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Offensive efficiency as a function of offensive possessions per minute

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  • Torch742
    Sorry for the double post. I ve written an article regarding Offensive efficiency (points per possession) as a function of offensive possessions. You can read
    Message 1 of 4 , Jul 10, 2004
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      Sorry for the double post.

      I've written an article regarding Offensive efficiency (points per
      possession) as a function of offensive possessions. You can read it
      here along with some of my other stuff:

      http://www.geocities.com/torch772/index.htm
    • Dean Oliver
      ... A couple things... 1. The band getting narrower with more poss simply expresses that you have more variance with fewer attempts, which is what
      Message 2 of 4 , Jul 10, 2004
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        --- In APBR_analysis@yahoogroups.com, "Torch742" <torch742@y...> wrote:
        > Sorry for the double post.
        >
        > I've written an article regarding Offensive efficiency (points per
        > possession) as a function of offensive possessions. You can read it
        > here along with some of my other stuff:
        >
        > http://www.geocities.com/torch772/index.htm

        A couple things...

        1. The band getting narrower with more poss simply expresses that you
        have more variance with fewer attempts, which is what statistically
        should happen. Variance of efficiency is theoretically inversely
        proportional to how many possessions you take.

        2. In Basketball on Paper, I generate functions that show how
        players' efficiencies vary with possession percentage (percentage of
        the team's possessions, with an average of 1 out of 5 or 20%). It's
        one of the most useful things I do. It suggests whether a player can
        use more possessions and still be efficient. It suggests how to
        optimize an offense. It says why Allen Iverson is valuable even if he
        is inefficient. You should take a look at that -- Chapter 19. (I
        know, the axes are backwards on my plots. It's a relic of how I had
        to originally generate them years ago.)

        3. You are definitely right in your theory that efficiency should go
        down with poss used. Part of that theory should also incorporate how
        good teammates are. That's the tough one to work with...

        DeanO

        Dean Oliver
        Author, Basketball on Paper
        http://www.basketballonpaper.com
        "Excellent writing. There are a lot of math guys who just rush from
        the numbers to the conclusion. . .they'll tell you that Shaq is a real
        good player but his team would win a couple more games a year if he
        could hit a free throw. Dean is more than that; he's really
        struggling to understand the actual problem, rather than the
        statistical after-image of it. I learn a lot by reading him." Bill
        James, author Baseball Abstract
      • Torch742
        ... Despite having taken several years of calc so far, Ive actually never taken stats, in highschool or college, so unfortunately all I have is what I can
        Message 3 of 4 , Jul 10, 2004
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          > A couple things...
          >
          > 1. The band getting narrower with more poss simply expresses that you
          > have more variance with fewer attempts, which is what statistically
          > should happen. Variance of efficiency is theoretically inversely
          > proportional to how many possessions you take.
          >
          > 2. In Basketball on Paper, I generate functions that show how
          > players' efficiencies vary with possession percentage (percentage of
          > the team's possessions, with an average of 1 out of 5 or 20%). It's
          > one of the most useful things I do. It suggests whether a player can
          > use more possessions and still be efficient. It suggests how to
          > optimize an offense. It says why Allen Iverson is valuable even if he
          > is inefficient. You should take a look at that -- Chapter 19. (I
          > know, the axes are backwards on my plots. It's a relic of how I had
          > to originally generate them years ago.)
          >
          > 3. You are definitely right in your theory that efficiency should go
          > down with poss used. Part of that theory should also incorporate how
          > good teammates are. That's the tough one to work with...
          >
          > DeanO

          Despite having taken several years of calc so far, Ive actually never
          taken stats, in highschool or college, so unfortunately all I have is
          what I can explain logically to myself, I can't compute the other
          stuff. I feel like I have a pretty good grasp of most things
          conceptually, but when it comes to things like variance etc, I dont
          actually know how to find it. That said...

          In response to 1, this is OP/M as opposed to OP. Unless I
          misunderstand something, the narrowing of the band is not due to
          variance in OP/M, as a low number of OP over an even lower number of
          minutes can still produce a high OP/M number, and yet the variance is
          only shown to the left of the graph, and not to the right.

          2. Yes, there is a subtle difference between % of team OP and OP/M, as
          you could probably argue that all of the players on a team like Dallas
          are "unfairly" getting a boost to their OP/M, but on the other hand
          using % of OP of total OP would "unfairly" reward players on slow
          tempo/defensive type teams, like say Rip on Detroit. I dont remember
          if I ever got to chapter 19, I put it down at some point and decided I
          wanted to know more about computing statistics to be able to look over
          the formulas myself before reading further. I might go back and look
          it over now :)

          3. Yes, but I believe the effects of playing with offensively good
          teammates is overstated a bit. Part of that effect overlaps with the
          OP/M effect. Good offensive teams have many offensive players, which
          means any given player is more likely to have a lower OP/M than he
          would on an average team, thereby indirectly raising his OE. The
          converse is also true, where bad offensive teams usually have very few
          good offensive players and a player's OP/M will likely be higher (See
          McGrady, Orlando).
        • Torch742
          Also, here are the top 20 players in adjusted offensive efficiency from the formula used in the article: 1. Brent Barry 2. Peja Stojakovic 3. Brian Cardinal 4.
          Message 4 of 4 , Jul 10, 2004
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            Also, here are the top 20 players in adjusted offensive efficiency
            from the formula used in the article:

            1. Brent Barry
            2. Peja Stojakovic
            3. Brian Cardinal
            4. James Posey
            5. Corey Maggette
            6. Shaquille Oneal
            7. Yao Ming
            8. Antawn Jamison
            9. Steve Nash
            10. Fred Hoiberg
            11. Ray Allen
            12. Mark Blount
            13. Reggie Miller
            14. Antonio Daniels
            15. Kobe Bryant
            16. Sam Cassell
            17. Elton Brand
            18. Jarron Collins
            19. Dirk Nowitzki
            20. Richard Jefferson
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