 APBR_analysis

 Restricted Group,
 457 members
Re: [APBR_analysis] Re: Measuring clutch performance
Expand Messages
 View Source
 0 Attachment
 John Maxwell <John.Maxwell@...>
wrote:> > "Clutch" play by a team is often reflected in the
Not only that, baseball teams with a good winning pct.
> percent of close
> > games they win.
>
> Actually, at least in baseball and the WNBA, this
> has not proven to be the
> case. Generally speaking, bad teams win more close
> games than any other kind
> and good teams win fewer close games than any other
> kind.
>
in onerun games generally decline the following
season (as myself and several other White Sox fans
found out this past summer). I suspect the same is
true in the NBA, though I have never looked at the
subject, nor am I aware of anyone who has.
I would have no idea how to analyze which players are
clutch and which ones aren't. Basketball isn't like
baseball where you can just look at what each player
does in each AB and go from there. In basketball
there's defense, rebounding and passing going on in
addition to shooting. Those things would have to be
looked at also, once clutch situations were defined.
I've always felt "clutch" was one of those terms
people used to describe players they wanted to like.
Jerry West was called Mr. Clutch, despite being on the
losing team in eight NBA finals and winning only once.
This isn't to say West wasn't a clutch player. I just
wonder why West got tagged with Mr. Clutch, when it
was Bill Russell who was the biggest winner of that
time. Probably a racial thing.
Ed
__________________________________________________
Do You Yahoo!?
Buy the perfect holiday gifts at Yahoo! Shopping.
http://shopping.yahoo.com  View Source
 0 Attachment
On Sat, 1 Dec 2001, Ed Weiland wrote:
> Not only that, baseball teams with a good winning pct.
I agree 100% with the statements that it is a mistake to look at teams'
> in onerun games generally decline the following
> season (as myself and several other White Sox fans
> found out this past summer). I suspect the same is
> true in the NBA, though I have never looked at the
> subject, nor am I aware of anyone who has.
records in close games, and to try to call the ones with good records
"clutch".
A minor quibble with the argument above however: while it is indeed true
that baseball teams with a good winning pct. in onerun games can be
expected to decline the following season, the same is true of ANY team in
ANY sport in ANY sort of games. Bill James many years ago thought he'd
discovered some profound truth in this and dreamt up some corny name for
it  "The Law of Elastic Reboound" or something  but it's been known
for about a century in statistics as "regression to the mean".
A team which wins 72 games in an NBA season is EXTREMELY likely to have
fewer wins the following season. A team which wins 90% of its 1point
games in a season is extremely likely to win a lower percentage the
following season. A team which wins 70% of its 1point games is very
likely to win a lower pct. the following season. Etc.
So while I agree 100% with both of the statements (that teams' 1run
records have little meaning, except of course to contribute to their
winloss record; and that teams with good 1run records are likely to see
a decline in those records the following season), it is not the case that
the latter statement is evidence in favor of the former statement.
> I would have no idea how to analyze which players are
True enough if we're looking for "total clutchness" but most of the NBA
> clutch and which ones aren't. Basketball isn't like
> baseball where you can just look at what each player
> does in each AB and go from there. In basketball
> there's defense, rebounding and passing going on in
> addition to shooting. Those things would have to be
> looked at also, once clutch situations were defined.
players who are known as clutch are known for being clutch as shooters
during crunch time. Maybe once in a very long while they'll get a
reputation for good D in crunch time (Havlicek steals the ball, Bird
steals the ball), and I can't think of a single player who had a
reputation as a clutch rebounder. Maybe, say, Wilt, Russell, Silas, et
al  but they were simply known as great rebounders period, it's not as
if people thought they only grabbed rebounds during crunch time and
lollygagged the rest of the game.
So to look for clutch players, I think it's an easy step to limit the
search to being a search for clutch *shooters*, and that is a more
limited, specific, easytodefine concept.
