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I did a quick analysis of the Charlotte Sting, who went 110 to start
the season and 174 to end the season, to finish at 1814. I'd like
to do a historical search for streaks of this kind in any league, but
knowing that such a search takes time and doing math doesn't, I
figured I'd present the odds of such a thing occurring.
Using basic binomial probability theory, the odds of a true 0.563 team
going 110 or worse over 11 games is 0.17% (not 17%, 0.17%). Really
low. The chances of a true 0.563 team going 174 or better in 21
games is 1.67%. The odds of having both things happening is 0.00285%
(the two numbers multiplied together), ridiculously low. I don't
think I did anything wrong. If anything, a true Bayesian analysis
would arrive at a lower number (it doesn't assume that the team is
truly 0.563).
So, now can anyone help me find the history of teams that might have
had such streaks in one season? This is truly amazing. (The odds
that the LA Sparks would go 180 is 9.04%, fairly high compared to
what Charlotte did.) I can look later at the game/personnel reasons
why Charlotte did this (someone is doing some of this analysis for
me).
Dean Oliver
Journal of Basketball Studies  View Source
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I can think of a couple of less dramatic but still streaky teams.
the 9596 cavs made the playoffs after a 07 (or 8) start. AFter a
really mediocre start, the Houston Rockets of 9091 had a ridiculous
hot streak midseason after Hakeem fractured his eye socket (courtesy
of Bill Cartwright's elbow).
 In APBR_analysis@y..., "Dean Oliver" <deano@t...> wrote:
>
> I did a quick analysis of the Charlotte Sting, who went 110 to
start
> the season and 174 to end the season, to finish at 1814. I'd
like
> to do a historical search for streaks of this kind in any league,
but
> knowing that such a search takes time and doing math doesn't, I
> figured I'd present the odds of such a thing occurring.
>
> Using basic binomial probability theory, the odds of a true 0.563
team
> going 110 or worse over 11 games is 0.17% (not 17%, 0.17%).
Really
> low. The chances of a true 0.563 team going 174 or better in 21
> games is 1.67%. The odds of having both things happening is
0.00285%
> (the two numbers multiplied together), ridiculously low. I don't
> think I did anything wrong. If anything, a true Bayesian analysis
> would arrive at a lower number (it doesn't assume that the team is
> truly 0.563).
>
> So, now can anyone help me find the history of teams that might
have
> had such streaks in one season? This is truly amazing. (The odds
> that the LA Sparks would go 180 is 9.04%, fairly high compared to
> what Charlotte did.) I can look later at the game/personnel
reasons
> why Charlotte did this (someone is doing some of this analysis for
> me).
>
> Dean Oliver
> Journal of Basketball Studies  View Source
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On Thu, 16 Aug 2001, Dean Oliver wrote:
>
Though you conclusion is correct, I don't think the formula you used is
> I did a quick analysis of the Charlotte Sting, who went 110 to start
> the season and 174 to end the season, to finish at 1814. I'd like
> to do a historical search for streaks of this kind in any league, but
> knowing that such a search takes time and doing math doesn't, I
> figured I'd present the odds of such a thing occurring.
>
> Using basic binomial probability theory, the odds of a true 0.563 team
> going 110 or worse over 11 games is 0.17% (not 17%, 0.17%). Really
> low. The chances of a true 0.563 team going 174 or better in 21
> games is 1.67%. The odds of having both things happening is 0.00285%
> (the two numbers multiplied together), ridiculously low. I don't
> think I did anything wrong. If anything, a true Bayesian analysis
> would arrive at a lower number (it doesn't assume that the team is
> truly 0.563).
>
> So, now can anyone help me find the history of teams that might have
> had such streaks in one season? This is truly amazing. (The odds
correct. E.g. if a team goes 110, with a true probability of winning of
.563, then although that event may have a 0.17% probability of occuring,
the next event, going 174, has a *100%* probability of occuring. Because
once a team goes 110, if it's going to end up 1814, it HAS to go 174
the rest of the way.
I.e. we're looking at a team with a fixed 1814 record (not a random
sample of 32 games), and have to use "sampling without replacement"
methods rather than "sampling with replacement", as the binomial equation
assumes.
