the model to learn on

00:11:53

its own. Yeah, I was going to say this

00:11:55

is a this is a specific term as you know

00:11:57

Brian in basian inference that's why it

00:11:59

also pays to be basian besides it's the

00:12:01

only coherent form of inference it's

00:12:03

called self-norming you don't have to

00:12:06

decide the weight it decides the weight

00:12:08

it's the ratio of of in some sense what

00:12:10

K described the effective number of

00:12:12

games in the prior in this case the

00:12:14

effective number of games in the

00:12:16

likelihood or the data but the data

00:12:18

decides that and so

00:12:21

>> with an extra loop where you cross valid

00:12:24

I mean the model doesn't inherently a

00:12:25

basing model does not decide that

00:12:28

>> you're adding an extra loop over kind of

00:12:30

like the you know cross validation kind

00:12:34

of loop over over possible waitings of

00:12:36

the prior and the likelihood I guess

00:12:38

I'll rephrase my question I'm surprised

00:12:40

that the priors are given by this

00:12:42

process a small weight

00:12:45

>> given he doesn't know he

00:12:48

>> you might be exaggerating you might be

00:12:50

overemphasizing his comment that things

00:12:51

move

00:12:52

>> it's smaller than college. I'll say that

00:12:53

it's small.

00:12:54

>> Well, the fact that you would move say

00:12:55

the Chiefs, the Chargers over the Chiefs

00:12:57

based on one game, I think shows that

00:13:00

the data is maybe

00:13:03

>> ranking that was the forecast. I was

00:13:06

>> right. So, we we we think the Chiefs are

00:13:08

slightly better than the Chargers still,

00:13:10

but uh because of that win in hand and

00:13:14

and the chance that they'll have the

00:13:16

tiebreaker uh at the end of the year as

00:13:18

well, just those those two things are

00:13:21

enough to give them a better chance uh

00:13:23

at the Super Bowl given at the current

00:13:26

snapshot. I'm asking my question because

00:13:29

I'm always trying to develop my own

00:13:30

intuition, but also helping our

00:13:32

listeners develop their intuition for

00:13:33

how much weight we should have on prior

00:13:36

because that's that's kind of the whole

00:13:37

game is how much we're reacting to new

00:13:39

information when it comes in. And there

00:13:41

are lots of people who aren't doing this

00:13:44

empirically and they're saying, "Well,

00:13:46

at this point in the season, we

00:13:47

shouldn't have any old information, so

00:13:49

we're just kind of writing it down to

00:13:50

zero." which we would oppose. But I

00:13:53

think that is so wrong because I think

00:13:55

if you let the model tell you what's

00:13:57

optimal and what we're talk what we're

00:13:59

saying is what's optimal for prediction.

00:14:01

So when you say I let the model tell me

00:14:03

what the weight should be that's because

00:14:04

the model's looking historically over 10

00:14:06

years if I wanted to predict as

00:14:08

effectively as possible I would keep

00:14:09

this weight. It would move like this.

00:14:11

Great. Do you have any sense? I know

00:14:13

that you didn't you weren't prepared for

00:14:14

this question, but I'm asking you, you

00:14:16

know, either h at the halfway point or

00:14:17

at the end of the season, at the end of

00:14:19

the season, what's the weight of the

00:14:21

prior still? Because I think most people

00:14:22

don't have good intuition. It

00:14:24

>> I don't know. I I can exactly the way I

00:14:28

can't give you a number. I know it's

00:14:29

still there. It never goes away. Um and

00:14:32

it probably shouldn't uh in in the vast

00:14:35

majority of cases. One of the things I

00:14:38

have been thinking of is that um

00:14:42

before I get into that I will say one of

00:14:44

the things I have studied in the past is

00:14:46

how much luck is involved in observed

00:14:49

outcomes. And I know that you get in the

00:14:52

NFL I think you have to get past maybe

00:14:54

11 games before sort of the observed

00:14:58

signal is as as strong as the the random

00:15:02

um share the variance in in game

00:15:04

outcome. So you can you can it's it's a

00:15:06

pretty easy exercise to do with a

00:15:08

binomial distribution. Um you know the

00:15:11

variance of a a purely random binomial

00:15:14

and then you have the variance of the

00:15:15

actual game outcomes and you can just

00:15:17

subtract the variances and you and so

00:15:20

you you I think you have to get past the

00:15:21

11th game before you are just even just

00:15:25

break even with the luck. So um

00:15:28

everybody take a breath um take a couple

00:15:31

breaths long.

00:15:34

>> Yeah. And and the the thing I mentioned

00:15:35

with the college um our college FBI is

00:15:38

at the other end of its kind of life

00:15:41

cycle. It is at the end of its life

00:15:43

cycle. It is is not as modern as as the

00:15:46

NFL model is. I'm not exactly sure. I

00:15:48

don't work directly on that project. Um

00:15:51

but we yeah we were criticized because

00:15:53

uh Texas lost Ohio State um and they

00:15:57

remained number one in in FBI and a lot

00:16:00

of people don't quite uh fully

00:16:03

understand what FBI is supposed doing

00:16:06

combined with a lot of conspiracy

00:16:08

thoughts and uh and and and other things

00:16:12

and we we we took a lot of arrows for

00:16:14

that but um I think the the prior are

00:16:17

stronger on the college side and that

00:16:19

that's probably why they stop.

00:16:21

>> No, no, no, no. It wasn't even it wasn't

00:16:22

even that the postgame expectancy was

00:16:25

not only pro Texas, but it was like

00:16:27

wildly pro Texas. Like the the signal in

00:16:30

the game to the extent that there was

00:16:31

signal in the game favored Texas like

00:16:33

like I think Bill Connley said it was

00:16:35

one of the biggest discrepancies. It was

00:16:37

the biggest discrepancy of week one. It

00:16:38

was one of the bigger ones you'll see.

00:16:41

Yeah, we we took um I think we took

00:16:43

solace. I think Bill had Texas like 15th

00:16:46

or something after that. So, we're like,

00:16:48

"Yeah, we know we maybe we're wrong, but

00:16:51

um we're not alone." Uh but yeah, saying

00:16:55

Bill dropped Bill dropped Texas to 15

00:16:58

after

00:16:58

>> I don't think they started very high. I

00:17:00

don't think they had nearly as high. We

00:17:02

had them as a solid number one

00:17:04

>> going into the season. I don't think he

00:17:06

was as high, but I don't know. I don't

00:17:10

>> Is Bill just putting his thumb on this

00:17:11

scale? He He doesn't He doesn't like

00:17:13

Texas. He's He's a Oklahoma guy at

00:17:15

heart. I'm I'm joking, of course, but I

00:17:17

I would take whether they're one or 15.

00:17:19

I think that's interesting. Who you you

00:17:21

moved too little, he moved too much, it

00:17:23

seems.

00:17:23

>> Yeah, we did move them down. I mean,

00:17:24

they lost by one score on the road, you

00:17:26

know, against a great team. And so,

00:17:28

they're not going to move very much. So,

00:17:29

it's just a a question of how tightly

00:17:31

were they bunched kind of going in uh to

00:17:34

that game. And and we we had, you know,

00:17:36

our priors are unlike the NF on the NFL

00:17:39

side, they're kind of purely um we don't

00:17:42

cheat with them. They are built on top

00:17:44

of how well you did last year. Uh how

00:17:46

many returning starters do you have? Did

00:17:49

your coach change? I think there's some

00:17:51

special attention placed on a returning

00:17:53

quarterback. Um and that's what drives

00:17:56

Oh, and how many, you know, four or five

00:17:58

star recruits do you have in the

00:17:59

pipeline over the last I don't know,

00:18:01

like two or three years or so. Um, and

00:18:04

that used to work really, really well.

00:18:06

And over the last, you know, two years

00:18:09

or three years, the sport has completely

00:18:11

changed. And so that no longer works as

00:18:14

well. And we should probably have a lot

00:18:15

less confidence in our in our preseason

00:18:17

estimates of these teams.

00:18:19

>> Well, it'll be fun. When the FBI, I

00:18:23

first got to know it on the college

00:18:24

scene, and when it first came out, I

00:18:26

thought it was one of the best public

00:18:27

models available. And it and we we found

00:18:30

it really tracked massive body, which

00:18:31

impressed us. And

00:18:34

this is just speaking to how hard it is

00:18:36

to keep up. I mean, the world evolves,

00:18:39

you know, not only the world evolves,

00:18:40

but other other people jump in,

00:18:42

competition evolves, and you got to stay

00:18:44

on top of things if you want to be a

00:18:45

cutting edge power ranking system. And

00:18:49

let me just say I mean this sounds it is

00:18:51

of course the realm of the geeks and we

00:18:54

and we are all geeks in this podcast but

00:18:57

the claim is

00:18:59

you you don't understand the game very

00:19:01

well or you don't understand what you're

00:19:03

looking at very well if you can't

00:19:04

predict what's going to happen. It's

00:19:05

almost the hallmark of how well you

00:19:06

understand it if you can predict what

00:19:08

happens next. And so I take it this is

00:19:10

kind of performance evaluation. You know

00:19:12

it's like do you understand? I mean can

00:19:14

you really tell us who's good and why

00:19:16

they're good? And if you could, then you

00:19:18

could predict what's going to happen

00:19:19

next. And that's really hard to do. So,

00:19:20

let's all just compete on who can

00:19:22

predict because it says something about

00:19:24

how well we understand the game.

