NBA All Stars by the Numbers

We have developed and enhanced a player and lineup evaluation system. We have been working on this for the last 8 years. Its far from perfect, and its greatest value is that over a period of years we have been able to identify trends to help us identify up and coming, starring and declining players. It also helps us understand what combinations of players work well together, and which dont.

To give you a basic understanding of the system, at its most basic its a plus minus system. Then we adjust it to take into account who the opponent is, is it home or away, are you playing against the other teams good lineup or bad lineup, what the score and game clock are (scoring the game winner is worth more than the 1st basket of the game. Scoring when up by 30 is worth nothing). If the team scores or gets a stop when the game is on the line, then your impact percentage goes up. We reward for getting the job done when it matters.

We track as you can see below by last games, and also track the variance. In other words, average is pretty much meaningless. You can score plus 20 one day, zero the next, but are you a 10 level
player ? We track the variance of the players performance. THe lower the variance, the more consistent the player on offense or defense.

One more point, these numbers don’t reflect necessarily the best players in the league, but what they do reflect is the players that are being best put in a position to succeed and are delivering.
When their teams have a need, they deliver. Thats why some names are not the biggest names. its also a reflection of their coaches. Some coaches don’t necessarily use their players in the
best lineups or matchups, which negatively impacts their ability to perform. Others are consistently good at it.

This is purely quantitative, nothing more or less. 
But Im sure it will lead to lots of discussion

                                                                             *******
                                   POINTS= OFFENSE-DEFENSE IMPACT%  MINUTES  Z-SCORE  

   1 CLE LeBron James               24.16   17.18   -6.98   62.49%  1807.01   3.2034
        CLE   last    3/ 48         17.73   13.28   -4.45   33.24%   120.40
        CLE   last    5/ 48         19.78   14.07   -5.71   35.22%   195.22
        CLE   last    8/ 48         22.44   19.96   -2.48   62.95%   314.52
        CLE  window   6/  6 tm      18.11   15.06   -3.05   38.41%   232.74
        CLE  varies  48/ 48       ( 13.98)(  9.61)( 11.59)( 33.61%) 1807.01

   2 DAL Jason Kidd                 17.12   11.69   -5.44   64.94%  1761.98   2.7861
        DAL   last    3/ 50          5.16    7.90    2.74   54.54%   102.94
        DAL   last    5/ 50         23.37   15.72   -7.65   84.60%   173.52
        DAL   last    8/ 50         23.35   17.62   -5.73   72.93%   273.98
        DAL  window   7/  7 tm      21.01   16.33   -4.68   71.25%   240.58
        DAL  varies  50/ 50       ( 18.86)( 11.26)( 13.35)( 44.76%) 1761.98

   3 MIA Dwyane Wade                17.56   21.34    3.78   58.90%  1867.31   2.7435
        MIA   last    3/ 49         20.80   28.17    7.36   61.64%   123.53
        MIA   last    5/ 49          8.99   20.71   11.72   41.22%   195.08
        MIA   last    8/ 49         14.03   21.13    7.09   51.66%   294.94
        MIA  window   7/  7 tm      12.34   20.23    7.89   47.16%   258.72
        MIA  varies  49/ 49       ( 14.63)( 10.87)( 11.52)( 41.38%) 1867.31

   4 NOH Chris Paul                 21.12    7.93  -13.19   51.91%  1685.00   2.7183
        NOH   last    3/ 45         17.54    4.67  -12.86   16.65%    97.48
        NOH   last    5/ 45         20.71    5.81  -14.90   30.71%   177.90
        NOH   last    8/ 45         19.80    6.77  -13.02   39.15%   292.83
        NOH  window   5/  7 tm      20.71    5.81  -14.90   30.71%   177.90
        NOH  varies  45/ 45       ( 14.11)( 11.01)(  9.39)( 44.43%) 1685.00

   5 PHI Andre Iguodala             17.19    6.63  -10.56   56.90%  1896.28   2.6702
        PHI   last    3/ 49         11.57    0.78  -10.79   17.94%   121.20
        PHI   last    5/ 49         10.46   -1.02  -11.49   11.77%   201.57
        PHI   last    8/ 49         12.05    2.09   -9.97   33.83%   318.73
        PHI  window   7/  7 tm       9.75   -0.63  -10.38   21.10%   277.32
        PHI  varies  49/ 49       ( 12.19)(  9.86)(  8.69)( 53.51%) 1896.28

   6 BOS Ray Allen                  16.35    8.67   -7.68   43.50%  1897.46   2.2523
        BOS   last    3/ 52         14.36    7.84   -6.52   45.06%   124.18
        BOS   last    5/ 52         15.95    9.57   -6.37   54.50%   196.90
        BOS   last    8/ 52         20.13   13.09   -7.03   64.21%   300.94
        BOS  window   7/  7 tm      18.81   14.46   -4.35   61.31%   263.15
        BOS  varies  52/ 52       ( 16.46)( 12.76)( 10.89)( 48.47%) 1897.46

   7 ORL Rashard Lewis              12.91    6.86   -6.05   36.79%  1807.13   1.8585
        ORL   last    3/ 49          6.35    2.39   -3.96   16.82%   107.88
        ORL   last    5/ 49         14.77    6.01   -8.76   37.84%   188.08
        ORL   last    8/ 49         13.99    5.39   -8.60   29.24%   296.12
        ORL  window   6/  6 tm      19.24    9.51   -9.73   41.18%   221.53
        ORL  varies  49/ 49       ( 14.65)( 13.63)(  8.43)( 46.65%) 1807.13

