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player minutes chart
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 A chart showing players who played in 2 consecutive seasons,
listing the year, number of players qualifying, minutes from previous
season, minutes from year indicated, ratio of minutes
(previous/current), and cumulative factor. The cumulative factor is
the given year's "concentration factor" multiplied by the previous
cumulative factor.
Players who played for more than one team during either year are
not eligible in this list. That's just the way I did it.
Here is the complete listing, as it now stands:
concentration
year players prev. min. factor cumul. expansion/contraction
1953 59 1777 1928 .921 .921 9 teams to 10
1954 56 1981 1928 1.028 .947 10 teams to 9
1955 51 2080 1910 1.089 1.03 9 teams to 8
1956 58 1944 1817 1.070 1.10
1957 54 1783 1682 1.060 1.16
1958 54 1944 1817 1.047 1.22
1959 66 1781 1799 .990 1.21
1960 68 1797 1725 1.042 1.26
1961 66 1797 1825 .985 1.24
1962 65 1914 2036 .940 1.16 8 teams to 9
1963 75 2091 1908 1.096 1.28
1964 75 1943 1827 1.064 1.36
1965 68 1836 1763 1.041 1.42
1966 68 1892 1846 1.025 1.45
1967 66 1975 2122 .931 1.35 9 teams to 10
1968 93 1682 2045 .822 1.11 10 to 23 (11 ABA teams)
1969 155 1955 1982 .987 1.09 23 to 25 (11 ABA)
1970 172 1949 1893 1.030 1.13
1971 185 1971 2070 .952 1.07 25 to 28 (11 ABA)
1972 208 2001 1867 1.072 1.15
1973 205 2069 1974 1.048 1.21 28 to 27 (10 ABA)
1974 220 1888 1834 1.029 1.24
1975 214 1897 1827 1.039 1.29 27 to 28 (10 ABA)
1976 216 1864 1719 1.084 1.40 28 to 25 (7 ABA)
1977 189 1890 1645 1.149 1.61 25 to 22
1978 164 1892 1859 1.017 1.64
1979 171 1906 1860 1.024 1.68
1980 170 1894 1800 1.052 1.77
1981 169 1883 1888 .997 1.76 22 teams to 23
1982 196 1825 1708 1.069 1.88
1983 198 1731 1655 1.046 1.97
1984 204 1780 1768 1.007 1.98
1985 227 1767 1667 1.060 2.10
1986 223 1761 1690 1.042 2.19
1987 220 1724 1659 1.039 2.28
1988 211 1687 1665 1.013 2.31
1989 224 1532 1553 .986 2.27 23 teams to 25
1990 223 1751 1778 .985 2.24 25 teams to 27
1991 247 1750 1675 1.045 2.34
1992 253 1710 1674 1.022 2.39
1993 271 1736 1611 1.078 2.58
1994 263 1666 1577 1.056 2.72
1995 271 1664 1576 1.056 2.88
1996 271 1584 1600 .990 2.85 27 teams to 29  In APBR_analysis@y..., "Mike Goodman" <msg_53@h...> wrote:
Mike 
A few things.
1. Are you saying that if players average fewer minutes, then talent
is more dilute? This is weird and I'd have to think about it.
2. One factor in minutes going down may be that more games are played
now than in the 50's. I don't imagine this to be a big factor.
3. I've never understood the arguments that people made about Jordan
having it easy because talent was more dilute in his period than in
the '60's, for instance. On a statistical first order basis, it is
easier to win a title in a 12 team league than a 30 team league.
4. I like to think about talent dilution only in terms of the Bell
Curve argument Bill James made. He said that pro players come from
the right side of the bell curve. The NBA is made up of the top
0.0001% of society's basketball players. If you double the size of
the league, it is the top 0.0002%. This is true if you assume that
scouting is perfect, which it ain't, and all of the top players
actually want to play, which they don't. So there is some fluff
 you actually have half of the top 0.0002% in the first case and
are missing half of that true talent (for example). What is
interesting to me about your numbers is that the league (in terms
of teams) grows roughly 3 times, but your factor grows only 2.85
times. Does this mean that the league is doing a better job of
getting the top players into the league? Given the money in it and
the globalization, that would make some sense.... But I'm really not
sure whether I can make the statement (which gets back to point #1).
Dean Oliver
Journal of Basketball Studies
> A chart showing players who played in 2 consecutive seasons,
> listing the year, number of players qualifying, minutes from
previous
> season, minutes from year indicated, ratio of minutes
> (previous/current), and cumulative factor. The cumulative factor is
> the given year's "concentration factor" multiplied by the previous
> cumulative factor.
> Players who played for more than one team during either year are
> not eligible in this list. That's just the way I did it.