00:19:25

>> Absolutely. Yeah. I mean, just and

00:19:27

that's true of science in general. You

00:19:30

have a theory and theories make

00:19:31

predictions and you test the theory

00:19:32

based on, you know, how how closely the

00:19:35

observations uh track with with their

00:19:37

predictions. And that um you know that

00:19:40

basically what we're talking about is

00:19:41

like a a science of football just

00:19:44

applying the scientific method to to

00:19:46

something.

00:19:47

just yeah it's wildly you know hard to

00:19:49

pin down of course there's so much that

00:19:51

we don't understand yet and there's so

00:19:53

much that we don't understand because

00:19:54

we're not as deep in the game as the

00:19:55

people who do it for a living and let's

00:19:57

talk about a detail of the game another

00:19:59

thing that you've done in your football

00:20:01

life is build fourth down models

00:20:05

and so I know you've you continually

00:20:08

tweaked those as well because we think

00:20:09

they're really good and then we find

00:20:11

holes and we make them better we find

00:20:12

some more holes we make them better but

00:20:14

there was a a questionable

00:20:16

fourth down call. The high-profile game,

00:20:19

one of the highest profile games of the

00:20:20

weekend was Baltimore going up to

00:20:22

Buffalo. Lost a 15-point lead in the

00:20:25

last four minutes of the game, including

00:20:27

uh a decision to punt the ball away um

00:20:30

late. Do you have a take on that

00:20:32

decision? And how could how should we

00:20:34

think about it? How are you thinking

00:20:35

about it?

00:20:37

>> Yeah, the model the model had I think

00:20:40

80% for go for it. The initial

00:20:45

uh play by play I think had it a fourth

00:20:47

and two and I think later they change it

00:20:49

to a fourth and three which isn't going

00:20:51

to change things very much because we

00:20:52

had the punt at 70%. So I'm

00:20:54

approximating. So I think it might have

00:20:56

been like 81 versus 72 or something like

00:20:58

that. So the order of magnitude of the

00:21:00

error is like 10% like win probability

00:21:03

which Brian you're you're going a little

00:21:05

shortand some who may not be familiar

00:21:06

including possibly me. 81 versus 72.

00:21:09

>> Oh chance to win. So, if I go for it,

00:21:12

your chance of winning in that situation

00:21:15

would be about 81%.

00:21:17

If you uh punted in that situation, we

00:21:20

would estimate it's about 70 to 71,

00:21:23

something like that. It was about 80 to

00:21:25

70. Um, which is a huge difference. Most

00:21:28

even big sort of enormous obvious fourth

00:21:32

down errors can be maybe like two

00:21:34

percentage points of win or three

00:21:36

percentage points of win probability. So

00:21:38

when you get you get somebody like John

00:21:39

Harbaugh with a 10% error, you know

00:21:42

something something is up. Something

00:21:45

something is off. Um and after the game,

00:21:49

I think there there's reporting that uh

00:21:52

Lamar Jackson had was cramping in that

00:21:55

final series. So and he's integral

00:21:57

obviously to any kind of short yardage.

00:21:59

Um and they elected to punt. So I think

00:22:02

that that played a part. I watched the

00:22:04

play before. or if you watch very

00:22:05

closely, Ed Oliver absolutely

00:22:07

obliterates Lamar Jackson on the play

00:22:09

before and I think that was probably

00:22:12

there's some some you know that would

00:22:14

cause some quote unquote cramping for

00:22:16

sure. I think that must have played into

00:22:18

it because the Ravens have really been

00:22:19

the best at this for a long time. And in

00:22:21

fact, there was almost an identical

00:22:23

situation. I think it was 2021 week two

00:22:27

against the Chiefs and they had a small

00:22:30

lead, you know, within a field goal

00:22:31

lead. And there's about two minutes left

00:22:34

to play. They were faced with a fourth

00:22:35

and two uh on their own side of the

00:22:37

field about their own, you know, 39 38

00:22:40

yard line, exact same place spot on the

00:22:41

field. They went for it, converted and

00:22:44

kept kept the win. So, uh, very

00:22:46

surprising, but I think the the answer

00:22:48

lies with, uh, just a banged up

00:22:51

quarterback.

00:22:52

>> Something we don't know. Interesting.

00:22:54

Really interesting. Shane, were you

00:22:55

trying to get in?

00:22:57

>> No, I mean, I I I I guess it is

00:22:59

interesting to kind of hear that maybe

00:23:00

that context uh that Brian just brought

00:23:03

up just because, you know, I was going

00:23:04

to kind of jump in and say like, you

00:23:05

know, of all the teams to go forward on

00:23:07

fourth and three, I think maybe only

00:23:09

Philadelphia would be a higher chance of

00:23:11

converting. I think in general the

00:23:13

personnel that the Ravens have. But now

00:23:15

you say that, you know, if Lamar was,

00:23:17

you know, kind of banged up and very

00:23:19

kind of locally right around that play

00:23:21

that that that would that would help

00:23:23

explain it. That would be the only thing

00:23:25

in my mind that would

00:23:26

>> Do the Bills know that though?

00:23:27

>> Do the Bills know that? And and do do

00:23:30

you have any other players on your team

00:23:31

who can pick up three yards on the

00:23:32

Ravens? Like so I And is and it's 10%.

00:23:36

you know, and like so I I still would

00:23:40

have probably gone for it if that were

00:23:43

me. I'm I'm not trying to make excuses.

00:23:45

I think they still should have gone for

00:23:47

it and I'm on the record and I you know

00:23:48

said so on on on Twitter but uh I mean

00:23:52

that that probably explains and it

00:23:54

probably you know we we when we look at

00:23:56

the end of the season and we look back

00:23:58

and do a complete sort of meta analysis

00:24:00

of like fourth down trends and who are

00:24:02

what which decisions were the biggest

00:24:04

errors.

00:24:06

Oftentimes we find the biggest errors

00:24:08

are on the like the go for it side like

00:24:10

which is very unexpected and the reason

00:24:12

is like injured kickers or injured

00:24:15

punters

00:24:16

>> long snapper you is is in the locker

00:24:19

room getting x-rays

00:24:22

>> there's always there's usually some sort

00:24:24

of context that we're the models aren't

00:24:26

picking up. Isn't this isn't this

00:24:27

literally the broken leg problem that

00:24:29

people talk about with when you go with

00:24:31

models and when you don't go with

00:24:32

models? Like you you the one of the few

00:24:34

times if you have a good algorithm, one

00:24:35

of the few times that you don't go with

00:24:38

the algorithm is if you know it's

00:24:39

missing something. And the example

00:24:40

people give is like if you're supposed

00:24:41

to model who's going to win a race and

00:24:43

you know the runner has a broken leg. So

00:24:45

then you don't use the model and you're

00:24:47

literally giving those examples from

00:24:49

your from your review of Sports.

00:24:51

>> So this is an interesting example. So I

00:24:53

would assume Brian whether Baltimore won

00:24:56

the game lost the game. Your estimate of

00:24:59

their FBI or strength parameter wouldn't

00:25:02

actually change that much. But what does

00:25:04

change a lot is now their probability of

00:25:08

let's say winning the AFC or going to

00:25:11

the Super Bowl. So can you give us a

00:25:13

sense of how big a magnitude of an

00:25:15

effect you had there? It's not like if I

00:25:17

if Baltimore I don't even know who

00:25:18

they're playing next week. Whether they

00:25:20

m went for it and won 38-37 or lost 40

00:25:24

to 38. The probability of win ain't

00:25:26

going to change Buckus as we say now. I

00:25:30

mean there's only I hate to say it

00:25:32

there's only 16 games left and they got

00:25:34

to catch up two games essentially on the

00:25:36

Bills and that's not going to be easy.

00:25:37

So how much is their let's call it go to

00:25:40

the Super Bowl probability change?

00:25:42

>> Yeah, I honestly Eric you asked the

00:25:44

exact same question I was going to

00:25:45

anyway. It's like can we kind of think

00:25:47

about because you talked about this 10%

00:25:48

on a game level being dramatic like can

00:25:51

can we talk about season level kind of

00:25:53

errors given the sort of the the

00:25:55

tiebreaking consequences of this

00:25:57

particular victory and stuff like that?

00:26:00

>> Yeah, we don't I'm I'm trying to look at

00:26:01

it right now. Um so we would say I think

00:26:05

we had them 19.6 to make the Super Bowl

00:26:09

percent. Right now it's gone up

00:26:13

>> 24 down. probably lost too.

00:26:18

>> They weren't, you know, that was a 50-50

00:26:20

game going into the season against

00:26:22

Buffalo.