   8 CHA Emeka Okafor               10.37   -0.14  -10.50   40.37%  1679.99   1.7337
        CHA   last    3/ 49         -7.12   -5.99    1.13  -12.95%    91.05
        CHA   last    5/ 49          7.75   -0.68   -8.44   38.49%   166.86
        CHA   last    8/ 49         13.98    1.66  -12.33   44.26%   253.02
        CHA  window   6/  6 tm       9.59   -0.65  -10.24   41.10%   198.14
        CHA  varies  49/ 49       ( 15.66)( 11.01)( 11.83)( 48.23%) 1679.99

   9 MIN Randy Foye                  9.09    9.93    0.84   43.24%  1768.34   1.6464
        MIN   last    3/ 49          6.34    5.95   -0.39   52.70%   128.74
        MIN   last    5/ 49          3.43   10.77    7.35   34.21%   201.94
        MIN   last    8/ 49          4.37    6.20    1.83   33.23%   325.41
        MIN  window   8/  8 tm       4.37    6.20    1.83   33.23%   325.41
        MIN  varies  49/ 49       ( 12.65)( 11.81)( 12.67)( 45.62%) 1768.34

  10 HOU Yao Ming                    9.64   -0.18   -9.83   34.83%  1579.31   1.5627
        HOU   last    3/ 48          7.81    7.42   -0.39   33.89%    99.06
        HOU   last    5/ 48          7.42    5.80   -1.62   25.29%   167.78
        HOU   last    8/ 48          8.26    5.11   -3.15   31.07%   245.35
        HOU  window   5/  7 tm       7.42    5.80   -1.62   25.29%   167.78
        HOU  varies  48/ 48       ( 13.21)( 11.94)( 10.35)( 48.19%) 1579.31

                                                                             *******
                                   POINTS= OFFENSE-DEFENSE IMPACT%  MINUTES  Z-SCORE  
  11 ATL Joe Johnson                 8.69    3.09   -5.59   37.43%  1917.51   1.5229
        ATL   last    3/ 48          3.67    4.44    0.77   26.02%   121.73
        ATL   last    5/ 48         -1.44    0.51    1.95    9.43%   205.94
        ATL   last    8/ 48          2.25    2.08   -0.17   15.91%   324.04
        ATL  window   6/  8 tm      -1.95   -0.15    1.81    6.23%   249.22
        ATL  varies  48/ 48       ( 13.21)(  9.57)( 12.38)( 38.42%) 1917.51

  12 BOS Kevin Garnett               9.43    3.16   -6.27   32.43%  1594.00   1.4937
        BOS   last    5/ 49         12.52    7.36   -5.16   49.46%   156.75
        BOS   last    8/ 49         17.92    4.95  -12.97   53.54%   250.13
        BOS  window   5/  7 tm      12.52    7.36   -5.16   49.46%   156.75
        BOS  varies  49/ 49       ( 18.27)( 13.57)( 12.38)( 50.83%) 1594.00

  13 DEN Nene                       10.79    2.87   -7.92   27.47%  1642.84   1.4643
        DEN   last    3/ 50          8.22    7.28   -0.94   49.03%    89.46
        DEN   last    5/ 50         11.18    9.19   -1.99   39.02%   156.01
        DEN   last    8/ 50         12.80    6.92   -5.88   32.15%   246.48
        DEN  window   8/  8 tm      12.80    6.92   -5.88   32.15%   246.48
        DEN  varies  50/ 50       ( 16.25)( 14.16)( 10.14)( 52.28%) 1642.84

  14 DET Rasheed Wallace             8.63    2.58   -6.05   33.16%  1481.02   1.4407
        DET   last    3/ 44         15.98    9.32   -6.65   65.70%   124.29
        DET   last    5/ 44         13.37    4.73   -8.64   66.60%   201.85
        DET   last    8/ 44          9.38    4.49   -4.89   47.81%   292.05
        DET  window   6/  6 tm      12.45    5.93   -6.52   56.45%   240.59
        DET  varies  44/ 44       ( 15.85)( 11.22)( 11.86)( 54.42%) 1481.02

  15 LAL Kobe Bryant                 9.19    8.50   -0.70   27.97%  1793.17   1.3706
        LAL   last    3/ 49         14.01   16.74    2.73   63.87%   119.44
        LAL   last    5/ 49         12.59   19.61    7.02   49.08%   189.05
        LAL   last    8/ 49         12.41   15.76    3.35   42.00%   294.68
        LAL  window   7/  7 tm      10.59   14.99    4.39   43.58%   266.58
        LAL  varies  49/ 49       ( 13.82)( 10.12)( 12.01)( 47.40%) 1793.17

  16 UTA Andrei Kirilenko           12.17    6.78   -5.39   21.92%  1139.46   1.3604
        UTA   last    3/ 38         11.53   13.86    2.33   19.61%    75.18
        UTA   last    5/ 38          9.32   14.02    4.70    9.43%   156.50
        UTA   last    8/ 38         14.04   16.55    2.51   15.45%   243.33
        UTA  window   0/  7 tm       0.00    0.00    0.00    0.00%     0.00
        UTA  varies  38/ 38       ( 18.17)( 11.89)( 13.66)( 56.91%) 1139.46