I think (I'm not sure) that the correct formula is along these lines:
there are 32 games, and 18 victories. If we look at the first 11 games,
and then the last 21 games, let's call those the "first portion" and "last
portion" of the season. How many different combinations are there of
winning 0 or 1 games in the first portion (and therefore 17 or 18 in the
last portion)?
A bit of notation: xCy (pronounced "x choose y") is the combinatoric
function [more commonly written like this: ( x ) ].
y
The formula for xCy is x!/(y!(xy)!), where ! means factorial.
Let "w" stand for the number of wins in the first portion of the season.
Then, looking at the first portion, there are 11Cw ways of winning w games
in the first portion. The Sting will therefore have won 18w games in the
last portion of the season, with 21C(18w) ways of doing this. Total
number of combinations for w wins is:
11Cw * 21C(18w)
For w = 0 and 1, this is
11C0 * 21C18 plus
11C1 * 21C17
which is
1 * (21*20*19/3*2*1) plus
11 * (21*20*19*18/4*3*2*1)
or
1,330 plus 65,835 =
67,165 ways for the Sting to win 0 or 1 games in their first 11
How many combinations of 18 wins and 14 defeats are there? The answer is
32C18, which is 471,435,600 (there's danger of memory overflows with
factorials this big, so I confirmed this calculation by summing all the
11Cw * 21C(18w) values for w = 0 to 11).
So the probability of the Sting winning 0 or 1 games in their first 11 is
65,385 / 471,435,600 = .00001425, or .001425%.
This is exactly half of the figure that you came up with, which is
probably not a coincidence, though I can't figure out what the reason for
the relationship would be. (Possibly, either you or I made an arithmetic
mistake somewhere.)
MKT  View Source
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I once looked at the question of whether baseball teams were "streaky"
from a mathematical point of view. For anybody familiar with generating
functions I'd be happy to explain the basic approach, though I don't have
time now (work keeps me *very* busy at the moment).
However, the correct mathematical approach (which can be exact or
approximate, as this approach will give you an series to sum for the
probability) is to, in this case, assume that there are randomly 18 wins
and 14 losses put together and to look at all 32!/(18!14!) combinations
for how many have a longest streak of exactly N wins or losses. This can
be solved directly if in a messy way, and I'll see if I still have my
program for summing the series that I wrote a few years ago.
If I don't, then I might be able to rewrite it if I have time.
The answer for baseball, if you're curious, was that teams have many more
streaks than expected of length 34, many fewer of length 5 or 6, and
many more of length 8+.
This can be explained to a large extent by noting that every 5 games, a
team will have its best and its worst pitchers both pitching, and a team's
performance is most sensitive (by far!) to its starting pitcher.
In basketball, I might guess that there would be fewer streaks than
expected of any length greater than about 3 because the homestands are so
short and because basketball is so sensitive to the home team. For
example, in baseball when I did my study, the home team was .511 in the
previous decade. In basketball I don't know that number, but from seeing
the best teams in the league frequently just break .500 on the road
(particularly the Celtics back when they were very good), I might guess
the home winning percentage to be around .625 or so, good enough to skew
this sort of result.
Then again, the other major difference is that in baseball the best team
in the league will lose to the worst team about 1/3 of the time. In
basketball, this would be more like 1/20 of the time I believe, or
certainly more than their records alone would indicate. So while in
baseball I could generally make the assumption that beyond the length of
one 34 game series (the reason for more of those streaks IMO) the
schedule was about constant, in basketball this is not true.
Any thoughts?
>
>
> On Thu, 16 Aug 2001, Dean Oliver wrote:
>
> >
> > I did a quick analysis of the Charlotte Sting, who went 110 to start
> > the season and 174 to end the season, to finish at 1814. I'd like
> > to do a historical search for streaks of this kind in any league, but
> > knowing that such a search takes time and doing math doesn't, I
> > figured I'd present the odds of such a thing occurring.
> >
> > Using basic binomial probability theory, the odds of a true 0.563 team
> > going 110 or worse over 11 games is 0.17% (not 17%, 0.17%). Really
> > low. The chances of a true 0.563 team going 174 or better in 21
> > games is 1.67%. The odds of having both things happening is 0.00285%
> > (the two numbers multiplied together), ridiculously low. I don't
> > think I did anything wrong. If anything, a true Bayesian analysis
> > would arrive at a lower number (it doesn't assume that the team is
> > truly 0.563).
