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Re: [APBR_analysis] (unknown)

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  • igor eduardo küpfer
    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
    • igor eduardo küpfer
      ... 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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