> I've always felt "clutch" was one of those terms
I agree with this also, although I would add the following hypothesis:
> people used to describe players they wanted to like.
> Jerry West was called Mr. Clutch, despite being on the
> losing team in eight NBA finals and winning only once.
> This isn't to say West wasn't a clutch player. I just
> wonder why West got tagged with Mr. Clutch, when it
> was Bill Russell who was the biggest winner of that
> time. Probably a racial thing.
some players are given (or demand) the ball a lot in clutch situations.
And they thus shoot a lot of those crucial shots. I have no idea if some
players have a systematically higher probability of making those shots,
but if they take enough of them, some of them will go in. And people will
remember those, and tend to forget the shots that they missed. And that
will lead to the player getting a clutch reputation.
E.g. maybe Jerry West shot in his career 100 clutch shots, and made 47 of
them. That'd be identical to his career shooting percentage (both regular
season and playoff). So unless there's a tendency for clutch shots to
have a lower percentage overall (which actually might be the case), Jerry
West shot no better in clutch situations than in nonclutch. But
sportswriters, fans, and coaches would remember those 47 clutch shots
made, whereas maybe Wilt only made 15 and Gail Goodrich only made 8, and
thus Jerry West would get the reputation as Mr. Clutch.
I would add that the notion that Mike Goodman and others have advocated,
of looking at playoff games as clutch situations, is I think a good one,
and the fact that West's FG% was as high in the playoffs as it was in the
regular season is in itself a fairly remarkable, one might even say
clutch, performance. Especially given that his scoring per game INCRASED.
MKT  View Source
 0 Attachment
 "Michael K. Tamada" <tamada@...> wrote:>
Increased shooting could also be a case of a player
> I would add that the notion that Mike Goodman and
> others have advocated,
> of looking at playoff games as clutch situations, is
> I think a good one,
> and the fact that West's FG% was as high in the
> playoffs as it was in the
> regular season is in itself a fairly remarkable, one
> might even say
> clutch, performance. Especially given that his
> scoring per game INCRASED.
trying to shoulder too much of the load. It's
interesting that in West's case the season his team
finally broke through and won the championship, 1972,
was the only year he averaged fewer points in the
playoffs than the regular season. West also shot only
.376 during the 1972 playoffs, by far the worst
showing of his career. He did post a career playoff
high in assists per game during the '72 playoffs.
Here are some other championship performances:
Wilt in '67 averaged a then careerlow 21.7 ppg in the
playoffs, shot 104 points below his regular season FG
pct. (albeit a morethanadequate .579), but posted a
career high with 9.0 assists per game.
Hakeem in '94 and '95 had FG pct. similar to his
regular season and career totals, but posted two of
his three highest playoff assist per game totals, 4.5
and 4.3 apg, both well above his career playoff
average of 3.3. Hakeem scored 33.0 ppg in the '95
playoffs, so it's not like he was sacrificing his
shots.
I'm not sure if the spike in assists is most
responsible for the championships, but I don't think
it can be ignored. Especially considering that star
players who aren't point guards, but possessed
goodtogreat passing skills like Russell, Barry,
Walton, Bird and Jordan tended to win championships.
Sometimes the the most clutch thing for a player to do
is to get his teammates involved.
btw, I don't mean to knock West as nonclutch. HIs
Laker teams lost three game sevens to the Celtics by a
total of seven points. There had to be some bad luck
involved in all that.
Ed Weiland
__________________________________________________
Do You Yahoo!?
Buy the perfect holiday gifts at Yahoo! Shopping.
http://shopping.yahoo.com  View Source
 0 Attachment
When considering clutch it seems weird to think about players
actually improving over how they would in normal (nonpressure)
situations. Rather, it seems to me that we might better define
clutch by looking at who did not become worse in clutch situations.
How you define clutch situations, incidentally, is a question I can't
really answer.