>
> Here is the complete listing, as it now stands:
>
> concentration
> year players prev. min. factor cumul. expansion/contraction
> 1953 59 1777 1928 .921 .921 9 teams to 10
> 1954 56 1981 1928 1.028 .947 10 teams to 9
> 1955 51 2080 1910 1.089 1.03 9 teams to 8
> 1956 58 1944 1817 1.070 1.10
> 1957 54 1783 1682 1.060 1.16
> 1958 54 1944 1817 1.047 1.22
> 1959 66 1781 1799 .990 1.21
> 1960 68 1797 1725 1.042 1.26
> 1961 66 1797 1825 .985 1.24
> 1962 65 1914 2036 .940 1.16 8 teams to 9
> 1963 75 2091 1908 1.096 1.28
> 1964 75 1943 1827 1.064 1.36
> 1965 68 1836 1763 1.041 1.42
> 1966 68 1892 1846 1.025 1.45
> 1967 66 1975 2122 .931 1.35 9 teams to 10
> 1968 93 1682 2045 .822 1.11 10 to 23 (11 ABA teams)
> 1969 155 1955 1982 .987 1.09 23 to 25 (11 ABA)
> 1970 172 1949 1893 1.030 1.13
> 1971 185 1971 2070 .952 1.07 25 to 28 (11 ABA)
> 1972 208 2001 1867 1.072 1.15
> 1973 205 2069 1974 1.048 1.21 28 to 27 (10 ABA)
> 1974 220 1888 1834 1.029 1.24
> 1975 214 1897 1827 1.039 1.29 27 to 28 (10 ABA)
> 1976 216 1864 1719 1.084 1.40 28 to 25 (7 ABA)
> 1977 189 1890 1645 1.149 1.61 25 to 22
> 1978 164 1892 1859 1.017 1.64
> 1979 171 1906 1860 1.024 1.68
> 1980 170 1894 1800 1.052 1.77
> 1981 169 1883 1888 .997 1.76 22 teams to 23
> 1982 196 1825 1708 1.069 1.88
> 1983 198 1731 1655 1.046 1.97
> 1984 204 1780 1768 1.007 1.98
> 1985 227 1767 1667 1.060 2.10
> 1986 223 1761 1690 1.042 2.19
> 1987 220 1724 1659 1.039 2.28
> 1988 211 1687 1665 1.013 2.31
> 1989 224 1532 1553 .986 2.27 23 teams to 25
> 1990 223 1751 1778 .985 2.24 25 teams to 27
> 1991 247 1750 1675 1.045 2.34
> 1992 253 1710 1674 1.022 2.39
> 1993 271 1736 1611 1.078 2.58
> 1994 263 1666 1577 1.056 2.72
> 1995 271 1664 1576 1.056 2.88
> 1996 271 1584 1600 .990 2.85 27 teams to 29 I am just curious about theory of minutes and dilution. I assume the
theory is that if a player's minutes dropped from the season before
this is an indication that better players have come in to take his
missing minutes. Thus the player who loses his minutes (from the
season before) occupyies the lower niche which creates a chain
reaction of other lower niches eventually dumping lower rung players
out of the league. THe end result of this is a higher concentration
of talent. Is this the correct assumption?
If it is, it seems pretty sound. The one flaw in the reasoning is
that it is assumes players retain their abilities from the seasons
before. However, player ability does not remain static. I think
most people view a players career as a line that moves up hits its
peak and then goes down. In that scenario most players are not
always, from season to season, the same as the season before. Thus,
a change in minutes for players could be a result of new younger
talent blowing into the league while another groups has aged. This
is not to say that the two groups both at their peaks are equal. SO,
a drop in minutes could be in some cases a result of an influx of new
not so great players while the current previously great group became
old. If the old players lose their minutes and talent from the year
before is the league's overall talent level really higher than the
year before. Maybe one could argue that it is on some level but I'm
not sure. Anyway, I'll stop because I think I might be babbling at
this point.
 In APBR_analysis@y..., "Dean Oliver" <deano@t...> wrote:
>  In APBR_analysis@y..., "Mike Goodman" <msg_53@h...> wrote:
>
> Mike 
>
> A few things.
>
> 1. Are you saying that if players average fewer minutes, then
talent
> is more dilute? This is weird and I'd have to think about it.
>
> 2. One factor in minutes going down may be that more games are
played
> now than in the 50's. I don't imagine this to be a big factor.
>
> 3. I've never understood the arguments that people made about
Jordan
> having it easy because talent was more dilute in his period than in
> the '60's, for instance. On a statistical first order basis, it is
> easier to win a title in a 12 team league than a 30 team league.
