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

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  • john wallace craven
    ... 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.
    Message 1 of 9 , Oct 10, 2002
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      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.
      >
      >
    • Dean Oliver
      ... NBA to, to do this. The exponent, of course, is radically different. ... don t have ... well. ... point ... 13 works well for the current slow pace.
      Message 2 of 9 , Oct 10, 2002
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        --- In APBR_analysis@y..., john wallace craven <john1974@u...> wrote:
        >
        >
        >
        > 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.

        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.

        >
        > 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
        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.
        > >
        > >
      • Michael K. Tamada
        ... [...] ... Good points. What is both a strength and weakness of the normal probability approach is: it uses more information and thus can make more
        Message 3 of 9 , Oct 15, 2002
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          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
          > 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.

          Good points. What is both a strength and weakness of the normal
          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
        • Dean Oliver
          ... Not ... approach ... points ... little ... any ... Pythagorean ... to ... make more ... mean ... takes ... Yup. The covariance is actually quite
          Message 4 of 9 , Oct 15, 2002
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            --- In APBR_analysis@y..., "Michael K. Tamada" <tamada@o...> wrote:
            > [...]
            >
            > > 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.
            >
            > Good points. What is both a strength and weakness of the normal
            > 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,

            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.
            >
            > As usual, there's a choice of the quick-and-dirty vs the more-
            accurate-
            > but-more-work calculations.
            >
            >
            > --MKT
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