 In APBR_analysis@y..., Ed Weiland <weiland1029@y...> wrote:
>
>  "Michael K. Tamada" <tamada@o...> wrote:
> >
> > I would add that the notion that Mike Goodman and
> > others have advocated,
> > of looking at playoff games as clutch situations, is
> > I think a good one,
> > and the fact that West's FG% was as high in the
> > playoffs as it was in the
> > regular season is in itself a fairly remarkable, one
> > might even say
> > clutch, performance. Especially given that his
> > scoring per game INCRASED.
>
>
> Increased shooting could also be a case of a player
> trying to shoulder too much of the load. It's
> interesting that in West's case the season his team
> finally broke through and won the championship, 1972,
> was the only year he averaged fewer points in the
> playoffs than the regular season. West also shot only
> .376 during the 1972 playoffs, by far the worst
> showing of his career. He did post a career playoff
> high in assists per game during the '72 playoffs.
>
> Here are some other championship performances:
>
> Wilt in '67 averaged a then careerlow 21.7 ppg in the
> playoffs, shot 104 points below his regular season FG
> pct. (albeit a morethanadequate .579), but posted a
> career high with 9.0 assists per game.
>
> Hakeem in '94 and '95 had FG pct. similar to his
> regular season and career totals, but posted two of
> his three highest playoff assist per game totals, 4.5
> and 4.3 apg, both well above his career playoff
> average of 3.3. Hakeem scored 33.0 ppg in the '95
> playoffs, so it's not like he was sacrificing his
> shots.
>
> I'm not sure if the spike in assists is most
> responsible for the championships, but I don't think
> it can be ignored. Especially considering that star
> players who aren't point guards, but possessed
> goodtogreat passing skills like Russell, Barry,
> Walton, Bird and Jordan tended to win championships.
> Sometimes the the most clutch thing for a player to do
> is to get his teammates involved.
>
> btw, I don't mean to knock West as nonclutch. HIs
> Laker teams lost three game sevens to the Celtics by a
> total of seven points. There had to be some bad luck
> involved in all that.
>
> Ed Weiland
>
> __________________________________________________
> Do You Yahoo!?
> Buy the perfect holiday gifts at Yahoo! Shopping.
> http://shopping.yahoo.com  View Source
 0 Attachment
Dean Oliver wrote:> Ed 
Here's the correlation matrix. (I hope it formats ok.)
>
> Nice. Is there correlation between variables? One that is key to
> understand is whether distance from previous game and days off are
> correlated. A home stand could be hiding some aspect of time off
> between games.
Days Dist Home MatchupP
Dist 0.076
0.000
Home 0.173 0.249
0.000 0.000
MatchupP 0.014 0.021 0.001
0.509 0.315 0.975
PtsDiff 0.060 0.041 0.226 0.465
0.003 0.048 0.000 0.000
Cell Contents: Pearson correlation
PValue
The correlations are generally pretty low.
> (Something also irks me about the p_win variable being
I'm not quite sure what endogenous means. If it means being related to the
> endogenous.)
>
other variables, I'm not quite sure if that's true: the matchup probability
calculation uses only team winning percentage and opponent winning
percentage, neither of which have any relationship to the other variables.
Maybe I misunderstood.
> I know I did a study of time off between games and saw that there is
I did something like that, too. I can't remember which season I used, but I
> an optimal period of time off (more than 2 wasn't good, but neither
> was 0). That would imply a squared term in days off. But I didn't do
> it as rigorously as you did.
>
found that most wins came on 2 day rests (I think). However, I didn't
include any other variables, so I could have just been looking at a
scheduling quirk for that season. I'll probably rerun this study on another
season to see if the results hold. If anyone else wants to give it a shot,
here's a table showing travel distances between NBA cities:
http://members.rogers.com/brothered/junk/TravelDistances.htm
ed  View Source
 0 Attachment
 In APBR_analysis@yahoogroups.com, igor eduardo küpfer
<edkupfer@r...> wrote:> Dean Oliver wrote:
Yeah, pretty low. Probably not much to worry about.