>
> 4. I like to think about talent dilution only in terms of the Bell
> Curve argument Bill James made. He said that pro players come from
> the right side of the bell curve. The NBA is made up of the top
> 0.0001% of society's basketball players. If you double the size of
> the league, it is the top 0.0002%. This is true if you assume that
> scouting is perfect, which it ain't, and all of the top players
> actually want to play, which they don't. So there is some fluff
>  you actually have half of the top 0.0002% in the first case and
> are missing half of that true talent (for example). What is
> interesting to me about your numbers is that the league (in terms
> of teams) grows roughly 3 times, but your factor grows only 2.85
> times. Does this mean that the league is doing a better job of
> getting the top players into the league? Given the money in it and
> the globalization, that would make some sense.... But I'm really
not
> sure whether I can make the statement (which gets back to point #1).
>
> Dean Oliver
> Journal of Basketball Studies
>
> > A chart showing players who played in 2 consecutive seasons,
> > listing the year, number of players qualifying, minutes from
> previous
> > season, minutes from year indicated, ratio of minutes
> > (previous/current), and cumulative factor. The cumulative factor
is
> > the given year's "concentration factor" multiplied by the
previous
> > cumulative factor.
> > Players who played for more than one team during either year
are
> > not eligible in this list. That's just the way I did it.
> >
> > Here is the complete listing, as it now stands:
> >
> > concentration
> > year players prev. min. factor cumul. expansion/contraction
> > 1953 59 1777 1928 .921 .921 9 teams to 10
> > 1954 56 1981 1928 1.028 .947 10 teams to 9
> > 1955 51 2080 1910 1.089 1.03 9 teams to 8
> > 1956 58 1944 1817 1.070 1.10
> > 1957 54 1783 1682 1.060 1.16
> > 1958 54 1944 1817 1.047 1.22
> > 1959 66 1781 1799 .990 1.21
> > 1960 68 1797 1725 1.042 1.26
> > 1961 66 1797 1825 .985 1.24
> > 1962 65 1914 2036 .940 1.16 8 teams to 9
> > 1963 75 2091 1908 1.096 1.28
> > 1964 75 1943 1827 1.064 1.36
> > 1965 68 1836 1763 1.041 1.42
> > 1966 68 1892 1846 1.025 1.45
> > 1967 66 1975 2122 .931 1.35 9 teams to 10
> > 1968 93 1682 2045 .822 1.11 10 to 23 (11 ABA teams)
> > 1969 155 1955 1982 .987 1.09 23 to 25 (11 ABA)
> > 1970 172 1949 1893 1.030 1.13
> > 1971 185 1971 2070 .952 1.07 25 to 28 (11 ABA)
> > 1972 208 2001 1867 1.072 1.15
> > 1973 205 2069 1974 1.048 1.21 28 to 27 (10 ABA)
> > 1974 220 1888 1834 1.029 1.24
> > 1975 214 1897 1827 1.039 1.29 27 to 28 (10 ABA)
> > 1976 216 1864 1719 1.084 1.40 28 to 25 (7 ABA)
> > 1977 189 1890 1645 1.149 1.61 25 to 22
> > 1978 164 1892 1859 1.017 1.64
> > 1979 171 1906 1860 1.024 1.68
> > 1980 170 1894 1800 1.052 1.77
> > 1981 169 1883 1888 .997 1.76 22 teams to 23
> > 1982 196 1825 1708 1.069 1.88
> > 1983 198 1731 1655 1.046 1.97
> > 1984 204 1780 1768 1.007 1.98
> > 1985 227 1767 1667 1.060 2.10
> > 1986 223 1761 1690 1.042 2.19
> > 1987 220 1724 1659 1.039 2.28
> > 1988 211 1687 1665 1.013 2.31
> > 1989 224 1532 1553 .986 2.27 23 teams to 25
> > 1990 223 1751 1778 .985 2.24 25 teams to 27
> > 1991 247 1750 1675 1.045 2.34
> > 1992 253 1710 1674 1.022 2.39
> > 1993 271 1736 1611 1.078 2.58
> > 1994 263 1666 1577 1.056 2.72
> > 1995 271 1664 1576 1.056 2.88
> > 1996 271 1584 1600 .990 2.85 27 teams to 29   In APBR_analysis@y..., harlanzo@y... wrote:
> I am just curious about theory of minutes and dilution. I assume
the
> theory is that if a player's minutes dropped from the season before
players
> this is an indication that better players have come in to take his
> missing minutes. Thus the player who loses his minutes (from the
> season before) occupyies the lower niche which creates a chain
> reaction of other lower niches eventually dumping lower rung
> out of the league. THe end result of this is a higher
concentration
> of talent. Is this the correct assumption?
Pretty much so. New players come into the league, young players
get better, in general.
> If it is, it seems pretty sound. The one flaw in the reasoning is
Thus,
> that it is assumes players retain their abilities from the seasons
> before. However, player ability does not remain static. I think
> most people view a players career as a line that moves up hits its
> peak and then goes down. In that scenario most players are not
> always, from season to season, the same as the season before.
