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Minutes projections
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Dan Rosenbaum



Joined: 03 Jan 2005
Posts: 541
Location: Greensboro, North Carolina

PostPosted: Mon Feb 14, 2005 5:46 pm    Post subject: Reply with quote

jkubatko wrote:
I did a small study using player-seasons from 1978-2004. To be included in the study, a player had to (a) see an increase of at least 50% in minutes per game from one season to the next and (b) play at least 41 games in each season. These criteria gave me 465 player-seasons. In 346 of these seasons (74.41%), the player's PER increased with an increase in playing time. The mean change in PER was 1.55 and the median was 1.58. The range of changes for the middle 50% was -0.05 to 3.18.

Because younger players are more likely to improve (and see an increase in playing time) than older players, I decided to add an age requirement to the criteria above. Now players had to be at least 30 years old in the target season to be inlcuded in the sample. The results were similar: in 42 of the 58 seasons (72.41%) the player's PER increased with an increase in playing time. The mean change in PER was 0.98 and the median was 1.19. The range of changes for the middle 50% was -0.21 to 2.32.

Very nice work, Justin. Thank you!

If it is easy to do, what are the parallel statistics for decreases in playing time?
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jkubatko



Joined: 05 Jan 2005
Posts: 702
Location: Columbus, OH

PostPosted: Mon Feb 14, 2005 5:53 pm    Post subject: Reply with quote

Dan Rosenbaum wrote:
If it is easy to do, what are the parallel statistics for decreases in playing time?


Decrease of at least 50% in minutes per game, no age requirement: 65 out of 91 (71.43%) saw a decrease in PER with a decrease in playing time.

Decrease of at least 50% in minutes per game, at least 30 years old in target season: 35 out of 41 (85.37%) saw a decrease in PER with a decrease in playing time.
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NickS



Joined: 30 Dec 2004
Posts: 384

PostPosted: Mon Feb 14, 2005 5:55 pm    Post subject: Reply with quote

Justin, about the study you described, even after you control for age you're going to have a selection bias for players that are recovering from injury. Look at AI this season, his minutes and productivity are up from last season but both can be explained by an increase in health.

You might mitigate this by comparing production in the season in which minutes increased with the average production from the previous 3 seasons.

On a related note, I wonder if there is any way to find people who's PER increased while their minutes decreased.

If one believes that production/minute should decrease as playing time increases it would seem logical that there should be players that would gain in efficiency when they lose minutes.

Obviously most players that lose minutes do so because they aren't playing as well. But there must be examples of players that have a back-up or starter at the same position that has a similar PER allowing the coach to adjust minutes.

An obvious example would be Jamison moving from a starter in GS to a bench player in Dallas but 1 sample does not a study make. Is there any way to isolate criteria to search for these players?
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NickS



Joined: 30 Dec 2004
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PostPosted: Mon Feb 14, 2005 5:58 pm    Post subject: Reply with quote

jkubatko wrote:
Dan Rosenbaum wrote:
If it is easy to do, what are the parallel statistics for decreases in playing time?

Decrease of at least 50% in minutes per game, at least 30 years old in target season: 35 out of 41 (85.37%) saw a decrease in PER with a decrease in playing time.


Shouldn't you make the age limit players under 30 (or 32 if you like)? You want to exclude players who have productivity and minutes declining due to age.
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jkubatko



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PostPosted: Mon Feb 14, 2005 6:03 pm    Post subject: Reply with quote

NickS wrote:
Shouldn't you make the age limit players under 30 (or 32 if you like)? You want to exclude players who have productivity and minutes declining due to age.


Players less than 30 years of age in target season: 30 out of 50 (60%) saw a decrease in PER with a decrease in playing time.
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jkubatko



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PostPosted: Mon Feb 14, 2005 6:08 pm    Post subject: Reply with quote

NickS wrote:
Justin, about the study you described, even after you control for age you're going to have a selection bias for players that are recovering from injury. Look at AI this season, his minutes and productivity are up from last season but both can be explained by an increase in health.


The players are listed below. Anyone fit that description?