> >
> > So, now can anyone help me find the history of teams that might have
> > had such streaks in one season? This is truly amazing. (The odds
>
> Though you conclusion is correct, I don't think the formula you used is
> correct. E.g. if a team goes 110, with a true probability of winning of
> .563, then although that event may have a 0.17% probability of occuring,
> the next event, going 174, has a *100%* probability of occuring. Because
> once a team goes 110, if it's going to end up 1814, it HAS to go 174
> the rest of the way.
>
> I.e. we're looking at a team with a fixed 1814 record (not a random
> sample of 32 games), and have to use "sampling without replacement"
> methods rather than "sampling with replacement", as the binomial equation
> assumes.
>
>
> I think (I'm not sure) that the correct formula is along these lines:
> there are 32 games, and 18 victories. If we look at the first 11 games,
> and then the last 21 games, let's call those the "first portion" and "last
> portion" of the season. How many different combinations are there of
> winning 0 or 1 games in the first portion (and therefore 17 or 18 in the
> last portion)?
>
> A bit of notation: xCy (pronounced "x choose y") is the combinatoric
> function [more commonly written like this: ( x ) ].
> y
>
> The formula for xCy is x!/(y!(xy)!), where ! means factorial.
>
> Let "w" stand for the number of wins in the first portion of the season.
> Then, looking at the first portion, there are 11Cw ways of winning w games
> in the first portion. The Sting will therefore have won 18w games in the
> last portion of the season, with 21C(18w) ways of doing this. Total
> number of combinations for w wins is:
>
> 11Cw * 21C(18w)
>
> For w = 0 and 1, this is
>
> 11C0 * 21C18 plus
> 11C1 * 21C17
>
>
> which is
>
> 1 * (21*20*19/3*2*1) plus
> 11 * (21*20*19*18/4*3*2*1)
>
> or
>
> 1,330 plus 65,835 =
> 67,165 ways for the Sting to win 0 or 1 games in their first 11
>
>
> How many combinations of 18 wins and 14 defeats are there? The answer is
> 32C18, which is 471,435,600 (there's danger of memory overflows with
> factorials this big, so I confirmed this calculation by summing all the
> 11Cw * 21C(18w) values for w = 0 to 11).
>
> So the probability of the Sting winning 0 or 1 games in their first 11 is
> 65,385 / 471,435,600 = .00001425, or .001425%.
>
>
> This is exactly half of the figure that you came up with, which is
> probably not a coincidence, though I can't figure out what the reason for
> the relationship would be. (Possibly, either you or I made an arithmetic
> mistake somewhere.)
>
>
>
> MKT
>
>
> To unsubscribe from this group, send an email to:
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>
>
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>
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 In APBR_analysis@y..., "Michael K. Tamada" <tamada@o...> wrote:>
is
> Though you conclusion is correct, I don't think the formula you used
> correct. E.g. if a team goes 110, with a true probability of
winning of
> .563, then although that event may have a 0.17% probability of
occuring,
> the next event, going 174, has a *100%* probability of occuring.
Because
> once a team goes 110, if it's going to end up 1814, it HAS to go
174
> the rest of the way.
The multiplication of the two numbers doesn't seem quite right. My
>
calculations would have been the same (except for the multiplication)
if it was a team that went 180140. The assumption that it is a 0.563
team is where traditional statistics fail in comparison to Bayesian
stats. The bigger question that Bayesian can help with is whether
this team truly is a 0.563 team. Their victory over Cleveland last
night suggests even further that they're better than a 0.563 team.
I just use the BINOMDIST() function in excel to calculate the
probabilities of exceedences. This function assumes a fixed
probability of winning (0.563 in this case). Then it calculates the
chances of a certain number of victories in a certain number of games.
What you're looking at are the number of ways to get to 1814 and the
chances that they do it the way they did. That is probably a better
way to calculate the odds of both such streaks occurring in the
season.
> I.e. we're looking at a team with a fixed 1814 record (not a random
equation
> sample of 32 games), and have to use "sampling without replacement"
> methods rather than "sampling with replacement", as the binomial
> assumes.
