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

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  • Dean Oliver
    ... NBA to, to do this. The exponent, of course, is radically different. ... them ... There s ... I once compared the normal probability approach to different
    Message 1 of 9 , Oct 10, 2002
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      --- In APBR_analysis@y..., "Michael K. Tamada" <tamada@o...> 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.
      >
      > And then there's DeanO's normal probability model approach. All of
      them
      > I'm sure lead to similar results, I wonder if some of them are more
      > accurate than others? Or if some are more accurate at the extremes,
      > others more accurate at predicting teams with "average" stats.
      There's
      > 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
    • 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 2 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 3 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 4 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 5 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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