Code:

                              -- MP/G --  -- PER ---
 Name                   Year  Yr-1    Yr  Yr-1    Yr
+----------------------+----+-----+-----+-----+-----+
 Greg Anderson          1997   7.5  20.2   7.8  11.4
 Ron Anderson           1989  14.8  31.9  16.2  15.4
 Greg Anthony           2002  14.8  25.2  12.0  12.6
 Stacey Augmon          2001  11.7  17.9  12.2  12.3
 Stacey Augmon          2004  12.3  20.5   9.3  10.0
 Ron Boone              1980  19.3  29.5   9.3  12.1
 Dudley Bradley         1988  13.2  22.1  10.3  11.4
 Chucky Brown           1999  15.6  24.8  10.5  11.6
 Mark Bryant            1996  13.4  22.4  13.2  13.1
 Mark Bryant            1999  15.9  26.8  10.2  12.1
 Jud Buechler           1999   8.2  21.1  14.1  12.2
 Matt Bullard           2000  10.1  18.3   9.6  11.9
 Don Buse               1982  18.9  30.8  11.7  14.8
 Adrian Caldwell        1997   6.4  12.6  10.4   7.5
 Tyrone Corbin          1997  18.1  32.9  12.6  11.0
 Earl Cureton           1989  16.3  25.0   9.2  10.1
 Emanual Davis          2001  13.0  20.8  10.2  11.8
 Andrew DeClercq        2003  10.4  17.2  10.1  11.3
 Greg Dreiling          1994   5.6  12.7   5.0   7.2
 Blue Edwards           1996  16.6  33.8  11.6  11.9
 James Edwards          1990  16.5  27.8  11.6  13.3
 Duane Ferrell          1997  10.9  18.0  10.9  11.6
 Vern Fleming           1996  12.5  22.7  14.6  11.3
 Rick Fox               2001  18.0  27.9  11.7  13.8
 Mike Gale              1982  14.4  23.9  13.7  10.9
 Winston Garland        1995  15.2  26.5  12.2  10.1
 Rickey Green           1991  13.4  28.5  11.0  13.1
 Carl Herrera           1997   8.9  24.5   4.7  10.6
 Fred Hoiberg           2004  12.4  22.8  10.3  13.6
 Jaren Jackson          1998  15.1  27.1  10.1   9.7
 Jim Jackson            2004  20.8  39.0  12.4  12.2
 Phil Jackson           1979  10.4  18.1   8.1  10.7
 Avery Johnson          2004   9.0  13.8  12.2  11.7
 Popeye Jones           2002  14.2  24.3  13.1  14.4
 Sam Lacey              1983  12.0  20.5   7.4   6.5
 John Long              1990  13.5  21.5   9.5  10.7
 John Lucas             1990  11.4  19.1  16.7  11.5
 Tony Massenburg        1999  14.7  26.6  14.9  15.9
 Sam Mitchell           1996  17.0  27.5  13.4  13.0
 Will Perdue            1997  17.5  29.5  13.7  16.7
 Wesley Person          2002  21.8  35.8  11.6  16.3
 Ed Pinckney            1996  13.5  23.1  10.9  14.3
 Kurt Rambis            1989  12.1  29.8  15.0  16.2
 J.R. Reid              1999  14.0  25.1  11.3  13.7
 Robert Reid            1989  15.8  26.2  10.9  12.3
 Dave Robisch           1980  15.2  32.6  11.9  17.2
 Sean Rooks             2003  11.9  19.2   7.6   7.7
 Brad Sellers           1993   5.3   9.9  13.7   8.3
 Purvis Short           1990  17.8  27.0  11.5  13.4
 Rory Sparrow           1989  18.0  32.7   8.3  11.7
 Bryant Stith           2001  15.4  32.1  13.8  11.7
 Damon Stoudamire       2004  22.3  38.0  12.1  14.8
 Darnell Valentine      1991  14.1  28.3  11.6  13.5
 Loy Vaught             2001   6.8  10.8   8.5  13.3
 Clarence Weatherspoon  2001  20.7  33.8  16.2  17.4
 Mark West              1995  15.1  23.0  12.2  12.8
 Joe Wolf               1996   9.3  16.6   6.8   8.8
 Orlando Woolridge      1991  22.9  34.4  17.6  19.5

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Justin Kubatko
Basketball-Reference.com
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NickS



Joined: 30 Dec 2004
Posts: 384

PostPosted: Mon Feb 14, 2005 6:10 pm    Post subject: Reply with quote

jkubatko wrote:

Players less than 30 years of age in target season: 30 out of 50 (60%) saw a decrease in PER with a decrease in playing time.


