00:00:01
Welcome, welcome to Wharton Moneyball. Welcome to a full hour of sports analytics
00:00:05
here on the Wharton Podcast Network. This is Cade Massey hosting today with two of my longtime collaborators, Audie Weiner and Shane
00:00:13
Jensen. Our fourth co-host Eric Bradlow is out this week doing Eric Bradlow things.
00:00:18
Some combination of us are here almost every week of the year. This is big talk, given that we're not
00:00:23
going to be on the air next week. Next week is a rerun week, but almost every week, 48, 49 weeks of the year,
00:00:29
we're here. Been doing it for more than 12 years now. Looking forward to the next hour.
00:00:35
We are going to run our usual format. This is Tuesday afternoon. We'll record, put it up on Wednesday morning.
00:00:42
We've got it going in your very favorite podcast locations, but also we have video these
00:00:47
days on YouTube if you want to catch us that way. Our format today, guests in the first half
00:00:52
of the show, open lines in the second. Our guest, our, you know, if anyone gets credited as the OG, the OG Wharton Moneyball
00:01:01
guest, it would be Neil Payne because Neil was there in the beginning when we were
00:01:05
still in the basement of a UPenn building and Neil was in Philadelphia, to which he
00:01:11
will return soon, but first he's going to visit with us. It's been a little while since we've had
00:01:15
Neil on the show. He's probably the leading, most repeated guest on the show. We're always happy to have him.
00:01:20
Neil, if you don't know, a sports data journalist. He really is this mix of good with
00:01:26
numbers and words. He was practically a co-founder of FiveThirtyEight, did a lot of work there.
00:01:31
You can find his work in a lot of places, but especially his sub stack. These days, Neil Payne, always good to see
00:01:38
you. Thanks for making time for us. Hey, thanks for having me back on and always great to see you guys.
00:01:43
Good to see you, man. And your stack O-hats. I'm especially seeing the Orioles and the Dodgers
00:01:50
and the Colts are peaking out today. I'm going to focus on the Pirates. I see the Pirates.
00:01:55
Pirates are in there. We've got some Pirates talk. I think Shane has queued us up for.
00:01:59
Let us begin. We have just, we've got the two longest playoff seasons are basketball and hockey.
00:02:06
They run concurrently. Both are a lot of fun. And this year they were kind of remarkably
00:02:12
parallel and they both wrapped up one night after the other, both with 4-1 wins in the final series and both champion teams
00:02:20
making 16-3 playoff run. 16 wins to get to the championship, of course, with only three losses, both very good
00:02:28
runs in the playoffs. We're talking, of course, about the New York Knicks winning for the first time in, what
00:02:33
was it, 52 years, 53, 53 years? And the Carolina Hurricanes, which 20 years might
00:02:39
sound like a long time, didn't seem like it, not really a long time. 20 years shouldn't be long in Stanley Cup
00:02:43
repeats. That's their second one. I think of them as an expansion team. They're not an expansion team.
00:02:47
They're the former Hartford Whalers. Hell, that probably makes them an expansion team,
00:02:51
but they've been in Carolina since 98 or so. Okay, Neil Payne, open us up. Take us whichever direction you go.
00:02:57
Which of those 4-1, which of those champions, Knicks or Hurricanes, are you more impressed
00:03:03
with? They both had 16-3 runs. They both had really, really good playoff runs. Who are you more impressed with?
00:03:09
Well, I think it's the answer based on just the playoffs alone has to be the Knicks.
00:03:15
They set a new NBA record for the greatest points per game differential, and I think
00:03:20
overall point differential, because they're synonymous with each other in the longest playoff format now.
00:03:26
And that was just incredible. If you think about where this team came from, and how it was built, and how
00:03:34
many years they had been trying, and then they kind of finally get this breakthrough, and
00:03:39
even to the finals, even midway through the finals, even during games of the finals, certainly,
00:03:45
nobody really thought they could do it, but they just kind of kept winning. It kept coming back and put together the
00:03:52
goat comeback as part of that, the 29 -point comeback. And it just felt like if there's ever
00:03:59
been a team almost in my lifetime that was like a team of destiny, capital T, capital D, it felt like this Knicks team
00:04:06
was that. So I was more impressed by them. Now, the other question is, who do we
00:04:10
think is more sustainable going forward? And I think probably Carolina. They've been here for a long time.
00:04:16
They famously had also tried really, really hard for many years to get a breakthrough to
00:04:20
go to the finals, longer than the Knicks did, and have been one of the upper -tier hockey teams for a long time.
00:04:26
And they just have such a sustainable program in place that feels like it can ride
00:04:30
out the chaos and randomness of hockey. And there was a lot of that in this series as well, to be able to
00:04:36
have long-term success. So it's kind of two sides of the same coin, depending on what you're looking at.
00:04:42
But I subscribe to probably the first definition of just like who was most impressive during
00:04:46
the postseason and during the finals and what we just saw. And that was the Knicks.
00:04:51
If anybody had that on their bingo card of like, hey, who's going to break the, I think it was the 2017 Warriors or
00:04:57
2018 Warriors or maybe the 01 Lakers, they were all in that conversation, 71 bucks record
00:05:03
for highest point differential in a single postseason. I do not think anyone on the planet,
00:05:09
except like maybe some rabid diehard Knick fans hanging around outside Madison Square Garden would have
00:05:14
thought that that was even remotely possible. And yet here we are. Here we are. Neil, it was only a few weeks ago
00:05:21
on the show, I remember saying, well, it's just presumed that the winner of the Western
00:05:26
Conference is going to win the title, right? It's who's going to win, thunder or spurs.
00:05:31
Here's a Western final for the whole, for all she wrote. That's kind of where everybody was.
00:05:36
What do we take away about, I mean, what, I guess the main question is like roster construction.
00:05:42
It's always a mystery in the NBA. What is, there can't just be one way, but what do we take away?
