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Re: [APBR_analysis] Re: Pace and another huge can of worms
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 Original Message 
From: "HoopStudies" <deano@...>
To: <APBR_analysis@yahoogroups.com>
Sent: Wednesday, January 30, 2002 9:53 AM
Subject: [APBR_analysis] Re: Pace
> John 
>
> The pace that I typically calculate is team possessions or
>
> FGA  OR + TO + 0.4*FTA
>
> Calculate it using both the offensive and defensive team stats, then
> average. The two estimates are usually within 12% of each other and
> I've checked the estimate against games I've scored. In theory, a
> team and its opponents have the same number of possessions in each
> game (+ or 2 and this + or  usually balances out over the season).
>
> I use that for creating "Offensive Ratings" and "Defensive Ratings",
> which are just Points scored and allowed per 100 possessions,
> respectively. Fortunately, my relative rankings are almost identical
> to yours so we're doing something similar. By my methodology,
> Milwaukee, LA, Dallas, then Utah were top offenses last year. My top
> 5 D's were almost the same, with Sacramento in there, but in
> different order (though all of them were _very_ similar). Bob
> Chaikin has another way of estimating possessions.
Interesting way of doing things. I think the primary reason why there's any
difference at all between our two methods (other than the fact that you use
FGA where I use FGM+OR+DR) is that you calculate the total per 100
possessions where I use the raw points scored per "average game (211.656
possessions). The reason I chose to do this was that some teams, due to
rebounding and not turning the ball over, get more possessions per game than
their opponents do. Last year's Lakers, for example, had the ball 103.19
times for every 100 opponent possessions. OTOH, the Bucks only had 98.31
possessions per 100 for their opponents. That's not a huge difference, but
it is an advantage due the Lakers for reasons other than luck.
The other reason for the slight difference (as well as a possible reason why
I have some teams with more possessions than others) might be that you don't
include offensive rebounds where I do. The way I figured it was, a
possession could end one of these ways:
 Made field goal
 Missed field goal, followed by a rebound
 Turnover
 Personal foul
Just curious, how did you rate players last year? I have a fairly complex
system that's still a little buggy, but here's everybody who was, according
to my methodology, worth 10 wins or more to their team in 20002001:
Tim Duncan 12.55
Shaquille O'Neal 12.11 (he actually had a higher pergame rating than
Duncan, but Duncan played in all 82 games where Shaq did not)
Karl Malone 11.83
Steve Francis 11.56
Jason Kidd 11.54
Dirk Nowitzki 11.37
Ray Allen 11.22
Kevin Garnett 10.93
John Stockton 10.87 (also the most efficient player in the league on a
perminute basis)
Allen Iverson 10.71
Tracy McGrady 10.55
David Robinson 10.34
Chris Webber 10.28
Vince Carter 10.28
Shawn Marion 10.19
Gary Payton 10.03
Kobe Bryant 10.03
>
> You can also do things like compensate for how teams slack off
> because they're involved in blowouts. That starts to get at why the
> Lakers' D improved so much in the playoffs, but, as you say, they
> turned it on then. (See Toying With 'Em at
> http://www.rawbw.com/~deano/articles/aa052097.htm or They Say Defense
> Wins Championships at
> http://www.rawbw.com/~deano/articles/aa082197.htm)
Hmm... the blowout thing is interesting... I tried to stay away from
standard deviations and the like because one thing that I think is important
for basketball stats while they're still in their infancy is simplicity. The
other reason was that, to be honest, figuring things out by individual games
would have drastically increased the amount of time I spent on the project.
Just curious... when you're looking at individual players, how do you
control for the Bad Team Effect (that is, the way that a Mike Bibby can
average 8 assists on a crappy team like Vancouver, but has trouble getting 5
a night when he's on a team that isn't built around his lessthanstellar
ballhandling skills)? I have a modifier for "team defense" that mostly ends
up uniformly driving totals of players on really bad teams down; since it's
based on the difference between Team Wins (my own Tendexish rating) and
Expected Wins (based on my own permutation of baseball's Pythagorean
Theorem), it can bring individual performances on good teams up, too. This
method is probably too pat for some, but how else can you factor in defense
when the league keeps track of very few defenserelated statistics?
John Craven
>
> The method I use for pace (and other things) is explained here:
>
> http://www.rawbw.com/~deano/estabmthf.html
>
> Dean Oliver
> Journal of Basketball Studies
>
>
>
>  In APBR_analysis@y..., "John Craven" <john1974@u...> wrote:
> > One thing that's always bothered me is that when people talk about
> the best
> > and worst offensive and defensive teams in the league, they nearly
> always
> > rank them by raw points scored for and against. Sometimes this is a
> valid
> > method, but not always  teams like the Mike Fratello Cavaliers are
> bound to
> > be overrated by this method, while teams like the George Karl
> Seattle
> > Supersonics (see my bias? ;) ) will be perennially underrated. Last
> year, I
> > observed that the Sacramento Kings really weren't halfbad on D,
> even though
> > they give up a lot of points per game, whereas Chicago wasn't even
> close to
> > average defensively.
> >
> > If you've ever watched the Kings play, you know that they like to
> push the
> > ball upcourt. Chicago last year did not. The net result is,
> Sacramento had a
> > ton more possessions (and opponent possessions) than did Chicago.
> Think of
> > this like park factors in baseball: just as some teams (the
> Rockies, for
> > instance) will have skewed hitting and pitching numbers because of
> the park
> > they play in, so will some teams in basketball have skewed offense
> and
> > defense numbers because of how fast they like to play.
> >
> > Without further ado, here are the top 5 and bottom 5 teams in pace:
> >
> > Top 5 (1000 = average)
> > 1. Golden State 1049
> > 2. Detroit 1046
> > 3. Sacramento 1045
> > 4. Orlando 1034
> > 5. Dallas 1017
> >
> > The two most averagepaced teams last year were Boston (999) and
> Cleveland
> > (1001).
> >
> > Bottom 5
> > 1. New York 943
> > 2. Miami 950
> > 3. Portland 973
> > 4. Charlotte 974
> > 5t. Utah 977
> > 5t. San Antonio 977
> >
> > You'll notice that the Knicks and Heat slowed things down more than
> any
> > single team sped things up. Overall, the bottom 5 pretty uniformly
> have good
> > defensive reputations; #7 (Chicago with a 978) is the first that
> does not.
> > Conversely, all five teams on the top of the list had reputations
> for
> > playing matador d, which isn't entirely true if you adjust for pace:
> >
> > Top 10 teams, PaceAdjusted Opp. PPG
> > 1. Philadelphia 90.2
> > 2. San Antonio 90.5
> > 3. Phoenix 91.1
> > 4. Miami 91.2
> > 5. New York 91.3
> > 6. Sacramento 91.8
> > 7. Charlotte 92.2
> > 8. Detroit 93.0
> > 9. Orlando 93.3
> > 10. Indiana 93.5
> >
> > Anyone who saw a lot of Philly last year knows that that #1 ranking
> is
> > accurate. Interestingly enough, a number of slowpaced teams (San
> Antonio,
> > New York, Miami) are still prominently featured. The biggest
> surprise is
> > Phoenix at #3. Some may be surprised that the Lakers aren't on the
> list; all
> > I have to say is, the team that played in the playoffs was not the
> same one
> > who played most of the season, defensively speaking. Let's take a
> look at
> > the worst 10:
> >
> > Bottom 10
> > 29. Washington 99.2
> > 28. Chicago 98.8
> > 27. New Jersey 97.8
> > 26. Denver 97.8
> > 25. Vancouver 97.2
> > 24. Golden State 96.8
> > 23. Boston 96.7
> > 22. Houston 96.6
> > 21. Cleveland 96.5
> > 20. Seattle 96.5
> >
> > Not surprisingly, these are some of the worst franchises in the
> league. The
> > only playoff team to make the list is Boston, though they only made
> it in
> > because of the weak East; most years, 36 wins isn't going to be
> enough to do
> > it. The only two teams with winning records on the list are Houston
> and
> > Seattle, and both are pretty far down it. The top offenses are a
> bit more
> > surprising:
> >
> > Top 10 PaceAdjusted Offenses
> > 1. Milwaukee 99.9
> > 2. Utah 99.4
> > 3. LA Lakers 99.3
> > 4. Houston 99.0
> > 5. Dallas 98.8
> > 6. San Antonio 98.4
> > 7. Portland 98.1
> > 8. Toronto 97.5
> > 9. Sacramento 97.4
> > 10. Minnesota 96.8
> > (Seattle was #11 with 96.6)
> >
> > 2 of the top 3 teams should surprise no one. In fact, the Lakers
> being
> > ranked so high should answer the question of how they won so many
> games
> > despite an average defense. But Utah? Yeah. Think about it.
> Stockton and
> > Malone are getting older, but they're still like clockwork, and
> last year
> > they had a third guy, Donyell Marshall, in the mix. They like to
> slow things
> > way down, but when they don't turn the ball over much and make a
> lot of
> > their shots. All the top 10 teams had plus.500 records, and only
> one
> > (Houston) missed the playoffs. And they were the only top10
> offense team
> > that was also on the bottom10 defense list (Seattle just missed
> out on that
> > "honor").
> >
> > The worst 10:
> > 29. Golden State 88.2
> > 28. Chicago 89.5
> > 27. Atlanta 90.4
> > 26. Detroit 91.3
> > 25. Vancouver 91.4
> > 24. Cleveland 92.3
> > 23. Washington 92.6
> > 22. New Jersey 92.7
> > 21. LA Clippers 93.0
> > 20. Indiana 93.3
> >
> > Again, the worst offensive teams in the league were also, by and
> large, the
> > worst teams in the league. None of these guys reached .500. Indiana
> came the
> > closest to making the playoffs; not surprisingly, they were only
> the 10th
> > worst offense, and they had the 10th best defense to boot. With the
> possible
> > exception of the Clippers, none of these names should be
> surprising. Chicago
> > had the 2nd worst offense and 5th worst defense; that's the recipe
> for
> > losing 65 games, folks.
> >
> > Questions? Comments?
> >
> > John Craven
>
>
>
> To unsubscribe from this group, send an email to:
> APBR_analysisunsubscribe@yahoogroups.com
>
>
>
> Your use of Yahoo! Groups is subject to http://docs.yahoo.com/info/terms/
>
>
> 0 Attachment
 In APBR_analysis@y..., "John Craven" <john1974@u...> wrote:> > John 
then
> >
> > The pace that I typically calculate is team possessions or
> >
> > FGA  OR + TO + 0.4*FTA
> >
> > Calculate it using both the offensive and defensive team stats,
> > average. The two estimates are usually within 12% of each other
and
> > I've checked the estimate against games I've scored. In theory, a
season).
> > team and its opponents have the same number of possessions in each
> > game (+ or 2 and this + or  usually balances out over the
> >
Ratings",
> > I use that for creating "Offensive Ratings" and "Defensive
> > which are just Points scored and allowed per 100 possessions,
identical
> > respectively. Fortunately, my relative rankings are almost
> > to yours so we're doing something similar. By my methodology,
top
> > Milwaukee, LA, Dallas, then Utah were top offenses last year. My
> > 5 D's were almost the same, with Sacramento in there, but in
there's any
> > different order (though all of them were _very_ similar). Bob
> > Chaikin has another way of estimating possessions.
>
> Interesting way of doing things. I think the primary reason why
> difference at all between our two methods (other than the fact that
you use
> FGA where I use FGM+OR+DR) is that you calculate the total per 100
(211.656
> possessions where I use the raw points scored per "average game
> possessions). The reason I chose to do this was that some teams,
due to
> rebounding and not turning the ball over, get more possessions per
game than
> their opponents do. Last year's Lakers, for example, had the ball
103.19
> times for every 100 opponent possessions. OTOH, the Bucks only had
98.31
> possessions per 100 for their opponents. That's not a huge
difference, but
> it is an advantage due the Lakers for reasons other than luck.
No time to answer everything right now, but I do want to clarify one
thing.
The term possession gets used very loosely. I define it as the time
between when one team gets the ball and the opponent gets the ball.
It's a definition, no arguing allowed. That is what makes it equal
for two teams in a game, a VERY useful thing. This means that a team
throws up 3 bricks, gets 3 offensive rebounds, and makes the 4th
shot  that is 1 possession. I designate everything within that
a "play". So it is 1 possession and 4 plays. Plays need not be
equal. Teams get offensive rebounds to maximize the number of plays
they have.
If you define points per 100 possessions, you are characterizing
offensive rebounds as part of the team's offense (duh). If you
define points per 100 plays, you are saying that offensive rebounds
are not part of the offense, but are something separate (there are 3
kingdoms: offense, defense, and rebounding). That may be useful
sometimes. I do calculate a Play %, or the chance that a team scores
on a play. I could calculate the points per 100 plays, but don't
often do that (because the play% is useful in some of my calculations
and points per 100 plays is mostly duplicative).
I hope that clarifies things. Your definition of pace means that all
that frantic offensive rebounding adds to pace and I think it's all
part of one possession. It's a matter of definition and what I'm
really hoping to accomplish here is a clarification of terminology.
My stats for last year can be downloaded as a (huge) PDF file from
http://www.rawbw.com/~deano/jobsstats_01.PDF
Individual winloss records, net points, etc.