With that small a sample size I still wonder if injuries (nagging or otherwise) are a significant factor (compare AK-47 this season to last season, for example).

What if you searched for players that had a drop in playing time of 25% or more, but excluded players who missed more than 10-12 games in the second season?

That won't exclude Elton Brand playing with a knee injury, but it might be a better filter.
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NickS



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PostPosted: Mon Feb 14, 2005 6:17 pm    Post subject: Reply with quote

jkubatko wrote:
NickS wrote:
Justin, about the study you described, even after you control for age you're going to have a selection bias for players that are recovering from injury. Look at AI this season, his minutes and productivity are up from last season but both can be explained by an increase in health.


The players are listed below. Anyone fit that description?


I can think of a few, but not many. That list convinces me that injuries are not playing a major cause. I'm not sure that it specifically proves anything but it is convincing.
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Mike G



Joined: 14 Jan 2005
Posts: 3628
Location: Hendersonville, NC

PostPosted: Tue Feb 15, 2005 10:27 am    Post subject: Reply with quote

Between '66 and '71, player minutes increased by an average of 8% per player per year. There are 86 instances of players whose minutes increased by 50% from one year to the next, and 42 cases of players' minutes dropping below 2/3 of the previous year.

Of the big minute-losers, 28/42 (.667) also had reduced PAR rates (simple pts+reb+ast, per minute). Their average age was 29.4, while the average age for the 14 other guys (whose minutes were reduced while PAR increased) was 27.5 .

Meanwhile, of the 86 MPG big-gainers, we have 47 whose PAR improved (.547). These win-win guys' average age was 26.2. The 39 players who responded negatively to big minute gains avg age was also 26.2 .

There was about a 1.1% average annual decline in league-wide PAR in this interval. This could be factored into the analysis. However, I still think the enormous dilution of talent that goes with expansion should swamp such a small effect. By 1971, almost half the league was expansion teams.
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NickS



Joined: 30 Dec 2004
Posts: 384

PostPosted: Tue Feb 15, 2005 12:08 pm    Post subject: Reply with quote

Another query for Justin.

I was thinking about the categories of people that have their minutes decreased or increased by 50% and that tells us something (I'm becoming more and more convinced that players don't, as a rule, lose efficiency by adding minutes) but doesn't really describe manu ginobili.

Can you do a query for all players that played between 26-31 mpg and had a win % over 65%? I'd just be curious to see a list and see if there are any comparisons to be made.

I can think of a couple examples.

Danny Fortson (98-99) had foul problems limit his minutes
Reggie Miller (02-03,03-04) was on the tail end of his career
Michael Redd (02-03) was playing behind ray allen

I don't know why Adrian Dantley (87-8Cool didn't play more.

Anyway, it would be interesting to see a list of names.
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gabefarkas



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PostPosted: Tue Feb 15, 2005 3:19 pm    Post subject: Reply with quote

regarding the PER differences for the increased minutes guys, i'm curious to see if there's a pattern by the amount or % of minutes by which they were increased.

for example, if someone went from 5 mpg to 15 mpg (ie, Mike Sweetney), was their PER increase different than if they went from 10 mpg to 35 mpg (ie, Zach Randolph)?
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jkubatko



Joined: 05 Jan 2005
Posts: 702
Location: Columbus, OH

PostPosted: Tue Feb 15, 2005 4:54 pm    Post subject: Reply with quote

gabefarkas wrote:
regarding the PER differences for the increased minutes guys, i'm curious to see if there's a pattern by the amount or % of minutes by which they were increased.


From 1978-2004, there were 5600 cases where a player played at least 41 games in back-to-back seasons. I calcuated the change in minutes played per game from one season to the next and rounded to the nearest minute. In the table below, the first column is the change in minutes played per game from year n-1 to year n, and the second column is the mean change in PER from year n-1 to year n.