00:05:47
Because nobody expected this team to do it. They didn't do it in following any pattern
00:05:52
that other people have been following. So what do we take away? Yeah, that's a, that's the $6 million question,
00:05:59
right? I saw some tweet where somebody was like, the hilarious thing about this Knicks run in
00:06:03
the finals is that like, nobody can really copycat anything off of it. It's impossible to replicate the specifics of like,
00:06:10
okay, go out, get an undersized guard that nobody thinks can be the best player in
00:06:14
a championship team, sign him to what seems like an above market deal, but ends up
00:06:18
being wildly below market. Then go out and get a bunch of guys who played together in college, but trade
00:06:23
one of them away and get a guy that was considered soft and incapable of stepping
00:06:27
up in the playoffs. Then fire your coach after making the conference finals multiple years and get a guy who
00:06:33
had been kind of floating around as a coach for many years without winning the championship
00:06:38
as the head coach, and then put it all together. And lo and behold, you have the best
00:06:42
playoff team of all time. There's just, how do you replicate that? But I do think maybe it should just
00:06:47
expand our horizons. And if that hadn't already been true, when we had previously seven different champions in seven
00:06:54
years, now we have eight in eight years. Whether it's the second apron, you guys know
00:07:00
all the theories abounding about why the NBA is potentially quite different now than it was
00:07:06
in its entire history before this. But I think maybe we just need to expand our horizons around what a championship team
00:07:13
looks like and how to build one, because it's like a totally different paradigm than the
00:07:17
one that we were used to 10 years ago when the Warriors and Cavs were going to the finals every year.
00:07:23
And basically the championship hinged. You could almost book it on the day that the free agency tweet was made by
00:07:32
Shams or whoever, Woj, about where KD was going and where LeBron was going. You basically say like, okay, well, that's your
00:07:40
championship in the summer before the following season. You can't do that anymore, in part because
00:07:46
there isn't that crazy free agent frenzy because of the restructuring of financial rules, but also
00:07:51
because it's just a lot more chaotic and random than it used to be. And a lot more players have led teams
00:07:56
to championships than the types of players and the small number of players that had done
00:08:01
that in previous eras. So Neil, you've gone from roster construction, gone to team-level analysis to league-level analysis.
00:08:10
And I want to come back to roster. Acknowledging that eight unique champions in eight years
00:08:14
is cool. I think this is something that most leagues would have striven for, right?
00:08:20
And so that's great. But let's come back to roster. And I wonder if there is, you know,
00:08:28
one way you're saying it is no one knows, you can't say, you wouldn't replicate this
00:08:32
what they did, like in terms of which type of player you get, which type of coach you get.
00:08:38
But is there, you know, think about it like a feature engineering exercise. So if what you just described was the
00:08:43
data, if this was a big data set and we were going to then do some analysis on it, we would engineer some features
00:08:49
out of that raw data, some higher level features. And could we think in that, like, can
00:08:54
we abstract up one level from 6-1 guards to what qualities the roster provides you?
00:09:03
Might that be a productive avenue for thinking about what was this, what were the key
00:09:08
features in this team? And again, we're acknowledging there's not one way to get it done, but is there something
00:09:13
we can learn? And maybe at that next level up, as opposed to the fundamental, you know, player level
00:09:17
stuff. Well, I think it's probably true that like their fifth best player, whether that's, you know,
00:09:23
Josh Hart or Bridges or someone like that, is probably much better than sort of the
00:09:30
median fifth best player on an NBA champion. And I think that that is maybe one of the things that you could take away
00:09:37
from this is they really invested, yes, they invested in Brunson, but they also invested a
00:09:42
lot, arguably more, because I think Ananobi and Bridges make as much or more than Brunson
00:09:48
does, and certainly Towns does. They invested heavily in the supporting cast, like
00:09:53
the rest of the starting five, and they all really complement each other well. They're very versatile players.
00:09:59
We saw each of them become like main characters at different times. I think we all recognize that Brunson was
00:10:05
sort of the engine that drove everything, but there were times in which like Bridges got
00:10:10
hot and kind of carried them early in the series. Ananobi, of course, hit probably, people are calling
00:10:15
it the hand of God, you know, maybe that's a soccer reference kind of coming in as well.
00:10:21
But I think that tip in was one of the biggest shots that anyone ever made in the history of the NBA finals to
00:10:26
kind of cap things off in game three, or game four. So really, to me, it's just maybe the
00:10:36
lesson is you know who your kind of offensive creator is as the key centerpiece, and
00:10:42
it's okay to build around a guy that maybe has defensive shortcomings. He literally has shortcomings height-wise.
00:10:48
If you get a good enough, versatile enough, and kind of just all-around strong core
00:10:54
around him, especially the rest of your starters. But you could get a lot of false
00:10:58
positives doing that, too, because we've seen a lot of teams be built that way that
00:11:01
kind of hit a wall, and they can't break through. I think that's one of the interesting things
00:11:05
about the Knicks is they were hitting that wall, too. They had the most, or I think second
00:11:10
most, playoff wins in a three-year span among a team that didn't make the finals
00:11:14
to then go on and make the finals the following year. The other team in that category was the
00:11:19
1991 Chicago Bulls. So, you know, you think about the Bulls fighting against the Pistons for so many years,
00:11:24
finally getting that breakthrough. We don't think of the Knicks in that conversation.
00:11:28
Maybe we should because they really also tried for many years to get through, and most
00:11:33
of the teams in that category that win a lot of playoff games but don't make the finals, they fall by the wayside in
00:11:38
that fourth year because things change. The team gets blown up. There's just, it's impossible to sustain that with
00:11:45
this sort of failed proof of concept or seemingly failed proof of concept, but they stuck
00:11:50
with it, and they were rewarded for doing that. So maybe you'll actually see more patience in
00:11:54
some ways as well. They changed the coach. They did change coach. That's a big deal.
00:11:59
It's easier to change the coach than it is to change the players, and especially in
00:12:03
this case because they were kind of capped out, and the players were almost borderline untradable
00:12:08
in some ways. So they pulled the only lever that they had, and maybe they kind of backed into
00:12:13
the right way to do it as a result. Right. Guys, as statisticians, what summary statistic would you
00:12:23
like to conjure to illustrate how unlikely the Knicks win was given the number of massive
00:12:32
leads that the Spurs had over various, in what, four of the games? So for example, a colleague sent me, Adi,
00:12:41
hold on. A colleague sent me a stat saying this is the most amazing stat I've ever seen,
00:12:45
that the Spurs led 72 percent of the time. I think that's impressive, and I'd like to
00:12:49
see that compared to other champions, but that doesn't capture the size of the lead, the
00:12:54
magnitude of the lead. So what could we do? What could we conjure to capture that?