00:26:23

>> So, we weren't banking on them winning

00:26:26

as far as the model goes. So, um you

00:26:29

know, all things considered, uh their

00:26:32

percentage actually went up. I think

00:26:33

let's be honest,

00:26:34

>> their offense looked really good and

00:26:36

offense right sticky and defense is like

00:26:39

kind of like more about who you're

00:26:41

playing off often times than uh and so

00:26:45

given that the Buffalo Bills has have a

00:26:47

great offense and a great quarterback,

00:26:49

the the Ravens defense just doesn't take

00:26:52

as big a hit as uh you might think.

00:26:54

>> This lets me ask this is a perfect lead

00:26:56

in Brian to the question I was going to

00:26:57

ask just a few minutes ago, but now that

00:26:59

we moved on to this game, I can talk

00:27:01

about it here.

00:27:03

Is FBI built

00:27:07

to do well at predicting who's going to

00:27:09

go to the Super Bowl or pred? No. That's

00:27:12

what I assumed. The answer was no. Like

00:27:13

you would build an entirely different

00:27:15

model if you were trying to predict

00:27:17

who's going to the Super Bowl versus

00:27:20

strength parameters on a given week A is

00:27:22

playing B, how much we expect them to

00:27:24

win by. So, I just want to I was going

00:27:26

to ask that earlier, but now I have a

00:27:28

perfect leadin for that. um it wasn't

00:27:31

built for super. So the fact that their

00:27:34

if you'd like their um FBI didn't change

00:27:38

very much, but their probability of

00:27:40

winning the Super Bowl actually went up,

00:27:42

that's not that surprising, right? It's

00:27:44

not inconsistent.

00:27:46

>> It's a bit surprising. I I'm personally

00:27:49

surprised. Um

00:27:51

>> but the yeah it's a combination of

00:27:53

things that offense is gets a you know

00:27:57

there's more confidence in an offense.

00:27:59

There's it's um if you were given the

00:28:02

choice between being the best team on

00:28:03

offense and the best team in defense on

00:28:05

the in the league. You would choose

00:28:07

offense just because the distribution of

00:28:09

offenses is is wider. The be the number

00:28:12

one offense is always better than the

00:28:13

number one defense. Um so it's it there

00:28:17

there are some sort of intricacies

00:28:21

uh whereas you know if you if it's like

00:28:23

a basically a 40 to 40 game um and both

00:28:27

the Bills and the Ravens offenses are um

00:28:31

kind of bumped up more than their

00:28:33

defenses are bumped down

00:28:36

and we again we're looking for the

00:28:37

differential of that particular play

00:28:40

right so so you know really what we

00:28:42

would be talking about is their change

00:28:44

probability if they had won versus Yeah,

00:28:47

I I I think for that particular uh

00:28:50

situation, you know, you could imagine

00:28:52

that yes, Baltimore still went up in

00:28:54

Super Bowl odds even after losing, but

00:28:56

they probably would have gone up even

00:28:57

more if they had executed

00:29:00

won and, you know, gone for it on fourth

00:29:03

down, executed and won.

00:29:06

>> Well, yes, if they had the win in hand,

00:29:09

absolutely. just they're just sitting,

00:29:12

you know, in a better perch. Um, but the

00:29:15

FBI model doesn't take the the win isn't

00:29:18

anything special. There's no bonus for

00:29:21

actually winning. I I I've built models

00:29:24

that that are hybrid that do both sort

00:29:26

of EPA per play like an efficiency.

00:29:28

>> But Brian, the Super Bowl prediction

00:29:30

comes from a simulation, right? Like a

00:29:32

forward simulation. So there it would

00:29:34

definitely weight heavily the fact that

00:29:36

they have a win when trying to do the

00:29:38

project. and you presumably condition on

00:29:40

the actual outcomes in that sim.

00:29:42

>> Yeah.

00:29:42

>> Yeah. They're completely separate

00:29:44

enterprises. You're you're estimating

00:29:46

power models and then you're putting

00:29:47

them through a simulation and these are

00:29:49

completely

00:29:49

>> that's what you guys have talked about

00:29:50

Kate for a long time about using the

00:29:52

Massie Pbody. I forget the name of the

00:29:54

platform you've talked about but uh you

00:29:56

know

00:29:56

>> yeah you can you can we we we there some

00:29:59

sims are better than others and we've

00:30:01

always massive people body always had I

00:30:03

think an advantage of having a good sim

00:30:05

because we we put a lot of uncertainty

00:30:06

into it. Um, yes, and that's the key.

00:30:09

That's the key, right? So, Brian Bryant

00:30:11

does that, but he's saying within his

00:30:12

power ranking model, it doesn't care if

00:30:14

you actually put in more uncertainty

00:30:16

than your It's kind of weird like if you

00:30:21

I should say it's surprising because if

00:30:23

you put in like the mathematically

00:30:24

proper amount of uncertainty,

00:30:27

then you get a result that's still

00:30:29

overconfident. It's still under It's

00:30:32

still overcalibrated to

00:30:33

>> No. Yeah, that's right. You have to

00:30:35

>> you actually have to fudge it to to

00:30:38

you're kind of like where is this

00:30:39

uncertainty extra uncertainty coming

00:30:40

from?

00:30:42

>> But your whole but your whole game is to

00:30:43

match the historical spread, right? So

00:30:46

you have to parameterize whatever you

00:30:48

need. You got to get in there to match

00:30:49

the historical spread. Hey, by the way,

00:30:51

let me let's let's take a moment since

00:30:52

we're talking about uncertainty and

00:30:54

we're at the very end of this

00:30:55

conversation with Brian. I want to I

00:30:57

want to take a moment to to celebrate a

00:30:59

few examples of c of of of of

00:31:03

communicating uncertainty because it's

00:31:05

so important and so hard and there just

00:31:07

aren't good examples of it. So, we had

00:31:09

um Russo on our show um last was it last

00:31:16

week talking college football? Three,

00:31:17

no, two weeks ago talking college

00:31:18

football.

00:31:19

>> And he did his um his his playoff picks

00:31:23

a very particular way. And then I

00:31:25

noticed that Barnwell did his playoff

00:31:26

picks the same way,

00:31:28

>> which was not like expected value

00:31:30

maximizing, but rather it was

00:31:32

representative. And so both of them, so

00:31:35

Russo, when he went to his playoff

00:31:36

picks, he said, "Look, I looked

00:31:37

historically. I said, "In the preseason

00:31:39

polls, how many how many of the top five

00:31:42

usually make the playoffs?" He said,

00:31:43

"Four out of five." So, I got to kick

00:31:45

one of those guys out when I make my

00:31:47

playoff picks. And then of the next

00:31:48

five, how many make the playoffs? He

00:31:49

said, "Two." So, I picked two. And then

00:31:51

he goes on down. He says, "How many who

00:31:53

are outside the top 25 historically make

00:31:55

the playoff?" Three. So, I got to pick

00:31:57

three teams. He says he has to just to

00:32:00

be entertaining. He's This again is not

00:32:01

an expected value maximizing bracket.

00:32:05

This is a representative bracket which I

00:32:09

want to celebrate because what he's

00:32:10

doing is communicating to the reader how

00:32:13

much uncertainty there is. It turns out

00:32:15

Bill Barnwwell does his playoff picks

00:32:17

exactly the same way I just read him.

00:32:19

Before Brian answers that, what I'd

00:32:21

rather him do, by the way, as being a

00:32:23

pure basian here, is sample from that

00:32:26

historical distribution, compute some

00:32:28

sort of bracket, do it many, many times

00:32:30

and average over that as opposed to just

00:32:33

saying the mode is three or so. And I'm

00:32:35

just saying what I would like him to do.

00:32:37

Who cares what I want? I'm just telling

00:32:38

you.

00:32:39

>> I I'm just trying to celebrate the

00:32:42

communication of uncertainty because his

00:32:44

because it's really hard to do. And so

00:32:46

anytime it happens, well, it's notable

00:32:48

to me. I want to give you one other

00:32:49

example. We talked about Connley. Bill

00:32:52

Connley is doing a new thing this year

00:32:54

which I think is freaking fantastic. We

00:32:55

do a version of it on our show

00:32:57

periodically, but he's nailed it. He's

00:32:59

saying, "Look, let's pick four long shot

00:33:01

games and construct the forsome such

00:33:05

that in expectation one of the long

00:33:08

shots is going to win." And so I think

00:33:11

each week he's going to in his weekly

00:33:13

column say here's four, here's the

00:33:14

quartet or whatever he calls it. and he

00:33:16

picks four. So that the expected

00:33:18

probability of a long shot winning a

00:33:21

game is 0.5. And it's fantastic because

00:33:24

he says look you know any one of these

00:33:25

you think no way but probability

00:33:28

suggests that on I'm going to build it

00:33:30

so that it's half and half that we'll

00:33:32

actually get one. And again I think this

00:33:33

is terrific communication of uncertainty

00:33:36

which is a very hard thing to do.

00:33:38

>> Yeah. No chalk is boring. Um

00:33:42

uh you you you you know that you know

00:33:47

one out of these top five teams is not

00:33:49

going to make the playoffs on average.