Code:

 -22 |  0.58    1 out of   1 (100.0%) improved
 -20 | -3.64    0 out of   2 (  0.0%) improved
 -19 | -3.67    0 out of   7 (  0.0%) improved
 -18 | -2.33    0 out of   5 (  0.0%) improved
 -17 | -2.74    2 out of  14 ( 14.3%) improved
 -16 | -3.07    1 out of  13 (  7.7%) improved
 -15 | -1.61    6 out of  17 ( 35.3%) improved
 -14 | -1.79    9 out of  32 ( 28.1%) improved
 -13 | -1.51   10 out of  35 ( 28.6%) improved
 -12 | -1.99    9 out of  50 ( 18.0%) improved
 -11 | -1.47   19 out of  74 ( 25.7%) improved
 -10 | -1.99   16 out of  78 ( 20.5%) improved
  -9 | -1.02   32 out of 108 ( 29.6%) improved
  -8 | -1.29   40 out of 129 ( 31.0%) improved
  -7 | -1.26   42 out of 152 ( 27.6%) improved
  -6 | -1.08   61 out of 206 ( 29.6%) improved
  -5 | -1.17   72 out of 239 ( 30.1%) improved
  -4 | -0.75  101 out of 294 ( 34.4%) improved
  -3 | -0.49  129 out of 310 ( 41.6%) improved
  -2 | -0.62  159 out of 410 ( 38.8%) improved
  -1 | -0.29  198 out of 473 ( 41.9%) improved
   0 | -0.12  229 out of 468 ( 48.9%) improved
   1 |  0.16  224 out of 418 ( 53.6%) improved
   2 |  0.34  222 out of 394 ( 56.3%) improved
   3 |  0.37  164 out of 298 ( 55.0%) improved
   4 |  0.80  165 out of 265 ( 62.3%) improved
   5 |  0.76  153 out of 236 ( 64.8%) improved
   6 |  0.79  101 out of 158 ( 63.9%) improved
   7 |  0.93   89 out of 134 ( 66.4%) improved
   8 |  1.22   79 out of 110 ( 71.8%) improved
   9 |  1.17   73 out of 104 ( 70.2%) improved
  10 |  1.32   74 out of  95 ( 77.9%) improved
  11 |  1.09   36 out of  52 ( 69.2%) improved
  12 |  1.63   37 out of  47 ( 78.7%) improved
  13 |  1.81   40 out of  52 ( 76.9%) improved
  14 |  1.73   17 out of  23 ( 73.9%) improved
  15 |  2.50   18 out of  22 ( 81.8%) improved
  16 |  2.63   15 out of  18 ( 83.3%) improved
  17 |  1.68    9 out of  13 ( 69.2%) improved
  18 |  1.79    7 out of   9 ( 77.8%) improved
  19 |  1.71    5 out of   7 ( 71.4%) improved
  20 |  2.95   11 out of  13 ( 84.6%) improved
  21 |  3.13    6 out of   7 ( 85.7%) improved
  22 |  3.99    3 out of   3 (100.0%) improved
  23 |  3.78    3 out of   3 (100.0%) improved
  25 |  3.36    1 out of   1 (100.0%) improved
  26 |  4.59    1 out of   1 (100.0%) improved


For example, players with an increase of 10 minutes played per game saw an average increase of 1.32 in PER.
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gabefarkas



Joined: 31 Dec 2004
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PostPosted: Tue Feb 15, 2005 10:10 pm    Post subject: Reply with quote

Justin - is there any chance I can get the raw data for that? the specific per difference for the 1 with -22, for both with -20, for all 7 with -18, etc?

i am curious to run some correlative stuff with the data....
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Kevin Pelton
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PostPosted: Tue Feb 15, 2005 11:00 pm    Post subject: Reply with quote



That's pretty remarkably linear.
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HoopStudies



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PostPosted: Tue Feb 15, 2005 11:18 pm    Post subject: Reply with quote

admin wrote:
That's pretty remarkably linear.


And that actually makes me skeptical.

I think the question of causality in all this is not clear.

If guys get more minutes because they're playing better, that's a vindication of a coach's ability to recognize a guy's ability.

If guys are playing better because they're getting more minutes, why is that? Does that just mean that they don't get used to the system in short time? Shouldn't there be some threshold past which they shouldn't be playing better with more minutes?
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