>
reason why
> The other reason for the slight difference (as well as a possible
> I have some teams with more possessions than others) might be that
you don't
> include offensive rebounds where I do. The way I figured it was, a
complex
> possession could end one of these ways:
>
>  Made field goal
>  Missed field goal, followed by a rebound
>  Turnover
>  Personal foul
>
> Just curious, how did you rate players last year? I have a fairly
> system that's still a little buggy, but here's everybody who was,
according
> to my methodology, worth 10 wins or more to their team in 20002001:
than
>
> Tim Duncan 12.55
> Shaquille O'Neal 12.11 (he actually had a higher pergame rating
> Duncan, but Duncan played in all 82 games where Shaq did not)
on a
> Karl Malone 11.83
> Steve Francis 11.56
> Jason Kidd 11.54
> Dirk Nowitzki 11.37
> Ray Allen 11.22
> Kevin Garnett 10.93
> John Stockton 10.87 (also the most efficient player in the league
> perminute basis)
the
> Allen Iverson 10.71
> Tracy McGrady 10.55
> David Robinson 10.34
> Chris Webber 10.28
> Vince Carter 10.28
> Shawn Marion 10.19
> Gary Payton 10.03
> Kobe Bryant 10.03
>
> >
> > You can also do things like compensate for how teams slack off
> > because they're involved in blowouts. That starts to get at why
> > Lakers' D improved so much in the playoffs, but, as you say, they
Defense
> > turned it on then. (See Toying With 'Em at
> > http://www.rawbw.com/~deano/articles/aa052097.htm or They Say
> > Wins Championships at
important
> > http://www.rawbw.com/~deano/articles/aa082197.htm)
>
> Hmm... the blowout thing is interesting... I tried to stay away from
> standard deviations and the like because one thing that I think is
> for basketball stats while they're still in their infancy is
simplicity. The
> other reason was that, to be honest, figuring things out by
individual games
> would have drastically increased the amount of time I spent on the
project.
>
you
> Just curious... when you're looking at individual players, how do
> control for the Bad Team Effect (that is, the way that a Mike Bibby
can
> average 8 assists on a crappy team like Vancouver, but has trouble
getting 5
> a night when he's on a team that isn't built around his lessthan
stellar
> ballhandling skills)? I have a modifier for "team defense" that
mostly ends
> up uniformly driving totals of players on really bad teams down;
since it's
> based on the difference between Team Wins (my own Tendexish
rating) and
> Expected Wins (based on my own permutation of baseball's Pythagorean
too. This
> Theorem), it can bring individual performances on good teams up,
> method is probably too pat for some, but how else can you factor in
defense
> when the league keeps track of very few defenserelated statistics?
about
>
> John Craven
>
> >
> > The method I use for pace (and other things) is explained here:
> >
> > http://www.rawbw.com/~deano/estabmthf.html
> >
> > Dean Oliver
> > Journal of Basketball Studies
> >
> >
> >
> >  In APBR_analysis@y..., "John Craven" <john1974@u...> wrote:
> > > One thing that's always bothered me is that when people talk
> > the best
nearly
> > > and worst offensive and defensive teams in the league, they
> > always
is a
> > > rank them by raw points scored for and against. Sometimes this
> > valid
are
> > > method, but not always  teams like the Mike Fratello Cavaliers
> > bound to
Last
> > > be overrated by this method, while teams like the George Karl
> > Seattle
> > > Supersonics (see my bias? ;) ) will be perennially underrated.
> > year, I
even
> > > observed that the Sacramento Kings really weren't halfbad on D,
> > even though
> > > they give up a lot of points per game, whereas Chicago wasn't
> > close to
to
> > > average defensively.
> > >
> > > If you've ever watched the Kings play, you know that they like
> > push the
Chicago.
> > > ball upcourt. Chicago last year did not. The net result is,
> > Sacramento had a
> > > ton more possessions (and opponent possessions) than did
> > Think of
of
> > > this like park factors in baseball: just as some teams (the
> > Rockies, for
> > > instance) will have skewed hitting and pitching numbers because
> > the park
offense
> > > they play in, so will some teams in basketball have skewed
> > and
pace:
> > > defense numbers because of how fast they like to play.
> > >
> > > Without further ado, here are the top 5 and bottom 5 teams in
> > >
than
> > > Top 5 (1000 = average)
> > > 1. Golden State 1049
> > > 2. Detroit 1046
> > > 3. Sacramento 1045
> > > 4. Orlando 1034
> > > 5. Dallas 1017
> > >
> > > The two most averagepaced teams last year were Boston (999) and
> > Cleveland
> > > (1001).
> > >
> > > Bottom 5
> > > 1. New York 943
> > > 2. Miami 950
> > > 3. Portland 973
> > > 4. Charlotte 974
> > > 5t. Utah 977
> > > 5t. San Antonio 977
> > >
> > > You'll notice that the Knicks and Heat slowed things down more
> > any
uniformly
> > > single team sped things up. Overall, the bottom 5 pretty
> > have good
reputations
> > > defensive reputations; #7 (Chicago with a 978) is the first that
> > does not.
> > > Conversely, all five teams on the top of the list had
> > for
pace:
> > > playing matador d, which isn't entirely true if you adjust for
> > >
ranking
> > > Top 10 teams, PaceAdjusted Opp. PPG
> > > 1. Philadelphia 90.2
> > > 2. San Antonio 90.5
> > > 3. Phoenix 91.1
> > > 4. Miami 91.2
> > > 5. New York 91.3
> > > 6. Sacramento 91.8
> > > 7. Charlotte 92.2
> > > 8. Detroit 93.0
> > > 9. Orlando 93.3
> > > 10. Indiana 93.5
> > >
> > > Anyone who saw a lot of Philly last year knows that that #1
> > is
(San
> > > accurate. Interestingly enough, a number of slowpaced teams
> > Antonio,
the
> > > New York, Miami) are still prominently featured. The biggest
> > surprise is
> > > Phoenix at #3. Some may be surprised that the Lakers aren't on
> > list; all
the
> > > I have to say is, the team that played in the playoffs was not
> > same one
a
> > > who played most of the season, defensively speaking. Let's take
> > look at
made
> > > the worst 10:
> > >
> > > Bottom 10
> > > 29. Washington 99.2
> > > 28. Chicago 98.8
> > > 27. New Jersey 97.8
> > > 26. Denver 97.8
> > > 25. Vancouver 97.2
> > > 24. Golden State 96.8
> > > 23. Boston 96.7
> > > 22. Houston 96.6
> > > 21. Cleveland 96.5
> > > 20. Seattle 96.5
> > >
> > > Not surprisingly, these are some of the worst franchises in the
> > league. The
> > > only playoff team to make the list is Boston, though they only
> > it in
Houston
> > > because of the weak East; most years, 36 wins isn't going to be
> > enough to do
> > > it. The only two teams with winning records on the list are
> > and
many
> > > Seattle, and both are pretty far down it. The top offenses are a
> > bit more
> > > surprising:
> > >
> > > Top 10 PaceAdjusted Offenses
> > > 1. Milwaukee 99.9
> > > 2. Utah 99.4
> > > 3. LA Lakers 99.3
> > > 4. Houston 99.0
> > > 5. Dallas 98.8
> > > 6. San Antonio 98.4
> > > 7. Portland 98.1
> > > 8. Toronto 97.5
> > > 9. Sacramento 97.4
> > > 10. Minnesota 96.8
> > > (Seattle was #11 with 96.6)
> > >
> > > 2 of the top 3 teams should surprise no one. In fact, the Lakers
> > being
> > > ranked so high should answer the question of how they won so
> > games
only
> > > despite an average defense. But Utah? Yeah. Think about it.
> > Stockton and
> > > Malone are getting older, but they're still like clockwork, and
> > last year
> > > they had a third guy, Donyell Marshall, in the mix. They like to
> > slow things
> > > way down, but when they don't turn the ball over much and make a
> > lot of
> > > their shots. All the top 10 teams had plus.500 records, and
> > one
Indiana
> > > (Houston) missed the playoffs. And they were the only top10
> > offense team
> > > that was also on the bottom10 defense list (Seattle just missed
> > out on that
> > > "honor").
> > >
> > > The worst 10:
> > > 29. Golden State 88.2
> > > 28. Chicago 89.5
> > > 27. Atlanta 90.4
> > > 26. Detroit 91.3
> > > 25. Vancouver 91.4
> > > 24. Cleveland 92.3
> > > 23. Washington 92.6
> > > 22. New Jersey 92.7
> > > 21. LA Clippers 93.0
> > > 20. Indiana 93.3
> > >
> > > Again, the worst offensive teams in the league were also, by and
> > large, the
> > > worst teams in the league. None of these guys reached .500.
> > came the
the
> > > closest to making the playoffs; not surprisingly, they were only
> > the 10th
> > > worst offense, and they had the 10th best defense to boot. With
> > possible
recipe
> > > exception of the Clippers, none of these names should be
> > surprising. Chicago
> > > had the 2nd worst offense and 5th worst defense; that's the
> > for
http://docs.yahoo.com/info/terms/
> > > losing 65 games, folks.
> > >
> > > Questions? Comments?
> > >
> > > John Craven
> >
> >
> >
> > To unsubscribe from this group, send an email to:
> > APBR_analysisunsubscribe@y...
> >
> >
> >
> > Your use of Yahoo! Groups is subject to
> >
> >
> > 0 Attachment
 Original Message 
From: "HoopStudies" <deano@...>
To: <APBR_analysis@yahoogroups.com>
Sent: Wednesday, January 30, 2002 11:38 AM
Subject: [APBR_analysis] Re: Pace and another huge can of worms
>  In APBR_analysis@y..., "John Craven" <john1974@u...> wrote:
> > > John 
> > >
> > > The pace that I typically calculate is team possessions or
> > >
> > > FGA  OR + TO + 0.4*FTA
> > >
> > > Calculate it using both the offensive and defensive team stats,
> then
> > > average. The two estimates are usually within 12% of each other
> and
> > > I've checked the estimate against games I've scored. In theory, a
> > > team and its opponents have the same number of possessions in each
> > > game (+ or 2 and this + or  usually balances out over the
> season).
> > >
> > > I use that for creating "Offensive Ratings" and "Defensive
> Ratings",
> > > which are just Points scored and allowed per 100 possessions,
> > > respectively. Fortunately, my relative rankings are almost
> identical
> > > to yours so we're doing something similar. By my methodology,
> > > Milwaukee, LA, Dallas, then Utah were top offenses last year. My
> top
> > > 5 D's were almost the same, with Sacramento in there, but in
> > > different order (though all of them were _very_ similar). Bob
> > > Chaikin has another way of estimating possessions.
> >
> > Interesting way of doing things. I think the primary reason why
> there's any
> > difference at all between our two methods (other than the fact that
> you use
> > FGA where I use FGM+OR+DR) is that you calculate the total per 100
> > possessions where I use the raw points scored per "average game
> (211.656
> > possessions). The reason I chose to do this was that some teams,
> due to
> > rebounding and not turning the ball over, get more possessions per
> game than
> > their opponents do. Last year's Lakers, for example, had the ball
> 103.19
> > times for every 100 opponent possessions. OTOH, the Bucks only had
> 98.31
> > possessions per 100 for their opponents. That's not a huge
> difference, but
> > it is an advantage due the Lakers for reasons other than luck.
>
> No time to answer everything right now, but I do want to clarify one
> thing.
>
> The term possession gets used very loosely. I define it as the time
> between when one team gets the ball and the opponent gets the ball.
> It's a definition, no arguing allowed. That is what makes it equal
> for two teams in a game, a VERY useful thing. This means that a team
> throws up 3 bricks, gets 3 offensive rebounds, and makes the 4th
> shot  that is 1 possession. I designate everything within that
> a "play". So it is 1 possession and 4 plays. Plays need not be
> equal. Teams get offensive rebounds to maximize the number of plays
> they have.
>
> If you define points per 100 possessions, you are characterizing
> offensive rebounds as part of the team's offense (duh). If you
> define points per 100 plays, you are saying that offensive rebounds
> are not part of the offense, but are something separate (there are 3
> kingdoms: offense, defense, and rebounding). That may be useful
> sometimes. I do calculate a Play %, or the chance that a team scores
> on a play. I could calculate the points per 100 plays, but don't
> often do that (because the play% is useful in some of my calculations
> and points per 100 plays is mostly duplicative).
>
> I hope that clarifies things. Your definition of pace means that all
> that frantic offensive rebounding adds to pace and I think it's all
> part of one possession. It's a matter of definition and what I'm
> really hoping to accomplish here is a clarification of terminology.
Sure. The primary reason I decided not to include offensive rebounds in my
conception of possessions is that often (I have no idea what the percentage
is) they result in the ball being kicked out to the PG and an entirely new
play being called. If I could, I'd lump putback attempts in with made field
goals, but I can't.
>
> My stats for last year can be downloaded as a (huge) PDF file from
>
> http://www.rawbw.com/~deano/jobsstats_01.PDF
>
> Individual winloss records, net points, etc.
