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

is

> Though you conclusion is correct, I don't think the formula you used

> correct. E.g. if a team goes 1-10, with a true probability of

winning of

> .563, then although that event may have a 0.17% probability of

occuring,

> the next event, going 17-4, has a *100%* probability of occuring.

Because

> once a team goes 1-10, if it's going to end up 18-14, it HAS to go

17-4

> the rest of the way.

The multiplication of the two numbers doesn't seem quite right. My

>

calculations would have been the same (except for the multiplication)

if it was a team that went 180-140. The assumption that it is a 0.563

team is where traditional statistics fail in comparison to Bayesian

stats. The bigger question that Bayesian can help with is whether

this team truly is a 0.563 team. Their victory over Cleveland last

night suggests even further that they're better than a 0.563 team.

I just use the BINOMDIST() function in excel to calculate the

probabilities of exceedences. This function assumes a fixed

probability of winning (0.563 in this case). Then it calculates the

chances of a certain number of victories in a certain number of games.

What you're looking at are the number of ways to get to 18-14 and the

chances that they do it the way they did. That is probably a better

way to calculate the odds of both such streaks occurring in the

season.

> I.e. we're looking at a team with a fixed 18-14 record (not a random

equation

> sample of 32 games), and have to use "sampling without replacement"

> methods rather than "sampling with replacement", as the binomial

> assumes.

11 is

>

> So the probability of the Sting winning 0 or 1 games in their first

> 65,385 / 471,435,600 = .00001425, or .001425%.

reason for

>

>

> This is exactly half of the figure that you came up with, which is

> probably not a coincidence, though I can't figure out what the

> the relationship would be. (Possibly, either you or I made an

arithmetic

> mistake somewhere.)

Hmm. You calculate lower odds than I. I would have expected higher,

if anything. Either way, we seem to be saying that the odds of such a

season (assuming that 0.563 is their true p_win) are ridiculously low.

This is going to be the case even if you look at home/road

distribution or at the quality of competition (which was something of

a factor for Charlotte).

(Ignore my comment in my previous email about 0.17% vs. 0.14% -- I was

trying to work on memory and mine is horrible.)

The apparent big factor in turning Charlotte around was improved

defense. This seemed to have been sparked by placing Tammy

Sutton-Brown in the starting lineup over Machanguana (who they got

for defense, ironically). The team was 16-3 with her starting, I

believe (I can check tonight). Allison Feaster, despite her 3-point

shooting, might also have been most influential on the defensive

end, but I don't have a lot of evidence for that. On the offensive

side, somehow they got Dawn Staley to cut down on her turnovers, but

that was not as big as the defensive turnaround.

Dean Oliver

Journal of Basketball Studies - Very interesting (and obviously not what I'd expect looking from the point

of view of a fan).

Anybody know why the FT disparity has dropped (and how strongly that

disparity correlates with winning %)? Have there been new instructions to

officials?

On Fri, 17 Aug 2001, Dean LaVergne wrote:

>

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

> From: Charles Steinhardt [mailto:charles@...]

>

>

>

>

> 2) There is no equivalent of the free throw in baseball, and in fact all

> new stadia are forced to put some sort of blue/black screen in

> straightaway center so that the batter has a good line of sight. Fans can

> have a very direct impact in basketball that I'd guess is worth as much as

> 5 points per game (some in increasing the home FT%, some in decreasing

> that of opponents. Maybe somebody has statistics on this?

>

>

> [Dean LaVergne] It doesn't seem to hold out. For the last ten years:

>

> Free Throw Percentage:

>

> Season Away Home Diff

> 1992 75.62% 76.12% 0.51%

> 1993 75.19% 75.65% 0.46%

> 1994 73.60% 73.25% -0.35%

> 1995 73.49% 73.84% 0.34%

> 1996 73.91% 74.03% 0.12%

> 1997 73.98% 73.67% -0.31%

> 1998 73.54% 73.82% 0.28%

> 1999 72.29% 73.32% 1.02%

> 2000 74.55% 75.44% 0.89%

> 2001 74.48% 74.99% 0.51%

>

>

> However, free throws attempted seem a little more significant:

>

>

> Season Away Home Diff % Diff

> 1992 28,522 30,553 2,031 7.12%

> 1993 29,715 31,659 1,944 6.54%

> 1994 28,688 30,131 1,443 5.03%

> 1995 29,248 30,690 1,442 4.93%

> 1996 30,587 32,178 1,591 5.20%

> 1997 29,522 30,272 750 2.54%

> 1998 30,704 31,799 1,095 3.57%

> 1999 18,399 19,000 601 3.27%

> 2000 29,410 30,352 942 3.20%

> 2001 28,570 29,308 738 2.58%

>

>

> Dean L

>

>