> > Ed 
> >
> > Nice. Is there correlation between variables? One that is key to
> > understand is whether distance from previous game and days off are
> > correlated. A home stand could be hiding some aspect of time off
> > between games.
>
> Here's the correlation matrix. (I hope it formats ok.)
>
> Days Dist Home MatchupP
> Dist 0.076
> 0.000
>
> Home 0.173 0.249
> 0.000 0.000
>
> MatchupP 0.014 0.021 0.001
> 0.509 0.315 0.975
>
> PtsDiff 0.060 0.041 0.226 0.465
> 0.003 0.048 0.000 0.000
>
> Cell Contents: Pearson correlation
> PValue
>
> The correlations are generally pretty low.
>
> > (Something also irks me about the p_win variable being
to the
> > endogenous.)
> >
>
> I'm not quite sure what endogenous means. If it means being related
> other variables, I'm not quite sure if that's true: the matchup
probability
> calculation uses only team winning percentage and opponent winning
variables.
> percentage, neither of which have any relationship to the other
> Maybe I misunderstood.
Basically, I assume you use 2004 winloss records to evaluate p_win.
Well, those winloss records are built from the things you are looking
at  whether a team is at home or on the road, how many days off,
their whole schedule. Maybe the winloss records of teams prior to
the matchup of the game you're looking at is exogenous (known a
priori). i.e., San Antonio faces the Lakers when one team is 125 and
the other is 107  use those records rather than their end of season
records. Maybe that's what you're doing, I dunno. I have doubt that
it would make a significant difference.
>
used, but I
>
> > I know I did a study of time off between games and saw that there is
> > an optimal period of time off (more than 2 wasn't good, but neither
> > was 0). That would imply a squared term in days off. But I didn't do
> > it as rigorously as you did.
> >
>
> I did something like that, too. I can't remember which season I
> found that most wins came on 2 day rests (I think). However, I didn't
another
> include any other variables, so I could have just been looking at a
> scheduling quirk for that season. I'll probably rerun this study on
> season to see if the results hold.
Just include the variable Days^2 in your regression and rerun that.
See what comes out significant.
DeanO
Dean Oliver
Author, Basketball on Paper
http://www.basketballonpaper.com
"Oliver goes beyond stats to dissect what it takes to win. His breezy
style makes for enjoyable reading, but there are plenty of points of
wisdom as well. This book can be appreciated by fans, players,
coaches and executives, but more importantly it can be used as a text
book for all these groups. You are sure to learn something you didn't
know about basketball here." Pete Palmer, coauthor, Hidden Game of
Baseball and Hidden Game of Football  View Source
 0 Attachment
Dean Oliver wrote:
<snip>
>>>
Ah. I will try to use contemporary win/loss records in my next analysis.
>>
>> I'm not quite sure what endogenous means. If it means being related
> to the
>> other variables, I'm not quite sure if that's true: the matchup
>> probability calculation uses only team winning percentage and
>> opponent winning percentage, neither of which have any relationship
>> to the other variables. Maybe I misunderstood.
>
> Basically, I assume you use 2004 winloss records to evaluate p_win.
> Well, those winloss records are built from the things you are looking
> at  whether a team is at home or on the road, how many days off,
> their whole schedule. Maybe the winloss records of teams prior to
> the matchup of the game you're looking at is exogenous (known a
> priori). i.e., San Antonio faces the Lakers when one team is 125 and
> the other is 107  use those records rather than their end of season
> records. Maybe that's what you're doing, I dunno. I have doubt that
> it would make a significant difference.
<snip>
> Just include the variable Days^2 in your regression and rerun that.
You'll have to help me out here, as I don't know anything about transforming
> See what comes out significant.