11 is
>
> So the probability of the Sting winning 0 or 1 games in their first
> 65,385 / 471,435,600 = .00001425, or .001425%.
reason for
>
>
> This is exactly half of the figure that you came up with, which is
> probably not a coincidence, though I can't figure out what the
> the relationship would be. (Possibly, either you or I made an
arithmetic
> mistake somewhere.)
Hmm. You calculate lower odds than I. I would have expected higher,
if anything. Either way, we seem to be saying that the odds of such a
season (assuming that 0.563 is their true p_win) are ridiculously low.
This is going to be the case even if you look at home/road
distribution or at the quality of competition (which was something of
a factor for Charlotte).
(Ignore my comment in my previous email about 0.17% vs. 0.14%  I was
trying to work on memory and mine is horrible.)
The apparent big factor in turning Charlotte around was improved
defense. This seemed to have been sparked by placing Tammy
SuttonBrown in the starting lineup over Machanguana (who they got
for defense, ironically). The team was 163 with her starting, I
believe (I can check tonight). Allison Feaster, despite her 3point
shooting, might also have been most influential on the defensive
end, but I don't have a lot of evidence for that. On the offensive
side, somehow they got Dawn Staley to cut down on her turnovers, but
that was not as big as the defensive turnaround.
Dean Oliver
Journal of Basketball Studies  View Source
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 In APBR_analysis@y..., Charles Steinhardt <charles@p...> wrote:>
"streaky"
> I once looked at the question of whether baseball teams were
> from a mathematical point of view. For anybody familiar with
generating
> functions I'd be happy to explain the basic approach, though I don't
have
> time now (work keeps me *very* busy at the moment).
Horrible how work gets in the way of actual quality science.
>
>
more
> The answer for baseball, if you're curious, was that teams have many
> streaks than expected of length 34, many fewer of length 5 or 6,
and
> many more of length 8+.
games, a
>
> This can be explained to a large extent by noting that every 5
> team will have its best and its worst pitchers both pitching, and a
team's
> performance is most sensitive (by far!) to its starting pitcher.
Pitching and the 45 man rotation really is important in analyzing
>
baseball streaks. I can't think of any other sports like this.
> In basketball, I might guess that there would be fewer streaks than
are so
> expected of any length greater than about 3 because the homestands
> short and because basketball is so sensitive to the home team. For
the
> example, in baseball when I did my study, the home team was .511 in
> previous decade. In basketball I don't know that number, but from
seeing
> the best teams in the league frequently just break .500 on the road
guess
> (particularly the Celtics back when they were very good), I might
> the home winning percentage to be around .625 or so, good enough to
skew
> this sort of result.
Basketball is between 58% and about 65% typically. It's a strong
>
force. I'd love to understand why it's so much more important. We
can qualitatively say that the fans are much closer, but we should
also be able to say that baseball teams can tailor their teams to the
quirkiness of their ballpark. I guess the emotional power is
stronger.
> Then again, the other major difference is that in baseball the best
team
> in the league will lose to the worst team about 1/3 of the time. In
length of
> basketball, this would be more like 1/20 of the time I believe, or
> certainly more than their records alone would indicate. So while in
> baseball I could generally make the assumption that beyond the
> one 34 game series (the reason for more of those streaks IMO) the
First, a 6517 team vs. a 1765 team should win about 94% of the time,
> schedule was about constant, in basketball this is not true.
so good guess.
Second, in baseball, a 10458 team vs. a 58104 team should win about
76% of the time, so about 3/4, not 2/3.
Basketball streakiness should be pretty much on par with what stats
predict, I'd think. NBA teams don't have too many long homestands
(the 7 game streak John brought up notwithstanding) to bias those
streaks. They don't play too many homehome matchups, so their season
ends up a pretty good random sampling through time. If the East or
West is particularly weak, that might have an effect.
Dean Oliver
Journal of Basketball Studies  View Source
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On Fri, 17 Aug 2001, Dean Oliver wrote:
>  In APBR_analysis@y..., Charles Steinhardt <charles@p...> wrote:
Well, the other work I'm doing is (hopefully) quality science too... :)
> >
> > I once looked at the question of whether baseball teams were
> "streaky"
> > from a mathematical point of view. For anybody familiar with
> generating
> > functions I'd be happy to explain the basic approach, though I don't
> have
> > time now (work keeps me *very* busy at the moment).