00:33:52

You just don't know which one and you

00:33:54

don't know which team to replace them

00:33:56

with. So if if you really want to kind

00:33:58

of maximize Yeah. you you keep the the

00:34:01

top five in there and that's no fun.

00:34:03

That's not That's not interesting. As as

00:34:05

dry as I am with all these FBIs and

00:34:10

fourth down computations and just pure

00:34:13

numbers and everything, I'm I'm a fan,

00:34:17

too. And I completely endorse

00:34:20

completely endorse that. It reminds me

00:34:22

of a strategy you need when with uh

00:34:25

brackets for the NCAA basketball

00:34:28

tournament. If you you can maximize you

00:34:30

can do just you can have the best model

00:34:33

in the world like you know an FPI or BPI

00:34:36

rather you know but but times a million

00:34:39

with the smartest AI and all that stuff

00:34:41

and you have the best bracket ever and

00:34:43

you're you're going to come in second

00:34:45

place because you know the the rando in

00:34:48

the cubicle down the hall from you um at

00:34:53

at the office is going to have a lucky

00:34:56

one. there's just going to be one if

00:34:58

your league is big enough there's going

00:34:59

to be somebody out there that's just

00:35:00

kind of a little bit lucky and so we

00:35:02

used to enter the you know like the

00:35:03

Kaggle basketball competition stuff the

00:35:06

way we would do it we would have our BPI

00:35:08

kind of chalk bracket and then we would

00:35:10

pick a couple

00:35:13

and maybe we would enter multiple of

00:35:16

these brackets each time picking a

00:35:18

different kind of upset and that was the

00:35:20

way actually to win those competitions

00:35:22

is but you got to be lucky too

00:35:25

>> what Kade said is really interesting to

00:35:27

me because I remember um both in a

00:35:31

sports context and in in my dissertation

00:35:34

case and a survey context, we did

00:35:36

exactly what Kade did. We said when

00:35:38

simulating data,

00:35:40

what should I condition on? Like in

00:35:42

other words, should I condition on

00:35:44

there's going to be at least three

00:35:45

upsets? Should I condition on there's

00:35:47

going to be at least four out of the top

00:35:48

five teams, but no more than that? Like

00:35:50

it's a really interesting exercise to

00:35:53

think about what features of the history

00:35:56

do you want to bring in to your

00:35:58

simulation. Those are priors, but

00:36:01

they're priors of a different kind. And

00:36:03

I really think that's a fascinating and

00:36:06

I'm glad K brought that up. It brought

00:36:07

back some fond memories. But it's an

00:36:09

interesting question about what aspects

00:36:11

of a picking from a as you said NCA pool

00:36:13

or what if you're simulating future NFL

00:36:16

outcomes. You know, we've always talked

00:36:18

on this show, roughly half the teams in

00:36:20

the NFL playoffs don't make it. Do you

00:36:21

want to condition on that? What do you

00:36:23

do? I I think that's fascinating.

00:36:25

>> It's It's so hard. It's so It's It's

00:36:27

something we come back to every football

00:36:29

season this time of year. It's like what

00:36:31

the that especially like consider, you

00:36:33

know, we're going to have random teams

00:36:36

in the college football playoffs. We're

00:36:37

way too sure we know what teams are

00:36:39

going to be in there. And we have to

00:36:40

somehow we have to keep learning that

00:36:41

lesson over and over. And hopefully if

00:36:44

you're paying attention, you do learn a

00:36:46

little bit over time. Hopefully at our

00:36:48

best, we're walking away from these

00:36:49

conversations a little bit more of the

00:36:51

uncertainty in the world as a result of

00:36:53

them. Hopefully. Brian, we're going to

00:36:55

have to let you go, man. We've kept you

00:36:56

longer than we expected to. Always a

00:36:58

pleasure. Please let us get you back in

00:37:00

here before the season goes away. We

00:37:02

always enjoy talking to you.

00:37:04

>> Yeah, thanks for having me.

00:37:07

>> Absolutely. Brian Burke, longtime friend

00:37:09

of the show. He is with ESPN. You can

00:37:12

see his work there. He's behind the

00:37:14

scenes on a lot of important work that

00:37:16

they do. And every now and then we get

00:37:18

him out in front get him front of house

00:37:20

as well because he's always fun to talk

00:37:21

to. Brian Burke, welcome back to Wharton

00:37:23

Moneyball. Welcome to the second half of

00:37:26

this week's show. An open lines segment.

00:37:29

What we used to call open lines, open

00:37:31

topics. Just off the phone with Brian

00:37:34

Bert from ESPN. Always catch Brian early

00:37:37

in the season. Find out what his

00:37:38

offseason project has been. find out how

00:37:41

he's taken in the NFL. He's a diehard.

00:37:43

He didn't he didn't come clear as much,

00:37:46

but he's from the Northern Virginia,

00:37:48

Annapolis area, and he's a diehard

00:37:50

Ravens fan. Um, and uh, so he was

00:37:53

talking dispassionately about something

00:37:54

that is really kind of killing him,

00:37:57

guys. Anything else coming out of that

00:37:59

conversation or about the NFL in

00:38:00

general, week one? Um, you know, we had

00:38:03

we had the Eagles kicking us off with

00:38:04

the Well, heck, Jaylen Carter kicked us

00:38:07

off. That was a hell of a beginning to

00:38:09

the season.

00:38:10

Um then we had games like all weekend

00:38:13

Chargers took down the Chiefs on Friday

00:38:15

night. Um we had a big great Monday

00:38:18

night game last night with Minnesota

00:38:19

coming back. I mean what a debut from

00:38:22

the QB out of Michigan for those guys.

00:38:24

They go down big pick six even and then

00:38:26

21 points in the fourth quarter. I mean

00:38:29

I completely everybody had given up on

00:38:31

those guys. So exciting for Minnesota.

00:38:34

Um what else jumps out to you about NFL

00:38:36

week one?

00:38:36

>> Just for me just a couple other things.

00:38:39

um you know, under the right scheme and

00:38:42

under look, Aaron Rogers can still play,

00:38:44

right? I mean, threw four touchdown

00:38:47

passes. I watched a lot of that game. He

00:38:49

looked pretty good. Now, again, he's

00:38:50

still immobile. If you rush him, he's

00:38:53

still going to make some bad decisions.

00:38:54

He's made that his whole career, even

00:38:56

when he was more mobile than he is. But,

00:38:58

you know, um I could see now why the

00:39:03

I'll call it the conservatism of Mike

00:39:05

Tomlin together with when you really

00:39:08

need it from Aaron Rodgers. I'm

00:39:10

upgrading my belief on the Steelers this

00:39:13

year. The problem is they're in the AFC

00:39:15

and I there's no chance to put them

00:39:17

above Buffalo or Baltimore or really or

00:39:20

Kansas City right now. But I can see now

00:39:23

why the combination of Tomlin's coaching

00:39:26

style and Rogers could work effectively.

00:39:28

So that impressed me. I think I Oh,

00:39:31

sorry. Let's stay with that one. Shane,

00:39:32

you want to go ahead and

00:39:33

>> Yeah. I just I was going to kind of ask

00:39:35

you both whether you feel like, you

00:39:36

know, because we're fresh off of Brian

00:39:38

Burke talking about how offense is

00:39:40

stickier maybe or more predictive

00:39:42

retweet than defense. would concern me

00:39:44

about the, you know, the Steelers kind

00:39:47

of, I guess the Steelers like long-term

00:39:49

chances is more that their defense led

00:39:51

up that many, you know, this is supposed

00:39:52

to be an elite defense and, you know,

00:39:55

maybe Justin Fields just had a great

00:39:56

game, etc. But, you know,

00:39:58

>> if if if that, you know, if that was

00:40:00

just kind of a a oneoff kind of

00:40:03

performance from the Steelers defense, I

00:40:05

think they really are a contender.

00:40:07

If they've taken if they've taken a step

00:40:09

back, even like a marginal improvement

00:40:12

that Rogers brings is not going to be

00:40:14

enough to really Yeah. as you said, push

00:40:15

them into the conversation of real AFC

00:40:17

contenders.

00:40:18

>> No, I think you would agree, Shane, if

00:40:19

it if I told you right now at the end of

00:40:21

the season the Steelers end up with a

00:40:23

top 10 offense, you'd be like, "Whoa,

00:40:26

that team could do something."

00:40:28

>> Cuz I'd be assuming a top 10 defense

00:40:30

just cuz the Steelers Exactly. So, that

00:40:32

was one team. Yeah, look, obviously, um,

00:40:35

you know, with the Colts, wasn't it

00:40:37

Danny Dimes, right? Played for the Colts

00:40:39

and, you know,

00:40:42

the thought was I didn't see a lot of

00:40:44

the game. I saw some of it. I was

00:40:45

watching on a lot of screens. Um, and

00:40:48

so, um, he looked good. Now,

00:40:52

it could be the team they were playing

00:40:54

was bad, but all I'm commenting on is um

00:40:58

you know, this idea that he can't play

00:41:01

good football is just not true. Now,

00:41:04

that doesn't mean he won't have 16 other

00:41:06

bad games, but I'm a believer, you know,

00:41:09

same way with academics when I evaluate

00:41:10

people for tenure. Shane, if someone can

00:41:12

run write one great paper, maybe you

00:41:16

could do it again. So there's no reason

00:41:18

for me to believe that Daniel Jones

00:41:20

under the right circumstances

00:41:22

can't have, you know, maybe he's a Sam

00:41:25

Darnold like season. Why not Sam

00:41:28

Darnold?