  17 PHO Grant Hill                  8.33    3.63   -4.70   30.18%  1382.00   1.3534
        PHO   last    3/ 48         19.33   14.43   -4.90   11.05%    89.98
        PHO   last    5/ 48          8.57   11.90    3.33    3.77%   166.17
        PHO   last    8/ 48          5.08    8.13    3.04    6.70%   236.57
        PHO  window   7/  7 tm       9.89   11.36    1.47   12.46%   210.07
        PHO  varies  48/ 48       ( 21.14)( 14.26)( 14.93)( 54.56%) 1382.00

  18 UTA Paul Millsap               11.20    3.62   -7.58   22.82%  1444.24   1.3473
        UTA   last    3/ 45         24.72    8.32  -16.40   41.92%    88.94
        UTA   last    5/ 45         18.68   10.25   -8.44   27.36%   158.35
        UTA   last    8/ 45          7.85    4.91   -2.95    1.34%   267.38
        UTA  window   7/  7 tm       9.85    5.53   -4.32    9.80%   226.25
        UTA  varies  45/ 45       ( 17.56)( 10.11)( 13.49)( 46.96%) 1444.24

  19 NJN Jarvis Hayes                8.04   -0.86   -8.90   29.31%  1164.38   1.3109
        NJN   last    3/ 46         23.51   -1.09  -24.59   28.15%    78.13
        NJN   last    5/ 46         16.82   -6.82  -23.64   48.06%   131.01
        NJN   last    8/ 46         13.98   -6.05  -20.03   23.98%   194.42
        NJN  window   7/  7 tm      11.23   -8.71  -19.94   14.09%   172.93
        NJN  varies  46/ 46       ( 19.24)( 14.35)( 14.12)( 59.98%) 1164.38

  20 POR LaMarcus Aldridge           7.69    3.77   -3.92   30.72%  1789.65   1.3095
        POR   last    3/ 49         -0.04   -1.68   -1.64   19.85%   123.20
        POR   last    5/ 49          7.81    2.71   -5.10   30.56%   192.67
        POR   last    8/ 49          4.06    1.89   -2.18   23.90%   305.24
        POR  window   6/  6 tm       7.45    2.81   -4.63   35.02%   226.91
        POR  varies  49/ 49       ( 15.23)( 13.36)(  8.97)( 50.43%) 1789.65

                                                                             *******
                                   POINTS= OFFENSE-DEFENSE IMPACT%  MINUTES  Z-SCORE  

  21 UTA Mehmet Okur                 6.72    3.48   -3.24   35.80%  1491.46   1.3032
        UTA   last    3/ 43         22.27    9.73  -12.54   69.04%   104.35
        UTA   last    5/ 43         14.79    9.82   -4.97   49.40%   173.36
        UTA   last    8/ 43          7.97    5.26   -2.71   32.81%   272.60
        UTA  window   7/  7 tm       8.89    5.80   -3.09   37.37%   250.76
        UTA  varies  43/ 43       ( 14.34)( 10.51)( 10.17)( 43.33%) 1491.46

  22 MIN Sebastian Telfair           5.65    1.78   -3.87   46.12%  1126.73   1.2886
        MIN   last    3/ 44          5.53   -0.43   -5.96   55.48%    92.25
        MIN   last    5/ 44          4.88    6.03    1.15   44.59%   160.05
        MIN   last    8/ 44          4.11    1.89   -2.22   49.30%   254.67
        MIN  window   8/  8 tm       4.11    1.89   -2.22   49.30%   254.67
        MIN  varies  44/ 44       ( 15.03)( 14.42)( 14.56)( 41.52%) 1126.73

  23 DAL Dirk Nowitzki               7.04    7.08    0.04   30.27%  1836.86   1.2430
        DAL   last    3/ 49          4.89    2.60   -2.29   36.74%   115.33
        DAL   last    5/ 49         16.08    7.51   -8.57   56.35%   189.51
        DAL   last    8/ 49         14.55   10.92   -3.63   44.59%   291.47
        DAL  window   7/  7 tm      12.94    9.48   -3.45   43.81%   254.69
        DAL  varies  49/ 49       ( 18.02)( 10.46)( 13.44)( 47.24%) 1836.86

  24 PHO Steve Nash                 11.71   14.90    3.19   18.33%  1497.80   1.2248
        PHO   last    3/ 44          7.52   21.05   13.53   -4.42%    93.21
        PHO   last    5/ 44          3.44   19.26   15.82  -12.94%   173.73
        PHO   last    8/ 44          4.17   15.48   11.31    1.84%   263.71
        PHO  window   7/  7 tm       8.98   18.86    9.89    8.29%   242.28
        PHO  varies  44/ 44       ( 17.71)( 12.53)( 12.46)( 57.52%) 1497.80

  25 TOR Chris Bosh                 10.15    7.38   -2.77   19.85%  1948.03   1.2039
        TOR   last    3/ 51         -0.33    4.72    5.04    6.45%    97.50
        TOR   last    5/ 51         -3.84    2.34    6.18    4.43%   172.91
        TOR   last    8/ 51          5.99    6.86    0.87   20.57%   286.96
        TOR  window   6/  8 tm       0.70    3.85    3.15   14.38%   210.41
        TOR  varies  51/ 51       ( 15.42)( 10.30)( 12.11)( 49.95%) 1948.03