>
data. Do you mean include Days^2 in addition to Days or instead of Days? I
did both, and here's how they turned out:
PtsDiff =  21.2 + 2.59 Days +0.000021 Dist + 5.59 Home + 30.0 MatchupP 
0.394 Days_2
Predictor Coef SE Coef T P
Constant 21.169 1.195 17.71 0.000
Days 2.5924 0.7956 3.26 0.001
Dist 0.0000214 0.0003555 0.06 0.952
Home 5.5937 0.4858 11.51 0.000
MatchupP 29.991 1.134 26.45 0.000
Days_2 0.3938 0.1350 2.92 0.004
PtsDiff =  18.1 +0.000099 Dist + 5.87 Home + 29.9 MatchupP + 0.0236 Days_2
Predictor Coef SE Coef T P
Constant 18.0811 0.7304 24.76 0.000
Dist 0.0000990 0.0003555 0.28 0.781
Home 5.8677 0.4794 12.24 0.000
MatchupP 29.890 1.136 26.32 0.000
Days_2 0.02362 0.04275 0.55 0.581
ed  View Source
 0 Attachment
 In APBR_analysis@yahoogroups.com, igor eduardo küpfer
<edkupfer@r...> wrote:> >> I'm not quite sure what endogenous means. If it means being
I should note that, not being an economist, I like throwing this word
around without as great an appreciation or understanding for it as I
should.
> You'll have to help me out here, as I don't know anything about
transforming
> data. Do you mean include Days^2 in addition to Days or instead of
Days? I
> did both, and here's how they turned out:
Include both, which you did in the first set below. Looks like it got
you significant on both days and days^2. And the signs are as
expected. It suggests optimal rest at about 3 days, longer than the 2
days we saw before. (Potentially important for the talk about rust vs
rest, esp if the Lakers wrap up on M.) Let me also ask  is Days = 0
if a team plays back to back nights or is that Days = 1?
I'm sure there are other ways to manipulate things, but this looks
like a pretty good thing. I'm saving it.
Home is a binary 1/0 indicator for home/road, resp?
>
MatchupP 
> PtsDiff =  21.2 + 2.59 Days +0.000021 Dist + 5.59 Home + 30.0
> 0.394 Days_2
DeanO
>
> Predictor Coef SE Coef T P
> Constant 21.169 1.195 17.71 0.000
> Days 2.5924 0.7956 3.26 0.001
> Dist 0.0000214 0.0003555 0.06 0.952
> Home 5.5937 0.4858 11.51 0.000
> MatchupP 29.991 1.134 26.45 0.000
> Days_2 0.3938 0.1350 2.92 0.004
>
>
Dean Oliver
Author, Basketball on Paper
http://www.basketballonpaper.com
"Dean Oliver looks at basketball with a fresh perspective. If you
want a new way to analyze the game, this book is for you. You'll
never watch a game the same way again. We use his stuff and it helps
us." Yvan Kelly, Scout, Seattle Sonics  View Source
 0 Attachment
Okay, I ran the test again, this time using 0304 results. Before I show you
what I got, let me address a couple of things.
Dean Oliver wrote:>  In APBR_analysis@yahoogroups.com, igor eduardo küpfer
Hell, that's nothing. Once during the course of an argument with an
> <edkupfer@r...> wrote:
>>>> I'm not quite sure what endogenous means. If it means being
>
> I should note that, not being an economist, I like throwing this word
> around without as great an appreciation or understanding for it as I
> should.
exgirlfriend I used the word "heretofore." I still don't know what it
means.
>
Questions: I don't understand a couple of things about the squared term. How
>> You'll have to help me out here, as I don't know anything about
>> transforming data. Do you mean include Days^2 in addition to Days or
>> instead of Days? I did both, and here's how they turned out:
>
> Include both, which you did in the first set below. Looks like it got
> you significant on both days and days^2. And the signs are as
> expected. It suggests optimal rest at about 3 days, longer than the 2
> days we saw before. (Potentially important for the talk about rust vs
> rest, esp if the Lakers wrap up on M.)
did you know that squaring the Days variable would give a better fit? And,
just exactly how does it suggest the optimal 3 day rest?