00:41:29

>> I I don't think we can write off any

00:41:31

quarterback based on kind of mediocre

00:41:35

performance in a single system,

00:41:37

especially if that system is a New York

00:41:39

based system, it seems like, you know.

00:41:41

So no, I agree. Sam Darnold, Eugino

00:41:44

Smith, and we have countless examples of

00:41:46

these of of quarterbacks kind of, you

00:41:48

know, looking mid or worse, not really

00:41:51

kind of being legitimate starters in

00:41:52

their first goaround and and, you know,

00:41:54

they get in a different system with

00:41:56

different personnel and an offensive

00:41:58

line. Um, and and and watch them do

00:42:01

something. I I don't know, that's not

00:42:03

really a statement about whether the

00:42:04

Danny Danny Dimes and Indie is going to

00:42:06

be a sustainable thing. Uh, or whether

00:42:08

they're, you know, going to be at all a

00:42:10

good team, but

00:42:11

>> find out a little bit. Yeah, we'll find

00:42:13

out.

00:42:13

>> He's definitely behind a better

00:42:14

offensive line.

00:42:15

>> Just quickly, we'll find out a little

00:42:16

bit this week. It's Broncos at Colts and

00:42:18

the Broncos are at least a good team if

00:42:21

nothing else. So, we'll find out

00:42:22

something.

00:42:24

>> A couple of the things about next

00:42:25

weekend. Um the Philly KC game. I mean,

00:42:29

that's exciting. Uh give us a Super Bowl

00:42:30

rematch in case.

00:42:31

>> Can't wait for the highlights. The

00:42:33

highlights are going to be so fun to

00:42:34

watch.

00:42:34

>> Can we say if the Eagles beat the Chiefs

00:42:36

that the Chiefs are in quotes whatever

00:42:39

this means, trouble?

00:42:41

Well, two games down. I mean, even if

00:42:43

you're still a good team, that's a hole.

00:42:45

>> Yeah, team teams dig out of it. I mean,

00:42:47

I think Baltimore started 0 and2 last

00:42:49

year and dug out of it. But,

00:42:50

>> but still, that's you don't you it's not

00:42:53

a great spot to be in. Other ones that

00:42:54

jump out to me, um I'm really curious

00:42:57

about Washington and Green Bay. I mean,

00:42:59

Green Bay looked so good, but with

00:43:00

Jaylen Daniels, I mean, it's a good test

00:43:02

for Washington. And then here's a sneaky

00:43:03

one, guys. I just noticed it. I was

00:43:05

going through the schedule. Atlanta goes

00:43:06

to Minnesota. Atlanta lost a

00:43:08

heartbreaker to to to Eric's team last

00:43:11

weekend.

00:43:11

>> I was so upset about that.

00:43:13

>> Yeah. Here's the thing about Atlanta

00:43:14

Minnesota. The quarterbacks in that game

00:43:17

are the quarterbacks from the NCAA final

00:43:19

two years ago. So, Michael Penn was the

00:43:22

Washington quarterback when Michigan's

00:43:24

JJ McCarthy won the national

00:43:27

championship in McCarthy's senior year.

00:43:28

And those guys will be facing each other

00:43:30

again in Minnesota Monday night.

00:43:32

>> Monday night.

00:43:33

>> I watched all

00:43:34

>> I'm sorry. Sunday night.

00:43:35

>> Sunday night. Sunday night. I watched

00:43:36

all of the Bucks Falcons game just to

00:43:39

let you know. And I'm going to say this

00:43:41

right now. I'm going to say the same

00:43:42

thing I said about Jaden Daniels last

00:43:44

year when I watched Washington against

00:43:46

the Eagles in week one. I'm sorry, the

00:43:48

Bucks again, sorry, Washington against

00:43:49

the Bucks in week one. You're not going

00:43:52

to want to play Michael Pennock Jr. very

00:43:54

soon cuz I'm going to tell you

00:43:56

something, that guy can play football.

00:43:59

He's got great decision making. He's got

00:44:01

a great arm. I mean, he made some throws

00:44:04

no more than five quarterbacks in the

00:44:06

NFL can make. Michael Pennix Jr. can

00:44:08

really play. Falcons were a good team.

00:44:12

They were right there with the Bucks,

00:44:13

man. I mean, it was a great football

00:44:15

game.

00:44:15

>> Well, Bjon scored on that first uh drive

00:44:18

as well, which is like Oh, yeah.

00:44:19

Remember, they've got Bjon Robinson,

00:44:21

which is a good place to be as well.

00:44:23

>> Don't they have the highest paid? They

00:44:24

must have the highest paid backup in all

00:44:26

of football,

00:44:28

>> too, as well, right? I mean they, you

00:44:30

know,

00:44:30

>> right?

00:44:32

>> Okay. So, um, real quickly, I just a

00:44:35

note that ESPN put up today that is

00:44:37

striking is how much kickoff returns are

00:44:39

up. And it's fun. It really is fun. You

00:44:41

forgot how much fun we lost with. So,

00:44:44

guys, did you realize that we had

00:44:45

dropped down at the low in 2023? The

00:44:49

season was the average 22% of kicks were

00:44:52

returned in 2023. Last year when they

00:44:55

changed the rules, which is great, but

00:44:57

they had the the the the you could get

00:44:59

the ball at the 30 if you put it in the

00:45:01

end zone. We only went from 22% to 33%.

00:45:05

So, they got a lift, but only up to 33%

00:45:07

this weekend when the penalty was

00:45:10

bringing the ball out to the 35. We saw

00:45:12

70 76% of the kickoffs were

00:45:15

>> Kate, I'm not You're not making me

00:45:16

reflect. I watched 10 different games

00:45:19

this weekend, maybe more. I don't

00:45:21

remember what I'm not saying what didn't

00:45:23

happen. I would have told you was 98%. I

00:45:26

don't remember one ball kicked into the

00:45:27

end zone and I watched every a as many

00:45:30

games as I could. I don't remember one.

00:45:32

>> Yeah. And they're they're even higher

00:45:34

variance I think than they than they

00:45:35

have been in the past. Um it's it's a

00:45:37

it's an major props to the NFL for

00:45:39

playing with the rules like that. really

00:45:41

gives it gives kickers kind of I mean

00:45:43

we're already seeing I feel like elite

00:45:45

like like like kickers kicking 60 field

00:45:48

yard field goals routinely and stuff

00:45:50

like gives kickers another thing to like

00:45:51

kind of become elite at to to kind of

00:45:53

work up and land right on the one yard

00:45:56

line

00:45:56

>> drop it right at the five.

00:45:58

>> Okay, one last note on football before

00:46:00

we change football. We do have college

00:46:02

football. It was a bit of a quiet

00:46:04

weekend, fun weekend. It would no huge

00:46:06

games except for Florida going down to

00:46:08

South Florida. Um, but next weekend, got

00:46:11

a couple notables just to kind of sell

00:46:12

you guys. This is my weekly note to sell

00:46:15

y'all on some college football.

00:46:16

Clemson's going into Atlanta to play

00:46:18

Georgia Tech. And they're only

00:46:20

four-point favorites. And this is kind

00:46:22

of a test on Clemson because they lost

00:46:25

game one. And they looked bad last week

00:46:27

against Troy. They were down like 16

00:46:30

nothing at halftime. They won.

00:46:31

>> People are skeptical on Clemson. Georgia

00:46:33

Tech's solid. They're only four-point

00:46:35

favorites. That's going to be

00:46:37

interesting. the big one. Um,

00:46:40

Florida's going to LSU. People were more

00:46:42

excited about that before Florida lost

00:46:44

their nine-point dogs. But there's a big

00:46:46

SEC game. Let's see if Florida can

00:46:47

bounce back. Texas A&M going up to Notre

00:46:51

Dame. A night game in South Bend. 6 and

00:46:54

a half point underdog.

00:46:55

>> You just skipped on. You put these on

00:46:57

here. You just skipped the most

00:46:59

interesting game in my mind. You know

00:47:00

what it is. You know, USF at Miami, man.

00:47:04

>> Well, it is USF Miami. Wait, wait, wait.

00:47:07

Let's be clear.

00:47:07

>> This is because USF beats Miami, you got

00:47:10

to put them in the playoffs.

00:47:11

>> No, they've beaten two ranked teams

00:47:12

already, Shane. If they beat Miami, come

00:47:15

on. They're in.

00:47:16

>> No, I forgot about two or week three or

00:47:19

whatever.