>
> >
> > The other reason for the slight difference (as well as a possible
> reason why
> > I have some teams with more possessions than others) might be that
> you don't
> > include offensive rebounds where I do. The way I figured it was, a
> > possession could end one of these ways:
> >
> >  Made field goal
> >  Missed field goal, followed by a rebound
> >  Turnover
> >  Personal foul
> >
> > Just curious, how did you rate players last year? I have a fairly
> complex
> > system that's still a little buggy, but here's everybody who was,
> according
> > to my methodology, worth 10 wins or more to their team in 20002001:
> >
> > Tim Duncan 12.55
> > Shaquille O'Neal 12.11 (he actually had a higher pergame rating
> than
> > Duncan, but Duncan played in all 82 games where Shaq did not)
> > Karl Malone 11.83
> > Steve Francis 11.56
> > Jason Kidd 11.54
> > Dirk Nowitzki 11.37
> > Ray Allen 11.22
> > Kevin Garnett 10.93
> > John Stockton 10.87 (also the most efficient player in the league
> on a
> > perminute basis)
> > Allen Iverson 10.71
> > Tracy McGrady 10.55
> > David Robinson 10.34
> > Chris Webber 10.28
> > Vince Carter 10.28
> > Shawn Marion 10.19
> > Gary Payton 10.03
> > Kobe Bryant 10.03
> >
> > >
> > > You can also do things like compensate for how teams slack off
> > > because they're involved in blowouts. That starts to get at why
> the
> > > Lakers' D improved so much in the playoffs, but, as you say, they
> > > turned it on then. (See Toying With 'Em at
> > > http://www.rawbw.com/~deano/articles/aa052097.htm or They Say
> Defense
> > > Wins Championships at
> > > http://www.rawbw.com/~deano/articles/aa082197.htm)
> >
> > Hmm... the blowout thing is interesting... I tried to stay away from
> > standard deviations and the like because one thing that I think is
> important
> > for basketball stats while they're still in their infancy is
> simplicity. The
> > other reason was that, to be honest, figuring things out by
> individual games
> > would have drastically increased the amount of time I spent on the
> project.
> >
> > Just curious... when you're looking at individual players, how do
> you
> > control for the Bad Team Effect (that is, the way that a Mike Bibby
> can
> > average 8 assists on a crappy team like Vancouver, but has trouble
> getting 5
> > a night when he's on a team that isn't built around his lessthan
> stellar
> > ballhandling skills)? I have a modifier for "team defense" that
> mostly ends
> > up uniformly driving totals of players on really bad teams down;
> since it's
> > based on the difference between Team Wins (my own Tendexish
> rating) and
> > Expected Wins (based on my own permutation of baseball's Pythagorean
> > Theorem), it can bring individual performances on good teams up,
> too. This
> > method is probably too pat for some, but how else can you factor in
> defense
> > when the league keeps track of very few defenserelated statistics?
> >
> > John Craven
> >
> > >
> > > The method I use for pace (and other things) is explained here:
> > >
> > > http://www.rawbw.com/~deano/estabmthf.html
> > >
> > > Dean Oliver
> > > Journal of Basketball Studies
> > >
> > >
> > >
> > >  In APBR_analysis@y..., "John Craven" <john1974@u...> wrote:
> > > > One thing that's always bothered me is that when people talk
> about
> > > the best
> > > > and worst offensive and defensive teams in the league, they
> nearly
> > > always
> > > > rank them by raw points scored for and against. Sometimes this
> is a
> > > valid
> > > > method, but not always  teams like the Mike Fratello Cavaliers
> are
> > > bound to
> > > > be overrated by this method, while teams like the George Karl
> > > Seattle
> > > > Supersonics (see my bias? ;) ) will be perennially underrated.
> Last
> > > year, I
> > > > observed that the Sacramento Kings really weren't halfbad on D,
> > > even though
> > > > they give up a lot of points per game, whereas Chicago wasn't
> even
> > > close to
> > > > average defensively.
> > > >
> > > > If you've ever watched the Kings play, you know that they like
> to
> > > push the
> > > > ball upcourt. Chicago last year did not. The net result is,
> > > Sacramento had a
> > > > ton more possessions (and opponent possessions) than did
> Chicago.
> > > Think of
> > > > this like park factors in baseball: just as some teams (the
> > > Rockies, for
> > > > instance) will have skewed hitting and pitching numbers because
> of
> > > the park
> > > > they play in, so will some teams in basketball have skewed
> offense
> > > and
> > > > defense numbers because of how fast they like to play.
> > > >
> > > > Without further ado, here are the top 5 and bottom 5 teams in
> pace:
> > > >
> > > > Top 5 (1000 = average)
> > > > 1. Golden State 1049
> > > > 2. Detroit 1046
> > > > 3. Sacramento 1045
> > > > 4. Orlando 1034
> > > > 5. Dallas 1017
> > > >
> > > > The two most averagepaced teams last year were Boston (999) and
> > > Cleveland
> > > > (1001).
> > > >
> > > > Bottom 5
> > > > 1. New York 943
> > > > 2. Miami 950
> > > > 3. Portland 973
> > > > 4. Charlotte 974
> > > > 5t. Utah 977
> > > > 5t. San Antonio 977
> > > >
> > > > You'll notice that the Knicks and Heat slowed things down more
> than
> > > any
> > > > single team sped things up. Overall, the bottom 5 pretty
> uniformly
> > > have good
> > > > defensive reputations; #7 (Chicago with a 978) is the first that
> > > does not.
> > > > Conversely, all five teams on the top of the list had
> reputations
> > > for
> > > > playing matador d, which isn't entirely true if you adjust for
> pace:
> > > >
> > > > Top 10 teams, PaceAdjusted Opp. PPG
> > > > 1. Philadelphia 90.2
> > > > 2. San Antonio 90.5
> > > > 3. Phoenix 91.1
> > > > 4. Miami 91.2
> > > > 5. New York 91.3
> > > > 6. Sacramento 91.8
> > > > 7. Charlotte 92.2
> > > > 8. Detroit 93.0
> > > > 9. Orlando 93.3
> > > > 10. Indiana 93.5
> > > >
> > > > Anyone who saw a lot of Philly last year knows that that #1
> ranking
> > > is
> > > > accurate. Interestingly enough, a number of slowpaced teams
> (San
> > > Antonio,
> > > > New York, Miami) are still prominently featured. The biggest
> > > surprise is
> > > > Phoenix at #3. Some may be surprised that the Lakers aren't on
> the
> > > list; all
> > > > I have to say is, the team that played in the playoffs was not
> the
> > > same one
> > > > who played most of the season, defensively speaking. Let's take
> a
> > > look at
> > > > the worst 10:
> > > >
> > > > Bottom 10
> > > > 29. Washington 99.2
> > > > 28. Chicago 98.8
> > > > 27. New Jersey 97.8
> > > > 26. Denver 97.8
> > > > 25. Vancouver 97.2
> > > > 24. Golden State 96.8
> > > > 23. Boston 96.7
> > > > 22. Houston 96.6
> > > > 21. Cleveland 96.5
> > > > 20. Seattle 96.5
> > > >
> > > > Not surprisingly, these are some of the worst franchises in the
> > > league. The
> > > > only playoff team to make the list is Boston, though they only
> made
> > > it in
> > > > because of the weak East; most years, 36 wins isn't going to be
> > > enough to do
> > > > it. The only two teams with winning records on the list are
> Houston
> > > and
> > > > Seattle, and both are pretty far down it. The top offenses are a
> > > bit more
> > > > surprising:
> > > >
> > > > Top 10 PaceAdjusted Offenses
> > > > 1. Milwaukee 99.9
> > > > 2. Utah 99.4
> > > > 3. LA Lakers 99.3
> > > > 4. Houston 99.0
> > > > 5. Dallas 98.8
> > > > 6. San Antonio 98.4
> > > > 7. Portland 98.1
> > > > 8. Toronto 97.5
> > > > 9. Sacramento 97.4
> > > > 10. Minnesota 96.8
> > > > (Seattle was #11 with 96.6)
> > > >
> > > > 2 of the top 3 teams should surprise no one. In fact, the Lakers
> > > being
> > > > ranked so high should answer the question of how they won so
> many
> > > games
> > > > despite an average defense. But Utah? Yeah. Think about it.
> > > Stockton and
> > > > Malone are getting older, but they're still like clockwork, and
> > > last year
> > > > they had a third guy, Donyell Marshall, in the mix. They like to
> > > slow things
> > > > way down, but when they don't turn the ball over much and make a
> > > lot of
> > > > their shots. All the top 10 teams had plus.500 records, and
> only
> > > one
> > > > (Houston) missed the playoffs. And they were the only top10
> > > offense team
> > > > that was also on the bottom10 defense list (Seattle just missed
> > > out on that
> > > > "honor").
> > > >
> > > > The worst 10:
> > > > 29. Golden State 88.2
> > > > 28. Chicago 89.5
> > > > 27. Atlanta 90.4
> > > > 26. Detroit 91.3
> > > > 25. Vancouver 91.4
> > > > 24. Cleveland 92.3
> > > > 23. Washington 92.6
> > > > 22. New Jersey 92.7
> > > > 21. LA Clippers 93.0
> > > > 20. Indiana 93.3
> > > >
> > > > Again, the worst offensive teams in the league were also, by and
> > > large, the
> > > > worst teams in the league. None of these guys reached .500.
> Indiana
> > > came the
> > > > closest to making the playoffs; not surprisingly, they were only
> > > the 10th
> > > > worst offense, and they had the 10th best defense to boot. With
> the
> > > possible
> > > > exception of the Clippers, none of these names should be
> > > surprising. Chicago
> > > > had the 2nd worst offense and 5th worst defense; that's the
> recipe
> > > for
> > > > losing 65 games, folks.
> > > >
> > > > Questions? Comments?
> > > >
> > > > John Craven
> > >
> > >
> > >
> > > To unsubscribe from this group, send an email to:
> > > APBR_analysisunsubscribe@y...
> > >
> > >
> > >
> > > Your use of Yahoo! Groups is subject to
> http://docs.yahoo.com/info/terms/
> > >
> > >
> > >
>
>
>
> To unsubscribe from this group, send an email to:
> APBR_analysisunsubscribe@yahoogroups.com
>
>
>
> Your use of Yahoo! Groups is subject to http://docs.yahoo.com/info/terms/
>
>
> 0 Attachment
On Wed, 30 Jan 2002, HoopStudies wrote:
> No time to answer everything right now, but I do want to clarify one
[...]
> thing.
>
> The term possession gets used very loosely. I define it as the time
> between when one team gets the ball and the opponent gets the ball.
> It's a definition, no arguing allowed. That is what makes it equal
> for two teams in a game, a VERY useful thing. This means that a team
Brian Harper has a useful terminology here: the Dean O definition of a
possession he calls a "major possession". I.e. all the offensive rebounds
still count as part of the possession. The John Craven definition is a
"minor possession"; missing a shot means a lost possession and getting an
offensive rebound means a new possession has started.
Both definitions have their uses.
> If you define points per 100 possessions, you are characterizing
Yes, I think there's two areas where the "minor possession" is useful: if
> offensive rebounds as part of the team's offense (duh). If you
> define points per 100 plays, you are saying that offensive rebounds
> are not part of the offense, but are something separate (there are 3
> kingdoms: offense, defense, and rebounding). That may be useful
> sometimes. I do calculate a Play %, or the chance that a team scores
we want to start looking at rebounding as separate from the other aspects
of basketball, e.g. to calculate the value or importance of rebounding,
compared to offense or defense. And to get a potentially better or at
least more pure measure of a team's offensive and defensive capabilities:
sometimes an offensive rebound means that the team gets to (really, has
to) reset its offense and run a play again and try to score. If a team
is really bad at that offense, but really good at offensive rebounding,
its overall offense (in terms of major possessions) might look okay, but
this measure would hide the team's woeful "true" or "isolated" offense,
which is hidden by its great offensive rebounding.
Some people would say that offensive rebounding IS a part of offense, and
the team truly is okay overall. After all, it is scoring in the end,
thanks to its offensive rebounds.
But much of the analysis that we do involves continually cutting finer and
finer divisions into the data. It's not just "offense", it's "offense"
and "rebounding" (and of course defense). And needless to say,
"rebounding" is not just rebounding, it's truly two different things:
"offensive rebounding" and "defensive rebounding". And there are even
finer rebounding distinctions to be made: the team rebounds and deadball
rebounds that we talked about last week, rebounds off of missed FTAs vs
rebounds off of missed FGAs, etc. etc. Finer and finer.
When it comes to overall, summary, bottom line measures, then of course we
do want single measures which combine all the elements. The hierarchy
might be something like this:
1. First level: one stat. Power Rating (or whatever we want to call the
one single overall measure of how good a team is).
2. Second level: two stats. "Offense" and "defense" using the "major
possession" measure.
3. Third level: three or four stats, depending on how you choose to
count. "Offense" and "defense" and "rebounding". But rebounding can,
be divided into "offensive rebounding" and "defensive rebounding" (though
one can average them to get an overall rebounding percentage if one
wants). At this third level we'd use the "minor possession" measure.
4. Fourth level: pretty much anything goes. Start subdividing between
fastbreaks and halfcourt, or assisted vs unassisted FGs (as we've been
doing this week), or inside and outside, or starters vs bench, or whatever
subdivision of stats we wanted to look at.
Brian Harper's terminology helps us distinguish between "major" and
"minor" possessions. What we need is a terminology to distinguish between
"offense" at the second vs the third level, as well as "defense" at the
2nd vs 3rd levels. Something like
"major offense" vs "minor offense" or
"overall offense" vs "pure offense" or
"OFF" vs "off" or
"2nd level offense" vs "3rd level offense"
"total offense" vs "isolated offense"
or something.
[...]
> I hope that clarifies things. Your definition of pace means that all
This is where we might be able to decide what a better measure of pace is.