> Let me also ask  is Days = 0
The latter. I am subtracting game dates from each other.
> if a team plays back to back nights or is that Days = 1?
>
> I'm sure there are other ways to manipulate things, but this looks
Yes.
> like a pretty good thing. I'm saving it.
>
> Home is a binary 1/0 indicator for home/road, resp?
Okay. Here are the results for 0304. For the Matchup Probability, I used
the team records heading into the game. For example, for two teams playing
their first games of the season, I would use 00 records for each team in my
probability calculation. Interestingly, this doesn't seem to affect the
regression results too much. The effect of Days between games is reduced in
this sample. Weird.
PtsDiff =  13.6 + 7.31 Home +0.000027 Distance + 18.1 WinProb + 0.722
Days  0.122 Days^2
Predictor Coef SE Coef T P
Constant 13.582 1.173 11.58 0.000
Home 7.3056 0.5010 14.58 0.000
Distance 0.0000269 0.0003734 0.07 0.943
WinProb 18.054 1.163 15.53 0.000
Days 0.7221 0.7202 1.00 0.316
Days^2 0.1216 0.1138 1.07 0.286
S = 11.48 RSq = 16.7% RSq(adj) = 16.6%
Analysis of Variance
Source DF SS MS F P
Regression 5 62072 12414 94.19 0.000
Residual Error 2343 308806 132
Total 2348 370877
ed  View Source
 0 Attachment
 In APBR_analysis@yahoogroups.com, igor eduardo küpfer
<edkupfer@r...> wrote:> Okay, I ran the test again, this time using 0304 results. Before I
show you
> what I got, let me address a couple of things.
I've had those moments, often inspired by arguments with soontobe
>
> Dean Oliver wrote:
> >  In APBR_analysis@yahoogroups.com, igor eduardo küpfer
> > <edkupfer@r...> wrote:
> >>>> I'm not quite sure what endogenous means. If it means being
> >
> > I should note that, not being an economist, I like throwing this word
> > around without as great an appreciation or understanding for it as I
> > should.
>
> Hell, that's nothing. Once during the course of an argument with an
> exgirlfriend I used the word "heretofore." I still don't know what it
> means.
exgirlfriends. What the hell is "visavis"?
> >
term. How
> >> You'll have to help me out here, as I don't know anything about
> >> transforming data. Do you mean include Days^2 in addition to Days or
> >> instead of Days? I did both, and here's how they turned out:
> >
> > Include both, which you did in the first set below. Looks like it got
> > you significant on both days and days^2. And the signs are as
> > expected. It suggests optimal rest at about 3 days, longer than the 2
> > days we saw before. (Potentially important for the talk about rust vs
> > rest, esp if the Lakers wrap up on M.)
>
> Questions: I don't understand a couple of things about the squared
> did you know that squaring the Days variable would give a better
fit? And,
> just exactly how does it suggest the optimal 3 day rest?
I didn't _know_ it would give a better fit. I hoped it would because
of what we were observing  that there was an optimal number of days
off. The only way to get an optimum out of a regression is to throw
in higher order terms. Usually a squared term is plenty. It doesn't
answer the bigger question of whether teams get rusty, though. It
suggests an answer (another lesson in how to lie with statistics), one
that I wouldn't trust from this study.
Look at the results of your regression. Take just the Days and Days^2
coefficients and calculate the marginal net points those terms
contribute for Days = 1, 2, 3, 4, etc. You'll see a max at 3.
>
So 2 days of rest is optimal.
> > Let me also ask  is Days = 0
> > if a team plays back to back nights or is that Days = 1?
> >
>
> The latter. I am subtracting game dates from each other.