> a change in minutes for players could be a result of new younger
SO,
> talent blowing into the league while another groups has aged. This
> is not to say that the two groups both at their peaks are equal.
> a drop in minutes could be in some cases a result of an influx of
new
> not so great players while the current previously great group
became
> old. If the old players lose their minutes and talent from the
year
> before is the league's overall talent level really higher than the
I'm
> year before. Maybe one could argue that it is on some level but
> not sure. Anyway, I'll stop because I think I might be babbling at
I am so glad to know I am not the only one who may be babbling.
> this point.
We could assume, in a large sampling of players, there are always
about as many guys in decline as there are in ascension. And as many
good and average guys are entering the league as are retiring. If
the concentration factors I have calculated are not equal to 1, then
there is either a net influx, or a net decline in talent
concentration.
In theory.
>
>   In APBR_analysis@y..., "Dean Oliver" <deano@t...> wrote:
>  In APBR_analysis@y..., "Mike Goodman" <msg_53@h...> wrote:
talent
>
> Mike 
>
> A few things.
>
> 1. Are you saying that if players average fewer minutes, then
> is more dilute? This is weird and I'd have to think about it.
Other way around. More competition = fewer minutes. Again, the
>
minutes listed are for individuals, so if you got fewer minutes,
someone else was better.
> 2. One factor in minutes going down may be that more games are
played
> now than in the 50's. I don't imagine this to be a big factor.
Dadgummit! I completely forgot this. It is definitely a factor,
>
and I will have to go back and redo everything before 1967. No big
changes from one year to the next, but the cumulative index will
change for sure.
Fewer games in the earlier seasons will mean an even larger
difference in talent concentration. (More games should yield more
minutes.) This is going to make the NBA of 1953 look even more like
a minor league affair.
> 3. I've never understood the arguments that people made about
Jordan
> having it easy because talent was more dilute in his period than in
Just between you and me and other "analysts", I think a title in
> the '60's, for instance. On a statistical first order basis, it is
> easier to win a title in a 12 team league than a 30 team league.
>
1960 is about equivalent to a conference finals appearance today;
the Celtics only had to win 2 rounds, to be called champs.
Another way to look at it is: the Bulls beat out 28 other teams 3
times straight, which is 84 teamseasons. The Celts beat out 78
other teams 9 years straight, some 67 teamseasons.
And, the Bulls did it twice.
> 4. I like to think about talent dilution only in terms of the Bell
not
> Curve argument Bill James made. He said that pro players come from
> the right side of the bell curve. The NBA is made up of the top
> 0.0001% of society's basketball players. If you double the size of
> the league, it is the top 0.0002%. This is true if you assume that
> scouting is perfect, which it ain't, and all of the top players
> actually want to play, which they don't. So there is some fluff
>  you actually have half of the top 0.0002% in the first case and
> are missing half of that true talent (for example). What is
> interesting to me about your numbers is that the league (in terms
> of teams) grows roughly 3 times, but your factor grows only 2.85
> times. Does this mean that the league is doing a better job of
> getting the top players into the league? Given the money in it and
> the globalization, that would make some sense.... But I'm really
> sure whether I can make the statement (which gets back to point #1).
Money surely draws the best players more effectively today than in
>
> Dean Oliver
> Journal of Basketball Studies
1952, when factory jobs paid better.
Connie Hawkins may or may not have originated this
statement: "The older we get, the better we were."
I know Oscar and Cousy (not to mention Wilt) have been vociferous
about how much better the talent used to be.   In APBR_analysis@y..., "Mike Goodman" <msg_53@h...> wrote:
>  In APBR_analysis@y..., harlanzo@y... wrote:
before
> > I am just curious about theory of minutes and dilution. I assume
> the
> > theory is that if a player's minutes dropped from the season
> > this is an indication that better players have come in to take
his
> > missing minutes. Thus the player who loses his minutes (from the
...
> > season before) occupyies the lower niche which creates a chain
> > reaction of other lower niches eventually dumping lower rung
> players
> > out of the league. THe end result of this is a higher
> concentration
> > of talent. Is this the correct assumption?
>
> We could assume, in a large sampling of players, there are
always
> about as many guys in decline as there are in ascension. And as
many
> good and average guys are entering the league as are retiring. If
then
> the concentration factors I have calculated are not equal to 1,
> there is either a net influx, or a net decline in talent
So a concentration factor > 1 means a net influx of talent? I think
> concentration.
> In theory.
that is what this all means. (Please correct me.) Actually the
concentration factor is a measure of net influx of talent, but not
dilution. Right? Getting a sense of dilution would mean comparing
the concentration factor increase to the increase in the number of
teams or the number of players. So the 2.85 factor over the last 50
years (when the league has gone from 10 teams to 29) means that
talent isn't all that diluted (2.85/2.9 ~1). It should have gone up
by a factor of 2.9 to keep up with the number of teams. On the other
hand, the number of players you are calculating this for went from 59
to 271, an increase of 4.5. So maybe it's diluted a lot (2.85/4.5).
