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Re: New file uploaded to APBR_analysis

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  • HoopStudies
    ... of the ... This was my initial main point to him as well, and the one he seems to understand the least. I ll try to run the APBR_A words past him. I was
    Message 1 of 77 , Jan 4, 2002
      --- In APBR_analysis@y..., "Michael K. Tamada" <tamada@o...> wrote:
      > Mike G's critique I think makes a number of worthwhile points. One
      of the
      > most important ones is the linkage, or lack thereof, between and
      > individual player's performance and his team's performance.
      >

      This was my initial main point to him as well, and the one he seems
      to understand the least. I'll try to run the APBR_A words past him.
      I was struggling to explain exactly why what he did was wrong. My
      best explanation (I felt) was, while his team model is linear, some
      of the parameters in that model aren't linear, especially PPS and
      ASTO. The sum of PPS and Ast/TO over all players does not lead to
      the team value. Assuming that the weights on team efficiency
      parameters apply at an individual scale assumes linearity in the
      parameters, not only in the model. (I think this is effectively what
      MikeT says below.) Berri also mentioned to me that ASTO was not
      necessary in his team regression (it did not explain any more
      variation in team pts than without it), but he felt it should be in
      there for the extrapolation to individuals; I think this also points
      out the lack of connectedness between the team regression and the
      individual value.

      > But, as Mike G pointed out, it is almost surely a mistake to apply
      these
      > team weights to individual players. Points scored and PPS (or FG%)
      being
      > the prime example, one which several of us have mentioned before.
      A 55%
      > FG% player such as Bo Outlaw or the elderly Artis Gilmore may sound
      great,
      > but if they're only scoring 8 points a game, they aren't really
      helping
      > the offense very much. Conversely, an Alan Iverson can be helping
      his
      > team, even with his wretched 42% FG%. (Although he still did not
      deserve
      > that MVP award he won last year.)

      This type of argument worked little with Berri..

      >
      >
      > What is missing from Prof Berri's article is a true model of how
      > individual players' contributions lead to overall team success (or
      lack of
      > success). This is IMO the Holy Grail of sports statistics
      research, most
      > especially basketball research. It is unfortunately exceedingly
      > difficult. Even without fancy statistics or models though, there
      are a
      > couple of obvious features that such a model must have:

      Let me interrupt and put in what I think the Holy Grail must be able
      to do:

      1. Honor conservation of possessions. A team and its opponent have
      the same number of possessions in a game (+ or -2) - that's pretty
      much a definition. If you're hypothetically putting 5 high scoring
      guys together, your model better acknowledge that they aren't all
      going to score so high. There just aren't enough possessions to go
      around. Their opponents would have to also speed things up, but that
      is unlikely to occur unless you have Paul Westhead as coach.

      2. Honor conservation of wins. The sum of a team individual wins
      and losses should be pretty close to the team total. If you are
      putting together a team of 5 guys who each have individual win/loss
      records of 19-1, you aren't going to end up with a team that is 95-
      5. (I'm not even sure you end up with a team that wins 95% of its
      games, but that is not a _rule_.)

      3. Be context-sensitive. The (point or win) values of assists,
      rebounds, and blocks should depend on what else is happening on the
      team. Assists are most valuable on teams with many players who can
      shoot. Teams can be successful without assists if only a few guys
      shoot well, something you see at lower levels of basketball in
      particular. The value of a defensive rebound also varies. If you're
      forcing a ton of missed shots, but not getting defensive boards, the
      boards you get are highly valuable. If you're not forcing any missed
      shots, it is better to expend energy forcing misses. This gets at
      why you can't have a team of 5 PGs or 5 PFs.

      4. With "normal" substitution patterns, be close to additive. It
      would be nice that this model, when used with normal substitution
      patterns, be easy to use and not require a computer to calculate.
      You have some statistic for individuals that you can add to say how
      many net points you will be up on your opponent. (Net points are
      what I'm personally going for because it is easily convertible into
      wins/losses.)

      5. Account for fatigue. We're not even trying on this one, I don't
      think. We typically assume that player performance is independent of
      how much time they spend on the court. If it truly were independent,
      everyone's best players would play 48 minutes each.

      > In its simplest form, if "Q" is the quantity of output produced in
      a given
      > period of time, "L" the amount of labor used, and "K" the amount of
      > physical capital used, then the Cobb-Douglas form assumes that
      >
      > Q = b * L^a * K^(1-a)
      >
      > It's usually more convenient to take logarithms of these variables,
      so
      >
      > ln(Q) = ln(b) + a*ln(L) + (1-a)*ln(K)
      >
      > which is a nice simple linear equation which has a number of nice
      > mathematical characteristics. Probably too nice, as real world
      production
      > functions are more complex than what is contained in these
      equations.
      > For example it assume that there are constant returns to scale, and
      thus
      > no economies of scale.

      Prof. Berri has mentioned that he is on one side of a rather
      substantial Economics of Sports debate about the appropriate form of
      the model for wins and points in basketball (and attendance, I
      believe). It sounds like we all are on a 3rd side that doesn't
      believe either the linear or Cobb-Douglas forms (at least for winning
      and points). (Coming from a stochastic hydro background, I know how
      you can _assume_ nice models that don't capture any of the "how",
      because the models that capture how things work are quite challenging
      to work with, especially when you assume statistical variations.)

      I have generally been trying to improve the communication between
      that academic side that Berri represents and us. As I've mentioned
      to him, his goals are good (evaluating compensation, racism, etc.),
      but if the method that underlies his evaluations is so flawed to not
      pass the laugh test, no one who can make a difference is going to
      listen to his conclusions.

      DeanO
    • APBR_analysis@yahoogroups.com
      Hello, This email message is a notification to let you know that a file has been uploaded to the Files area of the APBR_analysis group. File : /Warriors
      Message 77 of 77 , May 5, 2005
        Hello,

        This email message is a notification to let you know that
        a file has been uploaded to the Files area of the APBR_analysis
        group.

        File : /Warriors Stats.pdf
        Uploaded by : skauffman <skauffman@...>
        Description : Analysis of the Golden State Warriors 2004-05 Season

        You can access this file at the URL:
        http://groups.yahoo.com/group/APBR_analysis/files/Warriors%20Stats.pdf

        To learn more about file sharing for your group, please visit:
        http://help.yahoo.com/help/us/groups/files

        Regards,

        skauffman <skauffman@...>
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