Loading ...
Sorry, an error occurred while loading the content.
 

Re: All-Time Lakers Team

Expand Messages
  • Mike Goodman
    ... Sorry to take so long to get back to you, Ed. I don t claim to have a decent method of estimating steals, blocks, or turnovers. However, I have come up
    Message 1 of 5 , Jul 22, 2001
      --- In APBR_analysis@y..., Ed Weiland <weiland1029@y...> wrote:
      > > Players are credited an estimated number of
      > > blocks, steals, and
      > > turnovers from times before such stats were
      > > recorded. I gave Wilt 4
      > > blocks per game.
      >
      Sorry to take so long to get back to you, Ed.
      I don't claim to have a decent method of estimating steals,
      blocks, or turnovers. However, I have come up with an average
      correlation with other stats, that I find relatively inoffensive.
      Steals seem to correlate most closely with assists, and I guess
      that a player's steals are related to his normalized assist rate,
      thus: Steals = SqRt(Ast)-0.4
      SqRt means "square root of"
      For example, Maurice Stokes averaged 5.4 assists (per 36 minutes,
      standardized). SqRt(5.4) = 2.3, and 2.3 - .4 = 1.9. So I credit
      Stokes with 1.9 steals per 36 minutes, and his 3 year
      career "equivalent steals" total is 391.
      For sure, there are major deviations, from player to player, in
      this estimate, and there are a handful of cases in which I use
      anecdotal evidence to adjust my statistical "guess". However, for
      the purposes of career evaluation, a difference of even a steal a
      game very seldom changes a player's career ranking more than one
      place, usually none.
      In other words, assigning Maurice Stokes .9 steals, or 1.9, or 2.9
      does not make much difference in the career rankings scheme.
      While both assists and steals are a result of "good hands", and
      anticipation, and quickness, etc; blocked-shots are mostly correlated
      to rebounds, being a big-man thing.
      The formula for blocks is : Blk = SqRt(Reb) - 1.6. For Mr.
      Stokes, his career standardized rebound rate is 13.5, and his
      estimated shot-blocking is 2.1. This could be way off, but it seems
      to be a good average when applied to modern players. Again, there
      are major deviations.
      My feeling is that an educated guess is better than nothing, and I
      am constantly open to what I call "anecdotal evidence" (as opposed to
      statistical), and I consider any first-hand witnessing to be better
      than no evidence at all.
      Turnovers can be predicted, too, and the factors are many. The
      traditional belief is that assists are most closely related to
      turnovers, but in fact scoring and rebounding are, also.
      If you have any idea what I am referring to when I talk about
      standardized rates (and I have posted a few), the turnover formula is
      this: TO = .08(Sco)+.07(Reb)+.16(Ast)+.05(Stl)+.10(Blk)-.005(MPG).
      Here, MPG is minutes per game, and the other factors are multiples of
      the standardized per-36-minute scoring rate, etc.
      Strangely, blocks are 2nd to assists in correlation with turnovers.
      Assists, obviously, require passing, and some turnovers are
      inevitable.
      Scorers lose the ball more than non-scorers, in general.
      Apparently, rebounding causes a player to have possession, and he
      can then be stripped or throw a bad pass.
      Steals may indicate a gambling mentality, thus turnovers on the
      other end.
      Minutes are a factor because the jitters go away after the first
      few; and retrospectively, players with good numbers but few minutes
      may have been turnover-prone.
      I would love to know how to actually plug in a few hundred player
      stats and have my computer generate these correlations; all I have
      managed is to tinker with the numbers until a good average is
      achieved. After a tinkering, I just check the extremes at either
      end, trying to minimize.
      As always, I am open to any suggestion, except to "stop trying"
      or "give up".



      > Just curious here, Mike. How did you estimate the
      > blocks, steals and turnovers for each player?
      >
      > And when is that all-time Bulls list coming? I'm dying
      > to know where Leon Benbow rates. : )
      >
      > Ed Weiland
      > >
      > >
      >
      >
      > __________________________________________________
      > Do You Yahoo!?
      > Get personalized email addresses from Yahoo! Mail
      > http://personal.mail.yahoo.com/
    • Mike Goodman
      ... Alright, Ed, read em and weep: Alltime Chicago Bulls Lineup: RegSea PlaOf Total 1 Michael Jordan 11157 5447 16604 2 Scottie Pippen 6430 3126
      Message 2 of 5 , Jul 22, 2001
        --- In APBR_analysis@y..., Ed Weiland <weiland1029@y...> wrote:
        > And when is that all-time Bulls list coming? I'm dying
        > to know where Leon Benbow rates. : )
        >
        > Ed Weiland
        > >
        > >
        Alright, Ed, read 'em and weep:

        Alltime Chicago Bulls Lineup:

        RegSea PlaOf Total
        1 Michael Jordan 11157 5447 16604
        2 Scottie Pippen 6430 3126 9556
        3 Artis Gilmore 5105 632 5737
        4 Horace Grant 3856 1762 5617
        5 Toni Kukoc 3477 1371 4849
        6 Chet Walker 3605 883 4487
        7 Bob Love 3459 960 4419
        8 Jerry Sloan 3106 747 3853
        9 Norm Van Lier 2900 727 3626
        10 Tom Boerwinkle 2892 733 3625
        11 Reggie Theus 3025 407 3432
        12 Mickey Johnson 2864 453 3317
        13 David Greenwood 2832 444 3276
        14 B.J. Armstrong 2345 897 3241
        15 Clifford Ray 2383 645 3028
        16 Charles Oakley 2369 588 2956
        17 Dennis Rodman 1916 964 2880
        18 Orlando Woolridge 2602 265 2867
        19 Luc Longley 1858 960 2819
        20 Bob Boozer 2344 454 2797
        21 Ron Harper 1813 951 2764
        22 Dave Corzine 2367 339 2706
        23 Bill Cartwright 1804 840 2644
        24 John Paxson 1890 713 2603
        25 Elton Brand 2596 0 2596

        Leon Benbow is not this side of the horizon.
        >
        >
        > __________________________________________________
        > Do You Yahoo!?
        > Get personalized email addresses from Yahoo! Mail
        > http://personal.mail.yahoo.com/
      • Michael K. Tamada
        On Mon, 23 Jul 2001, Mike Goodman wrote: [...] ... [...] ... What you want to use is multivariate regression analysis also known as ordinary least squares
        Message 3 of 5 , Jul 23, 2001
          On Mon, 23 Jul 2001, Mike Goodman wrote:

          [...]

          > Turnovers can be predicted, too, and the factors are many. The
          > traditional belief is that assists are most closely related to
          > turnovers, but in fact scoring and rebounding are, also.
          > If you have any idea what I am referring to when I talk about
          > standardized rates (and I have posted a few), the turnover formula is
          > this: TO = .08(Sco)+.07(Reb)+.16(Ast)+.05(Stl)+.10(Blk)-.005(MPG).

          [...]

          > I would love to know how to actually plug in a few hundred player
          > stats and have my computer generate these correlations; all I have
          > managed is to tinker with the numbers until a good average is
          > achieved. After a tinkering, I just check the extremes at either
          > end, trying to minimize.

          What you want to use is "multivariate regression analysis" also known as
          "ordinary least squares regression". I believe that Excel will only do
          univariate regression. There are however freeware regression programs
          available; I don't use any of them because I've got paid-for programs but
          I know they are out there ... I know there was a shareware or freeware
          econometrics program available at Penn State University's website. Also
          there is a package called "R" which is a shareware or freeware version of
          "S", a package widely used by statisticians. However S, and I imagine R,
          are aimed more at theoretical statisticians and people who need to develop
          and program their own statistics, rather than being aimed at users who
          simply want to crunch some numbers using standard techniques.

          The technique you describe is a standard one for filling in missing data;
          i.e. run regressions to come up with equations predicting what a player's
          turnovers per minute will be.

          Obviously the technique becomes shakier as the amount of missing data
          increases, in particular for years prior to 197? when there are NO data at
          all on turnovers. Then you have to make assumptions that the turnover
          equations for, say, 1957, are the same as the ones for 197?-2001. In
          other words, extrapolation is a lot more difficult than interpolation, and
          for years with no turnover data whatsoever, we're extrapolating rather
          than interpolating.

          So the equations should be double-checked by, e.g. looking at
          season-by-season data to see if there are time trends. E.g. I believe
          that offensive rebounding percentages gradually increased during the
          1970s and 1980s. I believe that turnover rates (certainly per minute, and
          possibly relative to scoring, rebounding, etc.) declined in the 1980s and
          1990s. And for sure, field goal percentages rose for decades, until some
          time in the 1990s when they started declining.

          So the equations for predicting turnovers in the "modern" NBA may not work
          for predicting turnovers in the NBA of the 1950s.

          On the bright side, OLS will be much much faster AND lead to better, more
          accurate equations than fiddling around by hand.


          --MKT
        Your message has been successfully submitted and would be delivered to recipients shortly.