> that frantic offensive rebounding adds to pace and I think it's all
> part of one possession. It's a matter of definition and what I'm
> really hoping to accomplish here is a clarification of terminology.
If there are a lot of "major possessions", then clearly that's a fast game
pace. What if there are a lot of "minor possessions," without an
unusually high number of major possessions (in other words a game with a
lot of missed shots and offensive rebounds). Would we call that a
fastpaced game?
I think the answer depends on the extent to which those minor possessions
simply were guys attempting tipins, or followup layins. Vs. where a guy
like Rodman brings down the offensive rebound and immediately passes to a
teammate, who then has to create a shot or pass or dribble ... in other
words, is initiating what in game pace terms really should count as a new
offensive possession.
My impression is that most offensive rebounds lead pretty quickly to
follow shots, as opposed to ones where the team gets the off. rebound and
uses up 10 or 20 seconds on the shot clock before getting its next shot.
So that might argue in favor of using major possessions as a measure of
game pace.
On the other hand, those tipins and follow shots really are real true
shots, they do represent a flurry of activity on the court. So I could
still be made to think that minor possesions measure how much activity is
going on on the court.
MKT 0 Attachment
"Major Possessions" vs "Minor possessions"
or
"Possessions" vs "Plays"
I don't really care beyond the simplicity of the latter. I just
think it is useful to distinguish between the two things.
MikeT's points about when things get used are certainly wellthought
out. I have generally taken a "tiered approach" to evaluating
players myself. Though I see his point about what do we call a game
with few possessions but lots of plays  fast or slow  I think
I'll not strain my brain with that one for a while.
Whatever happened with Brian Harper, by the way? His site hasn't
been updated in a long time and I haven't heard from him either in
years.
Dean Oliver
Journal of Basketball Studies
 In APBR_analysis@y..., "Michael K. Tamada" <tamada@o...> wrote:
>
>
> On Wed, 30 Jan 2002, HoopStudies wrote:
>
> > No time to answer everything right now, but I do want to clarify
one
> > thing.
> >
> > The term possession gets used very loosely. I define it as the
time
> > between when one team gets the ball and the opponent gets the
ball.
> > It's a definition, no arguing allowed. That is what makes it
equal
> > for two teams in a game, a VERY useful thing. This means that a
team
>
> [...]
>
> Brian Harper has a useful terminology here: the Dean O definition
of a
> possession he calls a "major possession". I.e. all the offensive
rebounds
> still count as part of the possession. The John Craven definition
is a
> "minor possession"; missing a shot means a lost possession and
getting an
> offensive rebound means a new possession has started.
>
> Both definitions have their uses.
>
> > If you define points per 100 possessions, you are characterizing
> > offensive rebounds as part of the team's offense (duh). If you
> > define points per 100 plays, you are saying that offensive
rebounds
> > are not part of the offense, but are something separate (there
are 3
> > kingdoms: offense, defense, and rebounding). That may be useful
> > sometimes. I do calculate a Play %, or the chance that a team
scores
>
> Yes, I think there's two areas where the "minor possession" is
useful: if
> we want to start looking at rebounding as separate from the other
aspects
> of basketball, e.g. to calculate the value or importance of
rebounding,
> compared to offense or defense. And to get a potentially better or
at
> least more pure measure of a team's offensive and defensive
capabilities:
> sometimes an offensive rebound means that the team gets to (really,
has
> to) reset its offense and run a play again and try to score. If a
team
> is really bad at that offense, but really good at offensive
rebounding,
> its overall offense (in terms of major possessions) might look
okay, but
> this measure would hide the team's woeful "true" or "isolated"
offense,
> which is hidden by its great offensive rebounding.
>
> Some people would say that offensive rebounding IS a part of
offense, and
> the team truly is okay overall. After all, it is scoring in the
end,
> thanks to its offensive rebounds.
>
> But much of the analysis that we do involves continually cutting
finer and
> finer divisions into the data. It's not just "offense",
it's "offense"
> and "rebounding" (and of course defense). And needless to say,
> "rebounding" is not just rebounding, it's truly two different
things:
> "offensive rebounding" and "defensive rebounding". And there are
even
> finer rebounding distinctions to be made: the team rebounds and
deadball
> rebounds that we talked about last week, rebounds off of missed
FTAs vs
> rebounds off of missed FGAs, etc. etc. Finer and finer.
>
> When it comes to overall, summary, bottom line measures, then of
course we
> do want single measures which combine all the elements. The
hierarchy
> might be something like this:
>
> 1. First level: one stat. Power Rating (or whatever we want to
call the
> one single overall measure of how good a team is).
>
> 2. Second level: two stats. "Offense" and "defense" using
the "major
> possession" measure.
>
> 3. Third level: three or four stats, depending on how you choose
to
> count. "Offense" and "defense" and "rebounding". But rebounding
can,
> be divided into "offensive rebounding" and "defensive rebounding"
(though
> one can average them to get an overall rebounding percentage if one
> wants). At this third level we'd use the "minor possession"
measure.
>
> 4. Fourth level: pretty much anything goes. Start subdividing
between
> fastbreaks and halfcourt, or assisted vs unassisted FGs (as we've
been
> doing this week), or inside and outside, or starters vs bench, or
whatever
> subdivision of stats we wanted to look at.
>
>
> Brian Harper's terminology helps us distinguish between "major" and
> "minor" possessions. What we need is a terminology to distinguish
between
> "offense" at the second vs the third level, as well as "defense" at
the
> 2nd vs 3rd levels. Something like
>
> "major offense" vs "minor offense" or
> "overall offense" vs "pure offense" or
> "OFF" vs "off" or
> "2nd level offense" vs "3rd level offense"
> "total offense" vs "isolated offense"
>
> or something.
>
> [...]
>
> > I hope that clarifies things. Your definition of pace means that
all
> > that frantic offensive rebounding adds to pace and I think it's
all
> > part of one possession. It's a matter of definition and what I'm
> > really hoping to accomplish here is a clarification of
terminology.
>
> This is where we might be able to decide what a better measure of
pace is.
> If there are a lot of "major possessions", then clearly that's a
fast game
> pace. What if there are a lot of "minor possessions," without an
> unusually high number of major possessions (in other words a game
with a
> lot of missed shots and offensive rebounds). Would we call that a
> fastpaced game?
>
> I think the answer depends on the extent to which those minor
possessions
> simply were guys attempting tipins, or followup layins. Vs.
where a guy
> like Rodman brings down the offensive rebound and immediately
passes to a
> teammate, who then has to create a shot or pass or dribble ... in
other
> words, is initiating what in game pace terms really should count as
a new
> offensive possession.
>
> My impression is that most offensive rebounds lead pretty quickly to
> follow shots, as opposed to ones where the team gets the off.
rebound and
> uses up 10 or 20 seconds on the shot clock before getting its next
shot.
> So that might argue in favor of using major possessions as a
measure of
> game pace.
>
> On the other hand, those tipins and follow shots really are real
true
> shots, they do represent a flurry of activity on the court. So I
could
> still be made to think that minor possesions measure how much
activity is
> going on on the court.
>
>
> MKT 0 Attachment
On Wed, 30 Jan 2002, HoopStudies wrote:
> "Major Possessions" vs "Minor possessions"
That's fine, but what do we call a teams' offensive efficiency on
>
> or
>
> "Possessions" vs "Plays"
"possessions" vs offensive effiency on "plays"? I think you've already
mentioned that you call the latter Play% or something like that, which is
fine as an abbreviation but not so good as a name for it.
[...]
> Whatever happened with Brian Harper, by the way? His site hasn't
He's very active on a Sonics email list and a Mariners email list (and
> been updated in a long time and I haven't heard from him either in
> years.
maybe a Seahawks and a UW Husky football email list for all I know, but
I'm not on any Seahawks email lists and the Husky list that I subscribe
too is moribund). I don't know what the story with his website is  I
assume you're referring to the "NBAStatSite  the Hidden Game" website?
One of his partners in that website, Gary Scott Simon, still pops up in
rec.sport.basketball.pro. However I hardly read r.s.bb.pro anymore.
MKT 0 Attachment
 In APBR_analysis@y..., "John Craven" <john1974@u...> wrote:>.....here's everybody who was, according
John, I think you have very nice methods. Partly I say this because
> to my methodology, worth 10 wins or more to their team in 20002001:
>
your rankings are pretty close to mine. I use "effective game pace"
(points and rebounds for and against the team) to modify the stats,
as well as hybridize pergame and perminute rates.
So, here are my 2001 ratings, next to yours (I factored mine to
achieve a similar scale):
> Tim Duncan 12.55 13.17 Shaq
I used Duncan's 12.55 rating for 82 games as my standard of
> Shaquille O'Neal 12.11 12.62 Malone
> Karl Malone 11.83 12.55 Duncan
> Steve Francis 11.56 12.03 Garnett
> Jason Kidd 11.54 11.29 Nowitzki
> Dirk Nowitzki 11.37 11.19 Pierce
> Ray Allen 11.22 11.16 McGrady
> Kevin Garnett 10.93 10.88 Carter
> John Stockton 10.87 10.80 Allen
> Allen Iverson 10.71 10.73 Webber
> Tracy McGrady 10.55 10.72 D Robinson
> David Robinson 10.34 10.66 Stackhouse
> Chris Webber 10.28 10.49 Walker
> Vince Carter 10.28 10.40 Stockton
> Shawn Marion 10.19 10.32 Payton
> Gary Payton 10.03 10.32 Francis
> Kobe Bryant 10.03 10.26 Marion
> 10.24 Iverson
> > 9.98 Wallace
> 9.87 Kidd
> 9.82 AbdurRahim
> 9.81 Andre Miller
> 9.75 Kobe
translation, so his is the same on both our lists.
It looks as though you rank guards higher than I do, and you may be
considering strengthofschedule (i.e., West is best), which I have
not done.
Having seen several other lists, I have far fewer disagreements with
yours  and no major problems with it.
>..... one thing that I think is
important
> for basketball stats while they're still in their infancy is
simplicity.
I could not agree more. Userfriendly stats!
Mike Goodman 0 Attachment
That last post certainly was hard to read. This is a bad place to
try to post in columns, as all spaces are compressed to a single
space.
Anyway, I did notice what was the big difference between my rankings
and John Craven's. My list had Paul Pierce pretty high up, where his
didn't include Pierce at all, nor Antoine Walker or Stackhouse.
These 3 guys played for very bad teams last season. This seems to
disqualify members of such teams from consideration as great players,
in these equivalentwins (or individualwins) methods.
What I wonder is, does anyone have a winsbased evaluation method
that dates back several years? If so, are players like Pierce
considered "bad" players as long as their team is bad, and then
suddenly become "good" players at the moment their team improves (or
they move to a better team)?
Mike Goodman
p.s. If you hit "Reply", a post will appear with columns restored. 0 Attachment
 In APBR_analysis@y..., "mikel_ind" <msg_53@h...> wrote:> That last post certainly was hard to read. This is a bad place to
rankings
> try to post in columns, as all spaces are compressed to a single
> space.
>
> Anyway, I did notice what was the big difference between my
> and John Craven's. My list had Paul Pierce pretty high up, where
his
> didn't include Pierce at all, nor Antoine Walker or Stackhouse.
players,
>
> These 3 guys played for very bad teams last season. This seems to
> disqualify members of such teams from consideration as great
> in these equivalentwins (or individualwins) methods.
(or
>
> What I wonder is, does anyone have a winsbased evaluation method
> that dates back several years? If so, are players like Pierce
> considered "bad" players as long as their team is bad, and then
> suddenly become "good" players at the moment their team improves
> they move to a better team)?
I don't have it with me here at work, but I have been doing wins
based stuff for a long time and it shows this kind of change for
supporting cast kind of players  the Steve Kerr's of the world who
are valuable on good teams, but not so valuable on poor teams. By my
numbers last year, Pierce was one of the best players in the league,
with a winloss record of 12.24.5. Kobe, for comparison, was 11.6
3.4. Those are some very solid numbers.
Another example I think of as an interesting one was Mitch Richmond.
I consistently had him winning a lot of games for Sacramento, then he
tanked upon being traded to Washington. That was weird.
I had Andre Miller at 8.95.1 last year. McDyess was 8.64.3.
Stackhouse was 10.08.3. Jamison was 6.210.5. AWalker was 7.710.1.
I think Miller's record may be typical for winloss records of very
good players on bad teams. The reason is defense. Miller and Pierce
and Kobe are not the kind of players who can completely turn around a
defense, make it good. Only big men can really do that (and maybe
Jason Kidd).
Also, if you have a team that wins only 15 games, it doesn't make
sense to have one player on that team who wins 16 games like a Shaq
or a Jordan or Duncan do. Those guys make winning teams. Elton
Brand, though a good player, clearly doesn't add more than the 15
wins that Chicago had last year; it's impossible. My first cut was
an addition of 6 wins by Brand. I frankly am coming to believe that
it was more like 9 wins, but that's all a little theoretical right
now. And it always seems strange to me if someone contributes more
than half of his team's wins (especially since the Bulls are on pace
to beat 15 wins this year). It's hard to argue that even Jordan ever
won half his team's games when he was scoring 35 ppg and the Bulls
were winning only 40 games. Actually, Jordan should be a very good
example of what MikeG was asking for. I don't have all my info in
front of me (again), but
http://www.rawbw.com/~deano/articles/JordanvsOlaj.html
shows that Jordan was 19.40.7 in 1988. I don't think the Bulls were
that good that year, maybe 4042 (help?). So, sure, it is indeed
very possible for winbased ranking methodologies to show stars (or
superstars in this case) on mediocre teams.