> >
>
> Horrible how work gets in the way of actual quality science.
>
> Pitching and the 45 man rotation really is important in analyzing
I might suggest a few things, though admittedly without facts to back them
> baseball streaks. I can't think of any other sports like this.
>
> > In basketball, I might guess that there would be fewer streaks than
> > expected of any length greater than about 3 because the homestands
> are so
> > short and because basketball is so sensitive to the home team. For
> > example, in baseball when I did my study, the home team was .511 in
> the
> > previous decade. In basketball I don't know that number, but from
> seeing
> > the best teams in the league frequently just break .500 on the road
> > (particularly the Celtics back when they were very good), I might
> guess
> > the home winning percentage to be around .625 or so, good enough to
> skew
> > this sort of result.
> >
>
> Basketball is between 58% and about 65% typically. It's a strong
> force. I'd love to understand why it's so much more important. We
> can qualitatively say that the fans are much closer, but we should
> also be able to say that baseball teams can tailor their teams to the
> quirkiness of their ballpark. I guess the emotional power is
> stronger.
>
up. Certainly the proximity of the fans (and buildings designed for
maximum noise  look at the decibel level in Utah during the playoffs
sometime!) helps. Some other ideas:
1) The rotation is an equalizer in baseball. More to the point, the best
teams in basketball have a much better winning percentage than in
baseball.
2) There is no equivalent of the free throw in baseball, and in fact all
new stadia are forced to put some sort of blue/black screen in
straightaway center so that the batter has a good line of sight. Fans can
have a very direct impact in basketball that I'd guess is worth as much as
5 points per game (some in increasing the home FT%, some in decreasing
that of opponents. Maybe somebody has statistics on this?
3) Basketball is by its very nature a fasterpaced game than baseball and
thus more prone to momentumbased runs. In baseball, it's always easy for
the struggling team to take the equivalent of a timeout. Can you imagine
what basketball would be like if there were a 20" timeout after every
posession? Whatever else you'd expect, I'd think that game would be less
prone to long runs. Limited timeouts increase the value of momentum, and
a home crowd is helpful in that respect.
4) Basketball is a younger sport than baseball, and as a result there are
many fewer traditional fans of one team that end up in another market. In
addition, the markets are a little bit better spaced than in baseball and
include a few more cities that only have one sports team. As a result,
95% of the fans at a game will be home fans. I was recently at a
YankeesPhillies game in Philadelphia at which there were 55000 people,
about 40000 of them Yankees fans. In that atmosphere any home crowd
advantage must go away.
5) Basketball generally sells out, whereas baseball only does in select
markets. 10000 people at an Expos game isn't going to provide much of an
advantage, and if anything all those empty seats could be demoralizing.
6) Baseball can tailor its field to one or two hitters, but rarely an
entire lineup of 8 or 9 people as well as pitchers. That is to say, the
park can favor pitching or hitting, and can favor right or lefthanded
hitters, but any pro team has all of those. So this effect should be
minor, if any, and is mostly in relation to how fielders react to
different plays. In addition, with the unbalanced schedule most teams
that visit play there 8 or 9 games a year, and the rest 3 or 4. So there
is plenty of time to adjust to a park for a veteran or even by the end of
a series for a rookie. Incidentally, basketball does have its share of
homecourt advantages if you know where to look: I remember that before
the Celtics moved out of the Garden, there used to be wellplaced/hidden
dead spots in the parquet for example. But certainly it's not as
prevalent  basketball doesn't exactly have ground rules to worry about.
Then again, there were a bunch of complaints a few years ago about playoff
rims (I forget where) being bad for jump shooters and favoring the home
team with a strong inside game. And, in basketball one can tailor the
team's style of play to almost always take inside shots much better than
in baseball one can get a group of players to always hit to left field,
say.
> > Then again, the other major difference is that in baseball the best
Where are you getting these numbers? Either way, the difference is again
> team
> > in the league will lose to the worst team about 1/3 of the time. In
> > basketball, this would be more like 1/20 of the time I believe, or
> > certainly more than their records alone would indicate. So while in
> > baseball I could generally make the assumption that beyond the
> length of
> > one 34 game series (the reason for more of those streaks IMO) the
> > schedule was about constant, in basketball this is not true.