  26 LAL Lamar Odom                  8.25    4.25   -4.01   22.85%  1254.11   1.1732
        LAL   last    3/ 46         11.66   10.50   -1.16   65.37%   106.50
        LAL   last    5/ 46          9.70   11.30    1.61   52.25%   159.61
        LAL   last    8/ 46          5.18    5.94    0.76   15.76%   242.97
        LAL  window   7/  7 tm       3.36    3.99    0.63   16.49%   220.27
        LAL  varies  46/ 46       ( 20.55)( 15.10)( 13.85)( 59.77%) 1254.11

  27 CHI Ben Gordon                  6.83    7.33    0.50   27.49%  1836.72   1.1702
        CHI   last    3/ 51          3.17   15.05   11.88   12.35%   104.44
        CHI   last    5/ 51          9.82   15.52    5.71   33.15%   175.44
        CHI   last    8/ 51          6.75   10.05    3.30   18.38%   281.08
        CHI  window   7/  7 tm       9.66   10.82    1.16   26.96%   249.30
        CHI  varies  51/ 51       ( 14.18)( 10.62)( 12.34)( 45.11%) 1836.72

  28 PHI Thaddeus Young              6.68    2.44   -4.24   28.05%  1662.23   1.1680
        PHI   last    3/ 49         14.82    5.86   -8.96   29.69%   105.76
        PHI   last    5/ 49          5.90   -1.58   -7.48    0.21%   189.84
        PHI   last    8/ 49          7.16    1.40   -5.76   21.08%   296.87
        PHI  window   7/  7 tm       7.49   -0.44   -7.93   18.03%   258.09
        PHI  varies  49/ 49       ( 14.84)( 11.93)(  9.73)( 56.64%) 1662.23

  29 SAS Matt Bonner                 9.88    4.38   -5.51   16.08%  1065.79   1.0762
        SAS   last    3/ 47         12.41    9.54   -2.88   24.40%    75.24
        SAS   last    5/ 47         14.13    9.21   -4.92   35.69%   133.79
        SAS   last    8/ 47          8.89    2.57   -6.32   23.54%   200.32
        SAS  window   6/  6 tm       7.80    4.19   -3.61   20.91%   155.16
        SAS  varies  47/ 47       ( 22.01)( 16.66)( 15.12)( 60.72%) 1065.79

  30 GSW Andris Biedrins             4.42    1.19   -3.23   35.21%  1548.46   1.0627
        GSW   last    3/ 50         20.95   22.70    1.75   86.50%    67.43
        GSW   last    5/ 50         15.08    9.25   -5.83   68.29%   115.38
        GSW   last    8/ 50          7.95    4.14   -3.80   52.27%   201.71
        GSW  window   7/  7 tm       9.09    4.08   -5.01   51.50%   166.84
        GSW  varies  50/ 50       ( 14.19)( 12.77)( 11.53)( 50.93%) 1548.46

82 thoughts on “NBA All Stars by the Numbers

  1. Ed Hardy shirts

    Comment by hrewj -

  2. “You don’t work hard for 8 years on something that is garbage (and then post the results on the ‘net). ”

    why not?

    History (and the Bush presidency) are full of wasted years, often in 8 year chunks.
    The ultimate goal of statistics is to both capture and predict performance, value and the future. That is why insurance actuaries develop these massive tables ; to calculate risk. The mortgage crisis however shows us that sometimes derivative calculations can be fundamentally flawed, based on erroneous assumptions.

    The problem with taking arbitrary (or well meaning) subsets of data out of the large pool available is that you invariably leave out elements which are contributory or have real value. How many of these performances occur on back to backs, or at the end of a long road trip? What about injury? What about proximity to the trade deadline? or a contract year?

    “lineup evaluation system”
    But what is a lineup? simplest way to say it: A combination of 5 players on the court designed to win the ball game. But we have offense/defense line ups, designed to win yes, but with a subset goal to get a stop or a score, and of course we have the “hack a shack” or similar lineups….

    Do these ‘efficiency’ statistics take into account that they may be self-limiting? a player making 30 points (and on a pace to make 59) makes “only” 31 points because the coach pulls him after the 3rd quarter? What about the coach? Terry Porter vs Mike Dantoni? Offensive philosophy?
    What about “lineup balance”? Is this guy a good Forward because he is intrinsically good or is it because his team is ‘thin’ at that position?

    I suspect that these statistics are like other “collectibles” that men collect, because, its what we do!
    Collect and store up all kinds of meaningless shit and give it more value (because we’ve collected it)

    Comment by steve -

  3. to everyone complaining about how this system is worthless because Kobe is 15th, you all need to realize that this is NOT the only thing is being used to evaluate players this is one of the millions of tools that owners, gms, etc. use and yes it is useful info. just because Kobe is not number 1 or 2 doesnt invalidate it. he put up 50 against phoenix and yet the lakers lost to a team not headed for the post season. this formula is telling you which players deliver the most when needed and when it counts. did you all know that Kobe is 9th in the league this year at making game deciding shots??? while Lebron, DWade, Ray Allen, Joe Johnson are all ahead of him, just like in this stat sheet.

    no one is saying Kobe is not one of the top 3 players in the game right now this stat is merely looking at one of a million things.