I'm not sure which is the more relevant comparison.
I hope I'm right in my interpretations because, with this
understanding, I really do think you have something there. And I
wasn't sure there was a way to measure dilution. So simple yet so
apparently reasonable.
What players do you calculate the values for? Do you think we need
to correct for the number of games played, too (which change from
year to year, esp. early on)?
Dean Oliver
Journal of Basketball Studies   In APBR_analysis@y..., "Dean Oliver" <deano@t...> wrote:
> So a concentration factor > 1 means a net influx of talent? I
think
> that is what this all means. (Please correct me.) Actually the
Assuming reduced minutesperplayer is the result of increased
> concentration factor is a measure of net influx of talent, but not
> dilution. Right?
competition, that seems to be the consensus.
Getting a sense of dilution would mean comparing> the concentration factor increase to the increase in the number of
50
> teams or the number of players. So the 2.85 factor over the last
> years (when the league has gone from 10 teams to 29) means that
up
> talent isn't all that diluted (2.85/2.9 ~1). It should have gone
> by a factor of 2.9 to keep up with the number of teams.
I don't think so. Increasing the number of teams (expansion) has
only reduced the talent concentration during that year, and it is
immediately made up in the next year or two. What I am reading is
that a player from 1965, transported to 1985, would only get half as
many minutes. This is, of course, an average; a Wilt or an Oscar
would still get beaucoup minutes, but a Bob Ferry might get only 1/4
the minutes.
On the other> hand, the number of players you are calculating this for went from
59
> to 271, an increase of 4.5. So maybe it's diluted a lot
(2.85/4.5).
> I'm not sure which is the more relevant comparison.
I think, by comparing individuals' yeartoyear minutes, we are
immune to such factors as # of teams or # of players. The proof is
in the pudding.>
Simple enough, when you have Excel. Yet I have overlooked its
> I hope I'm right in my interpretations because, with this
> understanding, I really do think you have something there. And I
> wasn't sure there was a way to measure dilution. So simple yet so
> apparently reasonable.
significance.
> What players do you calculate the values for? Do you think we need
Fewer games early on means I get scalped some more, for beating up
> to correct for the number of games played, too (which change from
> year to year, esp. early on)?
>
on the oldtimers. I counted everyone who played for one team in 2
consecutive years. Not counting players traded midseason should not
add up to anything in the long run, but I can't prove this. Partly,
I was concerned about guys getting 88 games, but mostly I didn't have
a ready list of added totals.  I am responding to my own earlier post. I am editing some of this
chart to factor in the changing schedule, from 66 games in 1952, to
82 games by 1967.
In this version, ABA seasons are not included, though the effects
on the NBA are measured.
I also completed the analysis from 19962000, previously missing.
 In APBR_analysis@y..., "Mike Goodman" <msg_53@h...> wrote:
> A chart showing players who played in 2 consecutive seasons,
> listing the year, number of players qualifying, minutes from
previous
> season, minutes from year indicated, ratio of minutes
> (previous/current), and cumulative factor. The cumulative factor
is
> the given year's "concentration factor" multiplied by the previous
> cumulative factor.
> Players who played for more than one team during either year are
> not eligible in this list. That's just the way I did it.