>
> > I'm sure there are other ways to manipulate things, but this looks
used
> > like a pretty good thing. I'm saving it.
> >
> > Home is a binary 1/0 indicator for home/road, resp?
>
> Yes.
>
> Okay. Here are the results for 0304. For the Matchup Probability, I
> the team records heading into the game. For example, for two teams
playing
> their first games of the season, I would use 00 records for each
team in my
> probability calculation.
I was curious to see how you handled the early games of the season,
especially the times where one team was undefeated. It looks like you
used Pythagorean projections, rather than real records anyway. That
helps. But 00 usually requires some other assumption, like a
Bayesian prior that carries through the first few games.
>Interestingly, this doesn't seem to affect the
reduced in
> regression results too much. The effect of Days between games is
> this sample. Weird.
Not sure what to make of that weakening of the Days. What was the R2
of the previous version? We may have to improve the prior matchup P
to get back a reasonable estimate of the value of Days. If you just
look at games beyond the first 20 in the season, does r2 get better
and does Days become more significant?
>
DeanO
>
> PtsDiff =  13.6 + 7.31 Home +0.000027 Distance + 18.1 WinProb + 0.722
> Days  0.122 Days^2
>
> Predictor Coef SE Coef T P
> Constant 13.582 1.173 11.58 0.000
> Home 7.3056 0.5010 14.58 0.000
> Distance 0.0000269 0.0003734 0.07 0.943
> WinProb 18.054 1.163 15.53 0.000
> Days 0.7221 0.7202 1.00 0.316
> Days^2 0.1216 0.1138 1.07 0.286
>
> S = 11.48 RSq = 16.7% RSq(adj) = 16.6%
>
> Analysis of Variance
>
> Source DF SS MS F P
> Regression 5 62072 12414 94.19 0.000
> Residual Error 2343 308806 132
> Total 2348 370877
>
Dean Oliver
Author, Basketball on Paper
http://www.basketballonpaper.com
"Excellent writing. There are a lot of math guys who just rush from
the numbers to the conclusion. . .they'll tell you that Shaq is a real
good player but his team would win a couple more games a year if he
could hit a free throw. Dean is more than that; he's really
struggling to understand the actual problem, rather than the
statistical afterimage of it. I learn a lot by reading him." Bill
James, author Baseball Abstract  View Source
 0 Attachment
Replies to DanR and DeanO
Dean Oliver wrote:
> I was curious to see how you handled the early games of the season,
I'm sorry I didn't make it clear. For the second analysis (on the 0304
> especially the times where one team was undefeated. It looks like you
> used Pythagorean projections, rather than real records anyway. That
> helps. But 00 usually requires some other assumption, like a
> Bayesian prior that carries through the first few games.
regular season results) I didn;t use Pythagorean records. I instead used
each team's record to date. Two teams facing each other on the first game of
the season each had a 0.5 chance of winning that game, since they had
identical 00 records.
The results don't deviate much from my first analysis, which used season's
end Pythagorean Win%. I supposed this is because after the first part of the
season, each team's Pyth is relatively stable. I must admit to being a
little surprised by this, though.
> Not sure what to make of that weakening of the Days. What was the R2
r = 0.06 for 0001, r = 0.03 for this season.
> of the previous version?
> We may have to improve the prior matchup P
Games 220: r = 0.048 (p = 0.261)
> to get back a reasonable estimate of the value of Days. If you just
> look at games beyond the first 20 in the season, does r2 get better
> and does Days become more significant?
>
Games 2182: r = 0.024 (p = 0.314
dan_t_rosenbaum wrote:
> Interesting results. Here are a couple of suggestions.
Okay, I tried this. The regression outputs follow. I'm afraid that I don't
>
> I would leave out the MatchupP variable, since it is a lot like the
> dependent variable. Including it probably increases Rsquared a
> lot, but probably doesn't do much else. (All in all, it probably is
> pretty harmless, since it unlikely to be correlated with your
> independent variables.)