    Comment by Mike -

  4. So where’s your context-neutral metric?

    Comment by Wondering... -

  5. Check out Brad Stenger’s charts at NBA Graphs…

    http://nbagraphs.tumblr.com/

    NBA Graphs visualizes basketball game data by showing the second-by-second +/- information for each player playing in a horizontal histogram. You can really pick out the player-player dynamics and coaching decisions in a game.

    Comment by Pete Skomoroch -

  6. Pingback: Back to Battier « The Wages of Wins Journal

  7. Pingback: SLAM ONLINE | » Old Vets New Roles

  8. Pretty good measurement of just how important certain players are to their teams. It takes more than scoring a lot of points or getting a lot of rebounds to win championships – if he were alive, you could ask Wilt Chamberlain about that.

    Comment by BillH -

  9. This info, in excel, would be epic!

    Comment by Palms Hotel & Casino -

  10. If you enjoyed this post, you’ll enjoy this NY Times article by Michael Lewis about Shane Battier of the Rockets.

    http://www.nytimes.com/2009/02/15/magazine/15Battier-t.html

    Comment by James -

  11. Hmm. Interesting to see everyone side by side. Thanks

    Comment by Steve L. -

  12. Cuban,

    This is great stuff man! I really appreciate you sharing this type of material.

    I absolutely LOVE the fact that my Jazz have 3 players before Dirk. Andrei, Millsap and Okur. Maybe you have already shared these stats with Dirk and that’s why he always tries to cheapshot our players?

    Strange, would have thought to see D-Will in there, but it makes sense as it’s been a bad year for him. Keep it up!

    Comment by Brandon -

  13. Hi,

    Quite new to stats, my questions:

    A. Is there a rating tool that combines two (or more) ranking methods.
    For example, Mark’s system with the conventional system?

    B. Is there a similar ranking for “Money Time”?

    C. This is not related to this post, but I’m curios about it, are there stats about double-up.
    Example – How many points does LA avrage on possesions when you double-up Kobe, and when you don’t.
    What is LA’s records when Kobe is doubled X amount of his possetions.

    Hope these are relevent Q’s,
    Thanks :-)

    Comment by eg -

  14. Pingback: The Numbers Guy : Mark Cuban's Surprising Player-Performance Numbers

  15. “scoring the game winner is worth more than the 1st basket of the game.” Are you serious? Doug Collins does a great job of pointing out the beginning of NBA games are important in determining the winner even though many people think the 1st quarter doesn’t matter much. Because its true that the team that falls behind early usually makes a run to make the game interesting it appears as if the beginning of the game doesn’t matter much. I don’t know the percents but I’d be curious to know the winning percentage of teams that build a 5 or 10 point lead in the first quarter. I’d guess its over 55% for a 5 point lead and over 60% for a 10 point lead. The best kind of win is a early blow out were you get to rest your starters and give significant minutes to your bench. Its true that a game winner is more valuable then the first basket if the game goes down to the wire, but the first basket is more valuable then a game winner if the game ends up as blow out because there is no game winning shot so the first basket wins by default.

    I understand the Spurs win without using Ginobili at the start of games but they have the luxury of still having two studs (Duncan, Parker) without Ginobili.

    The game winning shot being more valuable then the first shot is the same argument as late inning hitting being clutch while early game hitting isn’t clutch. Leading off a game with a HR increases your chance of winning around 10% which is extremely valuable even if people don’t view it as clutch.

    Comment by Derek Wahl -

  16. Interesting. This seems to be the main way that Baseball evaluates their players.

    How much do use this in choosing your players?

    Rick

    Comment by Rick Bicycle Hangar -

  17. Mark,
    I am not sure if you read your comments, but I am going to ask you one thing that makes my scouting analysis more unique than anything that is out there today. The scouting analysis I give Pro players is so right on the money that it get this auto generates the players salary to that players performance on the court. So the programmer you have gives you numbers but if it is not balanced to a players worth on the court. It is useless in building a franchise unless you can equate a players performance to his value/salary.
    This site:

    http://scoutingthenba.com/blog/category/2008-salary-evaluation/

    I created a moneyball concept for basketball about 5 years ago and I send the information to different GM/Directors across the NBA.
    My work has been tainted by people that promise one thing and produce absolute crap as an end result. *That was sugar coating it trust me.
    With this program you can see the game from an entirely different perspective. You anticipate players movements just from this program.
    2008/9 Summer Free agent report is almost complete 5months ahead and some GMs already have the jump on this.
    You absolutely cannot hire a programmer as a scout that truly doesnt understand how to read the game being played in front of him.
    To be honest your as hard to get a hold of as Harvey Benjamin. Everyone below you two ask me 1.2 million questions but it never leads to how the game, owners, player salaries and the state of the economy can balance. I seriously appreciate you going to basketball on your blog cause we know its close to your heart so yea good read above.

