Monthly Archives: June 2010

Draft Comparison by Numbers

With just over 24 hours left until the 2010 NBA Draft, scouting reports, opinions, analysis and comparisons are flying around at a fevered pace. The culmination of two and a half months worth of anticpation for many NBA fans, there is a huge mass of information and discussion available about each player, their strengths, weaknesses and potential. One of the most frustrating components of this coverage for me is the comparison of draft prospects to current NBA players. As the easiest way for casual fans and analysts to contribute, these can make up the bulk of discussion on many media outlets (I’m looking at you sports talk radio.) I don’t have a problem with these comparisons in theory, I have a problem with the criteria used to make the comparisons.

For years, every guard with exceptional leaping ability was potentially the next Michael Jordan. Every long white player who can shoot is the next Larry Bird, Keith Van Horn or Adam Morrison; depending on the era. Although, in some parts of Rhode Island they’re referred to as the second coming of Austin Croshere. Every point guard from Gonzaga is the next John Stockton, every huge, awkward center is the next Greg Ostertag and every shot-blocking center with African roots is the next Dikembe Mutombo. These comparisons, based on skin color, position, the college they attended or one singular attribute do a diservice to the players and fans alike.

I began this project almost two months ago, with the idea of creating a system for comparing draft prospects to current NBA players, based on statistical outputs alone. With limited math and statistics skills it was a serious undertaking, and I have arrived at a system with some serious flaws. We’ll address the flaws in a moment, let me start by explaining what I’ve done.

My goal was to create a database whereby I could plug in a statistical profile for a draft prospect and return the statistical profile for the past draftee who’s profile is most similar, in addition I was hoping to find a way to create a numeric representation of how similar their profiles were. In this way you could make accurate comparisons without any subjective opinions. I understand that this project is an over-reaction in the other direction, and that potential and subjective opinions are invaluable tools for making personnel decisions. I just wanted to remind everyone that there are other tools being largely ignored by the casual fan and analyst.

I started by selecting 27 statistical categories which I thought I would offer a semi-complete profile of a player’s skills and abilities. Some of these categories were simple statistical production (Ast/40), some were ratios designed to help inform a player’s tendencies (3PA/FGA). I was somewhat limited in what stats I could use because I needed ones that I could find for college players over the last decade for comparison. For example, I would have liked to use Rebound Rates, but couldn’t find those numbers for college seasons before 2004. The complete list of categories I decided on is below:

  1. Height
  2. Weight
  3. Minutes per Game
  4. Points per 40 minutes
  5. Rebounds per 40 minutes
  6. Offensive Rebounds per 40 minutes
  7. Defensive Rebounds per 40 minutes
  8. Assists per 40 minutes
  9. Steals per 40 minutes
  10. Blocks per 40 minutes
  11. Turnovers per 40 minutes
  12. Personal Fouls per 40 minutes
  13. 2PT Field Goal Percentage
  14. 3PT Field Goal Percentage
  15. Free Throw Percentage
  16. Effective Field Goal Percentage
  17. True Shooting Percentage
  18. Free Throw Attempts per 40 minutes
  19. 3PT Attempts per Field Goal Attempt
  20. Assists per Field Goal Attempt
  21. Assist to Turnover Ratio
  22. Pure Point Rating
  23. Percentage of Team’s Possessions Used
  24. Points per Possession
  25. Field Goal Attempts per Possession
  26. Turnovers per Possession
  27. Player Efficiency Rating (PER)

Once I had the categories for a statistical profile, I put together the database for comparison, including every player drafted in the 1st Round since the 2001 NBA Draft. Subtracting high schoolers and foreign players, this left me with 171 players for comparison. Adding the top 40 college players available in this year’s draft (I used Draftexpress.com’s mock draft as a reference.) The next step was to calculate the average and standard deviation for each statistical category in the database. With that information I could standardize the individual values for each player in each category. Adding those standardized values together then gives us a single numeric value for each player’s statistical profile which can be used for comparison.

Now the sum of these standardized values is not a measure of a player’s worth. Some the values you see below are negatives, as for certain categories it would be better to below average, turnovers for example. By comparing these sums I was able to find the previous college player with the most similar statistical profile to each of the 40 players available in this draft. The results are below with the sum of the standardized values for each player in paraentheses. After the tables we can talk about some of the flaws.
 