00:12:57
Adi, sorry. So first of all, that's very hard because assessing a statistic after a game is over
00:13:05
is subjective, subject to selection bias. So actually, I gave a talk today on the maximum win probability of the losing team,
00:13:13
a paper that I wrote with my grad student Jonathan Pippin, and one of the main points is that big, essentially, blown leads happen
00:13:23
much more frequently than you think. And actually, the simple point is that two teams that are equal in talent, the team
00:13:30
that loses is about 50-50 likely to have a big lead at some point in the game, and that's counterintuitive.
00:13:36
And also, the point which I've made many times, typically, one team will often go out
00:13:40
and lead most of the game, and that's the arc sine law. If you model a basketball game as a
00:13:46
random walk, which is a reasonable thing to do, you get those sort of funny statistics,
00:13:50
and most of the public doesn't realize the actual behavior of these random variables.
00:13:54
But what the Knicks did is ridiculous. I mean, we just know that intuitively. I mean, they came back from a, what
00:14:00
was it, 29-point deficit at some point, 20 points in the fourth quarter. Almost every statistical model suggests that's one in
00:14:08
several, I mean, it's a thousand event or approximately. Yeah, I think so, 99.6. I mean,
00:14:14
again, I mean, I'm kind of with you, Audie, what you were first saying. I think lead shifts, I mean, I would
00:14:20
like to know what the probability is, like, of, you know, a blown, I know it doesn't have, it's notable because it happened in
00:14:27
the finals. Right, of course, it's notable, but I will say one thing about the- I feel
00:14:31
like we're underestimating the denominator of these kinds of occurrences, like in basketball games.
00:14:38
It seems like the swing can be dramatic. The simple point that having a lead of that size generally isn't blown.
00:14:47
I mean, that's just the definition of a win probability that size. But I will say something about NBA win
00:14:52
probability models, at least the public ones, and that's the only ones that I have access
00:14:55
to. Our work does show that the NFL win probability models are much more calibrated than the
00:15:01
NBA. The NBA ones are bad. And most, particularly where they're bad, they give way too much weight to a big lead,
00:15:10
and particularly late in the game. Those things happen, I think, far more, much more frequently than the models undertake.
00:15:17
And I think a lot of people's intuition, we almost, you didn't present it this way
00:15:22
at all. You didn't do this, but like, you know, like conditional, and maybe this is what you
00:15:26
were getting at, Cade, with your kind of question of the general is, you know, conditional
00:15:30
on there being one big point lead in the series, one game going that way, do we then just apply every other game as
00:15:39
an independent event to multiply out these very rare events? No, I mean, maybe something- You can't.
00:15:44
Maybe these aren't correlated with each other. Whatever is systematically happening makes it more likely
00:15:48
to happen. I think what's important, we got to bring this front and center. And we've been talking about the Knicks the
00:15:52
entire time. Can we talk about the Spurs? I mean, the greatest, frankly, the greatest ex
00:15:57
comment I've ever seen in my entire life was the Spurs are really great at taking
00:16:01
the lead, but not so great at holding the lead. And the really funny thing about the lead
00:16:06
is the important part is the holding. That, of course, is a riff on Seinfeld with the rental car reservations.
00:16:12
But can we talk about the Spurs? That's the thing that I don't quite understand.
00:16:16
If you flip it over, are they really part of this outcome? Did they blow it in an epic fashion
00:16:23
based on psychological shortcomings, inability to play when it gets- all these things that we
00:16:29
- It's not as easy, I feel like, as football where you can be like, oh, well, they stopped running the ball or whatever.
00:16:35
It's hard, at least for my- I'm with Adi. It's hard for, I guess, a little bit more of a naive person to be like,
00:16:42
are there signs that they take their foot off their gas in some terrible way or whatever?
00:16:47
Well, they did make some mistakes late in those games, for sure, like, you know, in
00:16:51
terms of shot selection. And then especially you think of like, you know, potentially dribbling out the clock in one
00:16:57
of those possessions, the one where Ananobi came down and blocked the shot anyway.
00:17:01
A 29 point blown lead hinges on like if any one single- It was similar to the 28-3 with the Patriots and
00:17:09
the Falcons. If any one play in either of those games goes differently, the leading team still wins.
00:17:16
So like every single play mattered in that. And if you make a kind of boneheaded
00:17:21
decision at any point, it can kind of open the door for them to get back in. Now, I saw some people saying like the
00:17:27
Spurs were one of the youngest NBA Finals teams. We know that experience matters in these things
00:17:33
and that maybe you could chalk up some share of the collapse to that. It certainly was a collapse.
00:17:40
I mean, they had these huge leads. They blew a 14-point lead in Game 1. They obviously blew the 29-point lead in
00:17:47
Game 3. And so it just is like, there's something there to that. And I don't know if it's just more
00:17:54
time on this stage together. Because even Wimby, you saw some of the plays where like he passed it to a
00:18:01
guy who wasn't looking at the ball to turn the ball over and kind of give another possession back to the Knicks.
00:18:08
I do also think there's a New York media bias, though, to kind of make it be like, oh, the Knicks won this more
00:18:13
than the Spurs blew it. Because if the shoes were on the other foot, we absolutely would be talking about, and
00:18:19
think back to like John Stark's shooting performance in 1994, where they would be killing the
00:18:25
Knicks for losing these games in the media if the shoe was on the other foot. But because it's like, you know, relatively small
00:18:32
market San Antonio, we've seen them win before. The presumption is that Wimby is going to
00:18:36
win a bunch of championships in the future. And this is just part of the growing
00:18:40
process that I think we're maybe letting them off the hook a little bit compared to
00:18:45
how we would approach it if it were another team. And it's an important question because it speaks
00:18:50
to how repeatable this Knicks experience is for next year. I mean, everything went their way in this
00:18:57
little run, I feel like, to the championship. Are they even the betting odds favorites for
00:19:03
the champion? No. They're not even second. Yeah. Yeah. So I guess a lot of people are
00:19:10
attributing at least a lot of this to kind of the breaks going their way. Well, the ESPN was, or whoever was broadcasting
00:19:17
it, was good about showing field goal percentage, especially I think in that last game.