    Comment by basketballscience -

  18. I hope to see Bargnani and other Italians one day, here.
    Excuse me, I’m Italian ;)

    Comment by Francesco Piccinelli -

  19. Clearly, it’s a system to measure a players impact on the outcome of game by adjusting for what the oother players on both sides of the ball are doing. Got it. The question is can it any quantitative system fairly evaluate the impact of a player on a deep team? My example of Pau is a good one, because you can’t look at any five games this season and conclude that Pau isn’t at least in the top ten. Anyway, I’m sure there is a lot more to the system then is being revealed here and GM’s surely need more than what scouts can offer with the number of players worldwide today.

    Mark, do you use this system with college ball and international teams?

    Comment by Greg -

  20. Pingback: Mark Cuban’s Player Evaluation Model Puts Jason Kidd as the Second Best Player at Suceeding and Delivering | Digital Sports Daily

  21. So it took 8 years to devise a formula that has Jason Kidd the second most clutch player in the league? I can’t imagine what he would’ve ranked 5 years ago in his prime — no doubt the greatest athlete ever to play the game.

    Data mining is for people throwing darts against the board. And yes, please let the emperor in on the fact that he has no clothes.
    .

    Comment by mugwump -

  22. HEY MARK …ATLEAST YOUR STILL RICH!
    OHH YEAH STICK 2 UR DAY JOB

    Comment by jimmy -

  23. So I’m still trying to figure out where the Z score comes from, as points*impact% isn’t it, so I suspect the variance is weighted in some way to fully create the Z score.

    That said, how many people actually read the post? Clearly Mark states this is a snapshot in time.

    “… these numbers don’t reflect necessarily the best players in the league, but what they do reflect is the players that are being best put in a position to succeed and are delivering. When their teams have a need, they deliver.”

    I think it’s fair to say Mark isn’t trying to support the Kidd trade with these numbers. Rather I’d say he’s trying to point out Kidd is doing just as well as LBJ when put into the situations of making an impact on the team.

    You don’t work hard for 8 years on something that is garbage (and then post the results on the ‘net). This is merely a snapshot in time, which is why he’s got no problem sharing it with the world.

    From MC> Ding,Ding, Ding. Congrats. You understand what is going on here :)

    Comment by Ryan J. Parker -

  24. Lamar before Gasol? Everyone but Lebron after Jason Kidd? This is interesting and I would LOVE to hear more of an explanation into the finer workings of this “system.”

    Comment by Daniel -

  25. That’s why numbers turn me off, just watch the games and critique players that way.

    Comment by Ryan -

  26. Cool system. One question: Why did you sell it to the Lakers?

    Comment by DP -

  27. Mark is going to do anything to make the Kidd trade look good for him.
    This info is nothing but useless garbage. Thanks for wasting my time.

    Comment by Paul -

  28. I am curious where you have Devin Harris ranked though. Hope it’s not
    last place.

    Comment by Kanishka Ray -

  29. Hmm…LeBron at #1, Jason Kidd at #2…..Kobe Bryant at #15.

    Back to the drawing board, man.

    Comment by Kanishka Ray -

  30. Just a short response to the JH’s previos post about Defenses. Just about every team in the NBA empoloys at least one player that can totaly wreck even the most sophisticated defenses, and even the 10th best guy on the court is usualy as good as the best player in any given college game. That is why even years after it was legalized you don’t see teams playing zone on a regular basis because it can always be victomized by a superior athlete. Zones are a gimmick at the NBA level meant to hide guys who can’t match up 1 on 1. And Mark would know this being an Indiana guy and Bobby Knight fan because Bobby never played a zone because he knew it was a cop out.

    Comment by Ben D -

  31. Any stats that have Andre Iguodala, Randy Foye, and Nene over
    Kobe Bryant simply tell same not to pay attention to the stat.
    Cube, sometimes stats should be left for fantasy stuffers like that
    moron Rick Kamla. There are things that players do on the court that are
    not measured by stats. This is more John Hollinger type of analysis,
    that tries to measure things that are sometimes better left for
    the basketball eye.

    From MC> Actually it shows the opposite. That people who evaluate players based on the box scores really have no understanding of the game

    Comment by Ron Lefort -

  32. Pingback: Mark Cuban’s horrible ranking system | Nothin But BBall

  33. The “basic understanding” of the evaluation system isn’t all that helpful. To see whether I agree with the system, or what holes of logic are in, I have to know what the exact formular. On just a intuitive sense of how players are ranked, the system seems really flawed. Maybe the method evaulation explains the Mavs lack of success.

    Comment by Joe -

  34. This is very cool, and I’m sure its a tool that may have helped the Mavs land a guy like Brandon Bass, but I have to notice one thing. The Mavs biggest rival–the San Antonio Spurs have only one player in the top 30 and its Matt Bonner! No Duncan, Ginobili or Parker!?

    Comment by John -

  35. Pingback: Top Posts « WordPress.com

  36. Samuel Leghorn Clemmons (Mark Twain) used one of the best quotes I’ve ever heard when people crunch numbers and get results like this… “There are liars, there are damn liars and then there are statistics.”

    Comment by thebman -

  37. Interesting, has this system been used to make actual basketball decisions?

    Comment by Eric -

  38. So Kirilenko, Okur, and Millsap are all better than Dirk? Ok then.

    Comment by HotShot -

  39. Very interesting stuff. Would love to hear more about it.

    How much trial/error did it take to zero in on these measures? (You mentioned YEARS of development?)

