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

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  • Richard Scott
    Have also seen that Bill James pythagorean method applied to the NBA to, to do this. The exponent, of course, is radically different. ... From:
    Message 1 of 9 , Oct 9, 2002
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      Have also seen that Bill James pythagorean method applied to the NBA to, to do this.  The exponent, of course, is radically different.
      ----- Original Message -----
      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
      ... 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
      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.

        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.


        --MKT
      • 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 3 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 4 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 5 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 6 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 7 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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