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

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  • Dean Oliver
    ... 82 games ... similar), you ... differential is ... (actually ... for the ... wins, and ... winning six ... put won a ... two ... their ... plus or ... our
    Message 1 of 9 , Oct 9, 2002
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      --- In APBR_analysis@y..., bchaikin@a... wrote:
      > games (all years back to 67-68 except the strike year of 98-99, and
      82 games
      > because to calculate this properly you need all events to be
      similar), you
      > get the equation:
      >
      > Y = aX + b where a = 2.61 and b = 41
      >
      > or
      >
      > Y = (2.61 times X) + 41
      >
      > or
      >
      > Wins = 2.61 times (point diff) + 41
      >
      > 41 is the b parameter because, you guessed it, if your point
      differential is
      > zero, you should be a .500 ball club (W-L of 41-41)...
      >
      > so for the 92-93 bulls with a pt diff of 6.3, historical data
      (actually
      > includes the test data) predicts 57.4 wins, and they won 57 games.
      for the
      > 93-94 bulls with a pt diff of 3.1, historical data predicts 49.1
      wins, and
      > they won 55 games. so you could say the 93-94 bulls were lucky in
      winning six
      > more games than they "should have" based on their stats, or simply
      put won a
      > few more close games than the odds would have suggested......
      >
      > over the time period of 1967-68 to 2001-02 (not including 1998-99),
      two
      > thirds of the teams were within plus or minus 3 wins or losses of
      their
      > predicted W-L record based on the above equation, 80% were within
      plus or
      > minus 4 games, and 90% within plus or minus 5 games, so again for
      our
      > purposes i think the above formula gives you a good indication of
      what a
      > team's W-L record should be based on point differential....
      >

      Yup, lots of ways to do this. But all of them show that the '94
      Bulls record was a little lucky given their point differential.

      DeanO
    • 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 2 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 3 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 4 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 5 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 6 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 7 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 8 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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