>
> Here is the complete listing, as it now stands:
>
> concentration
> year players prev. min. factor cumul. expansion/contraction
> 1953 59 1777 1928 .977 .977 9 teams to 10
> 1954 56 1981 1928 1.057 1.033 10 teams to 9
> 1955 51 2080 1910 1.089 1.125 9 teams to 8
> 1956 58 1944 1817 1.070 1.20
> 1957 54 1783 1682 1.060 1.28
> 1958 54 1944 1817 1.047 1.34
> 1959 66 1781 1799 .990 1.32
> 1960 68 1797 1725 1.086 1.44
> 1961 66 1797 1825 1.037 1.49
> 1962 65 1914 2036 .952 1.42 8 teams to 9
> 1963 75 2091 1908 1.096 1.55
> 1964 75 1943 1827 1.064 1.65
> 1965 68 1836 1763 1.041 1.72
> 1966 68 1892 1846 1.025 1.76
> 1967 66 1975 2122 .942 1.66 9 teams to 10
> 1968 93 1682 2045 .849 1.41 10 to 23 (11 ABA teams)
> 1969 155 1955 1982 .962 1.36 23 to 25 (11 ABA)
> 1970 172 1949 1893 1.045 1.42
> 1971 185 1971 2070 .953 1.35 25 to 28 (11 ABA)
> 1972 208 2001 1867 1.069 1.44
> 1973 205 2069 1974 1.063 1.54 28 to 27 (10 ABA)
> 1974 220 1888 1834 1.007 1.55
> 1975 214 1897 1827 1.016 1.57 27 to 28 (10 ABA)
> 1976 216 1864 1719 1.022 1.61 28 to 25 (7 ABA)
> 1977 189 1890 1645 1.142 1.84 25 to 22
> 1978 164 1892 1859 1.017 1.87
> 1979 171 1906 1860 1.024 1.91
> 1980 170 1894 1800 1.052 2.00
> 1981 169 1883 1888 .997 2.01 22 teams to 23
> 1982 196 1825 1708 1.069 2.14
> 1983 198 1731 1655 1.046 2.24
> 1984 204 1780 1768 1.007 2.26
> 1985 227 1767 1667 1.060 2.39
> 1986 223 1761 1690 1.042 2.49
> 1987 220 1724 1659 1.039 2.59
> 1988 211 1687 1665 1.013 2.63
> 1989 224 1532 1553 .986 2.59 23 teams to 25
> 1990 223 1751 1778 .985 2.55 25 teams to 27
> 1991 247 1750 1675 1.045 2.66
> 1992 253 1710 1674 1.022 2.72
> 1993 271 1736 1611 1.078 2.93
> 1994 263 1666 1577 1.056 3.10
> 1995 271 1664 1576 1.056 3.27
> 1996 271 1584 1600 .990 3.24 27 teams to 29
1997 266 1615 1596 1.012 3.28
1998 265 1658 1597 1.038 3.40
1999* 277 1637 1572 1.041 3.54
2000 298 1640 1554 1.056 3.74
* 1999 minutes adjusted for short season   In APBR_analysis@y..., "Mike Goodman" <msg_53@h...> wrote:
> I am responding to my own earlier post. I am editing some of
this
> chart to factor in the changing schedule, from 66 games in 1952, to
Fascinating stuff, this really is utilizing the minutes played
> 82 games by 1967.
statistic for all it's worth. A couple of minor suggestions:
1. It looks like you're literally looking at total minutes played;
I'd suggest minutes per game instead. For two reasons: it
automatically corrects for the "games per season" problem that
you've been wrestling with. And it will help correct for injuries,
and comebacks from injuries, which can cause a player's minutes
to seemingly plummet or skyrocket. Injuries will also affect minutes
per game, but less so than total minutes.
2. The issue of players' minutes changing due to their individual
improvement or aging is a potentially important but complex one.
You mentioned that it sort of evens out, as young players improve
and old players decline, but that assumes a sort of long run
equilibrium. I can imagine that there have been times when the
NBA was in the middle of a period of influx of new talent (probably
most all the time) or conversely a period of decay in which old
players declined but failed to get replaced by an equal amount of new
talent (probably much rarer, except maybe when an unusual "baby boom"
of talent, such as 1984, starts aging).
Are the concentration factors based on players' total minutes?
That might be the best way to do things, but it might cause the
concentration factors to be overly influenced by the star players
who get the most minutes. E.g. if 2 players both doubled their
minutes from 400 to 800, but one superstar diminished from 3,200 to
2,800, the grand total is unchanged and the concentration factor would
be 1.0. But I wonder if we should instead give each of the three
players equal weight, with individual concentration factors of 2.0,
2.0, and .875, for an average of 1.625. (This is assuming I've got the
correct formula for calculating concentration factors.)
That example shows a danger of my suggestion, as there will be a lot
of marginal players with tiny minutes whose individual concentration
factors can be huge or tiny, and which might unduly influence the
overall index.
Maybe there's an intermediate way... logarithms, medians instead of
means or totals, etc.
I won't be reading email for almost a month, so I regrettably won't
be able to participate in this discussion for much longer, until July.
It occurs to me that my minutespergame suggestion might be
problematic when players change their number of games played, in
addition to their number of minutes per game ... a lot here to think
about.
MKT   In APBR_analysis@y..., tamada@o... wrote:
>  In APBR_analysis@y..., "Mike Goodman" <msg_53@h...> wrote:
to
> > I am responding to my own earlier post. I am editing some of
> this
> > chart to factor in the changing schedule, from 66 games in 1952,
> > 82 games by 1967.
minutes
>
> Fascinating stuff, this really is utilizing the minutes played
> statistic for all it's worth. A couple of minor suggestions:
>
> 1. It looks like you're literally looking at total minutes played;
> I'd suggest minutes per game instead. For two reasons: it
> automatically corrects for the "games per season" problem that
> you've been wrestling with. And it will help correct for injuries,
> and comebacks from injuries, which can cause a player's minutes
> to seemingly plummet or skyrocket. Injuries will also affect
> per game, but less so than total minutes.