Dean Oliver
Journal of Basketball STudies
>
> Mike Goodman
>
> p.s. If you hit "Reply", a post will appear with columns restored. 0 Attachment
> > What I wonder is, does anyone have a winsbased evaluation method
who
> > that dates back several years? If so, are players like Pierce
> > considered "bad" players as long as their team is bad, and then
> > suddenly become "good" players at the moment their team improves
> (or
> > they move to a better team)?
>
> I don't have it with me here at work, but I have been doing wins
> based stuff for a long time and it shows this kind of change for
> supporting cast kind of players  the Steve Kerr's of the world
> are valuable on good teams, but not so valuable on poor teams. By
my
> numbers last year, Pierce was one of the best players in the
league,
> with a winloss record of 12.24.5. Kobe, for comparison, was 11.6
Let me back up a little here. I do see some variation for even good
> 3.4. Those are some very solid numbers.
>
players who change teams IF the team defense of the two teams are
different. Most players do not have significant effects on team
defense, big men being the exception (and apparently Jason Kidd and
maybe MJ). Offensively, stars don't really change much from team to
team. Role players can, but don't necessarily. Defensively, it's a
mixed bag. And since I don't calculate wins and losses as an
explicit rating method (usual Bill Jameslike disclaimer: I don't
believe in one number for overall ratings of players), they are just
meant to reflect a player's contribution to his team. They are a
pseudomeasurement, not an overall rating. Because they are pseudo
measurements, they are not subjective and, hence, a bit more
predictable and meaningful than stupid awards.
Pierce, by the way, is a pretty unusual player. His winloss record
has been above 0.500 since entering the league, a starlike quality.
5.91.8 as a rookie. 8.54.5 as a 2nd year guy. 12.24.5 last
year. He's a good but not great defender. You put him on one of
these poor defensive teams (like Cleveland) and they may get a little
better defensively. They should get better offensively.
> Also, if you have a team that wins only 15 games, it doesn't make
that
> sense to have one player on that team who wins 16 games like a Shaq
> or a Jordan or Duncan do. Those guys make winning teams. Elton
> Brand, though a good player, clearly doesn't add more than the 15
> wins that Chicago had last year; it's impossible. My first cut was
> an addition of 6 wins by Brand. I frankly am coming to believe
> it was more like 9 wins, but that's all a little theoretical right
pace
> now. And it always seems strange to me if someone contributes more
> than half of his team's wins (especially since the Bulls are on
> to beat 15 wins this year).
This is an interesting case. The Clips are better with Brand. The
Bulls are better without Brand. So how good is Brand? Context
sensitive.
> It's hard to argue that even Jordan ever
were
> won half his team's games when he was scoring 35 ppg and the Bulls
> were winning only 40 games. Actually, Jordan should be a very good
> example of what MikeG was asking for. I don't have all my info in
> front of me (again), but
>
> http://www.rawbw.com/~deano/articles/JordanvsOlaj.html
>
> shows that Jordan was 19.40.7 in 1988. I don't think the Bulls
> that good that year, maybe 4042 (help?). So, sure, it is indeed
OK. I'm back home and the Bulls were 5032 in 1988. In 1987, the
> very possible for winbased ranking methodologies to show stars (or
> superstars in this case) on mediocre teams.
Bulls were 4042 and Jordan was 17.33.7. That's about as close to
half a team's wins I can quickly find. Jordan scored 37 ppg with
Oakley and John Paxson as principal surrounding cast. With Pippen
and Grant around the following year, the team D got a little better
and Jordan's O got a little more efficient (his FG% went from 48% to
54%). Was Jordan a better player in 1988 than in 1987 because his
winloss record was better, because his offensive and defensive
numbers improved? Hell, I don't know. I don't really care. It was
obvious that he would improve both an offense and a defense. If the
Lakers offered me Magic Johnson at the time for Jordan, would I have
taken it? I guess we'll never know....
Dean Oliver
Journal of Basketball Studies 0 Attachment
On Tue, 5 Feb 2002, HoopStudies wrote:
> Also, if you have a team that wins only 15 games, it doesn't make
^^^^^^^^^^^
> sense to have one player on that team who wins 16 games like a Shaq
> or a Jordan or Duncan do. Those guys make winning teams. Elton
> Brand, though a good player, clearly doesn't add more than the 15
> wins that Chicago had last year; it's impossible. My first cut was
> an addition of 6 wins by Brand. I frankly am coming to believe that
> it was more like 9 wins, but that's all a little theoretical right
> now. And it always seems strange to me if someone contributes more
> than half of his team's wins (especially since the Bulls are on pace
I think it depends on how were define "contributes"; see below.
> to beat 15 wins this year). It's hard to argue that even Jordan ever
> won half his team's games when he was scoring 35 ppg and the Bulls
The 19.40.7 wonloss record for Jordan I am okay with. From your
> were winning only 40 games. Actually, Jordan should be a very good
> example of what MikeG was asking for. I don't have all my info in
> front of me (again), but
>
> http://www.rawbw.com/~deano/articles/JordanvsOlaj.html
>
> shows that Jordan was 19.40.7 in 1988. I don't think the Bulls were
> that good that year, maybe 4042 (help?). So, sure, it is indeed
> very possible for winbased ranking methodologies to show stars (or
> superstars in this case) on mediocre teams.
article, it appears to be based on a solid notion of looking at a player's
offensive and defensive ratings, comparing those to what teams of similar
off. and def. ratings would achieve in wonloss terms, and calling that
the player's wonloss record. I have no problem with that.
But I don't think Jordan's 19.40.7 wonloss record can be regarded as
being on the same measurement scale as the Bulls' 4042 record (actually
they were 5032 in 1988, but that doesn't matter). Nor can Brand's 6 or 9
individual victories be compared to the Bulls' total of 15.
We can calculate the Bulls' individual wonloss records in 1988, but we
cannot say that the sum of those wonloss records should equal 4042 (or
5032), nor can we say that 19.4 is almost half of 40. Those are apples
and oranges.
If we do want to claim that those 19.4 individual wins can be directly
compared to the team's 40 or 50 wins, we are imposing an overly simplistic
model upon how a team's record is determined by its players production.
Implicitly, it requires that the model be: Bulls Wins == sum of Jordan's
wins + Pippen's wins + Grant's wins + etc. etc. And that equation is
almost certainly an incorrect one for determining how individual players,
when put on a team, determine the team's wonloss record. It is extremely
unlikely that the correct model is a simple linear sum.
And if it is not a simple linear sum, then we can't directly compare
Jordan's 19.4 wins to the Bulls' 40 or 50 wins.
An analogy: if someone gets a 1400 SAT score, and such students usually
get 3.6 GPAs in college, we cannot say that the student's college GPA is
3.6/1400 = .0026 of their SAT score. Well we can say it, but it's not a
useful calculation. Nor is it useful to say that Brand or Jordan
contributed to half of their teams totals, based on their individual
wonloss stats.
It's much the same problem that you've pointed out with linear weights
systems: much of the world is not linear. If 10% of Microsoft's costs
are spent on systems analysts, can we claim that systems analysts
contribute to 10% of Microsofts production? It's not a useful ratio (the
second one I mean; the 10% of costs figure is useful for analyzing costs);
if Microsoft cut its systems analysts roster in half, would its production
fall by half? If it doubled its roster, would its production double? No
and no. Nor can we say that Jordan's 19.4 wins are about half of the
Bulls 40 wins.
Yet another way of looking at it: divvying up the 40 wins and saying that
soandso is responsible for x of them is an exercise doomed to failure.
How many of the 40 wins were due to Jordan, Pippen, etc.? After we finish
divvying them up, we then better ask: wait, how many wins would the Bulls
have had if they didn't have a coach? And for that matter an equipment
manager, ticket takers, stadium maintenance, etc.
Just as we can't look at Microsoft's sales of x million pieces of software
and say "Bill Gates produced y million pieces of software, Steve Ballmer
produced z million of them, the new programmer they hired produced w of
them, etc." That linear divvying up of production is not how the
production function works. Nor can a team's wins be linearly divvied up
among its players.
What we CAN do with players is try to estimate their MARGINAL value: how
many wins did they contribute compared to how many player X would have
contributed (where X could be a player that Jordan was traded for, or a
chosen comparison player, or a replacement level player if we could agree
on what the replacement level is, or whatever). And the 19.4 wins and 0.7
losses might be good estimates of that marginal value.
BUT: there is no requirement that the sum of players' marginal wins
equate to the team's total wins. Only with (in economics terms) "constant
returns to scale"  e.g. a simple linear sum  would that happen.
The marginal values can often be wellapproximated by linear methods.
But at extreme values even the marginal wins can't be interpreted
literally, or in a linear fashion. Does adding Jordan add 19 wins to a
team's total? Could be, in the case of the 2002 Wizards compared to 2001.
But if we're looking at the 1972 6913 Lakers, it is mathematically
impossible that adding Jordan to their roster would add 19 wins to their
total.
Similarly, if Jordan were to play for a really bad team that won only 15
games, would we say that subtracting Jordan from that team would cause
them to decrease their win total by 19?
Yet 19.40.7 might still be quite a good measure of Jordan's prowess. But
we can't interpret that 19.4 figure as one that can be directly compared
to the Bulls' 40 wins, or 15 wins, or 69 wins, or whatever their total is.
We can say that Jordan "contributed" 19.4 wins at the margin, but that
does not literally mean that he would add 19 victories to a team's total.
Wizards, maybe yes. 1972 Lakers no.
Nonlinearity. Individual wonloss records can be a fine way of
measuring players' production, but the jump from those individual wonloss
records to the team's actual wonloss record is not a simple one. Team
stats are a complex function of the stats of the individual players. Not
a simple linear sum.
And therefore Brand's, or Jordan's, individual victories cannot be
directly compared to their team's victories.
MKT 0 Attachment
 In APBR_analysis@y..., "Michael K. Tamada" <tamada@o...> wrote:>
wonloss
> Nonlinearity. Individual wonloss records can be a fine way of
> measuring players' production, but the jump from those individual
> records to the team's actual wonloss record is not a simple one.
Team
> stats are a complex function of the stats of the individual
players. Not
> a simple linear sum.
Whether or not the theoretical analysis is ok, it is tempting to do
>
> And therefore Brand's, or Jordan's, individual victories cannot be
> directly compared to their team's victories.
exactly this because the sum of individual winloss records does
almost always come very close to the team winloss total. I agree
that the model is simplistic. The fact that the sum is the team
total is why I call it a pseudomeasurement. There is a reality
check on it to some degree. And the model for getting there is
simple. Eh. Whatevah.
In terms of predictions, individual winloss records don't work,
despite my hopes nearly 15 years ago. I have actually come close to
proving that it is theoretically impossible to have a simple number
that allows you to predict a team's winloss record with that player
in place of another. Even the net points stuff I have, which comes
closer. It is practically impossible to remove context. Your 6913
Laker team is a good example, I think, reflecting how context is
important in making predictions. Or consider a team that wins by 10
ppg. Replace a player who contributes net 1 ppg with one who
contributes net 6 ppg is very unlikely to make that team win by 15
ppg because the team doesn't need the extra 5 ppg to win. Very
context sensitive.
Anyway, we can find reasons to discard EVERY single number we
calculate here. Individual winloss records are simple scans of
contribution that do sum to the team total, giving them a reality
check that linear weights do not have. I like that conceptually. I
don't claim it's predictive (why I pointed out the Brand conundrum),
but no one is putting forth any way to make those predictions.
Unfortunately.
Dean Oliver
Journal of Basketball Studies 0 Attachment
On Wed, 6 Feb 2002, HoopStudies wrote:
>  In APBR_analysis@y..., "Michael K. Tamada" <tamada@o...> wrote:
[...]
> >
> > Nonlinearity. Individual wonloss records can be a fine way of
> > measuring players' production, but the jump from those individual
> wonloss
> > records to the team's actual wonloss record is not a simple one.
> despite my hopes nearly 15 years ago. I have actually come close to
If "simple" includes "linear" yes, models that are that simple will not
> proving that it is theoretically impossible to have a simple number
work.
> that allows you to predict a team's winloss record with that player
Yes, diminishing marginal returns, and other nonlinearities abound.
> in place of another. Even the net points stuff I have, which comes
> closer. It is practically impossible to remove context. Your 6913
> Laker team is a good example, I think, reflecting how context is
> important in making predictions. Or consider a team that wins by 10
> ppg. Replace a player who contributes net 1 ppg with one who
> contributes net 6 ppg is very unlikely to make that team win by 15
> ppg because the team doesn't need the extra 5 ppg to win. Very
> context sensitive.
> Anyway, we can find reasons to discard EVERY single number we
I hope I made it clear that I was not criticizing the individual wonloss
> calculate here. Individual winloss records are simple scans of
records as measures of player quality, nor as measures of marginal
contributions to wins.
> contribution that do sum to the team total, giving them a reality
This is the part that is troublesome. It's nice that they sum to the team
> check that linear weights do not have. I like that conceptually. I
total, but on the whole I think that doesn't really tell us much about the
validity of the model. With suitable normalization, most or at any
rate many rating schemes could be made to have sums which come close to
adding up to the team's win total.
