## Re: [APBR_analysis] (unknown)

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• Replies to DanR and DeanO ... I m sorry I didn t make it clear. For the second analysis (on the 03-04 regular season results) I didn;t use Pythagorean records.
Message 1 of 19 , Jun 2, 2004
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Replies to DanR and DeanO

Dean Oliver wrote:

> I was curious to see how you handled the early games of the season,
> especially the times where one team was undefeated. It looks like you
> used Pythagorean projections, rather than real records anyway. That
> helps. But 0-0 usually requires some other assumption, like a
> Bayesian prior that carries through the first few games.

I'm sorry I didn't make it clear. For the second analysis (on the 03-04
regular season results) I didn;t use Pythagorean records. I instead used
each team's record to date. Two teams facing each other on the first game of
the season each had a 0.5 chance of winning that game, since they had
identical 0-0 records.

The results don't deviate much from my first analysis, which used season's
end Pythagorean Win%. I supposed this is because after the first part of the
season, each team's Pyth is relatively stable. I must admit to being a
little surprised by this, though.

> Not sure what to make of that weakening of the Days. What was the R2
> of the previous version?

r = 0.06 for 00-01, r = 0.03 for this season.

> We may have to improve the prior matchup P
> to get back a reasonable estimate of the value of Days. If you just
> look at games beyond the first 20 in the season, does r2 get better
> and does Days become more significant?
>

Games 2-20: r = 0.048 (p = 0.261)
Games 21-82: r = 0.024 (p = 0.314

dan_t_rosenbaum wrote:

> Interesting results. Here are a couple of suggestions.
>
> I would leave out the MatchupP variable, since it is a lot like the
> dependent variable. Including it probably increases R-squared a
> lot, but probably doesn't do much else. (All in all, it probably is
> pretty harmless, since it unlikely to be correlated with your
> independent variables.)
>
> Another option with your day variable is to enter it as a series of
> dummy variables.
>
> DAY0 - equals 1 if 0 days of rest, 0 otherwise
> DAY1 - equals 1 if 1 day of rest , 0 otherwise
> DAY2 - equals 1 if 2 days of rest, 0 otherwise
> DAY3 - equals 1 if 3 days of rest, 0 otherwise
> DAY4+ - equals 1 if 4 days or more of rest, 0 otherwise
>
> Then run the regression leaving one of those variables out.
>
> If, for example, you left DAY0 out of the regression, the DAY1
> coefficient would give you the effect of playing on one day's rest
> versus playing in a back-to-back.
>
> The DAY2 coefficent would give you the effect of playing on two
> days' rest versus playing in a back-to-back.
>
> The DAY3 coefficent would give you the effect of playing on three
> days' rest versus playing in a back-to-back.
>
> The DAY4+ coefficent would give you the effect of playing on four or
> more days' rest versus playing in a back-to-back.
>

Okay, I tried this. The regression outputs follow. I'm afraid that I don't
know how to interpret the results -- very few of the coefficients are
significant. (Note that I use Day1 to mean 1 day between games, ie back to
back -- the 1 does not mean "rest days.")

Ommitting Days1

Predictor Coef SE Coef T P
Constant -3.8515 0.5971 -6.45 0.000
Home 7.1257 0.5267 13.53 0.000
Distance -0.0000105 0.0003920 -0.03 0.979
Days2 0.5837 0.6157 0.95 0.343
Days3 0.3022 0.7996 0.38 0.706
Days4 -2.363 1.394 -1.70 0.090
Days5+ 0.935 1.801 0.52 0.604

Omitting Days2

Predictor Coef SE Coef T P
Constant -3.2678 0.5624 -5.81 0.000
Home 7.1257 0.5267 13.53 0.000
Distance -0.0000105 0.0003920 -0.03 0.979
Days1 -0.5837 0.6157 -0.95 0.343
Days3 -0.2815 0.6981 -0.40 0.687
Days4 -2.947 1.338 -2.20 0.028
Days5+ 0.351 1.758 0.20 0.842

Omitting Days3

Predictor Coef SE Coef T P
Constant -3.5494 0.7833 -4.53 0.000
Home 7.1257 0.5267 13.53 0.000
Distance -0.0000105 0.0003920 -0.03 0.979
Days1 -0.3022 0.7996 -0.38 0.706
Days2 0.2815 0.6981 0.40 0.687
Days4 -2.665 1.426 -1.87 0.062
Days5+ 0.633 1.824 0.35 0.729

Omitting Days4

Predictor Coef SE Coef T P
Constant -6.215 1.385 -4.49 0.000
Home 7.1257 0.5267 13.53 0.000
Distance -0.0000105 0.0003920 -0.03 0.979
Days1 2.363 1.394 1.70 0.090
Days2 2.947 1.338 2.20 0.028
Days3 2.665 1.426 1.87 0.062
Days5+ 3.298 2.152 1.53 0.126

Omitting Days5+

Predictor Coef SE Coef T P
Constant -2.917 1.799 -1.62 0.105
Home 7.1257 0.5267 13.53 0.000
Distance -0.0000105 0.0003920 -0.03 0.979
Days1 -0.935 1.801 -0.52 0.604
Days2 -0.351 1.758 -0.20 0.842
Days3 -0.633 1.824 -0.35 0.729
Days4 -3.298 2.152 -1.53 0.126

ed
• ... http://www.shrpsports.com/nba/stand/2002.htm -- ed
Message 2 of 19 , Nov 2, 2004
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ivan ivan wrote:
> this is a simple question
> but i can't find it anywhere....
>
>
> I'm doing analysis on how a history of winning or losing affects your
> chances of winning at the end of close games... so does anyone know
> where i can standings for the 2001-2002 NBA season?
> i want the home and away records?
>

http://www.shrpsports.com/nba/stand/2002.htm

--
ed
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