00:19:21
Before the game started, and then it's bore out again, San Antonio's field goal shooting percentage
00:19:27
declined monotonically from first quarter through fourth. And dramatically, monotonically and dramatically, it was almost
00:19:33
half as good in the fourth quarter as it was in the first. You want to give the Knicks some credit
00:19:38
for that, and presumably analytics would help us parse it in some way, but there were
00:19:42
plenty of shots that looked like good looks that they just weren't getting down.
00:19:47
And sure, I think they deserve a lot of... And this isn't a trend that they showed
00:19:51
at all during the season, right? This was something unique to the finals. Right.
00:19:56
Good question. That would be... Right. Well, what's even the base for that? Yeah.
00:20:02
I mean, maybe if that's just something that they had in their general kind of characteristic,
00:20:09
it made them even more vulnerable, I guess, to the Knicks. Well, and I don't think you win 62
00:20:13
games by having that tendency. It's so tough to kind of put, especially in the NBA where most games kind of
00:20:20
don't matter throughout the year. You have this massive sample size of games that only a handful of them matter.
00:20:26
Maybe it would be interesting to go back and look. I've kind of forgotten how the NBA Cup...
00:20:30
I mean, it's silly to mention it, but that was sort of a previous example of these two exact teams facing off on kind
00:20:37
of an elevated stage. And the Knicks ended up winning that one as well. It's so tough to figure out what is
00:20:44
signal amidst the noise of the long and mostly meaningless NBA season. Can we bring this back to the...
00:20:51
Sebastian, you should call back to... Go ahead, Odd. Just want to bring it back a little
00:20:55
bit to the hockey and the Hurricanes, not that they... We'll come back to them. But as sports analytics types, I mean, we
00:21:03
typically think of the Hurricanes as one of the essentially sports analytics-oriented team from ownership
00:21:10
to management to their whole process. And this, their victory and their history vindicates
00:21:16
that approach. I don't think the Knicks are in that camp at all. I mean, I think this is...
00:21:22
We can't even... You couldn't even articulate why they won. I mean, it's almost like the anti-analytics.
00:21:28
So is it... I mean, how do we square this? I mean, we statisticians who believe that this
00:21:34
is the answer. What do we do with these two data points? Well, the null hypothesis is it was luck,
00:21:40
right? Yeah. Or just that sometimes it's rare. And I think maybe we can effectively most
00:21:48
years treat it like it's almost impossible for a team to catch us by surprise, whether
00:21:54
we're looking at our metrics or talent or whatever we think the betting odds going into
00:21:58
a season, that we have this assumption that it's always going to be one of our kind of core group of favorites.
00:22:05
And I think OKC was the team that was the team to beat most of the year. Boston was in that conversation.
00:22:12
San Antonio worked their way into that. And we kind of said, hey, we've seen enough of Wimby.
00:22:17
We can put them in that tier. The Knicks were at the, maybe the edge of that tier, certainly before they started going
00:22:23
crazy in the playoffs and dominating everyone. And even by the end, I mean, San Antonio was favored going into the series.
00:22:30
And it looked like when the Knicks seemed like they lost that game three, that the
00:22:36
momentum was shifting away. People were hopping on the Spurs and being like, OK, the Spurs are going to actually
00:22:41
come back and win this. And then New York shut it down. But my point is that we sometimes forget
00:22:47
that there are every so often, maybe like once a decade or so, weird teams that sneak into the mix and through some combination
00:22:54
of luck playing above their heads or just being way better than we expected. They do win the NBA championship, the twenty
00:23:00
nineteen Raptors, the twenty eleven Mavericks come to mind. The Royals won the World Series.
00:23:07
Yeah, the Royals. I mean, baseball is its own like baseball that happens quite frequently.
00:23:11
Basketball is a random game, predictable sport, the sport where the favorite wins more often.
00:23:17
And even there you have examples. The 0 4 Pistons were another team in that conversation.
00:23:22
So probably like once or twice a decade, you get a team that's not one of the absolute cream of the crop teams as
00:23:29
we expect them to be going into the playoffs comes through and still wins regardless.
00:23:34
And sometimes we weirdly just kind of discount that possibility or it's just like, well, you
00:23:39
got to pick a favorite, right? You know, of course, we were going to side with Oklahoma City as having the highest
00:23:44
odds. But maybe we need to back off on the degree to which we favored the top teams, especially now in light of what we
00:23:51
were saying earlier about the fact that, hey, eight different teams have won in the past
00:23:55
eight seasons, which is the first time that's ever happened in NBA history. That's got to mean something.
00:24:00
Yeah. Can I try a hypothesis out on you, Neil, and obviously all of you guys. Brent Tillis was on our show speaking of
00:24:06
Carolina a few, I guess, a couple of months ago. And he made a very interesting statement, which
00:24:13
almost speaks to the area in analytics that we fail to model. He said that we don't appreciate how the
00:24:21
top player is able to make everybody around them better. And it's essentially, mathematically, we would call that
00:24:29
interactions. So when we model in sports, almost always it's additive. We just add up the contributions, even in,
00:24:35
it makes most sense in baseball because your contributions really are, for the most part, independent
00:24:40
of the rest of the contributions of the rest of the team, makes the least sense in basketball, if you think about it.
00:24:47
And I don't know, I'll have to turn to you, Shane, and Neil to tell me where hockey is on that.
00:24:52
But in basketball and football, it really matters how you work together as a team, as
00:24:56
a group. And maybe the Knicks figured that out in a way that we just haven't modeled.
