Typically when someone on TV talks about a team being young or old, they just take the average age of everyone the
roster. Which of course is a worthless number. If your youngs never play, your team is older than your average. If
the old guys never play, its younger than its average.
To find out where all the teams stood, i went through and took the age of each player on dec 31st and multiplied
by the number of minutes they have played and calculated the weighted average age for each team.
These charts are going to look confusing unless you are into numbers, but the numbers are interesting. The teams
are ordered from youngest weighted average age to oldest. All teams show the most used lineup in minutes, except for
the MAVs. I just picked a lineup but excluded numbers. Hey, this is competitive info …..
ATL “team 23.49 weighted age
ATL 1.00 131.08 minutes 57
appearances
Harrington Johnson
Lue Pachulia J.
Smith 24.20 years
CHI “team 24.48 weighted age
CHI -7.02 109.54 minutes 38 appearances
Chandler Duhon
Hinrich Nocioni
Sweetney 24.17 years
POR “team 24.96 weighted age
POR -13.40 172.98 minutes 58 appearances
Miles Monia
Przybilla Randolph Telfair
23.64 years
(I used this lineup since it played so much more than the next most used)
CHA “team 25.33 weighted age
CHA 3.93 157.97 minutes 60 appearances
Brezec Knight
Okafor Rush
Wallace 25.65 years
LAL “team 25.72 weighted age
LAL 10.63 216.09 minutes 74 appearances
Bryant Cook
Mihm Odom
Parker 25.93 years
MIL “team 25.91 weighted age
MIL -4.09 213.05 minutes 76 appearances
Bogut Ford
Magloire Redd
Simmons 24.68 years
BOS “team 25.98 weighted age
BOS 1.63 279.20 minutes 112 appearances
Blount Davis
LaFrentz Pierce
West 27.32 years
NOH “team 26.20 weighted age
NOH 0.25 263.95 minutes 80 appearances
Brown Mason
Paul Smith
West 26.15 years
UTA “team 26.25 weighted age
UTA 0.84 81.75 minutes 29 appearances
Giricek Kirilenko
McLeod Okur
Ostertag 27.79 years
TOR “team 26.30 weighted age
TOR -16.07 91.45 minutes 26 appearances
Araujo Bosh
Calderon James
Peterson 26.06 years
TOR 16.22 90.61 minutes 46 appearances
Bosh Calderon
James Peterson
Villanueva 25.25 years
(I showed both since the minutes were almost exactly the same)
NYK “team 26.42 weighted age
NYK -19.93 35.75 minutes 17 appearances
Ariza Davis
Frye Marbury
Richardson 26.97 years
(*Knicks most used by minutes lineup had Matt Barnes, so I excluded it)
GSW “team 26.60 weighted age
GSW 0.01 389.30 minutes 111 appearances
Davis Dunleavy
Foyle Murphy
Richardson 26.69 years
ORL “team 26.68 weighted age
ORL -8.81 100.41 minutes 33 appearances
Battie Francis
Hill Howard
Stevenson 27.36 years
(i used the lineup with Grant Hill since he is back)
SEA “team 26.73 weighted age
SEA 6.79 175.39 minutes 67 appearances
Allen Collison
Evans Lewis
Ridnour 26.51 years
WAS “team 26.99 weighted age
WAS 3.03 162.62 minutes 49 appearances
Arenas Hayes
Haywood Jamison
Jeffries 25.62 years
PHI “team 27.24 weighted age
PHI 5.57 286.85 minutes 109 appearances
Dalembert Iguodala Iverson
Korver Webber 26.95 years
DAL “team 27.54 weighted age
DAL 15.17 xxxx minutes xx appearances
Daniels Diop
Harris Howard
Nowitzki 24.99 years
LAC “team 27.74 weighted age
LAC 3.10 248.86 minutes 96 appearances
Brand Cassell
Kaman Mobley
Ross 28.32 years
CLE “team 27.78 weighted age
CLE 2.11 341.82 minutes 116 appearances
Gooden Hughes
Ilgauskas James
Snow 27.09 years
IND “team 27.79 weighted age
IND 9.77 169.28 minutes 70 appearances
Artest Croshere
Jackson O’Neal
Tinsley 27.92 years
MIN “team 28.02 weighted age
MIN 9.01 408.75 minutes 111 appearances
Garnett Hassell
Jaric Olowokandi Szczerbiak 28.65
years
DEN “team 28.04 weighted age
DEN 4.65 94.58 minutes 39 appearances
Anthony Camby
Lenard Martin
Miller 28.76 years
SAC “team 28.15 weighted age
SAC 9.98 508.58 minutes 177 appearances
Abdur-Rahim Bibby
Miller Stojakovic
Wells 28.85 years
MEM “team 28.61 weighted age
MEM 4.15 309.19 minutes 89 appearances
Battier Gasol E.
