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
Ed Hardy shirts
Comment by hrewj -
“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 -
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 -
So where’s your context-neutral metric?
Comment by Wondering... -
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 -
Pingback: Back to Battier « The Wages of Wins Journal
Pingback: SLAM ONLINE | » Old Vets New Roles
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 -
This info, in excel, would be epic!
Comment by Palms Hotel & Casino -
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 -
Hmm. Interesting to see everyone side by side. Thanks
Comment by Steve L. -
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 -
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 -
Pingback: The Numbers Guy : Mark Cuban's Surprising Player-Performance Numbers
“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 -
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 -
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 -
I hope to see Bargnani and other Italians one day, here.
Excuse me, I’m Italian 😉
Comment by Francesco Piccinelli -
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 -
Pingback: Mark Cuban’s Player Evaluation Model Puts Jason Kidd as the Second Best Player at Suceeding and Delivering | Digital Sports Daily
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 -
HEY MARK …ATLEAST YOUR STILL RICH!
OHH YEAH STICK 2 UR DAY JOB
Comment by jimmy -
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 -
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 -
That’s why numbers turn me off, just watch the games and critique players that way.
Comment by Ryan -
Cool system. One question: Why did you sell it to the Lakers?
Comment by DP -
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 -
I am curious where you have Devin Harris ranked though. Hope it’s not
last place.
Comment by Kanishka Ray -
Hmm…LeBron at #1, Jason Kidd at #2…..Kobe Bryant at #15.
Back to the drawing board, man.
Comment by Kanishka Ray -
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 -
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 -
Pingback: Mark Cuban’s horrible ranking system | Nothin But BBall
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 -
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 -
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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 -
Interesting, has this system been used to make actual basketball decisions?
Comment by Eric -
So Kirilenko, Okur, and Millsap are all better than Dirk? Ok then.
Comment by HotShot -
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 -
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 -
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 -
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. -
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 -
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 -
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 -
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 -
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 -
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 -
In what world is Randy Foye anywhere near a 50 player in the league let alone a top 10 guy
Comment by Steve -
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 -
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 -
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 -
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 -
No wonder the mavericks can’t ever win it all!!!!!!!!!!!!!! This is the dumbest validation for creating team Cuban!
Comment by shoupdogg -
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 -
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 -
Thanks for sharing, but how about a shout-out to Prof. Winston?
Comment by Joel @ Kelley -
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 -
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 -
Just wondering if down the road this system will impact coaching hiring/firing decisions.
Comment by deb -
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 -
Pingback: Heard It Through the Grapevine 2-9-09 » The Two Man Game
@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 -
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 -
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 -
Is this the top 30 in the league according to this ranking?
Comment by david -
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 -
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 -
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. -
Like to see u get Wallace next year, him and Dirk would be interesting at the 4 and 5
Comment by Belize -
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 -
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 -
sorry…. should say 82games.com The comment box is broken on Mac Firefox
Comment by Mason -
@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 -
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 -
Wow awesome stuff, Mark!
Comment by dperilli -
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 -
Presenting intricate numbers with little or no explanation will cause a buzz. Genious.
Comment by KJ -
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 -
The formatting doesnt work on explorer or firefox.
Comment by Bill -
Can you reformat it so the text isn’t cutoff please?
Comment by Hubert -
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 -