>
> First, a 6517 team vs. a 1765 team should win about 94% of the time,
> so good guess.
>
> Second, in baseball, a 10458 team vs. a 58104 team should win about
> 76% of the time, so about 3/4, not 2/3.
>
in the rotation. That 10458 team usually has starters with the following
records (let's say records when the team has them start, including W/L by
the bullpen)
A: 276
B: 249
C: 1913
D: 1814 (usually a collection of people in this slot at this point)
E: 1616
Some have 3 top starters and two worse ones.
Meanwhile, the worst team will have pretty much the reverse, with those
last couple of slots usually a collection of people promoted and demoted
from starting or from AAA ball. That worst team also will very often
juggle the rotation to try to win one from the best team, as they're not
in a pennant race anyway but know that they'll have more people watching
the game against the good team and want to impress potential fans (and
play the role of spoiler). As a result, at least one of the three games
in the series will give the worst team an even chance or better of
winning, and quite possibly another. As a result, one rarely sees the
best team in the league sweep the worst team, or at least it is more rare
than you might predict.
In basketball, this is not the case. The teams are pretty much the same,
pending injuries/suspensions.
> Basketball streakiness should be pretty much on par with what stats
I might expect otherwise for a few reasons:
> predict, I'd think. NBA teams don't have too many long homestands
> (the 7 game streak John brought up notwithstanding) to bias those
> streaks. They don't play too many homehome matchups, so their season
> ends up a pretty good random sampling through time. If the East or
> West is particularly weak, that might have an effect.
>
1) Prevalence of young players. Meaning that veteran teams should do a
little better in the first half of the season and young teams in the
second half rather than being constant (does somebody have stats on this
one?)
2) Coaches make adjustments including an overhaul of the offense/defense
midseason if they struggle. Should cause the team to play differently,
one way or the other.
3) Relative importance of injuries. Baseball is not so sensitive to the
injury even of a superstar. For example, the Red Sox this year are 6653
without the best pitcher in the game, an allstar catcher, an allstar
shortstop, and a bunch of other players that are important to their team
all for at least a month (and in the case of the SS and C, 3 months).
With no injuries, they should not be better than about 7247, and even
that would be a very impressive record for this group of players.
However, what would the Magic have done with Grant Hill last year? The
Sixers without Iverson? While baseball doesn't have a lower injury rate
than basketball, an injury to one of five starters is more critical, and
thus more likely to affect the team's performance. Particularly an
important starter.
I'd be interested to see some statistics on this, particularly if somebody
actually tried to calculate the effect of different injuries/potential
injuries.
Charles
> Dean Oliver
> Journal of Basketball Studies
>
>
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 In APBR_analysis@y..., Charles Steinhardt <charles@p...> wrote:
>
:)
> Well, the other work I'm doing is (hopefully) quality science too...
Lucky bum.
> 3) Basketball is by its very nature a fasterpaced game than
baseball and
> thus more prone to momentumbased runs. In baseball, it's always
easy for
> the struggling team to take the equivalent of a timeout. Can you
imagine
> what basketball would be like if there were a 20" timeout after
every
> posession? Whatever else you'd expect, I'd think that game would be
less
> prone to long runs. Limited timeouts increase the value of
momentum, and
> a home crowd is helpful in that respect.
I have my doubts about this given the general info suggesting that
>
within game streaks don't exist. (I'm not convinced about the
validity of that research either.)
> 4) Basketball is a younger sport than baseball, and as a result
there are
> many fewer traditional fans of one team that end up in another
market. In
> addition, the markets are a little bit better spaced than in
baseball and
> include a few more cities that only have one sports team. As a
result,
> 95% of the fans at a game will be home fans. I was recently at a
people,
> YankeesPhillies game in Philadelphia at which there were 55000
> about 40000 of them Yankees fans. In that atmosphere any home crowd
You haven't been to Golden St, have you?
> advantage must go away.
>
> 5) Basketball generally sells out, whereas baseball only does in
select
> markets. 10000 people at an Expos game isn't going to provide much
of an
> advantage, and if anything all those empty seats could be
demoralizing.