1. John Wall (0.2779)Jerryd Bayless (0.2166)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
John Wall PG 76.0 196.0 34.8 19.1 4.9 0.9 4.0 7.5 2.0 0.6 4.6 2.2 50.9 32.5 75.4 50.0 56.0 7.2 0.26 0.55 1.62 0.78 23.4 1.01 0.72 0.24 22.2
Jerryd Bayless PG/SG 75.0 204.0 35.7 22.1 3.1 0.5 2.6 4.5 1.1 0.1 3.3 2.5 48.9 40.7 83.9 54.0 61.0 8.3 0.38 0.32 1.36 -0.85 25.3 1.19 0.76 0.18 23.7

2. Evan Turner (12.4739)George Hill (12.9405)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Evan Turner SG 79.0 214.0 35.8 22.8 10.2 2.2 8.0 6.7 1.9 1.0 4.9 3.1 54.0 36.4 75.8 54.0 58.0 6.6 0.12 0.40 1.36 -1.25 29.6 1.05 0.76 0.23 30.4
George Hill PG/SG 74.5 181.0 36.8 23.3 7.3 1.7 5.7 4.6 1.9 0.4 3.2 3.0 58.0 45.0 81.2 61.0 66.0 8.3 0.27 0.34 1.46 -0.30 26.5 1.26 0.74 0.17 32.4

3. Derrick Favors (1.2534)Al Horford (1.2752)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Derrick Favors PF 82.0 245.0 27.5 18.1 12.3 4.4 7.9 1.5 1.3 3.0 3.6 3.8 61.3 62.9 61.0 62.0 5.8 0.00 0.13 0.41 -6.63 15.9 1.11 0.72 0.22 24.8
Al Horford PF 82.0 246.0 27.8 19.0 13.6 3.6 10.0 3.1 1.1 2.6 2.6 3.6 61.4 0.0 64.4 61.0 63.0 7.6 0.01 0.27 1.21 -1.31 16.3 1.20 0.73 0.16 30.2

4. DeMarcus Cousins (10.0594)Jason Thompson (10.4672)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
DeMarcus Cousins PF/C 83.0 292.0 23.5 25.8 16.8 6.9 9.9 1.7 1.7 3.0 3.5 5.5 56.5 16.7 60.4 56.0 58.0 12.0 0.02 0.10 0.59 -5.93 18.8 1.14 0.73 0.15 34.4
Jason Thompson PF/C 83.0 250.0 34.6 23.6 14.0 3.9 10.1 3.2 1.2 3.1 3.4 3.4 57.8 32.4 58.1 57.0 58.0 7.8 0.07 0.19 0.94 -3.20 25.6 1.13 0.80 0.16 31.4

5. Wesley Johnson (2.6055)Luther Head (2.5294)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Wesley Johnson SG/SF 79.0 206.0 35.0 18.9 9.8 2.5 7.3 2.5 1.9 2.1 2.6 2.4 54.0 41.5 77.2 56.0 60.0 4.7 0.30 0.19 0.96 -2.41 19.7 1.18 0.84 0.17 25.4
Luther Head SG 75.0 179.0 33.3 19.2 4.8 0.7 4.0 4.6 2.1 0.3 2.1 1.7 54.7 41.0 78.8 59.0 61.0 3.1 0.61 0.32 2.17 2.31 19.1 1.27 0.94 0.14 25.5

6. Greg Monroe (5.3338)Chris Bosh (5.7637)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Greg Monroe PF/C 83.0 247.0 34.2 18.9 11.3 2.5 8.7 4.4 1.4 1.8 3.8 3.0 54.5 25.9 66.0 58.0 61.0 7.2 0.07 0.33 1.14 -2.36 23.1 1.05 0.73 0.21 25.9
Chris Bosh PF 83.0 225.0 31.0 19.5 11.2 3.8 7.3 1.5 1.2 2.6 2.9 2.9 57.6 46.8 73.0 60.0 63.0 7.0 0.16 0.13 0.53 -4.89 18.3 1.21 0.75 0.18 27.6

7. Al-Farouq Aminu (-3.3085) –  Spencer Hawes (-3.3208)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Al-Farouq Aminu SF/PF 80.5 216.0 31.3 20.2 13.7 5.5 8.2 1.7 1.8 1.8 4.1 3.8 48.4 27.3 69.8 47.0 53.0 8.2 0.18 0.11 0.41 -7.42 21.9 1.00 0.76 0.20 23.6
Spencer Hawes C 85.0 244.0 28.9 20.6 8.8 2.7 6.1 2.7 0.7 2.4 3.5 3.1 53.3 33.3 75.5 53.0 56.0 4.4 0.01 0.17 0.77 -4.29 19.4 1.08 0.85 0.18 22.8

8. Xavier Henry (-4.4071) – Tayshaun Prince (-4.4495)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Xavier Henry SG/SF 78.5 210.0 27.5 19.5 6.3 1.7 4.6 2.1 2.2 0.7 2.8 2.7 49.2 41.8 78.3 56.0 59.0 4.6 0.47 0.15 0.77 -3.44 16.3 1.18 0.86 0.17 21.9
Tayshaun Prince SF 81.0 215.0 34.0 20.5 7.4 2.4 4.9 1.9 1.2 1.6 2.3 1.9 56.4 34.0 70.3 54.0 56.0 4.3 0.43 0.12 0.81 -2.79 20.7 1.18 0.93 0.13 23.4