> don't claim it's predictive (why I pointed out the Brand conundrum),
I suspect it's part of the datafitting problem in statistics. When we
have a set of data, it's pretty easy to come up with a model that fits
that data set really well. But such models usually perform poorly when
used to make actual predictions on outofsample data (i.e. real world
predictions).
Good predictive models are very hard to create. Just ask any economist to
try to predict when the next recession will come. Or any geologist when
the next big earthquake will hit Los Angeles.
> but no one is putting forth any way to make those predictions.
Yes, part of the Holy Grail again: how do individual players' qualities
> Unfortunately.
(and statistics measuring those qualities) combine into determining the
team's outcome? A problem that is difficult enough in baseball and harder
still in basketball.
MKT 0 Attachment
 In APBR_analysis@y..., "HoopStudies" <deano@r...> wrote:>
"The Bulls are better without Brand" is only half a comment.
>...... The Clips are better with Brand. The
> Bulls are better without Brand. So how good is Brand? Context
> sensitive.
"...than they would be if they still had him"?
or "...than they were when they had him"?
One might imagine that 1036 is a better mark than 1567, but the
Bulls' average score is 85.794.4, compared to 87.596.6 last year.
No significant change in the scoring.
Ron Artest is suddenly a star this year. Brad Miller and Marcus
Fizer are suddenly serious players. Mercer and Hoiberg have dropped
off, but Anthony has come along, with Oakley, while nobody
significant has been dumped.
With the coaching change, I would agree "the Bulls are better"; but
with Brand they might actually be contending. 0 Attachment
 In APBR_analysis@y..., "mikel_ind" <msg_53@h...> wrote:> >...... The Clips are better with Brand. The
year.
> > Bulls are better without Brand. So how good is Brand? Context
> > sensitive.
>
> "The Bulls are better without Brand" is only half a comment.
>
> "...than they would be if they still had him"?
>
> or "...than they were when they had him"?
>
> One might imagine that 1036 is a better mark than 1567, but the
> Bulls' average score is 85.794.4, compared to 87.596.6 last
> No significant change in the scoring.
dropped
>
> Ron Artest is suddenly a star this year. Brad Miller and Marcus
> Fizer are suddenly serious players. Mercer and Hoiberg have
> off, but Anthony has come along, with Oakley, while nobody
Speculation. Would Fizer and Artest and Miller have "suddenly"
> significant has been dumped.
>
> With the coaching change, I would agree "the Bulls are better"; but
> with Brand they might actually be contending.
improved with Brand there? (I don't think Artest is a star, but
haven't fully looked at his numbers.) Maybe Brand was a negative
influence, keeping down the hopes of these guys. It's a plausible
story, if just because Brand was getting all the touches.
The only thing that is clear is that it wasn't that hard to make up
for Brand's loss; they didn't drop to a 4 win franchise and it's hard
to name any 15 win team that got worse by losing its "best player".
DeanO 0 Attachment
 In APBR_analysis@y..., "Michael K. Tamada" <tamada@o...> wrote:> > contribution that do sum to the team total, giving them a reality
conceptually. I
> > check that linear weights do not have. I like that
>
the team
> This is the part that is troublesome. It's nice that they sum to
> total, but on the whole I think that doesn't really tell us much
about the
> validity of the model. With suitable normalization, most or at any
close to
> rate many rating schemes could be made to have sums which come
> adding up to the team's win total.
I agree that normalization can make anything work and that is
cheating. The normalization I do is only on the "games" that
individuals play. I make that sum to 82 because there is no real
concept of what consists of an "individual game". The indiv win%
comes directly from the way James does it in baseball.
Dave Berri obviously had another method where the win sum was about
the team total. His results were different than mine. There are a
lot of rating schemes that can sum to the team's total. The
justification for using them is the process in which they were
developed. If you like Berri's theory, you can use his. At least he
makes an attempt to relate individual performance to team success. I
just personally don't like some aspects of how he does it. I don't
particularly like the determination of "individual games" in my
method, but I like it overall better than Berri's. And that's all
there is (maybe  I think Craven has something, but I don't know the
process).
>
conundrum),
> > don't claim it's predictive (why I pointed out the Brand
>
When we
> I suspect it's part of the datafitting problem in statistics.
> have a set of data, it's pretty easy to come up with a model that
fits
> that data set really well. But such models usually perform poorly
when
> used to make actual predictions on outofsample data (i.e. real
world
> predictions).
economist to
>
> Good predictive models are very hard to create. Just ask any
> try to predict when the next recession will come. Or any geologist
when
> the next big earthquake will hit Los Angeles.
Or ask a hydrogeologist like me when predicting how contaminants
>
migrate through groundwater. The best thing you can do is provide
ranges of realistic estimates, based on a rigorous process using as
much data as possible and using as much physics about the
interactions as possible. You can do better.
DeanO 0 Attachment
 In APBR_analysis@y..., "HoopStudies" <deano@r...> wrote:>
For that matter, would Mercer and Hoiberg have slipped, with Brand
> Speculation. Would Fizer and Artest and Miller have "suddenly"
> improved with Brand there?
still there?
>(I don't think Artest is a star, but
Artest's standardized rates are (thru 23 games):
> haven't fully looked at his numbers.)
18.9 pts, 6.5 reb, 3.6 ast, 3.1 steals, 2.9 TO, .9 blocks
.516 combined shooting
The 3.1 steals rate leads the league by a large margin (2nd is
Iverson at 2.4)
> Maybe Brand was a negative
I was never discouraged by having a great player on my team, so I
> influence, keeping down the hopes of these guys.
> It's a plausible
> story, if just because Brand was getting all the touches.
don't fathom this thinking.
> The only thing that is clear is that it wasn't that hard to make up
If 15 wins is good enough, I guess.
> for Brand's loss;
>they didn't drop to a 4 win franchise and it's hard
player".
> to name any 15 win team that got worse by losing its "best
>
The "regression to the mean" principle would suggest that any 15win
> DeanO
team is almost certain to improve, no matter how you mix it up. 0 Attachment
On Wed, 6 Feb 2002, HoopStudies wrote:
[...]
> The only thing that is clear is that it wasn't that hard to make up
> for Brand's loss; they didn't drop to a 4 win franchise and it's hard
> to name any 15 win team that got worse by losing its "best player".
This is one of the extreme cases where the nonlinearities become
important. If we measure players by their individual wins and losses and
furthermore require that those sum to 15, then no Bull can appear to be
as highly productive as Jordan was with his 19 individual wins.
But in situations such as these where we're looking at wonloss
percentages, it's probably a better idea to look at odds instead of
probabilities, or even the logarithm of odds (known as a logit
transformation). Things may become linear with respect to odds or to
logits, which are nonlinear with respect to probabilities.
Two examples: odds ratios are what's behind the log5 method for
predicting win probabilties that some mathematician friend introduced Bill
James to.
And here's an example of how logits could be applied: For the 1567 Bulls
is ln(15/67) ~ 1.50. (Their odds of winning were 15/67 = .22, and their
probability of winning of course was 15/82 = .18.)
If we were lucky and life were relatively simple, Elton Brand's
contributions to the Bulls might be linear with respect to a logit, e.g.
subtracting him from the Bulls and replacing him with a pretty much
useless (for this year) high school player might hurt the Bulls to the
tune of 0.4 logits. For a team that had been 1567, the new logit would
be 1.9, the new odds would be exp(1.9) = .13, the new probability would
be .13/(.13+1) = .13, and the number of victories would be 10.7. So
losing Brand would cost the Bulls about 4 victories (which could of course
be counteracted by increased production from Artest, etc.  another
nonlinearity that we'd have to deal with).
Adding Brand to the Clippers, assuming that the other players' didn't
change (probably not a good assumption, nonlinearities again), would help
them by +.4 logits. So their 3151 2001 team, which had had a logit of
.50, now has a logit of .10, and therefore odds of .90, probability of
.475, and 39 wins. So Brand adds 8 wins to the Clips, in contrast to the
loss of 4 wins by the Bulls. (Obviously some of the Clips' wins would
therefore have to come at the expense of some team other than the Bulls,
nonlinearity again.)
That's a nice simple yet nonlinear model: Brand's quality measure stays
constant at .4 logits, but that translates into 4 marginal victories for
the Bulls and 8 marginal victories for the Clippers.
Unfortunately, this all assumes that (a) the logit function is the correct
functional form and (b) that the other players' production stays constant
(and of course there will be other roster changes which add even further
complications).
Life is undoubtedly not so simple, so I'm not claiming that that model
will actually work in terms of predictive value.
One thing which I've been meaning to try for years but never gotten around
to however is to use this kind of model to look just at rebounding. It's
a smaller, simpler task than trying to model offenses, defenses, or team
wins. It's clearly going to be a nonlinear process: if Tim Duncan gets
added to the Spurs and replaces ... who'd he replace, Carl Herrera?
Anyway, if Herrera was getting 4.5 rebounds per game and Duncan gets 12
per game, it is clearly not correct to predict that the Spurs will gain an
additional 7.5 rebounds per game. Some of Duncan's rebounds will come at,
so to speak, the expense of teammates. Yet he clearly should cause some
improvement to the Spurs' rebounding. I wonder if odds or logit measures
could be used so that players' rebounding quality stays constant even
though their teammates' and team's rebounds may change.
Such a measure wouldn't meet the "David Wesley" test that alleyoop2
suggested: we know that players' rebound stats will change when they
change positions (centers get more than power forwards, thanks to their
inside position). But it might pass the Greg Anthony test: a good
rebounder going to different teams or having different teammates (maybe
Dennis Rodman, maybe Vin Baker) might end up with a constant rebounding
score using these models, even though his reboundsper48minutes would
change.
MKT 0 Attachment
 In APBR_analysis@y..., "Michael K. Tamada" <tamada@o...> wrote:> > The only thing that is clear is that it wasn't that hard to make
up
> > for Brand's loss; they didn't drop to a 4 win franchise and it's
hard
> > to name any 15 win team that got worse by losing its "best
player".
>
losses and
> This is one of the extreme cases where the nonlinearities become
> important. If we measure players by their individual wins and
> furthermore require that those sum to 15, then no Bull can appear
to be
> as highly productive as Jordan was with his 19 individual wins.
Some of my final thoughts (before I go absolutely insane from working
>
on too many things at the same time).
1. I like playing devil's advocate. It was clear that Brand was the
best Bull last year and that he is at least a good player. His loss
was easily replaced because shaking up a bad team just helps improve
things. You add noise to your team when you make big changes to
raise it out of the consistent stinkhole that it resides in. No
offense to Chicagoans intended. It is hard to see how the Bulls
would be much better than their current record if Brand were
2. It is possible, though unlikely, for your hypothetical situation
to occur, where a Jordan with 19 wins plays on a team with 15 wins.
This is because I do not normalize to constrain the sum to 15, nor
did I develop the individual games to make the wins sum to the right
number.
3. Thanks for the info on logits. Saves me from reading about them
in some bori..., err, fun economics book! They do illustrate the
effect that definitely occurs in basketball, where one player is more
beneficial for one team than for another. I'm not convinced that
logits are the right functional form or how I'd use them yet. But
it's good to understand them well enough to consider it as I continue
research. Haven't seen the log5 method. Where is it?
4. If basketball were simple, none of us would be discussing this.
5. Where is Bob Chaikin? He has his simulation program that can be
used to determine the effect of replacing Brand with other guys. I'd
be curious to hear what it says.
Gotta go have cake.
DeanO
> But in situations such as these where we're looking at wonloss
to
> percentages, it's probably a better idea to look at odds instead of
> probabilities, or even the logarithm of odds (known as a logit
> transformation). Things may become linear with respect to odds or
> logits, which are nonlinear with respect to probabilities.
introduced Bill
>
> Two examples: odds ratios are what's behind the log5 method for
> predicting win probabilties that some mathematician friend
> James to.
67 Bulls
>
> And here's an example of how logits could be applied: For the 15
> is ln(15/67) ~ 1.50. (Their odds of winning were 15/67 = .22, and
their
> probability of winning of course was 15/82 = .18.)
e.g.
>
> If we were lucky and life were relatively simple, Elton Brand's
> contributions to the Bulls might be linear with respect to a logit,
> subtracting him from the Bulls and replacing him with a pretty much
the
> useless (for this year) high school player might hurt the Bulls to
> tune of 0.4 logits. For a team that had been 1567, the new logit
would
> be 1.9, the new odds would be exp(1.9) = .13, the new probability
would
> be .13/(.13+1) = .13, and the number of victories would be 10.7. So
course
> losing Brand would cost the Bulls about 4 victories (which could of
> be counteracted by increased production from Artest, etc.  another
didn't
> nonlinearity that we'd have to deal with).