00:47:19

>> Favorite saw is is Group of Five. Group

00:47:22

of Five. I forgot about

00:47:23

>> usually doesn't kick in until like

00:47:25

November, but no, I I I like it. I like

00:47:26

it. It can really jump. You're always

00:47:28

ahead of the game, Eric. Yes. Well, so

00:47:30

so undoubtedly USF is one of the stories

00:47:32

of the season so far, but they are

00:47:34

16point underdogs against Miami. So

00:47:37

let's let's keep our powder dry on that

00:47:39

one. But it would be fun

00:47:40

>> if they win.

00:47:41

>> Yeah, it would be fun. Okay, that's it

00:47:43

for football for the week. That's a lot

00:47:45

of football. Of course, that's where we

00:47:46

are early season working some things

00:47:48

out. We had some other major sports in

00:47:51

particular the fourth and final tennis

00:47:54

championship, major championship, US

00:47:56

Open, of course. um both sides of the

00:47:58

bracket, but I I I didn't watch, but I

00:48:00

have to say I was surprised. Not only

00:48:02

did Alcarez beat center in the final,

00:48:04

but he beat beat him in four sets, and

00:48:06

they weren't even particularly close

00:48:07

sets, at least some of them. So, Eric,

00:48:09

give us the update here if he's how is

00:48:11

it that center is the best player in the

00:48:13

world, but Alcarz has beat him like six

00:48:15

of the last seven or something.

00:48:17

>> Seven of the last eight, actually.

00:48:18

>> Seven of eight now. Okay.

00:48:19

>> Yeah. So, in the last two years. So,

00:48:22

well, first, Alcarez now is number one

00:48:24

again in the world. So, let's just be

00:48:25

clear about that. He took over the

00:48:27

points lead. He is number one again. Um

00:48:30

look

00:48:30

>> well to be clear the way we talked about

00:48:32

center all summer was that he was the

00:48:33

next thing to freaking Jesus Christ. And

00:48:35

so this is a little bit surprising to

00:48:37

see to see this to see this go down.

00:48:40

>> Yeah. Or I guess to kind of more

00:48:42

contextualize it, how often do you see

00:48:43

like two kind of players at their kind

00:48:46

of elite Pete showing such an like an

00:48:48

eight eight out of nine kind of

00:48:49

imbalance in sort of matchup?

00:48:52

>> Good. Yeah. So there is there's an

00:48:53

imbalance there. Look, I think most

00:48:56

people tennis experts, you know, I know

00:48:58

uh one week we're trying to get them on.

00:49:00

We had him on. We had to switch him off.

00:49:01

We're trying to get Paul Anacone on.

00:49:03

Would be great to talk about this.

00:49:06

I think what's been shown over the last

00:49:08

two years. For now, tennis is not is

00:49:10

non-stationary, but for right now,

00:49:13

Garez's top end game is better than S's

00:49:17

top end game. It does not mean center

00:49:19

can't beat him. It does not mean center

00:49:21

doesn't have a better record against

00:49:23

common opponents. He does because his

00:49:26

game has much lower variance. But

00:49:30

Alcarez knows the importance of every

00:49:32

single match against Sinner and he's

00:49:36

going to bring his top end game against

00:49:39

S. And look, he's seven and one against

00:49:42

S. That is an imbalance. Look, when we

00:49:45

look back on their career, Shane, this

00:49:46

is what we do right now. Even though

00:49:48

their ears were a little different.

00:49:50

Djokovic has a slightly winning record

00:49:52

against Nadal. Djokovic has a slightly

00:49:55

winning record against Federer. Djokovic

00:49:57

has a winning record against the other

00:50:00

big two. It matters when we look back on

00:50:03

Sinner and Alcarez. I'm not saying S

00:50:06

can't get there. It's now I it's either

00:50:08

10 and five or 11 and five. I don't

00:50:09

remember which one it is. Alcarez has a

00:50:12

10 to5 lead I think it is over S. and

00:50:16

he's got six majors and center has four.

00:50:20

And if Alcarez wins the Australian,

00:50:22

which he says is his number one goal for

00:50:24

next year, he'll be 22 years old with

00:50:26

the career grand slam. Do you uh when

00:50:30

you think about kind of in in your kind

00:50:32

of personal sort of rankings of kind of

00:50:34

great going up against greats within a

00:50:36

particular era, do you think more about

00:50:39

total grand slam titles or do you think

00:50:41

more about the personal head-to-head? I

00:50:43

I think about I not I think about the

00:50:45

peak. I think about when the person was

00:50:47

at his best because here's the problem

00:50:49

with comparing Federer to Nadal.

00:50:51

>> It's they're not aligned exactly but not

00:50:53

really. Federer is five six years older

00:50:55

than Djokovic and so they're

00:50:56

headto-head. I'm actually surprised I

00:50:58

happen to know Federer's record against

00:51:00

Djokovic. I'm surprised he's even he

00:51:03

didn't have a winning record, but Feder

00:51:04

Jookovic was 27 and 23 against Federer.

00:51:08

And so that's a winning record, but it's

00:51:10

not that winning a record. And for five

00:51:12

of those years, Federra was definitely

00:51:14

not at his prime. Um, I think of the I

00:51:18

if you want to be and we know ELO has

00:51:20

its problems. I tend to look at peak

00:51:23

rating when the person was at their

00:51:25

best. Could you beat them? And I say the

00:51:29

same about center that I say about

00:51:30

Djokovic. Djokovic was is not the

00:51:33

greatest player I've ever seen. He's the

00:51:35

GOAT. He's the most accomplished player.

00:51:38

There's no debating it. But he's not the

00:51:40

greatest player I've ever seen. If Feder

00:51:43

has his best day, he won. If Nadal had

00:51:46

his best day, he won. I saw Stan Roinka

00:51:48

blow out Djokovic on his best day. But

00:51:52

those best days come very very

00:51:54

infrequently. And if you're not at your

00:51:56

best day, you had no chance against

00:51:58

Djokovic.

00:51:59

>> Okay. So, I just want to note that

00:52:01

you're you're you're making you're

00:52:03

there's a distinction here between their

00:52:05

peak EO ELO rating and what you're

00:52:08

talking about their best day because

00:52:09

you're saying even within an era when

00:52:12

you'd have an ELO rating on a guy, you

00:52:13

still have a distribution of performance

00:52:15

and you're talking about that right tail

00:52:18

that when the guy's at his best, how

00:52:20

good is that? And we don't have a number

00:52:22

around that. And it'd be nice to have

00:52:23

it'd be nice to have that assessed in

00:52:25

some way. But I I was struck in the I

00:52:27

was recalling our summerlong

00:52:28

conversations about center and Alcarez

00:52:31

and this characterization that some

00:52:32

quants some quants in tennis have put

00:52:34

numbers around it and have validated

00:52:36

this this there is a higher floor and a

00:52:38

lower ceiling for center. But what but

00:52:41

the way you talked about it Eric is that

00:52:43

it's almost as if Alcarez he isn't

00:52:45

taking a random draw from his

00:52:46

distribution when he gets in in these

00:52:48

matches against center. He's somehow

00:52:50

able to shift his draws to the right

00:52:53

side of the district.

00:52:54

>> It's not symmetry isn't symmetric. He's

00:52:56

somehow strategic in terms of when

00:53:01

also the the I don't call it the rumors.

00:53:04

Um

00:53:05

>> Alcarazz's coach, Juan Carlos Ferrer,

00:53:07

former number one in the world by the

00:53:08

way, said that they worked the entire

00:53:12

summer on making Alcarz more consistent

00:53:15

and raising his floor. Alcarz lost only

00:53:19

one set that was to sinner in the finals

00:53:22

the entire US Open. Had he gone under

00:53:24

had he won all three sets, he would have

00:53:27

been the first person in the open era of

00:53:29

men's tennis win the US Open without

00:53:32

>> incredible that he lost one set to

00:53:34

center and that happened. So he didn't

00:53:36

go on. But just to let you know, I think

00:53:38

we're going to see this is what's scary

00:53:40

for men's tennis. I think we're going to

00:53:43

see the same peak from Alcarez but a

00:53:46

higher floor. And that's even scary.

00:53:49

That's really scary. And by the way, the

00:53:51

other stat that I found interesting is

00:53:53

the last two years um Yannik Center

00:53:56

against everybody else in tennis but

00:53:59

Alcarz is 169 and4

00:54:04

and he's 1-7 against Alcare.

00:54:08

>> I mean that is

00:54:09

>> that's interesting. That's that's a

00:54:11

separation of one two big separation.

00:54:14

But this but and let me just say I think

00:54:16

we'd also want is you know we're now at

00:54:19

eight and what I mean by eight we're at

00:54:21

eight straight majors

00:54:23

>> where it's been Alcarz or Sinner. Let me

00:54:25

just give them both credit. It's four to

00:54:27

four. They both have four of them. Now

00:54:30

how many in a row are we going to go

00:54:33

where one of them wins?

00:54:36

That's I mean could it be another I'm

00:54:39

making it up is another five years.

00:54:41

>> What's No, we won't go five more years.