    Would also love to hear if it’s had a measurable effect in the quality of your player assessment

    Cheers,
    Jed

    Comment by Jed -

  40. To the person who asked where is Devin Harris:

    Devin is not on the list because he ranks 40th in the NBA in assist/turnover ratio.

    Devin is last in the NBA among starting point guards in assists-to-turnovers so even though he score, he doesn’t do it efficiently and doesn’t assist well and turns the ball over plus shoots a low percentage from three (about 30%) and doesn’t rebound.

    The question that should be asked about Devin Harris is this: If Devin is an all-star player and Vince Carter is among the best players in the league and NJ has a candidate for rookie of the year, then why are they 4 games under 0.500?

    The stats above will tell you the answer. Devin and Vince get the sexy stats but not the complete game that leads to wins.

    Comment by Jeff in Dallas -

  41. Oh…So that’s why you traded for Jason Kidd. No slight intended,
    but based on that information alone (JKidd ranked 2nd overall) I’d
    have to reevaluate your entire system.

    Comment by Jordan -

  42. Hi Mark,

    I’m currently a graduate student studying Quantitative Analysis and
    would love to see the details of this analysis. Are the details of
    the study proprietary? Just curious about the details of the study.
    Please contact me via the E-Mail I’ve included with this comment.
    One thing I have to notate though is that I’m a HUGE LA Lakers fan –
    Hope this doesn’t deter you from sharing the info!

    Thanks in advance,
    Will

    Comment by Will T. -

  43. we can probably pull another trade with nets for VC half man half amazing and Javaris Hayes since he’s up on this list

    Comment by obamaMFFL -

  44. Mark,

    Since this methodology rewards coming in in the clutch…wouldn’t it hurt players who’s teams are consistently dominant? Fewer opportunities to actually perform in the clutch could lead to a smaller sample size, more variance, etc…and may point to why Kobe would be so low in this list, relative to his perceived value elsewhere (and why JKidd would be so high, since the Mavs are not winning by an average of 10 points a game like the lakers)

    Comment by Mike -

  45. This system needs to be tweaked until Kobe Bryant comes out on top. If you tweak it and he comes out 2nd tweak it again. I’m serious. Once you have that established I’ll take this a little more take this with some merrit.

    Comment by Julian Johnson -

  46. This just shows that someone doesn’t have anything else better to do than to try to make a little money because they invented something to tell you who’s better in their eyes. I just say let the players play and lets all enjoy watching the performances put on by these great athletes, but that’s just me.

    Comment by AM -

  47. So Jason Kidd is impacting the Mavericks more than Devin Harris did? That doesn’t make any sense, particularly looking at the team’s win record with Jason Kidd.

    I think your system needs to be re-tooled. It just doesn’t make sense, or perhaps you need to provide a better explanation.

    Comment by DylanSq -

  48. This is mostly meaningless without explanation… is it pace-adjusted?
    What is a “good” lineup and a bad lineup? What is the “impact” of blocked shots on defensive play (assuming that the block is not performed by the player in question).

    Comment by Bill -

  49. In what world is Randy Foye anywhere near a 50 player in the league let alone a top 10 guy

    Comment by Steve -

  50. Ok…

    Jason Kidd is the second best player in the NBA ?
    Jarvis Hayes (Jarvis who ???) is in the top20 ??

    Maybe you should think a bit more about your formulas…

    Comment by bob -

  51. I like the effort, but any system that has Sebastian
    Telfair (8.2 pts__4.5 ast__1 stl__38% FG__32% 3pt)
    as the #22 ranked player in the ENTIRE NBA must be flawed.

    Comment by MadN -

  52. Based on this, Deron Williams would be the third best PG on the Timberwolves, since Telfair and Foye or on here and Deron is not. This formula doesn’t make sense. Sorry.

    Comment by Matt -

  53. So is this accurately saying that Rashard Lewis has more of an impact for the Orlando Magic than Dwight Howard? You may want to rethink the way you construct your numbers.

    Comment by McDiesel -

  54. No wonder the mavericks can’t ever win it all!!!!!!!!!!!!!! This is the dumbest validation for creating team Cuban!

    Comment by shoupdogg -

  55. Mark…have your techies check out Microsoft Solver Foundation. Pretty sweet little tool/foundation fo analyzing this kind of information. Might make their job easier and help you to get better analysis/intelligence form all your data.

    I’m using it for completely unrelated stuff (scheduling optimization and resource leveling).

    Comment by Paul -

  56. You’re system obviously is flawed if you have Jason Kidd rated 2nd. Everyone can see his decline and it’s pretty apparent the trade with New Jersey was lopsided win for them.

    Comment by Jim -

  57. Thanks for sharing, but how about a shout-out to Prof. Winston?

    Comment by Joel @ Kelley -

  58. Most important player there– Paul Milsap, because he’s making the league minimum this season. He is a great example of the importance of drafting well and taking chances on second-round draft picks. The Jazz would be sunk this season if they didn’t have a productive replacement for Boozer at almost no cost.

    Comment by Daniel -

  59. Pau Gasol is not in the top 20!!?? I think the system needs some tweaking. Are you penalizing players on deep teams?

    Comment by Greg -

  60. Just wondering if down the road this system will impact coaching hiring/firing decisions.

    Comment by deb -

  61. Interesting stuff! Can you elaborate on the numbers?
    Just the definitions of the terms you are using. What does lost 3/48
    mean? Why there are three “lost” rows? Variance I am assuming is the
    variance in players stats. True or false?