>
> Adding Brand to the Clippers, assuming that the other players'
> change (probably not a good assumption, nonlinearities again),
would help
> them by +.4 logits. So their 3151 2001 team, which had had a
logit of
> .50, now has a logit of .10, and therefore odds of .90,
probability of
> .475, and 39 wins. So Brand adds 8 wins to the Clips, in contrast
to the
> loss of 4 wins by the Bulls. (Obviously some of the Clips' wins
would
> therefore have to come at the expense of some team other than the
Bulls,
> nonlinearity again.)
stays
>
>
> That's a nice simple yet nonlinear model: Brand's quality measure
> constant at .4 logits, but that translates into 4 marginal
victories for
> the Bulls and 8 marginal victories for the Clippers.
correct
>
> Unfortunately, this all assumes that (a) the logit function is the
> functional form and (b) that the other players' production stays
constant
> (and of course there will be other roster changes which add even
further
> complications).
model
>
> Life is undoubtedly not so simple, so I'm not claiming that that
> will actually work in terms of predictive value.
around
>
>
> One thing which I've been meaning to try for years but never gotten
> to however is to use this kind of model to look just at
rebounding. It's
> a smaller, simpler task than trying to model offenses, defenses, or
team
> wins. It's clearly going to be a nonlinear process: if Tim
Duncan gets
> added to the Spurs and replaces ... who'd he replace, Carl Herrera?
gets 12
> Anyway, if Herrera was getting 4.5 rebounds per game and Duncan
> per game, it is clearly not correct to predict that the Spurs will
gain an
> additional 7.5 rebounds per game. Some of Duncan's rebounds will
come at,
> so to speak, the expense of teammates. Yet he clearly should cause
some
> improvement to the Spurs' rebounding. I wonder if odds or logit
measures
> could be used so that players' rebounding quality stays constant
even
> though their teammates' and team's rebounds may change.
they
>
> Such a measure wouldn't meet the "David Wesley" test that alleyoop2
> suggested: we know that players' rebound stats will change when
> change positions (centers get more than power forwards, thanks to
their
> inside position). But it might pass the Greg Anthony test: a good
(maybe
> rebounder going to different teams or having different teammates
> Dennis Rodman, maybe Vin Baker) might end up with a constant
rebounding
> score using these models, even though his reboundsper48minutes
would
> change.
>
>
> MKT 0 Attachment
 HoopStudies <deano@...> wrote:>  In APBR_analysis@y..., "mikel_ind" <msg_53@h...>
The Bulls appeared to be headed toward 1012 wins tops
> wrote:
> > >...... The Clips are better with Brand. The
> > > Bulls are better without Brand. So how good is
> Brand? Context
> > > sensitive.
> >
> > "The Bulls are better without Brand" is only half
> a comment.
> >
> > "...than they would be if they still had him"?
> >
> > or "...than they were when they had him"?
> >
> > One might imagine that 1036 is a better mark than
> 1567, but the
> > Bulls' average score is 85.794.4, compared to
> 87.596.6 last
> year.
> > No significant change in the scoring.
> >
> > Ron Artest is suddenly a star this year. Brad
> Miller and Marcus
> > Fizer are suddenly serious players. Mercer and
> Hoiberg have
> dropped
> > off, but Anthony has come along, with Oakley,
> while nobody
> > significant has been dumped.
> >
> > With the coaching change, I would agree "the Bulls
> are better"; but
> > with Brand they might actually be contending.
>
> Speculation. Would Fizer and Artest and Miller have
> "suddenly"
> improved with Brand there? (I don't think Artest is
> a star, but
> haven't fully looked at his numbers.) Maybe Brand
> was a negative
> influence, keeping down the hopes of these guys.
> It's a plausible
> story, if just because Brand was getting all the
> touches.
>
> The only thing that is clear is that it wasn't that
> hard to make up
> for Brand's loss; they didn't drop to a 4 win
> franchise and it's hard
> to name any 15 win team that got worse by losing its
> "best player".
early in the season. Then two things improved the team
dramatically. Bill Cartwright took over as coach and
Ron Artest returned.
Under Bill they're 713 with a 89.893.2 point diff.
Hardly a threat to the Lakers (even though they swept
that season series. Figure those odds), but definitely
out of the stinkhole. Artest has played like a star. I
wouldn't go so far as to call him a star just yet. He
plays in such a frenzied style that he can be
extremely erratic. I suspect he has a serious slump in
him before the season ends.
As for Brand, there's little doubt in my mind that
this would be a team on the fringes of the EC playoffs
had Brand stuck around. That said, I have to say that
I do like the deal the Bulls made. A solid, usable PF
is something that's pretty easy to find. Not one of
Brand's calibre, but one that a team can get by with.
Tyson Chandler has a chance to be a special player. It
may take another year or two, but the talent is
obvious. I've don't think I've ever seen a player this
tall who was as athletic.
Ed Weiland
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 In APBR_analysis@y..., Ed Weiland <weiland1029@y...> wrote:> Tyson Chandler has a chance to be a special player. It
You've said this before. Given how rarely the Bulls are on TV, it's
> may take another year or two, but the talent is
> obvious. I've don't think I've ever seen a player this
> tall who was as athletic.
no surprise I haven't seen him, but I am really curious, though. Who
do you think he is most similar to? Most people say "tall"
and "athletic" and they are referring to Kevin Garnett. Is that
realistic?
Dean Oliver
Journal of Basketball Studies 0 Attachment
Imagine a taller, faster David Robinson with no offensive game.
Original Message
From: deano@...
Sent: Thursday, February 07, 2002 9:27 AM
To: APBR_analysis@yahoogroups.com; deano@...
Subject: [APBR_analysis] Re: nice methods
 In APBR_analysis@y..., Ed Weiland <weiland1029@y...> wrote:
> Tyson Chandler has a chance to be a special player. It
> may take another year or two, but the talent is
> obvious. I've don't think I've ever seen a player this
> tall who was as athletic.
You've said this before. Given how rarely the Bulls are on TV, it's
no surprise I haven't seen him, but I am really curious, though. Who
do you think he is most similar to? Most people say "tall"
and "athletic" and they are referring to Kevin Garnett. Is that
realistic?
Dean Oliver
Journal of Basketball Studies
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On Thu, 7 Feb 2002, HoopStudies wrote:
>  In APBR_analysis@y..., Ed Weiland <weiland1029@y...> wrote:
I'm curious too. I've only seen him once, in the LA Pro Summer League
> > Tyson Chandler has a chance to be a special player. It
> > may take another year or two, but the talent is
> > obvious. I've don't think I've ever seen a player this
> > tall who was as athletic.
>
> You've said this before. Given how rarely the Bulls are on TV, it's
> no surprise I haven't seen him, but I am really curious, though. Who
> do you think he is most similar to? Most people say "tall"
> and "athletic" and they are referring to Kevin Garnett. Is that
> realistic?
(actually I think it was called the Dada Summer League this past summer).
At that point he was extremely raw and the two crossroads for him that I
saw led to (a) Kevin Garnett and (b) Brad Sellers. It was absolutely too
early to tell which way he would end up.
People who have seen him more often, throughout the season, are in a much
better position to evaluate. But unless he starts making some obvious
Kobetype teenage strides, it could easily be 34 years before we know
where he'll end up. Or maybe it'll only take 12 years as Ed Weiland
says.
MKT 0 Attachment
On Wed, 6 Feb 2002, Michael K. Tamada wrote:
> Two examples: odds ratios are what's behind the log5 method for
I deleted the email, but I think someone asked about Bill James' "log5
> predicting win probabilties that some mathematician friend introduced Bill
> James to.
method". That's simply his name for his formula (not as well known but
much cleverer than his Pythagorean formula) for calculating the expected
win probability when, say, a 75% winprobability team plays a 25%
winprobability team. I don't know where the log or the 5 comes from, but
the formula can be derived from standard probability formulas, I think
with a small assumption about functional form thrown in.
The really fantastic more general version of the formula is for situations
which are not inherently 5050 balanced, such as batters' probability of
getting a hit against a pitcher. Someone told me that version can also be
derived from probability theory, but I haven't been able to do it.
Despite the name, the formulas use odds ratios, not logarithms. Actually
come to think of it I don't think Bill James put the formulas in terms of
odds ratios, he used probabilities. But the formulas are much simpler
and cleaner when cast in odds terms.
MKT
P.S. For those who are interested, the formulas.
First, odds are calculated from probabilities by this definition:
odds = p/(1p)
e.g. a 75% probability is equivalent to 75/25 = 3:1 odds.
The log5 formula says to find the probability of Team A beating Team B,
look at Team A's odds of winning (based on their overall wonloss record,
or whatever source of probability estimates you want to use), call those
OddsA. Call Team B's odds OddsB.
Then Team A's odds of winning against Team B are simply OddsA/OddsB.
Example: if A wins 75% of the time, and B wins 25% of the time, then
intuitively Team A should have an extremely high probability of beating
Team B, we're talking Sacramento Kings vs Chicago Bulls. Their respective
odds are 3 and 1/3, so Team A's odds of beating Team B are 3/(1/3) = 9.
9:1 odds convert into a 90% probability, using the inverse of the odds
definition: p = (odds/(1+odds).
Next, the more general version of the formula, let's use a baseball
example: a .333 hitter faces a pitcher who gives up hits at a .333 rate.
What is the expected probability that the batter will get a hit?
Intuitively, it's going to be a lot larger than .333, because we're
clearly talking about an ace hitter and a bad pitcher here.
Because the overall odds (overall meaning throughout baseball) of getting
a hit are not 1:1 (the probability is not 5050, unlike teams' wonloss
records), we need a third parameter: the overall odds of getting a hit.
Let's assume that on average, batters hit .280. So their typical odds are
.280/.720 = .389, lets call this OddsO for overall odds.
The .333 hitter's odds, let's call them OddsH, are .333/.667 = .500. For
the pitcher, we need to look at his odds of success (not his odds of
giving up a hit); the pitcher gets batters out .667 of the time so his
odds of success are .667/.333 = 2, let's call the pitcher's odds OddsP.
The log5 formula for the batter's odds of getting a hit are
(OddsB/OddsP)/OddsO
i.e. the same formula, but divided by the overall odds, OddsO.
In our example this is (.500/2)/.389 = 9/14 = .643.
Converting that into probabilities, the batter has a .643/(1+.643) = .391
probability of getting a hit.
Obviously these are general, overall calculations. There may be
individual quirks in the matchup that cause one player or team to do
unusually well or poorly against certain opponents (Bulls vs Lakers this
season, although that's probably a random fluke).
Bill James said in one of his Baseball Abstracts that he got these
formulas from a mathematician friend, but I have not seen a complete
citation or derivation of them.
MKT 0 Attachment
 "Michael K. Tamada" <tamada@...> wrote:>
Garnett is probably a stretch. One spin on draft day
>
> On Thu, 7 Feb 2002, HoopStudies wrote:
>
> >  In APBR_analysis@y..., Ed Weiland
> <weiland1029@y...> wrote:
> > > Tyson Chandler has a chance to be a special
> player. It
> > > may take another year or two, but the talent is
> > > obvious. I've don't think I've ever seen a
> player this
> > > tall who was as athletic.
> >
> > You've said this before. Given how rarely the
> Bulls are on TV, it's
> > no surprise I haven't seen him, but I am really
> curious, though. Who
> > do you think he is most similar to? Most people
> say "tall"
> > and "athletic" and they are referring to Kevin
> Garnett. Is that
> > realistic?
>
> I'm curious too. I've only seen him once, in the LA
> Pro Summer League
> (actually I think it was called the Dada Summer
> League this past summer).
> At that point he was extremely raw and the two
> crossroads for him that I
> saw led to (a) Kevin Garnett and (b) Brad Sellers.
> It was absolutely too
> early to tell which way he would end up.
tried to sell Chandler and Curry as a future
ShaqGarnett tandem, which is pretty ridiculous, IMO.
I doubt Chandler will ever develop Garnett's
allaround game. He just doesn't seem to have the same
personality. He seems more like a Webber/Barkley type
personalitywise. That being an OK guy who sometimes
rubs folks the wrong way. Not exactly an MJ when it
comes to leadership. That's just my initial take on
him though. I could easily be way off the mark here. I
was concerned about Chandler being another Brad
Sellers at first too. I doubt that will happen.
Chandler is more athletic than Sellers and he doesn't
play soft. Reckless yes, but not soft.
Comparing him to other guys who skipped college in
their rookie years, right now his offense comes up
short compared to Garnett, Kobe and TMac. It seems
that most of Chandler's points come from put backs,
alleyoops and other high percentage shots. He's said
to have good range, but I've yet to see it during a
game. He also commits a ton of turnovers. On defense
he looks pretty good. He uses his height and quickness
very well. He does get burned on occasion, but that's
to be expected from any rookie, let alone one straight
from the preps. Also, he doesn't look completely
overmatched, as Jonathan Bender did his first couple
of seasons. Most of Chandler's problems seem to come
from being tentative on the court. That's a common
problem with rooks and it usually corrects itself in
time.
As far as comparing him to one guy, I would say
Stuart's assessment "A taller, quicker David Robinson
with no offense" is about as accurate as any. I
suppose the more pessimistic types would call him a
Brad Lohaus who can jump.
> People who have seen him more often, throughout the
Keeping in mind of course that Ed Weiland is just a
> season, are in a much
> better position to evaluate. But unless he starts
> making some obvious
> Kobetype teenage strides, it could easily be 34
> years before we know
> where he'll end up. Or maybe it'll only take 12
> years as Ed Weiland
> says.
fan and possibly an overly optimistic one at that, one
might be better off trusting the experts here. Since
Chandler has moved into the starting lineup, I'm
guessing he'll get about 1000 more minutes this
season. At season's end we should have a better idea
of what he's going to become.
Ed Weiland
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I hate to mention this, but can we change the Subject of the posts
when the topic changes?
I personally don't have too much of an interest in Tyson Chandler,
but I would love to read more about the mathmatical methods used
here. Changing the subject prevents everyone from reading through
irrelevant material.