00:54:43

But what's the longest run we've seen

00:54:45

with two guys taking all the slams?

00:54:47

>> I think this is it now.

00:54:49

>> Eight.

00:54:50

>> Cuz remember there was the big three.

00:54:52

>> Yeah. And how many? That was

00:54:54

>> No, there was the big three and there

00:54:56

was Snampis and Agassy and you know Jim

00:54:59

Courier won one every now and then and

00:55:01

there was Michael Chang won one and you

00:55:03

know and even during the you know the

00:55:05

Borg Mack andro Connor there was always

00:55:07

you know more than two and so I think

00:55:10

this is the longest. It would not

00:55:12

surprise me if this grows at least to

00:55:14

double digits like why next year would

00:55:16

you predict anybody but one of the two

00:55:18

of them?

00:55:19

>> This is a good this is a good overunder.

00:55:21

Eric, what do you give us an overhead or

00:55:22

me and Shane will take? We we'll bet.

00:55:24

Give us a number and we'll take

00:55:25

>> Okay, we're at eight right now.

00:55:26

>> Yeah,

00:55:27

>> I am going to go

00:55:30

13 and a half.

00:55:34

>> What do you want, Shane? So, that's

00:55:36

another year and a half basically.

00:55:38

>> Not basically. That's it. Next year and

00:55:40

a half,

00:55:43

>> I guess.

00:55:43

>> Before you pick before you pick, let me

00:55:45

say why I picked that number,

00:55:46

>> Eric. 14 and a half would be a year and

00:55:48

a half. No.

00:55:50

>> Oh, yeah. I'm sorry. You're right. Six.

00:55:51

14. I meant four. Then I'll go with 14

00:55:53

and a half. Let me say why I'm going

00:55:55

with that number.

00:55:58

Like a lot of times you could say, well,

00:56:01

you don't even know the set of potential

00:56:03

players. I do. Like no one's coming up

00:56:07

from the bottom. That's just like I

00:56:09

don't know about now. There's no

00:56:11

17-year-old. Like it's gonna have to be

00:56:12

someone like a Ben Shelton or it would

00:56:14

have to be someone like a Felix Aliim.

00:56:18

It's gonna have to be or is vera finally

00:56:21

gets his act together for seven matches

00:56:23

or Medvadev has some resurgence. Like

00:56:26

they're so good. It can't be some random

00:56:30

17year-old that's now eight. It's just

00:56:32

not it's not going. So it's it's within

00:56:34

the set of players we have. So I'll go

00:56:36

with 14 and a half. Thank you. 14 and a

00:56:38

half. That's my overunder. That's my

00:56:40

rationale.

00:56:41

>> Shane, what do you got? What do you got,

00:56:42

buddy? I guess I'll I'll I'll I'll

00:56:44

>> I'll I'll take the under just to be

00:56:46

interesting and because we just had a

00:56:47

conversation with Brian Burke about

00:56:49

uncertainty.

00:56:51

Um good for you.

00:56:52

>> And I guess and I guess you know what I

00:56:54

what what what would make it happen? I

00:56:56

my mechanism I guess is maybe you know

00:56:59

all takes a one or two injuries and all

00:57:01

of a sudden it's you know I mean that

00:57:03

that's probably that's probably one of

00:57:04

the biggest factors in this is both of

00:57:07

these players or at least one of them

00:57:09

staying healthy long enough to kind of

00:57:10

keep this streak going. My viewers, they

00:57:12

better both be injured because if you're

00:57:14

playing one of them right now, you know,

00:57:16

you it does sort of sound like you

00:57:17

almost you need both an injury and an

00:57:20

impressive right by

00:57:23

I'll get this stat and I'll post it on W

00:57:26

Moneyball.

00:57:27

>> I think this is the largest gap in the

00:57:29

history of men's tennis between number

00:57:31

two and number three.

00:57:33

S has like a 4,950 almost 5,000 point

00:57:37

lead on the number three player, which I

00:57:39

think is still Zerv. And just to let you

00:57:42

know, you get 2,000 points for winning a

00:57:43

major. And he has a 5,000 point lead on

00:57:47

number three. So just to let you know

00:57:49

how big the gap is to norm it, it's big.

00:57:51

And Shane, which Kate, which one are you

00:57:53

taking?

00:57:54

>> I'm going to go under also. I think it's

00:57:56

the only reasonable way to go. I think

00:57:58

we're supposed to go that way. It's

00:58:00

almost like, you know, in golf, Shane, I

00:58:01

don't know if you play enough golf, but

00:58:02

Eric, I think, does. There's the amateur

00:58:05

side of the cup and the pro side of the

00:58:06

cup. Amateurs tend to play too little

00:58:09

break. They're often below the cup at

00:58:11

the end of the putt. I feel like this,

00:58:13

you know, we're supposed to be

00:58:14

professional statisticians. Some of

00:58:16

y'all actually are professional

00:58:17

statisticians. I think you got to take

00:58:19

uncertainty. The things that we're

00:58:20

outside the model. And so, I'm I'm going

00:58:23

to go I don't love it and it's not fun,

00:58:24

but I'll go under as well.

00:58:25

>> I'll tell you what, I don't This is a

00:58:26

great discussion for statistical topic.

00:58:28

I don't think we have as good enough

00:58:30

intuition as to what I'll call the

00:58:32

distribution of the maximum of multiple

00:58:34

players cuz remember let's assume that

00:58:38

let's eliminate changes for one second

00:58:40

injuries. Now we have to talk about we

00:58:43

have two people who have this much

00:58:45

distance to number three. You're right

00:58:48

there's variability but now I'm taking

00:58:49

the maximum of the two cuz you know that

00:58:53

I don't think we have really good

00:58:54

intuition for. As a matter of fact, you

00:58:56

could argue that's even a farther

00:58:58

exceedence than we have intuition for,

00:59:00

which was why I'm comfortable with my 14

00:59:03

and a half.

00:59:04

>> You can imagine, Eric, to take that

00:59:05

further, like if you put three in the

00:59:07

set or four in the set, we probably have

00:59:08

really bad intuition because you

00:59:10

probably

00:59:11

more of 68 majors going to the big three

00:59:14

over a 17year period.

00:59:16

>> Exactly. That kind of thing. So, it's a

00:59:18

fun it's a fun little problem, actually.

00:59:20

All right, guys. We're down to just a

00:59:22

couple of minutes before we should wrap

00:59:23

this thing up. But let's talk baseball.

00:59:25

I mean, it's been a quiet week in my

00:59:27

mind mentally because there's been so

00:59:29

much football, but there have been

00:59:30

baseball games being played. What's

00:59:32

going on over there? What do we need to

00:59:33

be paying attention to?

00:59:35

>> Well, we had a couple uh It's worth

00:59:37

noting that this is uh continued to be

00:59:39

We haven't had a no hitter yet this

00:59:41

year, but

00:59:42

>> after average, we we've come so very

00:59:44

close over the last couple weeks. This

00:59:46

is I'm so I'm so glad you put this in

00:59:48

the rundown because like I there have

00:59:51

been no no hitters, but

00:59:53

>> can we actually just basically then say

00:59:56

that even if this year ends with none I

00:59:58

mean we had what one to eight and two/3

01:00:00

innings was were they both to eight and

01:00:02

two/3s innings? We should have like a no

01:00:04

hitters in expectation or something

01:00:06

extra stat that kind of like gives us

01:00:07

like 0.98 of a no hitter or something

01:00:10

>> or a distance metric like suppose I told

01:00:12

you we have none this year but we had 10

01:00:15

no hitters through 8 and 2/3 you'd be

01:00:18

like oh all right that seems reasonable.

01:00:21

So I like this hard cut right at zero

01:00:24

hits like

01:00:27

>> it. I understand what a no hitter is,

01:00:29

but I'm just saying

01:00:30

>> I know it's like weird it's a

01:00:33

particularly weird discretetized

01:00:36

>> consequence of what we're generally

01:00:37

seeing across baseball is is increased

01:00:40

offense um a little bit, you know, and

01:00:43

and and so another kind of example of

01:00:46

that kind of discretetized, you know,

01:00:47

consequence of that is we still got uh

01:00:50

four players now on track to hit over 50

01:00:52

home runs. And that's only happened

01:00:54

twice before in MLB history. So, you

01:00:57

know, if you know, Judge unfortunately

01:01:00

has dropped off a bit because he's, you

01:01:02

know, he hasn't been playing. Uh, but

01:01:04

Rally, Schwarber, Otani, and Suarez are

01:01:06

still all on pace for over 50 home runs.

01:01:08

And if they hit that, that's something

01:01:10

that also has not happened in a long

01:01:11

time.

01:01:12

>> When has that happened before? You said

01:01:13

twice before. What era?

01:01:14

>> It was uh n I looked up 2001. So, both

01:01:17

were steroid era.

01:01:18

>> That's what I figured. Okay.

01:01:19

>> 1998 and 2001. The last time it

01:01:22

happened, just to name the guys in 2001,

01:01:24

it was Alex Rodriguez, Juan Gonzalez,

01:01:26

Sosa, and Barry Bonds.