9. Ed Davis (-1.3980)Jason Maxiell (-1.5073)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Ed Davis PF 82.0 227.0 26.9 19.2 13.6 4.1 9.6 1.4 0.6 4.0 2.9 2.6 57.8 65.9 58.0 61.0 8.2 0.00 0.11 0.48 -4.88 15.2 1.16 0.72 0.17 26.8
Jason Maxiell PF 78.0 258.0 31.4 19.5 9.8 4.1 5.7 1.1 1.3 3.5 2.5 3.2 54.5 40.0 64.5 55.0 59.0 10.2 0.02 0.09 0.44 -4.39 18.8 1.17 0.70 0.15 26.3

10. Ekpe Udoh (-2.8560)Brandan Wright (-2.2947)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Ekpe Udoh PF/C 82.0 237.0 35.1 15.8 11.1 4.1 7.0 3.9 0.9 4.2 2.8 2.8 50.5 26.9 68.5 50.0 54.0 5.1 0.07 0.25 1.11 -1.82 19.7 1.04 0.81 0.18 25.1
Brandan Wright PF 82.0 200.0 27.4 21.5 9.0 3.0 6.0 1.5 1.4 2.6 2.3 2.2 64.6 56.7 65.0 64.0 6.2 0.00 0.10 0.63 -3.41 15.8 1.26 0.81 0.14 27.0

11. Patrick Patterson (-2.5771)Jared Jeffries (-3.0796)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Patrick Patterson PF 81.0 240.0 33.0 17.3 9.0 3.7 5.3 1.1 0.9 1.6 1.3 1.9 62.6 34.8 69.2 61.0 63.0 4.1 0.18 0.10 0.88 -1.37 15.6 1.30 0.89 0.10 24.0
Jared Jeffries SF/PF 82.0 215.0 32.6 19.4 9.8 3.3 6.6 2.7 1.9 1.7 3.7 3.2 47.2 38.0 66.7 49.0 53.0 7.2 0.17 0.18 0.73 -4.52 22.1 1.03 0.79 0.20 22.7

12. Luke Babbitt (6.2743) – Troy Bell (6.2797)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Luke Babbitt SF 81.0 218.0 37.1 23.6 9.6 2.2 7.4 2.3 1.1 0.9 2.6 2.8 52.1 41.6 91.7 54.0 62.0 6.9 0.20 0.14 0.89 -2.66 25.0 1.24 0.84 0.13 27.6
Troy Bell PG/SG 74.0 178.0 38.6 26.1 4.7 1.6 3.1 3.8 2.3 0.2 2.6 2.2 48.4 40.2 84.7 55.0 62.0 8.9 0.52 0.23 1.46 -0.26 27.7 1.28 0.83 0.13 29.5

13. Avery Bradley (-20.2629) – Demar Derozan (-15.7546)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Avery Bradley SG 75.0 180.0 29.5 15.8 3.9 1.3 2.6 2.8 1.8 0.7 2.1 3.2 45.7 37.5 54.5 49.0 50.0 2.6 0.30 0.19 1.37 -0.51 14.9 1.05 0.97 0.14 14.5
Demar Derozan SG 78.5 211.0 33.4 16.6 6.9 2.9 4.0 1.7 1.1 0.4 2.5 2.5 56.2 16.7 64.6 53.0 56.0 5.0 0.10 0.14 0.70 -3.37 19.4 1.09 0.83 0.16 19.3

14. Cole Aldrich (1.1862) – Charlie Villanueva (1.1378)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Cole Aldrich C 83.0 236.0 26.8 16.9 14.7 4.6 10.1 1.3 1.1 5.2 2.3 3.8 56.2 67.9 56.0 60.0 6.6 0.00 0.12 0.55 -3.69 13.9 1.16 0.76 0.16 28.7
Charlie Villanueva PF 81.0 237.0 25.8 21.1 12.9 4.1 8.8 2.0 1.0 2.9 3.5 3.4 52.2 50.0 68.8 53.0 57.0 6.7 0.04 0.13 0.57 -5.46 17.3 1.09 0.79 0.18 25.9

15. Gordon Hayward (-2.7436)Jared Jeffries (-3.0796)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Gordon Hayward SF 80.0 211.0 33.5 18.5 9.8 2.3 7.5 2.0 1.3 1.0 2.9 2.6 59.2 29.4 82.9 53.0 50.0 7.0 0.43 0.17 0.73 -3.56 20.6 1.17 0.76 0.17 24.9
Jared Jeffries SF/PF 82.0 215.0 32.6 19.4 9.8 3.3 6.6 2.7 1.9 1.7 3.7 3.2 47.2 38.0 66.7 49.0 53.0 7.2 0.17 0.18 0.73 -4.52 22.1 1.03 0.79 0.20 22.7