>
Uh, Golden St. again.
> 6) Baseball can tailor its field to one or two hitters, but rarely
an
> entire lineup of 8 or 9 people as well as pitchers. That is to say,
the
> park can favor pitching or hitting, and can favor right or
lefthanded
> hitters, but any pro team has all of those. So this effect should
be
> minor, if any, and is mostly in relation to how fielders react to
teams
> different plays. In addition, with the unbalanced schedule most
> that visit play there 8 or 9 games a year, and the rest 3 or 4. So
there
> is plenty of time to adjust to a park for a veteran or even by the
end of
> a series for a rookie. Incidentally, basketball does have its share
of
> homecourt advantages if you know where to look: I remember that
before
> the Celtics moved out of the Garden, there used to be
wellplaced/hidden
> dead spots in the parquet for example. But certainly it's not as
about.
> prevalent  basketball doesn't exactly have ground rules to worry
> Then again, there were a bunch of complaints a few years ago about
playoff
> rims (I forget where) being bad for jump shooters and favoring the
home
> team with a strong inside game. And, in basketball one can tailor
the
> team's style of play to almost always take inside shots much better
than
> in baseball one can get a group of players to always hit to left
field,
> say.
Well, other than the old Boston Garden, I don't remember hearing of
>
>
any physical reason for an arena to favor one team over another.
> > First, a 6517 team vs. a 1765 team should win about 94% of the
time,
> > so good guess.
about
> >
> > Second, in baseball, a 10458 team vs. a 58104 team should win
> > 76% of the time, so about 3/4, not 2/3.
Bill James:
> >
>
> Where are you getting these numbers? Either way, the difference is
Win% Team A vs. Team B
>
stats
> > Basketball streakiness should be pretty much on par with what
> > predict, I'd think. NBA teams don't have too many long homestands
season
> > (the 7 game streak John brought up notwithstanding) to bias those
> > streaks. They don't play too many homehome matchups, so their
> > ends up a pretty good random sampling through time. If the East
or
> > West is particularly weak, that might have an effect.
do a
> >
>
> I might expect otherwise for a few reasons:
>
> 1) Prevalence of young players. Meaning that veteran teams should
> little better in the first half of the season and young teams in the
this
> second half rather than being constant (does somebody have stats on
> one?)
Testable. However, the general hypothesis has actually been the other
way around. Young players supposedly hit a wall and do worse in the
2nd half. I tend to think you're right, but have had a hard time
testing it, not having a very uptodate player directory with
birthdays.
>
offense/defense
> 2) Coaches make adjustments including an overhaul of the
> midseason if they struggle. Should cause the team to play
differently,
> one way or the other.
No difference between baseball and basketball on this one.
>
> 3) Relative importance of injuries. Baseball is not so sensitive to
the
> injury even of a superstar. For example, the Red Sox this year are
6653
> without the best pitcher in the game, an allstar catcher, an
allstar
> shortstop, and a bunch of other players that are important to their
team
> all for at least a month (and in the case of the SS and C, 3
months).
> With no injuries, they should not be better than about 7247, and
even
> that would be a very impressive record for this group of players.
The
> However, what would the Magic have done with Grant Hill last year?
> Sixers without Iverson? While baseball doesn't have a lower injury
rate
> than basketball, an injury to one of five starters is more critical,
and
> thus more likely to affect the team's performance. Particularly an
somebody
> important starter.
>
> I'd be interested to see some statistics on this, particularly if
> actually tried to calculate the effect of different
injuries/potential
> injuries.
The problem is always how you replace a superstar. If you replace
them with a bad player, the team gets much worse. The Bulls without
Jordan actually did pretty well the first year, then suffered the
next. The Sixers did play a few without Iverson this year, so we can
check. The Raptors without Vince. I am forming this unjustified
opinion in my head that teams that play 13 games without their
superstar generally do about the same. Teams that play more than
about 10 games without their superstar really start to hurt. I need
to form the hypothesis a little better, but I think I've seen it.
Dean Oliver
Journal of Basketball Studies  View Source
 0 Attachment
Forgot to finish the formula on Win% calculations...
>
is
> > > First, a 6517 team vs. a 1765 team should win about 94% of the
> time,
> > > so good guess.