00:25:02
And what they did was get a bunch of players that really work together. And I don't even remember this, but they
00:25:08
were all together in college. And we've heard, Dean Oliver has been on our show and has talked about research that
00:25:16
they've done and how basketball players that play together become better as a pair, dramatically, throughout
00:25:23
the season. And I guess that would be magnified over a course of the career. So maybe we and the analysts are missing
00:25:29
it. We haven't modeled it. And maybe the Knicks are leading the way, or maybe I'm just bullshitting.
00:25:34
What do you think? I mean, no, go ahead, Shane. No, no, go ahead, Neil. Okay. Yeah.
00:25:40
I mean, I think there have, first of all, the things like the adjusted plus minus
00:25:44
type metrics, those try to pick up on at least an amplification of sort of the best player being able to make the team,
00:25:51
make the teammates better by essentially taking away the individual stats from the teammates and attributing
00:25:58
them to the star player who kind of made that more possible. And so there's that credit splitting.
00:26:03
I will say though, I hate to interrupt this, totally additive model. That is not an interaction model.
00:26:09
Oh, it is. Oh, for sure. I'm saying like, in effect, it's forcing into an additive kind of framework, just essentially taking
00:26:19
away credit from, linearly from the teammates and giving it to the player, even if it's
00:26:25
not appearing in their individual stats. So that's one attempt to do that. I also think there's been efforts to try
00:26:30
to model at the lineup level, try to kind of predict what combination of skills and
00:26:36
attributes of players will succeed together. That's kind of a holy grail type of
00:26:41
thing in basketball. And I think every team that has analysts are trying to do that in some way,
00:26:46
shape, or form. The problem with lineup data is it's notoriously kind of low sample, noisy.
00:26:51
There's a lot of garbage time that you have to kind of get out of the system. So there's huge challenges to that.
00:26:57
And oftentimes, yeah, it's the matchups as much as you want to try to find like proxies for the upcoming matchup.
00:27:04
Like when you've got Wimby going up against a small guard in Jalen Brunson, you know,
00:27:10
you might try to model that, but it's very difficult to find like seven, five guys
00:27:14
that are rim protectors against like five foot 10 or whatever he is lead guards.
00:27:19
So yeah, the specificity of the past data to your particular problem going forward is always
00:27:26
going to be a challenge. And I think that's why basketball is one of the most complicated and I think interesting
00:27:31
sports. Like somebody was saying, and I think this is funny, you guys will kind of appreciate
00:27:34
this. I saw somebody being like, well, the analytics didn't see this coming. So it means that it's kind of worthless
00:27:40
and it's like, okay, well, here's a quick guide for you guys. So if the analytics predict the winner correctly,
00:27:46
then the analytics are ruining the game because they're making it too predictable.
00:27:50
If the analytics don't predict the winner correctly, then the analytics are worthless because they're wrong.
00:27:54
I hope that that can kind of clear things up either way, the analytics are going
00:27:58
to be to blame for things. But honestly, like, isn't this why we watch sports? We love things like this as fans to
00:28:05
see a team that basically by all logic and reason and past performance and all these
00:28:12
things you would not think would be particularly likely to win the NBA title, much less
00:28:17
have the greatest point differential in postseason history, just goes and does that.
00:28:21
Isn't that, I mean, that's pretty awesome. I think even though it like confounds us
00:28:25
as predictors and analysts, I think it's cool. Neil, I'll just say two things. First, I'm a Knicks fan.
00:28:31
There you go, everyone. But mostly ignoring that because of all those years, the earliest championship that I can remember
00:28:38
growing up outside of New York was 1973. So that's the first one I remember. Now it's the last one that I remember.
00:28:45
So it's a short period of time. Hopefully a New York team will do something again pretty soon.
00:28:49
I wonder which one I'm thinking about. You all know, but not likely. But you know, when you mentioned that, I
00:28:56
mean, we love the game as game, right? It's so exciting, particularly when you're a fan
00:29:01
and when you have a rooting interest in the team that does this. But as analysts, let's just stand back.
00:29:08
We want to crack it open. I mean, and that's kind of the goal. And I'm wondering whether the big gap in
00:29:15
our analysis has been combinations in basketball. We really haven't done a good job figuring
00:29:21
out how to model players that work together, combination skills that work together, simply because it's
00:29:26
hard. But now the date is coming, and maybe we'll move forward. Yeah, and I'll only push back in that
00:29:32
one of the reasons we choose additive models and not interaction models is we typically don't
00:29:37
necessarily have the data to support that kind of that interaction model. It's more a matter of faith than it
00:29:42
is kind of an empirical thing. And so for I mean, New York, we will get some data.
00:29:47
I mean, again, we keep talking about like, what's going to, you know, prospectively, you know,
00:29:52
they had nobody could argue they had a gigantic positive residual by this year. If there's any signal in that, you know,
00:30:01
one piece of evidence that their signal is they keep going next year, if all of a sudden, like it bounces back down to
00:30:06
zero or negative next year, when there's some amount of you know, that's, that's some observation
00:30:11
on whether or not they've kind of figured out sort of a team building matchup kind
00:30:16
of thing that's, you know, kind of sustainable, or whether it was more of a kind
00:30:20
of Yeah, they caught lightning in the bottle, basically. Either. I mean, I don't want to take anything
00:30:25
away from their achievement. You know, they caught lightning in the bottle. That is like an established fact.
00:30:31
Well, I, for one thing, 29 point comebacks are totally sustainable. Right? Yeah, no, that's, that's the new way to
00:30:38
win. All right, go down by 29. On that note, we're gonna talk the remainder of the sports high on the agenda.
00:30:45
We talked the hell out of the Knicks and basketball. We alluded to some hockey stuff.
00:30:50
The hockey stuff is super interesting. I mean, is there any, what was it called?
00:30:55
There's an ESPN, great ESPN article out yesterday about the nerd jock marriage with the Hurricanes,
00:31:02
the Tolsky Brindamar. It's really an extreme version of nerd jock. It's hard to get more jock at a
00:31:08
head coach than Brindamar. It's hard to get nerdier than our friend Eric Tolsky was on the show just a
00:31:13
couple of months ago. But that combination certainly was effective for them, has been effective for them the last couple
00:31:17
years, even as an assistant GM, Tolsky's been there. So maybe a little bit on that.