16. Larry Sanders (-0.6325) – Hakim Warrick (-0.9993)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Larry Sanders PF 82.5 222.0 26.9 21.4 13.6 4.5 9.1 1.4 1.1 3.8 2.5 4.4 54.7 25.0 64.1 54.0 56.0 7.1 0.04 0.09 0.59 -3.78 18.4 1.15 0.84 0.13 30.0
Hakim Warrick PF 80.5 215.0 37.5 22.8 9.2 3.3 5.9 1.6 1.0 0.8 2.7 2.5 56.6 29.0 68.1 56.0 60.0 9.7 0.07 0.11 0.58 -4.16 26.4 1.19 0.76 0.14 26.9

17. Paul George (5.6852)Al Thornton (5.4371)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Paul George SG/SF 81.0 214.0 33.2 20.2 8.7 2.3 6.4 3.7 2.7 1.0 3.9 3.5 48.5 35.3 90.9 51.0 57.0 5.5 0.46 0.24 0.94 -3.73 23.1 1.09 0.81 0.21 25.4
Al Thornton SF 79.0 221.0 31.2 25.3 9.2 3.9 5.3 0.9 1.9 1.5 3.2 3.5 54.9 44.4 79.0 57.0 62.0 7.7 0.18 0.05 0.28 -6.52 23.7 1.22 0.81 0.15 30.0

18. Eric Bledsoe (-11.6073)Daequan Cook (-10.7018)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Eric Bledsoe PG/SG 73.5 182.0 30.3 14.9 4.1 0.7 3.4 3.8 1.9 0.4 4.0 2.9 51.6 38.3 66.7 54.0 57.0 4.4 0.41 0.34 0.96 -3.69 16.1 1.00 0.74 0.27 14.4
Daequan Cook SG 78.0 203.0 19.7 19.8 8.7 1.5 7.2 2.1 1.5 0.5 2.9 3.4 46.5 41.5 69.7 53.0 55.0 3.4 0.41 0.13 0.71 -3.84 13.3 1.12 0.93 0.16 21.2

19. Solomon Alabi (-4.9582)Marcus Haislip (-5.5872)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Solomon Alabi C 85.0 237.0 25.6 18.2 9.7 3.9 5.8 0.8 1.0 3.7 3.0 3.7 53.4 79.4 53.0 60.0 6.6 0.00 0.06 0.26 -6.15 15.3 1.13 0.75 0.18 24.6
Marcus Haislip PF 82.0 221.0 33.5 19.8 7.9 2.7 5.3 1.1 0.5 2.1 2.4 4.1 55.1 32.6 72.1 54.0 57.0 5.3 0.15 0.08 0.47 -4.20 20.5 1.16 0.87 0.14 21.6

20. Damion James (4.3035)Tyler Hansbrough (4.2418)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Damion James SF/PF 80.0 227.0 30.3 23.8 13.6 4.2 9.4 1.3 2.2 1.6 2.7 3.9 53.5 38.3 67.4 54.0 58.0 8.1 0.22 0.08 0.48 -4.59 20.4 1.18 0.81 0.13 28.6
Tyler Hansbrough PF 81.5 234.0 30.3 27.4 10.7 4.0 6.7 1.3 1.6 0.5 2.4 3.0 52.1 39.1 84.1 52.0 61.0 11.5 0.05 0.08 0.54 -3.94 21.7 1.26 0.78 0.11 30.4

21. Daniel Orton (-20.1845)Chris Wilcox (-15.6641)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Daniel Orton C 81.5 269.0 13.2 10.3 10.0 3.6 6.5 1.2 1.7 4.2 3.0 7.0 53.9 0.0 52.4 53.0 53.0 5.0 0.02 0.16 0.39 -5.60 5.2 0.92 0.65 0.27 15.8
Chris Wilcox PF 82.0 218.0 24.1 18.4 11.0 4.1 6.8 2.3 1.2 2.3 2.3 4.1 50.6 0.0 58.5 50.0 52.0 6.3 0.01 0.15 1.00 -2.08 14.6 1.08 0.86 0.13 23.7

22. Hassan Whiteside (3.5234)Demarre Carroll (3.5869)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Hassan Whiteside PF/C 83.5 227.0 26.1 20.1 13.6 4.1 9.5 0.4 0.9 8.2 2.9 3.7 52.2 60.0 58.8 53.0 55.0 8.4 0.02 0.03 0.16 -6.46 16.9 1.09 0.78 0.16 29.8
Demarre Carroll SF 80.0 207.0 28.0 23.6 10.3 3.5 6.8 3.1 2.2 0.9 2.1 3.2 57.9 36.4 63.4 58.0 59.0 7.2 0.10 0.19 1.46 -0.18 18.6 1.23 0.86 0.11 30.3