> > >
> > > Second, in baseball, a 10458 team vs. a 58104 team should win
> about
> > > 76% of the time, so about 3/4, not 2/3.
> > >
> >
> > Where are you getting these numbers? Either way, the difference
>
Win%A_B = [Win%A*(1Win%B)]/[Win%A*(1Win%B)+(1Win%A)*Win%B]
> Bill James:
>
> Win% Team A vs. Team B
>
http://www.rawbw.com/~deano/methdesc.html#matchup
has the detailed info.  View Source
 0 Attachment
Original Message
From: Charles Steinhardt [mailto:charles@...]
2) There is no equivalent of the free throw in baseball, and in fact all
new stadia are forced to put some sort of blue/black screen in
straightaway center so that the batter has a good line of sight. Fans can
have a very direct impact in basketball that I'd guess is worth as much as
5 points per game (some in increasing the home FT%, some in decreasing
that of opponents. Maybe somebody has statistics on this?
[Dean LaVergne] It doesn't seem to hold out. For the last ten years:Free Throw Percentage:Season Away Home Diff
1992 75.62% 76.12% 0.51%
1993 75.19% 75.65% 0.46%
1994 73.60% 73.25% 0.35%
1995 73.49% 73.84% 0.34%
1996 73.91% 74.03% 0.12%
1997 73.98% 73.67% 0.31%
1998 73.54% 73.82% 0.28%
1999 72.29% 73.32% 1.02%
2000 74.55% 75.44% 0.89%
2001 74.48% 74.99% 0.51%
However, free throws attempted seem a little more significant:
Season Away Home Diff % Diff
1992 28,522 30,553 2,031 7.12%
1993 29,715 31,659 1,944 6.54%
1994 28,688 30,131 1,443 5.03%
1995 29,248 30,690 1,442 4.93%
1996 30,587 32,178 1,591 5.20%
1997 29,522 30,272 750 2.54%
1998 30,704 31,799 1,095 3.57%
1999 18,399 19,000 601 3.27%
2000 29,410 30,352 942 3.20%
2001 28,570 29,308 738 2.58%
Dean L  View Source
 0 Attachment
Very interesting (and obviously not what I'd expect looking from the point
of view of a fan).
Anybody know why the FT disparity has dropped (and how strongly that
disparity correlates with winning %)? Have there been new instructions to
officials?
On Fri, 17 Aug 2001, Dean LaVergne wrote:
>
> Original Message
> From: Charles Steinhardt [mailto:charles@...]
>
>
>
>
> 2) There is no equivalent of the free throw in baseball, and in fact all
> new stadia are forced to put some sort of blue/black screen in
> straightaway center so that the batter has a good line of sight. Fans can
> have a very direct impact in basketball that I'd guess is worth as much as
> 5 points per game (some in increasing the home FT%, some in decreasing
> that of opponents. Maybe somebody has statistics on this?
>
>
> [Dean LaVergne] It doesn't seem to hold out. For the last ten years:
>
> Free Throw Percentage:
>
> Season Away Home Diff
> 1992 75.62% 76.12% 0.51%
> 1993 75.19% 75.65% 0.46%
> 1994 73.60% 73.25% 0.35%
> 1995 73.49% 73.84% 0.34%
> 1996 73.91% 74.03% 0.12%
> 1997 73.98% 73.67% 0.31%
> 1998 73.54% 73.82% 0.28%
> 1999 72.29% 73.32% 1.02%
> 2000 74.55% 75.44% 0.89%
> 2001 74.48% 74.99% 0.51%
>
>
> However, free throws attempted seem a little more significant:
>
>
> Season Away Home Diff % Diff
> 1992 28,522 30,553 2,031 7.12%
> 1993 29,715 31,659 1,944 6.54%
> 1994 28,688 30,131 1,443 5.03%
> 1995 29,248 30,690 1,442 4.93%
> 1996 30,587 32,178 1,591 5.20%
> 1997 29,522 30,272 750 2.54%
> 1998 30,704 31,799 1,095 3.57%
> 1999 18,399 19,000 601 3.27%
> 2000 29,410 30,352 942 3.20%
> 2001 28,570 29,308 738 2.58%
>
>
> Dean L
>
>
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