Thanks,
LKM
 In APBR_analysis@y..., Ed Weiland <weiland1029@y...> wrote:
>
>  "Michael K. Tamada" <tamada@o...> wrote:
> >
> >
> > On Thu, 7 Feb 2002, HoopStudies wrote:
> >
> > >  In APBR_analysis@y..., Ed Weiland
> > <weiland1029@y...> wrote:
> > > > Tyson Chandler has a chance to be a special
> > player. It
> > > > may take another year or two, but the talent is
> > > > obvious. I've don't think I've ever seen a
> > player this
> > > > tall who was as athletic.
> > >
> > > You've said this before. Given how rarely the
> > Bulls are on TV, it's
> > > no surprise I haven't seen him, but I am really
> > curious, though. Who
> > > do you think he is most similar to? Most people
> > say "tall"
> > > and "athletic" and they are referring to Kevin
> > Garnett. Is that
> > > realistic?
> >
> > I'm curious too. I've only seen him once, in the LA
> > Pro Summer League
> > (actually I think it was called the Dada Summer
> > League this past summer).
> > At that point he was extremely raw and the two
> > crossroads for him that I
> > saw led to (a) Kevin Garnett and (b) Brad Sellers.
> > It was absolutely too
> > early to tell which way he would end up.
>
> Garnett is probably a stretch. One spin on draft day
> tried to sell Chandler and Curry as a future
> ShaqGarnett tandem, which is pretty ridiculous, IMO.
> I doubt Chandler will ever develop Garnett's
> allaround game. He just doesn't seem to have the same
> personality. He seems more like a Webber/Barkley type
> personalitywise. That being an OK guy who sometimes
> rubs folks the wrong way. Not exactly an MJ when it
> comes to leadership. That's just my initial take on
> him though. I could easily be way off the mark here. I
> was concerned about Chandler being another Brad
> Sellers at first too. I doubt that will happen.
> Chandler is more athletic than Sellers and he doesn't
> play soft. Reckless yes, but not soft.
>
> Comparing him to other guys who skipped college in
> their rookie years, right now his offense comes up
> short compared to Garnett, Kobe and TMac. It seems
> that most of Chandler's points come from put backs,
> alleyoops and other high percentage shots. He's said
> to have good range, but I've yet to see it during a
> game. He also commits a ton of turnovers. On defense
> he looks pretty good. He uses his height and quickness
> very well. He does get burned on occasion, but that's
> to be expected from any rookie, let alone one straight
> from the preps. Also, he doesn't look completely
> overmatched, as Jonathan Bender did his first couple
> of seasons. Most of Chandler's problems seem to come
> from being tentative on the court. That's a common
> problem with rooks and it usually corrects itself in
> time.
>
> As far as comparing him to one guy, I would say
> Stuart's assessment "A taller, quicker David Robinson
> with no offense" is about as accurate as any. I
> suppose the more pessimistic types would call him a
> Brad Lohaus who can jump.
>
> > People who have seen him more often, throughout the
> > season, are in a much
> > better position to evaluate. But unless he starts
> > making some obvious
> > Kobetype teenage strides, it could easily be 34
> > years before we know
> > where he'll end up. Or maybe it'll only take 12
> > years as Ed Weiland
> > says.
>
> Keeping in mind of course that Ed Weiland is just a
> fan and possibly an overly optimistic one at that, one
>
> might be better off trusting the experts here. Since
> Chandler has moved into the starting lineup, I'm
> guessing he'll get about 1000 more minutes this
> season. At season's end we should have a better idea
> of what he's going to become.
>
> Ed Weiland
>
>
>
>
> __________________________________________________
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Fine idea on the topic change.
Regarding Chandler, I see him as a 7'1" Ralph Sampson. Like Sampson,
he has intriguing physical skills, including something of a jumper,
and makes a lot of athletic plays, but has no post game and a strange
allergy to defensive rebounds.
I saw Chandler play 3 games in Oregon as a high schooler. Some of you
brought it up before, but what struck me is that he didn't seem to
have that disposition to dominate. One team put a 6'3" football
player on him and the guy totally got under Chandler's skin; his team
was actually lucky to even win the game.
That made me think he didn't have the maturity to leap to the NBA,
but perhaps I was wrong on that. He's certainly been better than the
other high schoolers, although I believe his first year numbers will
end up short of what Garnett and TMac did.
 In APBR_analysis@y..., "lk_maxwell" <lk_maxwell@h...> wrote:
> I hate to mention this, but can we change the Subject of the posts
> when the topic changes?
>
> I personally don't have too much of an interest in Tyson Chandler,
> but I would love to read more about the mathmatical methods used
> here. Changing the subject prevents everyone from reading through
> irrelevant material.
>
> Thanks,
>
> LKM
>
>  In APBR_analysis@y..., Ed Weiland <weiland1029@y...> wrote:
> >
> >  "Michael K. Tamada" <tamada@o...> wrote:
> > >
> > >
> > > On Thu, 7 Feb 2002, HoopStudies wrote:
> > >
> > > >  In APBR_analysis@y..., Ed Weiland
> > > <weiland1029@y...> wrote:
> > > > > Tyson Chandler has a chance to be a special
> > > player. It
> > > > > may take another year or two, but the talent is
> > > > > obvious. I've don't think I've ever seen a
> > > player this
> > > > > tall who was as athletic.
> > > >
> > > > You've said this before. Given how rarely the
> > > Bulls are on TV, it's
> > > > no surprise I haven't seen him, but I am really
> > > curious, though. Who
> > > > do you think he is most similar to? Most people
> > > say "tall"
> > > > and "athletic" and they are referring to Kevin
> > > Garnett. Is that
> > > > realistic?
> > >
> > > I'm curious too. I've only seen him once, in the LA
> > > Pro Summer League
> > > (actually I think it was called the Dada Summer
> > > League this past summer).
> > > At that point he was extremely raw and the two
> > > crossroads for him that I
> > > saw led to (a) Kevin Garnett and (b) Brad Sellers.
> > > It was absolutely too
> > > early to tell which way he would end up.
> >
> > Garnett is probably a stretch. One spin on draft day
> > tried to sell Chandler and Curry as a future
> > ShaqGarnett tandem, which is pretty ridiculous, IMO.
> > I doubt Chandler will ever develop Garnett's
> > allaround game. He just doesn't seem to have the same
> > personality. He seems more like a Webber/Barkley type
> > personalitywise. That being an OK guy who sometimes
> > rubs folks the wrong way. Not exactly an MJ when it
> > comes to leadership. That's just my initial take on
> > him though. I could easily be way off the mark here. I
> > was concerned about Chandler being another Brad
> > Sellers at first too. I doubt that will happen.
> > Chandler is more athletic than Sellers and he doesn't
> > play soft. Reckless yes, but not soft.
> >
> > Comparing him to other guys who skipped college in
> > their rookie years, right now his offense comes up
> > short compared to Garnett, Kobe and TMac. It seems
> > that most of Chandler's points come from put backs,
> > alleyoops and other high percentage shots. He's said
> > to have good range, but I've yet to see it during a
> > game. He also commits a ton of turnovers. On defense
> > he looks pretty good. He uses his height and quickness
> > very well. He does get burned on occasion, but that's
> > to be expected from any rookie, let alone one straight
> > from the preps. Also, he doesn't look completely
> > overmatched, as Jonathan Bender did his first couple
> > of seasons. Most of Chandler's problems seem to come
> > from being tentative on the court. That's a common
> > problem with rooks and it usually corrects itself in
> > time.
> >
> > As far as comparing him to one guy, I would say
> > Stuart's assessment "A taller, quicker David Robinson
> > with no offense" is about as accurate as any. I
> > suppose the more pessimistic types would call him a
> > Brad Lohaus who can jump.
> >
> > > People who have seen him more often, throughout the
> > > season, are in a much
> > > better position to evaluate. But unless he starts
> > > making some obvious
> > > Kobetype teenage strides, it could easily be 34
> > > years before we know
> > > where he'll end up. Or maybe it'll only take 12
> > > years as Ed Weiland
> > > says.
> >
> > Keeping in mind of course that Ed Weiland is just a
> > fan and possibly an overly optimistic one at that, one
> >
> > might be better off trusting the experts here. Since
> > Chandler has moved into the starting lineup, I'm
> > guessing he'll get about 1000 more minutes this
> > season. At season's end we should have a better idea
> > of what he's going to become.
> >
> > Ed Weiland
> >
> >
> >
> >
> > __________________________________________________
> > Do You Yahoo!?
> > Send FREE Valentine eCards with Yahoo! Greetings!
> > http://greetings.yahoo.com 0 Attachment
 In APBR_analysis@y..., "Michael K. Tamada" <tamada@o...> wrote:>
introduced Bill
>
> On Wed, 6 Feb 2002, Michael K. Tamada wrote:
>
> > Two examples: odds ratios are what's behind the log5 method for
> > predicting win probabilties that some mathematician friend
> > James to.
James' "log5
>
> I deleted the email, but I think someone asked about Bill
> method". That's simply his name for his formula (not as well known
but
> much cleverer than his Pythagorean formula) for calculating the
expected
> win probability when, say, a 75% winprobability team plays a 25%
from, but
> winprobability team. I don't know where the log or the 5 comes
> the formula can be derived from standard probability formulas, I
think
> with a small assumption about functional form thrown in.
situations
>
> The really fantastic more general version of the formula is for
> which are not inherently 5050 balanced, such as batters'
probability of
> getting a hit against a pitcher. Someone told me that version can
also be
> derived from probability theory, but I haven't been able to do it.
Actually
>
> Despite the name, the formulas use odds ratios, not logarithms.
> come to think of it I don't think Bill James put the formulas in
terms of
> odds ratios, he used probabilities. But the formulas are much
simpler
> and cleaner when cast in odds terms.
Things are coming together for me. I didn't know the method was
>
called log5. I called them matchup probabilities and use them a lot
myself. I can't say that I could quite derive the formula either (it
always seemed that the league average had to be some sort of prior
probability, if you framed it in a Bayes perspective). James said he
got the formula from Dallas Adams. I asked him once about a citation
and he didn't give me a specific one, so I spent some time looking
for it in math/stat journals and couldn't find it there.
>
Or I've got them documented at
> MKT
>
>
> P.S. For those who are interested, the formulas.
>
http://www.rawbw.com/~deano/methdesc.html#matchup
Dean Oliver
Journal of Basketball Studies 0 Attachment
On Fri, 8 Feb 2002, HoopStudies wrote:
>  In APBR_analysis@y..., "Michael K. Tamada" <tamada@o...> wrote:
[...]
> > Despite the name, the formulas use odds ratios, not logarithms.
I found a sort of citation at
> Actually
> > come to think of it I don't think Bill James put the formulas in
> terms of
> > odds ratios, he used probabilities. But the formulas are much
> simpler
> > and cleaner when cast in odds terms.
> >
>
> Things are coming together for me. I didn't know the method was
> called log5. I called them matchup probabilities and use them a lot
> myself. I can't say that I could quite derive the formula either (it
> always seemed that the league average had to be some sort of prior
> probability, if you framed it in a Bayes perspective). James said he
> got the formula from Dallas Adams. I asked him once about a citation
> and he didn't give me a specific one, so I spent some time looking
> for it in math/stat journals and couldn't find it there.
http://www.baseballprospectus.com/news/19980728kushner.html
Apparently Bill James first published the formula in his 1981 Baseball
Abstract (I didn't buy my first one until 1983 and stupidly gave it away
as a present). It sounds as though he came up with the formula himself,
and Dallas Adams provided empirical rather than theoretical evidence in
favor of the formula.
In essence, James' log5 measures of team quality are simply half the
team's winning odds. This permits him to use an additive formula for
calculating expected win probabilities  if Team A has log5 value "Alog5"
and Team B has "Blog5", then in James' formula, Team A's probability of
winning is Alog5/(Alog5+Blog5).
The author of the article in the URL, James Kushner, points out the log5
is simply equal to W/2L, where W is the team's wins and losses.
What these guys are all missing is that "W/2L" is in fact onehalf the
team's odds of winning, and that things become simpler still when
you use odds instead of probabilities. I mean you can't beat a formula
like OddsA/OddsB; that's even simpler than the formula above. Or to put
it another way, the "2" in "W/2L" is redundant.
I've written to Bill James a couple of times about using odds ratios (he
once asked readers for a formula for measuring home field advantage, and I
sent him essentially the formula that DeanO has on his website, and I
pointed out how this all flows simply and naturally from looking at odds
ratios), but never got a reply.
There was apparently discussion of "Dallas Adams formula" in 1997 on the
SABR listserv  see
http://www.pacificnet.net/~sroney/SABRL/index97.html
but I couldn't access the listserv archives, presumably because I'm not a
member of SABR.
MKT 0 Attachment
On Fri, 8 Feb 2002, Ed Weiland wrote:
[...]
> might be better off trusting the experts here. Since
> Chandler has moved into the starting lineup, I'm
> guessing he'll get about 1000 more minutes this
> season. At season's end we should have a better idea
> of what he's going to become.
Yeah, the starting, and the accompanying minutes should
have two benefits: he'll presumably learn more and faster,
and we observers will get a much better sense of how he's
developing.
And I think in cases like this, firsthand observations
such as the ones you've made are necessary. Those of us from
afar can only look at his stats, which will probably be lousy.
But for a 19year old rookie, lousy stats have to be expected,
it what's you see him doing or not doing on the court that is
probably a better forecast of his future.
MKT
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