Jones Stoudamire Wright
29.90 years
DET “team 28.62 weighted age
DET 18.63 616.12 minutes 202 appearances
Billups Hamilton
Prince B. Wallace R. Wallace 29.12
years
PHO “team 28.79 weighted age
PHO 12.61 254.01 minutes 85 appearances
Bell Diaw
Marion Nash
Thomas 29.16 years
NJN “team 29.20 weighted age
NJN 12.43 315.84 minutes 98 appearances
Carter Collins
Jefferson Kidd
Krstic 27.35 years
MIA “team 29.39 weighted age
MIA 21.16 172.28 minutes 61 appearances
Haslem Mourning
Posey Wade
Williams 28.90 years
HOU “team 29.85 weighted age
HOU -2.97 97.47 minutes 21 appearances
Bowen Howard
McGrady Wesley
Yao 30.01 years
SAS “team 30.50 weighted age
SAS 0.37 192.76 minutes 69 appearances
Bowen Duncan
Finley Nesterovic
Parker 30.05 years
SAS 21.98 191.76 minutes 53 appearances
Bowen Duncan
Ginobili Nesterovic Parker
29.17 years
(I included both since the minutes were almost dead even)
And while Im at it, here is some more fun info
1 Atlanta 160 lineups used
2 Boston 205 lineups used
3 Charlotte 304 lineups used
4 Chicago 218 lineups used
5 Cleveland 120 lineups used
6 Dallas 187 lineups used
7 Denver 214 lineups used
8 Detroit 86 lineups used
9 Golden State 176 lineups used
10 Houston 179 lineups used
11 Indiana 182 lineups used
12 LA Clippers 208 lineups used
13 LA Lakers 193 lineups used
14 Memphis 189 lineups used
15 Miami 162 lineups used
16 Milwaukee 164 lineups used
17 Minnesota 164 lineups used
18 New Jersey 176 lineups used
19 New Orleans 132 lineups used
20 New York 281 lineups used
21 Orlando 203 lineups used
22 Philadelphia 170 lineups used
23 Phoenix 133 lineups used
24 Portland 253 lineups used
25 Sacramento 122 lineups used
26 San Antonio 203 lineups used
27 Seattle 181 lineups used
28 Toronto 119 lineups used
29 Utah 230 lineups used
30 Washington 153 lineups used
Oh – very nice, I be back.
Best regards for all.
Comment by Agroturystyka -
I see – very good !
Best regards for all.
————————————–
My web sides
http://www.jastrzebiagora.com.pl
http://www.wladyslawowo.biz
http://www.lato.turystyka.pl
Comment by Agroturystyka -
good
Comment by imdbcn -
yes very nice work!
Comment by Ogłoszenia anonse -
Very nice. you have drive and passion, so maybe it is you.
Comment by praca za granicą oferty pracy -
Of course, if Bird and Walsh are smart, they will trade him to a crappy team for next year’s 1st rounder and others to make the deal work which would make the Pacer average less.
Comment by runescape money -
That is, if something was done in a team, that went off all the charts statistics-wise, and that combination proved magical and led to a championship team, I bet that other teams would possibly try to emulate that trend numberically also, but that would not necessarily lead to their success also.
Comment by wow powerleveling -
very interested theme; I will back, thanks!
Comment by tworzenie stron -
Very nice. Yeah, there’s no way you have time to compile these stats. LOL. But you have drive and passion, so maybe it is you.
