- ... Alright, Ed, read em and weep: Alltime Chicago Bulls Lineup: RegSea PlaOf Total 1 Michael Jordan 11157 5447 16604 2 Scottie Pippen 6430 3126Message 1 of 5 , Jul 22, 2001View Source--- In APBR_analysis@y..., Ed Weiland <weiland1029@y...> wrote:
> And when is that all-time Bulls list coming? I'm dyingAlright, Ed, read 'em and weep:
> to know where Leon Benbow rates. : )
> Ed Weiland
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.
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- On Mon, 23 Jul 2001, Mike Goodman wrote: [...] ... [...] ... What you want to use is multivariate regression analysis also known as ordinary least squaresMessage 2 of 5 , Jul 23, 2001View Source
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
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.