    Finally how much you rely on these stats as a owner of Mavs. You know
    what they say, “statistics is like a bikini what it reveals is
    important but what conceals is vital.”

    Comment by VW -

  62. Pingback: Heard It Through the Grapevine 2-9-09 » The Two Man Game

  63. @JH
    Why do you say people have not noticed the defensive side of the NBA?
    Just because reporters care more about the big dunks and the buzzer
    beaters doesn’t mean it is going unnoticed.

    Comment by BettorFan -

  64. Very cool, Mark. I wonder what the team total z-scores look like?

    I hope they would be similar to the standings (Cavs, Celts, Lakers on top).

    How does the plus/minus work? Do you have someone go through the tape & grade each player/play?

    Comment by Scott Stewart -

  65. I did not grasp the definitions of all stats here fully, but do I
    understand correctly that based on the stats you provided Wade and
    Nash are defensive liabilities? I am not surprised about Nash, but I
    am a bit suprised about Wade’s stat defensively.

    Comment by Darius -

  66. Is this the top 30 in the league according to this ranking?

    Comment by david -

  67. Have you done regression testing using historical data on your system to determine if the NBA champs had the best player ratings?

    Comment by Derek -

  68. So assuming a higher Z-Score means better, Jason Kidd is #2 in the league after LeBron James. Nice trade Mark! It’s a travesty that Devin Harris is in the All-Star game and not Kidd! :-|

    Comment by Jonathan Wong -

  69. Oh, Mark, you shouldn’t have published this, now they will storm you
    with Jason Kidd Dwyane Wade trade requests.

    Fact that you are not treating this as a joke is best evidence
    that the basketball statistics world is seriously flawed.

    Comment by I.T. -

  70. Like to see u get Wallace next year, him and Dirk would be interesting at the 4 and 5

    Comment by Belize -

  71. So if I’m understanding this correctly, the numbers say Jarvis Hayes has had a bigger impact playing 25.3mpg on the 24-28 Nets than Paul Pierce has playing 36.8mpg on the 42-11 Celtics? Are you saying the Nets are a 15 win team without Jarvis’ 8.3ppg? I like that you’re continuing to innovate, but you’ve gotta look at those results and think “back to the drawing board, no?

    Comment by Jon -

  72. excellent work!
    W. Edwards Deming would be proud!

    I’m a professional sports coach in another sport, but spent 8 years coaching Divison I hoops…
    Nice to see other people putting in the time with the numbers…
    “There is more to basketball than just stats, but there’s more to stats than just numbers”

    The only thing I have noticed in the last several years that seems to be trending
    toward the negative is the defensive sophistication…
    People would be stunned if they knew how much defense was actually going on in the NBA…
    But I still think there’s a pretty big tactical upside still left there…
    Not sure its a quantum leap, but not sure its not the difference between a round or two in the playoffs…
    Rick Majerus probably is running the most sophisticated defense in Basketball today…
    Might be worth a conversation…

    Just my two cents…

    All the best,

    JH

    Comment by JH -

  73. sorry…. should say 82games.com The comment box is broken on Mac Firefox

    Comment by Mason -

  74. @Jay Last 3 and last 5 games of the 48 played so far this year. I’d like Mark to explain a little more how the numbers are gotten to. Also, if you could compare it to the analysis that 2gams does

    Comment by Mason -

  75. Damn! I was going to approach you about a business idea to build precisely this system! Always a step slow!

    Interesting stuff. Like others, I can’t see the full info but I am not surprised. I am not sure if you have run it for other seasons, but based on what I see (and my own less-than complete formulas), I wonder if you would have found Battier (the player I most wish you would find a way to get to the Mavs) or Adrian Griffin (the best overall plus-minus impact I can remember in 10 years of watching every single Mavs game) near the top of the list.

    Cool stuff. Thanks for sharing

    Comment by NovaSphere Blog -

  76. Wow awesome stuff, Mark!

    Comment by dperilli -

  77. Unless you’re using this to track or project specific player traits, and not using it as an overall rating, there is something seriously wrong with the results there.

    Where is Brandon Roy? Where is Devin Harris?

    I’m actually a little surprised you would share this openly. How much do you know about other teams evaluation methods?

    From MC > The numbers for 1 point in time couldnt help another team. And as far as to why players are or arent on there, it means the numbers say they arent impacting their teams more than the people who are listed. That could be a reflection of the coach, the system, the player. Thats the part we dont share.

    Comment by Karl Malone's Elbows -

  78. Presenting intricate numbers with little or no explanation will cause a buzz. Genious.

    Comment by KJ -

  79. Could you explain the numbers a bit more? For example, what do last 3 and 5 and 8 /4x mean? window 7/ 7? varies 50/ 50?

    From MC> its the last grouping of games

    Comment by Jay -

  80. The formatting doesnt work on explorer or firefox.

    Comment by Bill -

  81. Can you reformat it so the text isn’t cutoff please?

    Comment by Hubert -

  82. Mark,
    Helpful stuff, if only the format were such that it could be read, either in RSS or on your blog. There is no text wrap, and so much of the information is lost. Any chance of a reformat to make it easier for the commoner to read?
    Peace,
    David

    Comment by David -

Comments are closed.