Maybe you’ll figure out a secret mathematical formula to basketball that’ll make you win. Like the secret baseball formula that everyone’s talking about.
Comment by business -
Do you really write everything here? I doubt it considering that you have so much money, no way, I bet my chatmates from webdatedotcom were right
Comment by thynoe123 -
Thanks for the statistics about team ages, Mark. As someone who is getting into sports operation research, this is really cool information.
Comment by cnfalv -
very goooooood!!!
Comment by story -
very goooooood!!!
Comment by story -
very goooooood!!!
Comment by story -
ºÜgoooooood!!!
Comment by story -
Some interesting stats, how would you assess the sport of baseball lineups?
Comment by whales -
I just added Smartest Guys and War Within to my Netflix queue! I can’t wait! But I too am surprised that Mr. Direct-to-Consumer is suggesting Amazon instead of some downloadable method… ~M
Comment by IT中国 -
back in the day who was the oldest person who started played N.B.A?
Comment by james -
Thanks for the statistics about team ages, Mark. As someone who is getting into sports operation research, this is really cool information.
also, to val in post 10,
Do you really think personal maturity matters on a basketball court? See Portland Jailblazers. It would be interesting if Mark had some statistics on the Jailblazers teams and their plus/minus numbers. I guess you could make the argument that “Since everyone on the team had a low personal maturity level, they also had a correlating high personality compatibility.”
As for the rest of your thoughts, I am not quite sure you understand what Mark and Sagarin and that other guy are looking to measure with these numbers or why Mark has invested so much faith into these numbers. From what I have read, the Mavs use this info mostly to pinpoint players on hot streaks or cold streaks who the Mavs can exploit in one on one matchups. IOW, rather than the traditional “We Must Stop This One Guy”, the Mavs try and break down matchup for matchup on the team and see where they can take advantage of a player playing poorly and also where they will have to double team/get in foul trouble/mess with the head of a player who is on a hot streak,
Comment by John "Z-Bo" Zabroski -
to val in post 10.
“gelling, synergy, group-think, team-sensibility, positive energy, synchronicity, great communication skills, complementarity of skills, will to achieve, professional and personal maturity and flexibility, faith and belief, persistence, patience, personality compatibility, social contribution, giftedness, developmental potential, luck, interpersonal sensitivity, potential skills, or maybe just plain abuncance of serendipity, and also guts in risk taking to make decisions when the number trends don’t support it.”
we will call that the MJ formula, guranteed to win rings, unless abe pollin signs the cheques.
Comment by chris in australia -
That is very interesting. You should tell the NBA or TNT to use that statistic instead of the “useless” statistic you spoke of. Good thinking. Visit Solution Bound!!
http://www.solutionbound.com
Comment by Lewis -
When a relatively young team has a single player in his late 30s playing serious minutes and a young talented kid in the wings, this team’s average age (weighted or otherwise) can be very misleading. When doing statistical analysis, good statisticians make calls on what data to weigh and what data to throw away based on various principles and standards of the industry. It doesn’t always add up to you or me but often proves a more accurate prediction.
Comment by David-Plano -
Well… there might be a correlation between age and success, but it would not be linear.
Cleveland’s average age is 28 and without Lebron 21… There average age would be much higher and their success would be much lower.
Comment by penxv -
Mark,
– These numbers aren’t as useful for a statistical analysis as the other set that you posted (Back to Backs in the NBA). There are obvious and separate advantages to have youth and to have experience. Any statistical analysis done with these numbers would be inherently muddled.
– I wanted to do something with these numbers, but I just didn’t see how it could be useful.
– If you went back into your stats to find out the number of wins and losses that teams had on the second game of a back-to-back versus fresh teams… You could get a statistically accurate read on the effect of playing back-to-backs.
It could be broken down even further by doing separate home and away analysis.
Comment by penxv -
Have you run a regression – winning as a function of line-up consistency? Is there a correlation or pattern?
Comment by nate -
Mark — you may want to include 8 players in your analysis. Many of the NBA’s youngest players (esp rookies) get the majority of their minutes as 6th men.