>
> Another option with your day variable is to enter it as a series of
> dummy variables.
>
> DAY0  equals 1 if 0 days of rest, 0 otherwise
> DAY1  equals 1 if 1 day of rest , 0 otherwise
> DAY2  equals 1 if 2 days of rest, 0 otherwise
> DAY3  equals 1 if 3 days of rest, 0 otherwise
> DAY4+  equals 1 if 4 days or more of rest, 0 otherwise
>
> Then run the regression leaving one of those variables out.
>
> If, for example, you left DAY0 out of the regression, the DAY1
> coefficient would give you the effect of playing on one day's rest
> versus playing in a backtoback.
>
> The DAY2 coefficent would give you the effect of playing on two
> days' rest versus playing in a backtoback.
>
> The DAY3 coefficent would give you the effect of playing on three
> days' rest versus playing in a backtoback.
>
> The DAY4+ coefficent would give you the effect of playing on four or
> more days' rest versus playing in a backtoback.
>
know how to interpret the results  very few of the coefficients are
significant. (Note that I use Day1 to mean 1 day between games, ie back to
back  the 1 does not mean "rest days.")
Ommitting Days1
Predictor Coef SE Coef T P
Constant 3.8515 0.5971 6.45 0.000
Home 7.1257 0.5267 13.53 0.000
Distance 0.0000105 0.0003920 0.03 0.979
Days2 0.5837 0.6157 0.95 0.343
Days3 0.3022 0.7996 0.38 0.706
Days4 2.363 1.394 1.70 0.090
Days5+ 0.935 1.801 0.52 0.604
Omitting Days2
Predictor Coef SE Coef T P
Constant 3.2678 0.5624 5.81 0.000
Home 7.1257 0.5267 13.53 0.000
Distance 0.0000105 0.0003920 0.03 0.979
Days1 0.5837 0.6157 0.95 0.343
Days3 0.2815 0.6981 0.40 0.687
Days4 2.947 1.338 2.20 0.028
Days5+ 0.351 1.758 0.20 0.842
Omitting Days3
Predictor Coef SE Coef T P
Constant 3.5494 0.7833 4.53 0.000
Home 7.1257 0.5267 13.53 0.000
Distance 0.0000105 0.0003920 0.03 0.979
Days1 0.3022 0.7996 0.38 0.706
Days2 0.2815 0.6981 0.40 0.687
Days4 2.665 1.426 1.87 0.062
Days5+ 0.633 1.824 0.35 0.729
Omitting Days4
Predictor Coef SE Coef T P
Constant 6.215 1.385 4.49 0.000
Home 7.1257 0.5267 13.53 0.000
Distance 0.0000105 0.0003920 0.03 0.979
Days1 2.363 1.394 1.70 0.090
Days2 2.947 1.338 2.20 0.028
Days3 2.665 1.426 1.87 0.062
Days5+ 3.298 2.152 1.53 0.126
Omitting Days5+
Predictor Coef SE Coef T P
Constant 2.917 1.799 1.62 0.105
Home 7.1257 0.5267 13.53 0.000
Distance 0.0000105 0.0003920 0.03 0.979
Days1 0.935 1.801 0.52 0.604
Days2 0.351 1.758 0.20 0.842
Days3 0.633 1.824 0.35 0.729
Days4 3.298 2.152 1.53 0.126
ed  View Source
 0 Attachment
ivan ivan wrote:> this is a simple question
http://www.shrpsports.com/nba/stand/2002.htm
> but i can't find it anywhere....
>
>
> I'm doing analysis on how a history of winning or losing affects your
> chances of winning at the end of close games... so does anyone know
> where i can standings for the 20012002 NBA season?
> i want the home and away records?
>

ed
Your message has been successfully submitted and would be delivered to recipients shortly.