- --- In APBR_analysis@y..., "Michael K. Tamada" <tamada@o...> wrote:
>

NBA to, to do this. The exponent, of course, is radically different.

>

> On Thu, 10 Oct 2002, Richard Scott wrote:

>

> > Have also seen that Bill James pythagorean method applied to the

>

them

> And then there's DeanO's normal probability model approach. All of

> I'm sure lead to similar results, I wonder if some of them are more

There's

> accurate than others? Or if some are more accurate at the extremes,

> others more accurate at predicting teams with "average" stats.

> different functional forms one can use in the linear regressions

too:

> ratio vs difference, logarithms or straight points, etc.

I once compared the normal probability approach to different

pythagorean exponents and the normal approach is always better. Not

by enough to worry about, though. I'd expect any linear form or

ratio to be similar to the pythagorean. Since the normal approach

takes into account a little more than just points scored and points

allowed (how variable they were in doing so), it should be a little

more accurate. It also allows it to work without modification in any

league, whereas you need to change the exponent on the Pythagorean

approach from the WNBA to the NBA to college men to college women to

HS, etc.

DeanO - On Thu, 10 Oct 2002, Richard Scott wrote:

> Have also seen that Bill James pythagorean method applied to the NBA to, to do this. The exponent, of course, is radically different.

I found that an exponent somewhere around 13 (unfortunately, I don't have

my notes with me; I'm at school and they're at home) works really well.

Obviously, it's not perfect; just like baseball, factors other than point

differential (like luck) impact won-lost records.

John Craven

> ----- Original Message -----

> From: bchaikin@...

> To: APBR_analysis@yahoogroups.com

> Sent: Thursday, October 10, 2002 3:17 AM

> Subject: [APBR_analysis] predicting W-L record based on team point differential

>

>

> This is pretty radical. Does anyone know what team ppg differential

> typically produces in terms of W-L record? My guess is 6.3 ppg and

> 57-25 is closer to normal, like Bob says, than 3.1 ppg and 55-27 is.

>

> - --- In APBR_analysis@y..., john wallace craven <john1974@u...> wrote:
>

NBA to, to do this. The exponent, of course, is radically different.

>

>

> On Thu, 10 Oct 2002, Richard Scott wrote:

>

> > Have also seen that Bill James pythagorean method applied to the

>

don't have

> I found that an exponent somewhere around 13 (unfortunately, I

> my notes with me; I'm at school and they're at home) works really

well.

> Obviously, it's not perfect; just like baseball, factors other than

point

> differential (like luck) impact won-lost records.

13 works well for the current slow pace. Higher numbers work better

with older higher scoring games (16 worked better in the '80's).

WNBA exponent is around 9.

>

point differential

> John Craven

>

> > ----- Original Message -----

> > From: bchaikin@a...

> > To: APBR_analysis@y...

> > Sent: Thursday, October 10, 2002 3:17 AM

> > Subject: [APBR_analysis] predicting W-L record based on team

> >

differential

> >

> > This is pretty radical. Does anyone know what team ppg

> > typically produces in terms of W-L record? My guess is 6.3 ppg

and

> > 57-25 is closer to normal, like Bob says, than 3.1 ppg and 55-

27 is.

> >

> > - On Thu, 10 Oct 2002, Dean Oliver wrote:

> --- In APBR_analysis@y..., "Michael K. Tamada" <tamada@o...> wrote:

[...]

> I once compared the normal probability approach to different

Good points. What is both a strength and weakness of the normal

> pythagorean exponents and the normal approach is always better. Not

> by enough to worry about, though. I'd expect any linear form or

> ratio to be similar to the pythagorean. Since the normal approach

> takes into account a little more than just points scored and points

> allowed (how variable they were in doing so), it should be a little

> more accurate. It also allows it to work without modification in any

> league, whereas you need to change the exponent on the Pythagorean

> approach from the WNBA to the NBA to college men to college women to

> HS, etc.

probability approach is: it uses more information and thus can make more

accurate predictions. But one needs to have data on, not just the mean

points, but also the variance of points (and I think your formula takes

covariance into account too?). These are very easy calculations, but the

data are a bit less easy to get. Available, but a little more hunting and

a little more work to do, compared to just looking at points scored and

allowed.

As usual, there's a choice of the quick-and-dirty vs the more-accurate-

but-more-work calculations.

--MKT - --- In APBR_analysis@y..., "Michael K. Tamada" <tamada@o...> wrote:
> [...]

Not

>

> > I once compared the normal probability approach to different

> > pythagorean exponents and the normal approach is always better.

> > by enough to worry about, though. I'd expect any linear form or

approach

> > ratio to be similar to the pythagorean. Since the normal

> > takes into account a little more than just points scored and

points

> > allowed (how variable they were in doing so), it should be a

little

> > more accurate. It also allows it to work without modification in

any

> > league, whereas you need to change the exponent on the

Pythagorean

> > approach from the WNBA to the NBA to college men to college women

to

> > HS, etc.

make more

>

> Good points. What is both a strength and weakness of the normal

> probability approach is: it uses more information and thus can

> accurate predictions. But one needs to have data on, not just the

mean

> points, but also the variance of points (and I think your formula

takes

> covariance into account too?). These are very easy calculations,

Yup. The covariance is actually quite important. It shows how much

teams play up or down to opponents. Teams definitely play up or down

to opponents in the NBA. Not as clear in other leagues (or other

sports). Basically there is no reason to blow a team out by 45 when

you can win by 10 safely. That's also why you can't do a correlation

of Jordan's minutes to how well his team performed. If he's injured

and plays 20 minutes, the team could do poorly. But if he plays so

well that the team is up by 35 after 20 minutes and he doesn't play

again, the team can do well. I tried correlating playing time to

team success (by game, not by season) and found this to be an

impossible barrier to overcome. So, the correlation definitely

matters.

DeanO

> data are a bit less easy to get. Available, but a little more

hunting and

> a little more work to do, compared to just looking at points scored

and

> allowed.

accurate-

>

> As usual, there's a choice of the quick-and-dirty vs the more-

> but-more-work calculations.

>

>

> --MKT