23. Dominique Jones (1.6883)Marcus Williams (1.6426)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Dominique Jones SG 77.0 216.0 37.1 23.0 6.6 1.2 5.4 3.9 1.8 0.6 3.2 3.0 52.1 31.1 74.1 50.0 56.0 9.2 0.34 0.24 1.24 -1.45 28.8 1.13 0.79 0.16 26.3
Marcus Williams PG 75.0 215.0 33.3 14.8 4.6 0.9 3.7 10.3 1.1 0.3 4.4 2.0 41.0 40.0 86.2 47.0 56.0 5.7 0.29 0.97 2.33 5.96 17.9 0.96 0.69 0.29 19.3

24. Elliot Williams (-1.0605)Kirk Snyder (-1.0392)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Elliot Williams PG/SG 76.0 175.0 33.3 21.5 4.8 0.9 3.9 4.5 1.6 0.1 3.5 2.4 52.7 36.6 75.8 54.0 60.0 9.0 0.42 0.33 1.28 -1.37 23.3 1.15 0.73 0.19 23.8
Kirk Snyder SG 79.0 228.0 31.7 23.7 7.2 2.4 4.8 4.3 1.3 0.8 4.4 2.7 47.0 34.8 73.1 49.0 54.0 8.2 0.32 0.24 0.98 -3.87 25.6 1.05 0.80 0.20 23.4

25. Craig Brackins (-9.8072)Darrell Arthur (-9.2456)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Craig Brackins PF 82.0 229.0 35.1 18.8 9.7 2.4 7.3 2.5 0.9 1.4 2.4 2.3 44.6 31.0 76.0 45.0 50.0 5.5 0.19 0.15 1.01 -2.00 22.4 1.05 0.91 0.13 20.3
Darrell Arthur PF 80.5 216.0 24.7 20.6 10.1 3.7 6.4 1.3 0.8 2.1 3.0 4.8 55.5 16.7 70.2 55.0 57.0 4.6 0.03 0.08 0.44 -5.38 16.6 1.11 0.86 0.16 23.5

26. Greivis Vasquez (5.5496)Josh Childress (5.3564)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Greivis Vasquez PG/SG 78.5 211.0 33.9 23.1 5.5 1.4 4.1 7.4 2.0 0.4 4.0 2.5 47.0 35.9 85.7 50.0 55.0 5.8 0.37 0.41 1.87 2.35 25.7 1.08 0.85 0.18 25.8
Josh Childress SF 79.0 196.0 29.8 21.0 10.1 2.6 7.5 3.6 1.2 2.2 3.0 3.2 53.6 39.5 82.1 56.0 60.0 5.5 0.34 0.24 1.20 -1.57 19.5 1.19 0.84 0.17 28.5

27. Armon Johnson (-9.8069)Jordan Farmar (-9.7921)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Armon Johnson PG 75.0 195.0 34.5 18.3 3.9 0.9 3.0 6.5 1.0 0.4 3.9 2.1 54.4 23.9 67.8 51.0 54.0 4.0 0.16 0.43 1.66 0.92 22.2 1.00 0.83 0.21 18.4
Jordan Farmar PG 74.0 171.0 30.4 17.7 3.4 0.7 2.6 6.7 1.5 0.3 4.8 2.2 47.2 33.3 71.7 48.0 52.0 4.5 0.45 0.45 1.40 -0.91 22.3 0.95 0.80 0.26 17.9

28. James Anderson (5.1228)Quincy Douby (5.2268)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
James Anderson SG/SF 78.0 208.0 34.1 26.2 6.8 2.3 4.6 2.8 1.6 0.7 2.8 3.1 54.9 34.1 81.0 53.0 60.0 9.2 0.44 0.16 1.03 -2.24 26.3 1.23 0.83 0.13 29.1
Quincy Douby SG 75.0 175.0 36.7 27.7 4.7 0.9 3.8 3.4 1.9 0.9 3.0 2.0 51.5 40.1 84.7 56.0 60.0 5.8 0.47 0.17 1.12 -1.98 31.3 1.24 0.92 0.14 30.7

29. Stanley Robinson (-9.1744)Wilson Chandler (-10.0241)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Stanley Robinson SF/PF 80.0 213.0 34.2 17.0 8.9 3.1 5.8 1.2 1.0 1.4 2.6 1.8 57.1 34.2 62.9 56.0 57.0 3.3 0.20 0.09 0.44 -4.69 19.2 1.11 0.87 0.17 20.7
Wilson Chandler SF 80.0 210.0 31.7 18.4 8.7 2.3 6.4 1.8 0.8 1.7 2.0 3.6 49.4 33.3 65.4 50.0 52.0 4.0 0.27 0.11 0.87 -2.16 20.8 1.10 0.96 0.12 21.6