One problem with using minutespergame is that, while
mathematically compensating for the player who was injured, in fact
that player's minutes are picked up by other players; and so there
would be a skewed total for his team, and for the league.
> 2. The issue of players' minutes changing due to their individual
new
> improvement or aging is a potentially important but complex one.
> You mentioned that it sort of evens out, as young players improve
> and old players decline, but that assumes a sort of long run
> equilibrium. I can imagine that there have been times when the
> NBA was in the middle of a period of influx of new talent (probably
> most all the time) or conversely a period of decay in which old
> players declined but failed to get replaced by an equal amount of
> talent (probably much rarer, except maybe when an unusual "baby
boom"
> of talent, such as 1984, starts aging).
Completely valid points. But wouldn't a mass retirement or mass
>
influx be evened out over a few years at most? If there were serious
ups and downs not attributable to league expansion, I would wonder
about this, yet as the sample size grows in later years, the trend is
invariably toward talent concentration.
> Are the concentration factors based on players' total minutes?
would
> That might be the best way to do things, but it might cause the
> concentration factors to be overly influenced by the star players
> who get the most minutes. E.g. if 2 players both doubled their
> minutes from 400 to 800, but one superstar diminished from 3,200 to
> 2,800, the grand total is unchanged and the concentration factor
> be 1.0. But I wonder if we should instead give each of the three
the
> players equal weight, with individual concentration factors of 2.0,
> 2.0, and .875, for an average of 1.625. (This is assuming I've got
> correct formula for calculating concentration factors.)
lot
>
> That example shows a danger of my suggestion, as there will be a
> of marginal players with tiny minutes whose individual
concentration
> factors can be huge or tiny, and which might unduly influence the
July.
> overall index.
>
> Maybe there's an intermediate way... logarithms, medians instead of
> means or totals, etc.
>
> I won't be reading email for almost a month, so I regrettably won't
> be able to participate in this discussion for much longer, until
>
think
> It occurs to me that my minutespergame suggestion might be
> problematic when players change their number of games played, in
> addition to their number of minutes per game ... a lot here to
> about.
Again we have reached more agreement than I am comfortable with!
>
>
> MKT
I wish we could find some holes in the logic, or at least some seams.
Even casually asking my acquaintances seems to produce the same
response, intuitively or analytically: players must be getting
better.   In APBR_analysis@y..., "Mike Goodman" <msg_53@h...> wrote:
> Even casually asking my acquaintances seems to produce the same
I agree with your conclusion on player improvement. I was trying to
> response, intuitively or analytically: players must be getting
> better.
think of a way of independently verifying that point. The one way
that it struck me to do this is to check players' best years and see
whether their peaks coincide with the generally believed development
of players (ie rising production from 2127/28 and then gradual
decline). Indeed, it did seem that an inordinate number of players
hit their statistical peaks in 6162 well before we might believe
they would. I have not looked at this thoery in depth but its just a
thought.   In APBR_analysis@y..., harlanzo@y... wrote:
>
to
> I agree with your conclusion on player improvement. I was trying
> think of a way of independently verifying that point. The one way
see
> that it struck me to do this is to check players' best years and
> whether their peaks coincide with the generally believed
development
> of players (ie rising production from 2127/28 and then gradual
a
> decline). Indeed, it did seem that an inordinate number of players
> hit their statistical peaks in 6162 well before we might believe
> they would. I have not looked at this thoery in depth but its just
> thought.
Another suggestion (offline) has been that players have longer
careers these days. Whereas 13 years was about the limit for players
entering before 1965, there are now quite a few players who go for 15
20 years. In general, their last few seasons would consist of
minutes diminishing below that of their rookie seasons.
Which brings me to another point: I don't think it matters where
in your career you peak (early, middle, late), in terms of league
wide averages. Rather, it matters how many minutes you played as a
rookie, and how many you play in your last season, and that is all.
While a good many players hang on to the bitter end, perhaps
winding up their career with a 100minute season, there are very few
who get 100 minutes as a rookie, and build up to major minutes
later. Most good, longcareer players are good as rookies.
So, regardless of the intervening years, only one's first and last
seasons really add up to anything in the league totals. If you get
2000 minutes as a rookie, you may peak at 3000 or 2500, or whatever;
if you play 10 years and end up with a 500 minute season, you lost
1500 minutes over 10 years. When you are looking at large numbers of
players, the curve smooths out everyone's peaks and valleys, and it
looks as though every year it is tougher to get minutes; but at least
part of this measurement is bogus.
Now we come to another stickingpoint; we could figure everyone's
rookie minutes, finalseason minutes, and career length, to get an
average annual minuteslost number. But this would not distinguish
between an aging factor and a competition factor.
So these numbers may mean nothing. Or they may mean something.
Anyone?  Responding to one of my own posts, again.