00:31:21
And then World Cup, of course, I would love to hear y'all's thoughts on the, the draw fest that it has been so
00:31:28
far. 50% of the games coming into the day have been draws, whereas the international average
00:31:32
is something like 22. Now, technical, technical challenges on my end, I'm gonna step away.
00:31:37
Audie and Shane, Neil, you guys are doing fantastic. Leave it to you. Wish I could join you.
00:31:41
Y'all have a good time. Aud, safe travels back. Neil, great to see you. Shane, thanks for everything.
00:31:47
All right. So let's bring us back to hockey. I'm gonna let you guys take, take over
00:31:50
this, this wrap up. We talked about the Hurricanes and the, and the World Cup, not the World Cup, the
00:31:56
finales of the, the final of the hockey. But what do we got here? What, what, what, what's, what's the quick summary?
00:32:04
Well, I think one of the interesting things and I'm curious what you think about this,
00:32:08
Shane, as well, is if all you knew was the top line result, Carolina Hurricanes win
00:32:13
the Stanley Cup, certainly, like we said, been one of the best teams for a long time.
00:32:18
It made sense that they would have their breakthrough. If you knew anything else about how that
00:32:23
happened, though, you would be like, this is just the most chaotic possible outcome, both in
00:32:28
terms of there was a goalie switch in the middle of the Stanley Cup final, which
00:32:31
kind of turned the series around for Carolina, was like the single biggest factor.
00:32:36
I know Freddie Anderson was also hurt. They revealed later, but going to Brandon Bussie,
00:32:41
the rookie backup, he wasn't the backup during the season. And that's, I think what just makes hockey
00:32:47
so bizarre is they decided to go with Anderson at the start of the playoffs. It was kind of a toss up, but
00:32:52
they gave it to the veteran because he had been there before. And, and he goes off and has just
00:32:57
a, he looked like the favorite to win the consummate trophy. If it wasn't Mitch Marner of the golden
00:33:02
Knights through the first three rounds of the playoffs. And then all of a sudden he's not
00:33:07
playing so well in the first few games of the, um, the Stanley Cup final. Nobody is really playing that much defense or
00:33:14
goaltending all that well in those there's like a record or near record number of goals
00:33:18
and certainly comebacks just galore early in that series. Brendan Moore makes the switch to Bussie and
00:33:25
net. He comes in and relief. He actually enables them to do a record setting for goal third, uh, third period comeback,
00:33:32
uh, in game three, they end up losing it anyway, but it gave them the confidence
00:33:37
to go with him going forward. He's the goalie of record the rest of the series and they kind of shut them
00:33:42
down. They had a shut out in the deciding game, but also he had like a nine 50 save percentage.
00:33:48
And that was really the hinge point in the series. Um, as far as Vegas went, they stopped
00:33:55
converting as many chances. They had a number of prime chances, especially in the clinching game that they just like
00:34:01
hit a post or they sailed it over the net. Uh, and it was really that difference between
00:34:06
them finishing and not. And also early in the series, Jordan stall ended up winning the, um, the cons my
00:34:13
trophy. And he had really not done that much of note and was not really on many people's radar to win playoff MVP, uh, up
00:34:20
until having a five game goal streak in the first five games of the Stanley cup final, which ended up kind of clenching him
00:34:26
that as the oldest and one of the lowest scoring playoff MVPs. Uh, so I just felt like the series
00:34:32
was completely resistant to narratives aside from if you just zoom out and look at the
00:34:37
big picture. Yeah. Carolina was probably the best team and they ended up winning, but Shane, what are your
00:34:42
thoughts? Well, I think one thing with Jordan stall, I mean, I think specifically, I think he
00:34:49
is an interesting sort of, I guess, Stanley cup finals MVP, because I think obviously I
00:34:53
think he was kind of given, given it based on his, his, his goal scoring, but he's not really not the finals MVP.
00:35:01
It's the playoff MVP. Yeah. I misspoke. He was basically, I think given the playoff
00:35:07
MVP trophy based on his finals performance. And I think that, I think in most people's mind was driven by the goal scoring,
00:35:14
but it came from kind of a surprising place because he's, I think famously like, you
00:35:17
know, an amazing sort of defensively oriented center. And I, I'll kind of just point out
00:35:22
one of the things he, all other things besides goal scoring, he didn't have finals.
00:35:25
He won face offs at a 68% clip, which is, I think a record since that actually has been tracked in hockey over
00:35:33
the last 30 years. So that I think his contributions basically went, went beyond the goal scoring, but I agree
00:35:40
with you in general, it's, it's a, it's a hard kind of series to put a really strong narrative on.
00:35:46
We were talking a little bit in the basketball about like kind of, you know, luck
00:35:50
versus something systemic. I mean, I would like to say that I think in general, one of the reasons
00:35:58
Carolina was able to kind of manage that goaltender shift and everything like that and, and
00:36:03
kind of feed off it is that they are generally a very good defensive team, but Neil, as you pointed out, they weren't playing
00:36:09
very good defensively in those first few games with Anderson and net all of a sudden
00:36:13
they switched to Bussey. And I feel like the defense also got better in front of the goaltender.
00:36:19
And I don't know if that speaks to like some kind of, you know, Audie, you were talking about interaction kind of effects.
00:36:24
I think defense, goaltender, it's subtle. And maybe we're over, over reading this one
00:36:30
situation, but I think that that's an example of a hockey interaction that can kind of,
00:36:35
can kind of happen at least locally. Let me ask you guys a question. Our resident hockey expert, Shane and Neil in,
00:36:42
in soccer, we're going to get to that in a moment. You have XG, which is really important because
00:36:47
it's very low scoring basketball. We never bother with it because it's so high scoring.
00:36:50
We don't really think about points above expected. How does the, is there's, there is an
00:36:56
XG for hockey is how'd that play out? I mean, did the hurricanes win on the substantially on the XG score, but just squeaked
00:37:05
by more on the G score or vice versa? Or what, what's the story there? Well, the hurricanes per, well, the hurricanes perennially
00:37:11
dominate XG because they just have such possession of the puck. And they're sort of constantly creating these sort
00:37:17
of positive XG events at all times. So maybe the, the difference there is that like the finishing quality was better than it
00:37:27
has been in those playoff losses that they've had in the past. It's like, they're always going to have opportunities.