Comment by chris -
It seems that, with the exception of a few outliers, average age corresponds very well to success. (If Dallas were to have posted their lineup of Terry, Stackhouse, Howard, Nowitzki, Dampier which was used so often last year my guess is that their average age would be right up with the highest.) It is not rocket science as to why this stat is as such. When teams are bad they draft high and play these draft picks. Atlanta, Toronto, and NO have all been forced to play their first round picks from the last few years a significant amount of time. Also, if a player is good then he stays in the league and plays a lot. If he is bad then he does neither. So, if you are a good team then you are not looking to replace your god players as they get older with younger guys with anything close to the frequency that bad teams do.
Comment by Brian G -
Then there is the importance of good timing, as contexts can change….and a great decision one year can not work in another situation. No time to comment further, Val.
Comment by Val -
Interesting for sure. However, while stats are relative in that they are a representation of reality they never exactly can predict or match reality – leaving room for other things such as gut feel, experience, talent in personnel selection, teaching and training giftedness in development of human resources etc as further wildcards to throw an X factor into it possibly. That is, if something was done in a team, that went off all the charts statistics-wise, and that combination proved magical and led to a championship team, I bet that other teams would possibly try to emulate that trend numberically also, but that would not necessarily lead to their success also. The numbers really are not the important part of it, and numbers would not necessarily show key intangibles like gelling, synergy, group-think, team-sensibility, positive energy, synchronicity, great communication skills, complementarity of skills, will to achieve, professional and personal maturity and flexibility, faith and belief, persistence, patience, personality compatibility, social contribution, giftedness, developmental potential, luck, interpersonal sensitivity, potential skills, or maybe just plain abuncance of serendipity, and also guts in risk taking to make decisions when the number trends don’t support it. but the decision seems right otherwise, and when tried somehow works better than imagined. I doubt that any statistical analysis can ever reflect how individual elite athletes mesh or not as a group for example. Although relevant, helpful, insightful, and interesting, statistical analyses is still no match for great/inspired personnel selection/development imho, as long as people remain people. Val
Comment by Val -
Hey Mike (post 1),
Wouldn’t you think the time is the result, not the effect? Kind of the chicken and the egg thing. Clearly, the time can second.
Comment by David -
Aside from age, what about experience? And, not experience based on # of seasons played, but # of minutes played? Kobe Bryant is a young guy, but he has played a ton of minutes.
Comment by tim gibbons -
Correlation coefficient between age and winning %: 0.4096
Getting the most wins from their age:
1) DET
2) DAL (large jump to #3)
3) SAS
4) MIL
5) CLE
Worst:
1) TOR
2) NYK
3) ATL
4) HOU
5) CHA
If you’re looking for a new team to cheer for the next 5 years, DAL, MIL, and CLE (barring the loss of #23) look like good potentials.
Comment by Sam O -
Mike,
I don’t believe that Mark is compiling these stats. I live in Dallas and read the job postings on the Mavericks website. A couple of weeks ago or so I saw that he was hiring a statistician. Probably has more than one on the staff.
Comment by Todd -
Marc, can you post win/loss records by average age? Curious to see how the age factors into w&l.
I could look it up myself but I am lazy and you seem to have the number anyways.
Comment by Scott Pinkston -
Don’t count Artest against the great Pacer team. Of course, if Bird and Walsh are smart, they will trade him to a crappy team for next year’s 1st rounder and others to make the deal work which would make the Pacer average less. That would show Ronny, send him to the Raptors for some picks. They can have Jax too as someone needs to tell him he is not a shooter, but yet he plays shooting guard. Go figure.
Comment by JR Ewing -
No big surprise that Detroit’s used the fewest lineups. Not only do they avoid injury problems, but it looks like Larry Brown’s short rotation is holding over in the Flip Saunders era.
I wonder what that means for whether they’ll be able to keep up the pace they’re playing.
Comment by Jason -
Interesting and helpful info
Could you post the data as an excel file that can be downloaded which would be helpful for everyone to manipulate the data
Comment by dan devasto -
Mark,
Sometimes I wonder how you became a billionaire when you have so much time on your hands…
😉
Some interesting stats, how would you assess the sport of baseball lineups?
Comment by MIke -