30. Terrico White (-14.1043)Demar Derozan (-15.7564)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Terrico White PG/SG 77.0 203.0 31.5 19.3 5.8 2.0 3.8 1.9 1.1 0.3 1.7 2.3 48.7 34.3 71.4 50.0 53.0 4.3 0.40 0.12 1.11 -1.15 18.5 1.15 0.97 0.10 18.7
Demar Derozan SG 78.5 211.0 33.4 16.6 6.9 2.9 4.0 1.7 1.1 0.4 2.5 2.5 56.2 16.7 64.6 53.0 56.0 5.0 0.10 0.14 0.70 -3.37 19.4 1.09 0.83 0.16 19.3

31. Willie Warren (-4.4783)Frank Williams (-4.4793)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Willie Warren SG 76.0 208.0 32.3 20.2 4.1 0.8 3.2 5.1 1.2 0.1 4.7 2.8 52.4 30.9 79.5 50.0 57.0 7.5 0.40 0.36 1.08 -3.43 23.1 1.03 0.73 0.24 18.6
Frank Williams PG 75.0 212.0 32.5 19.8 5.7 1.1 4.6 5.3 2.5 0.3 3.4 2.5 42.3 34.0 80.8 45.0 53.0 7.1 0.36 0.35 1.59 0.44 21.8 1.06 0.83 0.18 22.5

32. Quincy Pondexter (2.2165)Antoine Wright (2.2119)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Quincy Pondexter SF 78.0 220.0 32.3 23.9 9.1 3.7 5.4 2.2 1.6 0.7 2.3 3.1 55.0 35.3 82.7 55.0 61.0 7.8 0.11 0.14 0.93 -2.27 21.4 1.24 0.83 0.12 28.2
Antoine Wright SG/SF 78.0 203.0 33.9 21.0 7.0 1.9 5.1 2.6 1.4 0.8 2.9 3.0 54.0 44.7 69.1 59.0 61.0 5.4 0.41 0.18 0.92 -2.80 22.0 1.22 0.85 0.17 24.7

33. Darrington Hobson (-1.7650)Aaron Brooks (-1.7392)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Darrington Hobson SF 78.5 204.0 33.6 18.9 11.1 2.6 8.5 5.4 1.5 0.5 3.5 2.3 46.5 36.1 65.3 48.0 52.0 6.8 0.25 0.36 1.54 0.14 22.9 1.02 0.80 0.19 24.0
Aaron Brooks PG 72.0 161.0 36.8 19.3 4.6 1.0 3.7 4.6 1.5 0.2 2.8 2.7 50.2 40.4 84.6 55.0 59.0 4.2 0.43 0.32 1.67 0.72 22.5 1.17 0.88 0.17 22.2

34. Devin Ebanks (-15.6870)Demar Derozan (-15.7654)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Devin Ebanks SF 80.0 208.0 34.1 14.1 9.5 3.4 6.1 2.9 1.2 0.8 2.6 1.9 49.5 10.0 77.0 46.0 53.0 5.2 0.10 0.26 1.11 -1.74 18.1 1.02 0.79 0.19 20.3
Demar Derozan SG 78.5 211.0 33.4 16.6 6.9 2.9 4.0 1.7 1.1 0.4 2.5 2.5 56.2 16.7 64.6 53.0 56.0 5.0 0.10 0.14 0.70 -3.37 19.4 1.09 0.83 0.16 19.3

35. Gani Lawal (-8.6004)Darrell Arthur (-9.2456)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Gani Lawal PF 81.0 233.0 25.8 20.3 13.1 4.5 8.6 0.6 0.7 2.1 3.4 3.3 53.1 57.2 53.0 55.0 9.5 0.00 0.05 0.19 -7.32 17.3 1.06 0.73 0.18 22.9
Darrell Arthur PF 80.5 216.0 24.7 20.6 10.1 3.7 6.4 1.3 0.8 2.1 3.0 4.8 55.5 16.7 70.2 55.0 57.0 4.6 0.03 0.08 0.44 -5.38 16.6 1.11 0.86 0.16 23.5

36. Jordan Crawford (-2.3259)Casey Jacobsen (-2.3250)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Jordan Crawford SG 76.5 198.0 32.8 25.0 5.8 1.2 4.6 3.5 1.6 0.2 3.0 2.0 50.1 39.1 77.3 53.0 57.0 5.4 0.36 0.18 1.19 -1.60 24.6 1.17 0.91 0.14 24.8
Casey Jacobsen SG/SF 78.0 215.0 35.2 24.0 4.9 1.6 3.3 3.9 0.7 0.1 2.7 2.0 48.1 37.2 77.6 51.0 57.0 8.6 0.37 0.23 1.45 -0.29 25.5 1.19 0.84 0.13 24.2