00:37:32
It's just a question of whether they eventually cash in on them at sort of a higher than expected rate.
00:37:38
And they finally got that, but certainly they did have, I believe the, the lion's share
00:37:44
of the events, you know, whether it's like scoring chances, expected goals, all those things during
00:37:51
the course of the series, they had let's see, there's a great site called natural stat
00:37:58
trick that I want to shout out. That is my kind of go-to source for this metric.
00:38:04
And so I think that that's one interestingly enough. So real-time correction on air while Carolina
00:38:11
had the 56% of the course events, those are like shot attempts basically. And they also had 52% of the
00:38:18
scoring chances. They actually only had 48% of the expected goals in the series.
00:38:23
Now I don't have broken out by game. So I'd be curious as to see like that typically speaking XG per 60 minutes on
00:38:33
D like allowed is a good measure of defense because it basically says like, is the
00:38:37
other team getting shots in an area of the ice? That's like particularly dangerous.
00:38:42
So I would expect, and again, I'll have to look at it because I was wrong. I was expecting them to have higher XG
00:38:47
percentage overall that they tighten that up as the series went on, but maybe it's also
00:38:52
like, really high XG on that one shot that he missed right in front that would have, I believe tied the game in, in,
00:39:00
in game six. Yeah, no, no, I do think Carolina in general are kind of masters of that because
00:39:06
of the, they're typically pretty good defensively are masters actually. Like I think that Montreal Carolina series, the
00:39:13
Sierra, the semifinal was ridiculous in terms of kind of the, the, the differential and sort
00:39:19
of expected goals and that, you know, kind of eventually realized in an outcome as well.
00:39:23
But I do kind of feel like, again, it maybe comes back to a little bit what we were talking about before of the
00:39:28
kind of feel like Carolina somehow got out of their defensive mode or maybe, you know,
00:39:34
the, you know, the, the, the Knights were just kind of getting, uh, getting some, uh,
00:39:39
lucky opportunities in those first few games. I do kind of think somehow the goalie
00:39:43
switch also led to this kind of defensive or, or at least them kind of getting their usual defensive group back, I guess, is
00:39:50
what the way I would maybe describe it. So I'm just going to pipe in there and say, I'm always looking for projects, um,
00:39:55
for our students and Neil, you're always looking for things to write about, but I'm kind
00:39:58
of curious, maybe you know the answer. What is the rough approximate relationship between XG
00:40:04
differential and winning on the game level and XG differential and winning at the, at the
00:40:09
seven game series level. And I would guess that the latter one is probably higher.
00:40:14
Uh, usually at the, at a game level, there's much weaker relationship between the XG differential
00:40:19
and winning, but cause it actually went the other way. I think it's pretty surprising.
00:40:23
I mean, and, and obviously one of the things that we just finished a, uh, a, a hockey data competition and the inside baseball
00:40:32
that we put into our little nugget into the model was the importance of the goalie.
00:40:36
And because goalies can, can really make a, a different, a real, real, uh, important contribution
00:40:44
to the, the dampening of the XG cause they can stop shots and, and, and that can change it.
00:40:51
So what do you think about that as a project? And, and maybe that it's really just a
00:40:54
goalie performance that, that saved them. Well, we know that sort of, um, the, the, if you had to pick whether you
00:41:02
would win the save percentage battle or sort of, I guess, save percentage above or expected
00:41:06
based on XG. Yeah. Or the XG battle, you absolutely would rather win the save percentage battle because goaltending, uh,
00:41:15
performance backward looking is like the biggest driver of who wins any hockey game and any
00:41:22
series. How predictable it is, is an open question. It's traditionally been quite noisy, uh, trying to
00:41:29
predict who would be the best goalie and which goalies would play the best going forward.
00:41:32
And in a weird way, we saw this, I mean, the, the hurricanes, in addition to being the masters of like, Hey, over a
00:41:38
large enough sample, if you control enough possession and have enough of the XG, uh, over,
00:41:43
over many years, even years worth of sample, you will eventually win the cup. They are also an example of how mercurial
00:41:51
goaltending is because they put in the, one of the worst goalies in the league from
00:41:55
the regular season, Freddie Anderson. He was the best goalie in the playoffs through three rounds and then was not good
00:42:01
early in the series. And they put a rookie backup in who then played the way he had been playing
00:42:06
earlier in the playoffs and they won the cup. So it's like this weird duality of goaltending
00:42:11
luck over layered over the long-term, Hey, let's just control all of the possession over
00:42:19
the course of a decade. All right. So that's great. Wrap up for hockey. Why don't we spend the remaining five or
00:42:24
so minutes giving us a quick recap of the world cup. Um, and my, um, distant attention as an
00:42:31
American, you know, I have to confess it. Um, I did follow the Americans. They won, they won surprisingly big.
00:42:38
They look great. And then the other data point that I paid attention to was Spain playing to a
00:42:42
tie against a terrible team. And, and, and there was a lot of ties. And we heard that from Cade before he
00:42:47
slipped out. Um, so what is your assessment of, at least from a statistical angle of the first
00:42:52
rounds of games that we've seen? It seems very chaotic. It's in keeping with what we saw.
00:42:58
It's just like the, the NBA and NHL kind of never left. And we're still, you know, continuing it with
00:43:03
the world cup because yeah, these draws, I think what was notable about them is that
00:43:07
they were also most of them. If not all of them were lopsided like pregame odds, where you would expect the favorite
00:43:15
to, to get the points out of the situation. And in a lot of cases, like a team like Cabo Verde, uh, playing against Spain,
00:43:22
a draw is a win for them. I mean, it's, they, they were not expected to win that game.
00:43:26
It was one of the biggest upsets, even though we think of upsets typically as being
00:43:29
like handing a loss to the favorite, this was handing them a tie and that was a huge, massive upset.
