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Re: Offensive efficiency as a function of offensive possessions per minute

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  • 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 1 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 2 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 3 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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