37. Mikhail Torrance (-0.4767)Mike Conley (-0.4634)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Mikhail Torrance PG/SG 76.0 207.0 32.9 19.2 4.6 0.4 4.2 6.2 1.0 0.3 3.3 1.9 52.1 35.8 86.5 53.0 59.0 6.0 0.31 0.47 1.88 2.01 21.0 1.13 0.78 0.19 23.3
Mike Conley PG 73.0 175.0 31.6 14.3 4.4 0.9 3.5 7.7 2.8 0.3 2.8 1.9 57.9 30.4 69.4 55.0 59.0 4.7 0.22 0.77 2.77 5.77 15.8 1.09 0.76 0.21 24.2

38. Lance Stephenson (-15.6001) – Demar Derozan (-15.7654)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Lance Stephenson SG 78.0 227.0 28.2 17.5 7.6 2.6 5.0 3.5 1.3 0.3 3.4 2.8 49.5 21.9 66.4 46.0 49.0 5.0 0.20 0.23 1.04 -2.66 18.8 0.97 0.85 0.19 17.7
Demar Derozan SG 78.5 211.0 33.4 16.6 6.9 2.9 4.0 1.7 1.1 0.4 2.5 2.5 56.2 16.7 64.6 53.0 56.0 5.0 0.10 0.14 0.70 -3.37 19.4 1.09 0.83 0.16 19.3

39. Jarvis Varnado (-0.2561)Hilton Armstrong (-0.2794)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Jarvis Varnado PF 82.0 210.0 31.7 17.4 12.9 3.8 9.2 1.1 0.8 6.0 2.5 3.1 58.2  – 61.0 58.0 60.0 7.4 0.00 0.10 0.45 -4.37 17.4 1.15 0.74 0.17 28.6
Hilton Armstrong PF 82.0 240.0 27.7 14.0 9.6 2.9 6.7 1.1 0.9 4.5 3.3 4.0 60.9 50.0 69.2 61.0 64.0 5.3 0.01 0.13 0.32 -6.42 12.3 1.09 0.64 0.26 19.8

40. Trevor Booker (-4.7462)Luol Deng (-4.7329)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Trevor Booker SF 79.5 236.0 30.8 19.8 10.9 3.6 7.3 3.3 1.7 1.8 2.5 2.6 54.7 26.5 59.1 47.0 53.0 7.0 0.09 0.22 1.31 -0.87 19.4 1.11 0.83 0.14 26.7
Luol Deng SF 80.0 220.0 31.1 19.4 8.9 2.9 6.0 2.4 1.7 1.4 2.9 2.9 51.4 36.0 71.0 52.0 55.0 4.7 0.25 0.15 0.82 -3.32 19.6 1.10 0.87 0.16 23.0

41. Sherron Collins (-6.7597)T.J. Ford (-6.7383)

  Position Height Weight Min/g Pts/40 Reb/40 Oreb/40 Dreb/40 Ast/40 Stl/40 Blk/40 TO/40 PF/40 2PT% 3PT% FT% eFG% TS% FTA/40 3PA/FGA Ast/FGA A/TO PPR % of Tm Pos. Pts/Pos. FGA/Pos TO/Pos PER
Sherron Collins PG 72.0 217.0 33.0 18.8 2.5 0.3 2.3 5.4 1.3 0.1 2.9 2.0 46.9 37.0 85.5 51.0 56.0 4.7 0.44 0.37 1.89 1.79 19.8 1.12 0.87 0.17 19.1
T.J. Ford PG 72.0 162.0 33.6 17.2 4.5 0.7 3.7 8.0 2.3 0.2 3.7 2.9 42.9 26.5 82.0 42.0 51.0 6.8 0.17 0.64 2.37 5.46 21.8 0.97 0.78 0.21 22.0

Now there are some obvious flaws with my method, as evidenced by the results. Since each of the categories are weighted equally, a close similarity in a subtle category like FGA/POS can make up for a large disparity in a more obvious category like Assists. In addition since my technique is really measuring how far away from average a player is in each of these categories, it doesn’t always lead to statistical profiles that match up perfectly. For example a guard could be above average in all the shooting categories  and come out looking the same as a power forward who is above average in all the rebounding or possession categories. The only subjective piece I had to add was factoring in a position played when a profile was close to two or more profiles.

I am hoping that this is a tool that I can continue to refine for next year’s draft. As this year’s draft classs gets incorporated into the comparison database I will have a larger sample for comparison. In addition if anyone has suggestions on how to improve or simply change my techniques I would love to hear them. Here is the link to the spreadsheet if anyone wants to look at the raw data.