00:43:35
And we're in the middle of one right now, as well, as we record, we don't know how it's going to, um, once you
00:43:39
listen to this, you want, you'll know what we don't know, but France is tied with
00:43:43
Senegal into the 65th minute of this game. And that's kind of been a continuation of
00:43:47
yesterday, uh, as we record this. So it would have been on Monday, four draws. That was the first time since 1958, that
00:43:55
there were four draws on a single day in the world cup. And only the second time that's ever happened.
00:44:00
And those draws, uh, like substantially changed the odds for what were already, I think this
00:44:05
was sort of the favorites were less favored than usual going into the tournament as well.
00:44:10
Like, uh, Spain and France were the co -favorites at about 16% each. If you kind of normalize, uh, everything to
00:44:16
add up to a hundred percent, normally the favorites closer to 20%, uh, rather than around
00:44:21
that 15, 16% mark. And we just saw like Spain's odds of winning the tournament went down by two percentage
00:44:28
points with that one draw in their opening game. Uh, Belgium, uh, was handed a draw.
00:44:33
They dropped in terms of their odds to kind of make a deep run into the knockouts.
00:44:37
Um, uh, Iran even lost, you know, 17 points off of their round of 32 odds because they had that draw, uh, as well.
00:44:45
So it really shows you like for the top teams, the expectation is to get three points, not one point.
00:44:51
But if you were conceived, if you're only getting one point against like the Cabo Verde
00:44:55
of your group, because everybody else is going to play those teams in your group, by
00:44:59
the way, you have to remember, even though it's much easier now to get out of the groups than it used to be, uh,
00:45:04
sometimes in the top eight, uh, third place teams can make it out. In addition to the top two teams in
00:45:10
each group, you still have to get the points that you're expected to get against the
00:45:14
weaker teams in your group to be able to kind of stay on pace with everybody else.
00:45:18
Cause the assumption is those draws are not going to continue and you're going to be
00:45:22
kind of left out in the cold because you only got one point against Cabo Verde and everybody else got three points in their
00:45:28
matches against them. So it does matter a lot. And we're already seeing some pretty big shifts
00:45:32
in the odds early, like just less than a week, just a few days into the tournament.
00:45:37
Speaker 3 I think it speaks to how just kind of stochastic this sport is and that, you know, even a top team can
00:45:44
struggle. I mean, the other match that we haven't talked about, cause it actually had scoring in
00:45:49
it was Australia upset Turkey to nothing. And I kind of like, I went to kind of quote, unquote, the box score for
00:45:55
this match and Turkey had possession 63% of the time and had 28 shots to Australia's a lot of shots in 28 shots
00:46:05
to Australia's eight and Australia one to nothing. So it's kind of like Australia have good
00:46:11
shots. I mean, what was the XG? I mean, that's really the issue. Yeah. I, I guess, I guess that's right.
00:46:17
But I think it just speaks to kind of like, you can kind of dominate, you know, like I watched, I watched both the
00:46:22
Spain one against Cape Verde and, you know, they were just basically in the Cape Verde
00:46:26
zone the entire time and couldn't convert. And I watched, you know, a day earlier,
00:46:30
two days earlier, Germany, Curacao, basically the same game, except the, the Germany goals went in
00:46:36
and it ended up being seven one. And so like, you know, I don't know how much it's just sort of like, you
00:46:42
know, like if there's something systematic here, or we're just sort of seeing kind of puts
00:46:47
a little less puts on realizations than we should for our puts on rates here. And I'll just throw in that.
00:46:52
I think the, the public tends to overreact to this, these early pieces of information, but
00:46:58
that's something that we can hash out later. Can you remark on the Americans? You have anything interesting to say about their
00:47:05
performance or it's just a relevancy at this point? Well, they're, they were relevant in the sense
00:47:11
that, amidst a tournament where all of the other favorites seem to be, except Germany, as
00:47:16
you mentioned, who loves to win matches seven to one in their history. But against a lot of the other favorites
00:47:24
are kind of top tier teams. Not that we even thought the Americans might be like borderline that going into the tournament,
00:47:30
certainly not among the absolute favorites. They were the ones that probably were, were
00:47:36
them and Germany showed out the most in terms of just being like most impressive in
00:47:41
their win. So, you know, the hype train is going to pick up for sure. And I'm sure that there will be some,
00:47:48
some moments of maybe coming back down to earth at different times, but they couldn't have
00:47:53
asked for a better start. I think that was sort of also, and the way that some of the goals, they
00:47:57
were kind of heroic goals. They were goals where one guy was kind of taking on multiple different defenders and sort
00:48:03
of, you know, doing some pretty precision strikes to make it happen. And they didn't let their foot off the
00:48:08
gas for one second. They literally scored in extra time in the last like 30 seconds of the match to
00:48:14
get that fourth goal, which normally you would say is unsportsmanlike and running up the score.
00:48:20
But I think in a sport like soccer, where we've just been talking about how hard
00:48:23
goals are to come by, you take every goal that you can get and you make no apologies for it.
00:48:28
And so I mean, they matter for tiebreaker and they're tie breakers. Yeah. It is consequential kind of beyond our, our
00:48:35
little like prediction exercises. Right. Well, that was a, that's a great point to end on.
00:48:39
I want to thank Neil for Neil Payne for joining us this hour. He'll be coming back to Philly.
00:48:44
So we expect to see you more in person. And that's great for the show. I'll thank Shane and Cade.
00:48:50
Eric is away doing, as Shane likes to say, Eric Bradlow things. I want to thank our producers, Jacob Grodnick,
00:48:57
Deep Patel, Marissa Renna for doing the things that they do to help make this show
00:49:03
run every single week. We are off next week. So in a couple of weeks, there'll be
00:49:06
a lot more information, lots to talk about. Maybe we'll even mention the word baseball in
00:49:11
two weeks, which would be exciting. We have heading to, we're actually in the doldrums of baseball.
00:49:16
So maybe it was fitting that we not talk about it at all this week. But hope you guys, our listeners come back
00:49:23
and join us in two weeks. We'll be running a rerun next week. Thanks, everyone. And enjoy your sports, enjoy your statistics, and
00:49:29
have a good week.