I went ahead and tabulated the careers of some 1500 players, using
seasons spent with a single team. I have broken them down by career
length, from singleseason careers to a 17+ year group.
The 2nd and 3rd columns are the minutes played as rookie, and in
final season.
career avg. minutes
length season annual decline
   
yrs. # first last net min. pct.
1 326 (500) (500)
2 156 685 561 123 123 .180
3 129 873 626 247 124 .142
4 93 916 688 229 76 .083
5 76 1032 678 354 88 .086
6 65 1150 784 367 73 .064
7 86 1368 660 709 118 .086
8 78 1415 734 681 97 .069
9 96 1266 953 313 39 .031
10 106 1448 912 537 60 .041
11 92 1393 983 410 41 .029
12 70 1534 1020 513 47 .030
13 57 1779 1101 678 57 .032
14 42 1734 1030 704 54 .031
15 24 1436 1053 383 27 .019
16 19 1881 1037 844 56 .030
17+ 16 1997 480 1516 89 .044
__________________________________________
7.7 1531 1240 805 435 65 .052
This thing has sat on my desktop long enough; I am not ashamed to
say I don't know what to make of it.
One thing is clear: "weak" players (those with brief careers) have
a steeper decline, both in minutes and pct. of minutes, than do
stronger (longer) players. Is it possible to produce a "natural
decline" factor, as distinguished from a "talent concentration"
factor, by comparing the decline rates of stronger and weaker players?
Something about guys who go past 16 years and hanging on to the
bitter end? I don't know how much these 16 players can skew the
overall group, but it does illustrate how a bias can result when
talented young players come in at 2000 minutes and leave at 500.
 In APBR_analysis@y..., "Mike Goodman" <msg_53@h...> wrote:
I don't think it matters where
> in your career you peak (early, middle, late), in terms of league
> wide averages. Rather, it matters how many minutes you played as a
> rookie, and how many you play in your last season, and that is all.
> While a good many players hang on to the bitter end, perhaps
> winding up their career with a 100minute season, there are very
few
> who get 100 minutes as a rookie, and build up to major minutes
> later. Most good, longcareer players are good as rookies.
> So, regardless of the intervening years, only one's first and
last
> seasons really add up to anything in the league totals. If you get
> 2000 minutes as a rookie, you may peak at 3000 or 2500, or
whatever;
> if you play 10 years and end up with a 500 minute season, you lost
> 1500 minutes over 10 years. When you are looking at large numbers
of
> players, the curve smooths out everyone's peaks and valleys, and it
> looks as though every year it is tougher to get minutes; but at
least
> part of this measurement is bogus.
> Now we come to another stickingpoint; we could figure
everyone's
> rookie minutes, finalseason minutes, and career length, to get an
> average annual minuteslost number. But this would not distinguish
> between an aging factor and a competition factor.
> So these numbers may mean nothing. Or they may mean something.
> Anyone?   In APBR_analysis@y..., "Mike Goodman" <msg_53@h...> wrote:
> Responding to one of my own posts, again.
using
> I went ahead and tabulated the careers of some 1500 players,
> seasons spent with a single team. I have broken them down by career
Mike 
> length, from singleseason careers to a 17+ year group.
> The 2nd and 3rd columns are the minutes played as rookie, and in
> final season.
>
> career avg. minutes
> length season annual decline
>    
> yrs. # first last net min. pct.
> 1 326 (500) (500)
> 2 156 685 561 123 123 .180
> 3 129 873 626 247 124 .142
> 4 93 916 688 229 76 .083
> 5 76 1032 678 354 88 .086
> 6 65 1150 784 367 73 .064
> 7 86 1368 660 709 118 .086
> 8 78 1415 734 681 97 .069
> 9 96 1266 953 313 39 .031
> 10 106 1448 912 537 60 .041
> 11 92 1393 983 410 41 .029
> 12 70 1534 1020 513 47 .030
> 13 57 1779 1101 678 57 .032
> 14 42 1734 1030 704 54 .031
> 15 24 1436 1053 383 27 .019
> 16 19 1881 1037 844 56 .030
> 17+ 16 1997 480 1516 89 .044
> __________________________________________
> 7.7 1531 1240 805 435 65 .052
I think all this work with minutes is very interesting. Not precisely
sure what to make of it either, but it _seems_ relevant and
informative. (Maybe for doing something like James' career projection
stuff...)
For instance, it's interesting that players with longer careers never
fall to the level of 2 year players  in terms of minutes. That
probably means that they are still better than the 2 year players even
after 16 years in the game.
Another way to look at the data would be to calculate the minutes for
players in their peak year and what year that typically was.
Calculate a decline rate in minutes per year from the year of peak.
I'm guessing that the peak minute year flatterns out at about 5 years,
based on the typical assumption that players' careers peak at age
2728.
Dean Oliver
Journal of Basketball Studies
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