01:01:28

>> That's all steroid. And how about 98?

01:01:31

>> 98. Well, okay. So 98. Uh

01:01:34

Boon, Ken Griffy Jr., Sosa, and

01:01:37

Magguire.

01:01:38

>> Steroids.

01:01:38

>> Half Well, nobody nobody's Ken Griffy

01:01:42

Jr. steroids here.

01:01:43

>> Alleged. In case anyone's listening,

01:01:45

alleged.

01:01:46

>> But yes, this would be the this would be

01:01:48

the first time outside of the steroid

01:01:50

era. We could call that the peak that

01:01:53

>> I prefer to think about that. That was

01:01:55

that was my grad school Chicago grad

01:01:57

school era. It was a good time to be a

01:01:58

Cubs fan. It was a good time to be in

01:01:59

Chicago.

01:02:00

>> Oh, people people slam it down, but it

01:02:03

was an exciting time to watch baseball.

01:02:04

I mean, we got, you know, we got what we

01:02:06

kind of like.

01:02:06

>> It's another one of those interesting

01:02:08

stats, by the way, cuz we've never had

01:02:09

more than four, right? obviously and um

01:02:12

you know there must have been many years

01:02:13

in the 20s where I'll make it up Babe

01:02:15

Ruth and Hack Wilson or Babe Ruth and

01:02:17

this person but like Garrick never hit

01:02:20

50 in a season. People don't know this.

01:02:22

Aaron never hit 50 in a season.

01:02:25

>> That's what people criticize Aaron for.

01:02:27

You know I consider him the home run

01:02:28

leader but whatever. Um he never hit 50.

01:02:31

May hit 50 in a season once in his

01:02:33

career. So you know let's not make it

01:02:37

seem like hitting 50 is that easy. It's

01:02:39

not. I think it's notable. I think what

01:02:41

we're seeing here is kind of the

01:02:42

combination of in some rule changes that

01:02:44

have increased offense and and and you

01:02:46

know helped hitters over pitchers in

01:02:48

general plus the fact that you know

01:02:51

we've got like a generation of like kind

01:02:53

of orientation towards home run hitting

01:02:56

maybe you know the three outcomes

01:02:59

whatever that I I think is kind of

01:03:00

driving a lot of this. We have a lot

01:03:01

more home run hitters now than we you

01:03:03

know had in the early years of baseball.

01:03:07

>> Yep. I'm I'm always

01:03:10

All right, guys. Well, it's only going

01:03:11

to get more interesting in baseball over

01:03:12

the next couple of weeks as we come down

01:03:13

to the wire, playoff races, etc. We'll

01:03:17

look forward to it. All right. Uh, why

01:03:19

don't we wrap it there? Been a full hour

01:03:21

here on Wharton Moneyball for the whole

01:03:24

crew. Shane Jensen been in here the

01:03:26

whole time. Eric Bradlo been in here the

01:03:28

whole time. Audi Winer in Absentia. This

01:03:29

has been Kade Massie. Many thanks to

01:03:33

Dion Simkins who I think we named the

01:03:34

fifth Beatle today. D Patel, Boss Lady,

01:03:38

and our producer, Marissa Rain. I

01:03:40

appreciate all y'all do and thank you

01:03:42

guys for listening. Come back and join

01:03:44

us next time. Between now and then,

01:03:47

enjoy your sports.

01:03:50

[Music]

Episode Highlights

Brian Burke Joins the Show
Brian Burke, a sports data scientist at ESPN, shares insights on football analytics.
“I feel like the fifth beetle.”
@ 01m 06s
September 19, 2025
Discussion on NFL Teams
Analyzing the performance of NFL teams after week one, including the Chiefs and Packers.
“Maybe it is time.”
@ 05m 09s
September 19, 2025
The Challenge of Predictions
Understanding the game means being able to predict outcomes, which is incredibly challenging.
“It's almost the hallmark of how well you understand it if you can predict.”
@ 19m 04s
September 19, 2025
Fourth Down Decisions
A questionable fourth down call by the Ravens raises questions about decision-making in critical moments.
“The model had an 80% chance to go for it, but they chose to punt.”
@ 20m 40s
September 19, 2025
Communicating Uncertainty
Celebrating the importance of communicating uncertainty in predictions and decision-making.
“It's really hard to do, and anytime it happens, it's notable to me.”
@ 32m 46s
September 19, 2025
NFL Playoffs Uncertainty
Half the teams in the NFL playoffs don't make it. It's a lesson we keep learning.
“It's fascinating how we keep learning that lesson over and over.”
@ 36m 46s
September 19, 2025
Daniel Jones's Potential
Despite skepticism, there's belief that Daniel Jones can excel under the right circumstances.
“You know, this idea that he can't play good football is just not true.”
@ 41m 01s
September 19, 2025
USF's Playoff Chances
If USF beats Miami, they could secure a playoff spot after beating two ranked teams.
“If they beat Miami, come on. They're in.”
@ 47m 11s
September 19, 2025
Alcaraz vs. Sinner
Alcaraz's dominance over Sinner continues, raising questions about their future matchups.
“Alcaraz's top end game is better than Sinner's top end game.”
@ 49m 13s
September 19, 2025
Alcaraz's Consistency
Alcaraz's potential for a higher floor in men's tennis is both exciting and scary.
“That's really scary.”
@ 53m 49s
September 19, 2025
Historic Gap in Tennis
The gap between the number two and number three players is unprecedented in men's tennis.
“This is the largest gap in the history of men's tennis.”
@ 57m 27s
September 19, 2025
Home Run Milestones
Four players are on track to hit over 50 home runs, a rarity outside the steroid era.
“Hitting 50 is not that easy.”
@ 01h 02m 39s
September 19, 2025

Episode Quotes

Maybe it is time.
NFL Week 1 Review: Fourth Down Decisions, Super Bowl Odds, and Kickoff Returns
Take a couple breaths long.
NFL Week 1 Review: Fourth Down Decisions, Super Bowl Odds, and Kickoff Returns
There's always some sort of context that the models aren't picking up.
NFL Week 1 Review: Fourth Down Decisions, Super Bowl Odds, and Kickoff Returns
It's fascinating how we keep learning that lesson over and over.
NFL Week 1 Review: Fourth Down Decisions, Super Bowl Odds, and Kickoff Returns
If they beat Miami, come on. They're in.
NFL Week 1 Review: Fourth Down Decisions, Super Bowl Odds, and Kickoff Returns
This is the largest gap in the history of men's tennis.
NFL Week 1 Review: Fourth Down Decisions, Super Bowl Odds, and Kickoff Returns

Key Moments

Guest Introduction01:06
Managing Expectations15:31
Fourth Down Decisions20:40
NFL Playoffs36:46
Daniel Jones41:01
Alcaraz's Peak53:43
Tennis Dominance54:21
Home Run Race1:00:52

Words per Minute Over Time

Vibes Breakdown

Data-Driven NFL Playoffs and College Football’s Shifting Power

January 12, 2026

01:08:34

Data-Driven NFL Playoffs and College Football’s Shifting Power

NFL Week 3 Analytics Insights and MLB Home Run Chase & Playoff Outlook

September 18, 2025

01:01:26

NFL Week 3 Analytics Insights and MLB Home Run Chase & Playoff Outlook

Bill Connelly on College Football Chaos, Coaching Carousel, and Predicting the Future of the Game

October 31, 2025

59:01

Bill Connelly on College Football Chaos, Coaching Carousel, and Predicting the Future of the Game

Baseball Analytics, NFL Parity, and College Football Playoff Odds

November 16, 2025

01:01:01

Baseball Analytics, NFL Parity, and College Football Playoff Odds

From Masters Victory to Motion Data: Golf’s Analytical Evolution

April 16, 2026

01:01:58

From Masters Victory to Motion Data: Golf’s Analytical Evolution

NFL Analytics Preview, QB Forecasts, and Team Rankings for 2025

August 05, 2025

01:05:14

NFL Analytics Preview, QB Forecasts, and Team Rankings for 2025

NBA Shockwaves, Why the Chiefs Still Rank No.1, and the Power of Data

December 01, 2025

01:00:01

NBA Shockwaves, Why the Chiefs Still Rank No.1, and the Power of Data

NBA Analytics, Tanking, and the Future of Team Building

February 19, 2026

01:04:12

NBA Analytics, Tanking, and the Future of Team Building

Inside College Football’s Data-Driven Evolution and Decision-Making

January 22, 2026

01:10:36

Inside College Football’s Data-Driven Evolution and Decision-Making

Rethinking Tennis Strategy Through Data and Coachability

February 12, 2026

01:07:36

Rethinking Tennis Strategy Through Data and Coachability

When Analytics Meet Chaos in Football Playoffs

January 15, 2026

01:10:28

When Analytics Meet Chaos in Football Playoffs

Billy Wagner Hall of Fame, MLB Pitching Trends & College Football Week Zero

August 27, 2025

01:07:31

Billy Wagner Hall of Fame, MLB Pitching Trends & College Football Week Zero