That being said, there were some interesting results. Keep in my mind that this is not meant as a tool to predict future production, just compare what a player has already done. I am not saying that Wesley Johnson will have Luther Head’s career arc, but it is interesting that those two players had such similar profiles. The others that were extremely close were Willie Warren and Frank Williams as well as Antoine Wright and Quincy Pondexter. Remember, I am not advocating using this for any specific purpose, but I thought it would be fun to see. Enjoy!

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Filed under 2010 Draft, NBA, Statistical Analysis

My Thoughts on the Finals

Kobe: Finals MVP or Finals Most Famous Scorer?

This is a few days late, but I wanted to chip in with a few thoughts on the Lakers winning their second consecutive championship, more specifically on Kobe winning his second consecutive Finals MVP. First of all congratulations to the Lakers. They earned their win, no doubts about it. It was an incredibly compelling series to watch and I enjoyed it as much as any recent playoff series.

Now, for full disclosure I have some serious problems with Kobe. I respect his individual skills, his work ethic and the incredible effort he plays with. I have no respect for the way he barely conceals his toleration of his teammates and seems to value his specific role on a championship team more than just being on a championship team. On his podcast last week, Bill Simmons shared an anecdote from Game 5 where during a timeout following another Paul Pierce basket, Kobe screamed at Jackson in front of Artest and the entire team that since Ron wasn’t doing his job that he would guard Pierce. If I am not mistaken this is the same sort of thing which has DeMarcus Cousins in a draft free-fall and earned him a reputation as an uncoachable hot-head. That being said let’s look at some Lakers’ numbers from the Finals, and see what Kobe did to earn his Finals MVP trophy.

 As Finals MVP Kobe must have been the team’s most efficient scorer. Nope, he barely shot 40% from the field and 30% on 3PTs. Odom was the most efficient from the field, and Artest was the team’s best 3PT shooter. In fact the only categories Kobe led the team in were Points, Assists, Steals, Turnovers and Personal Fouls. Only 3 out of those 5 are positive statistical categories. He didn’t even lead the team in minutes played, that was Pau Gasol. Well maybe he didn’t have the top numbers in each category, but surely his overall performance proves he was the best player on the floor. Surely the advanced stats will show he was on top?

Kobe doesn’t come out on top here either. Gasol led the team in Wins Produced and WP48. Farmar, Gasol, Artest, Fisher and Odom all had better raw +/- numbers than Kobe. I couldn’t find any PER numbers for just the Finals, but I am guessing that with his rebounding and shooting percentages Gasol would have come out on top here as well. Well maybe Kobe just performed at his best in the team’s wins. Perhaps his numbers were skewed by some bad performances in the Laker’s three losses, but he was at his best when it counted.

Except for rebounding numbers Kobe was MUCH better statistically in the Lakers losses. Well he didn’t earn the Finals MVP there. Maybe he had some defining performance, a historic 4th quarter where shook off a bad game and carried his team to victory.

Game 1 – Kobe goes 1-5 with 3 Pts. and 2 Ast in the 4th quarter. His one made basket was a 3PT with three seconds left when the Lakers were already up by 10.

Game 3 – Kobe goes 1-6 with 4 Pts. and 0 Ast in the 4th quarter.

Game 6 – With his team up 25 entering the 4th, Kobe goes 1-4 with 4 Pts. and 0 Ast in quarter.

Game 7 – In a close and hard fought 4th quarter goes 1-4 with 10 Pts. and 1 Ast.

He had a strong 4th quarter in the deciding game, but finished the series shooting 21% and averaging 5.3 points in the 4th quarter. Not great numbers for “One of the greatest closers ever.” I understand that I have a personal bias, and I understand that no one else on the Lakers produced consistently, but what exactly did he do to earn his second Finals MVP. Statistically, basic or advanced, he wasn’t the best player on the team. He performed much better when the Lakers lost, and he consistently underperformed in the clutch. Can anyone help me with this?

More Disciples of Clyde

I swear I am not auditioning for a job as their personal researchers, but Dan and Ken asked another interesting question on their podcast that I thought dovetailed nicely with this discussion. In Episode #96 Ken wondered what the Lakers record was of the past 3 season when Gasol had more shot attempts than Kobe. I looked through their games logs for the last three seasons, playoffs included. A quick analyis showed the Lakers having a 160-58 record when Gasol and Kobe both played and Kobe led the team in shot attempts. This translates to an impressive .734 winning percentage. It was a very small sample size, but the Lakers had a 17-1 record when both played, and Gasol led the team in shot attempts. This translates to an astronomical .944 winning percentage. There is certainly more to the story than a simple shot comparison between the two, but as Kobe’s athleticism continues to decline, it might be to their benefit to feature Pau more often and let Kobe play off of him.

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Filed under Los Angeles Lakers, NBA, Statistical Analysis

Reaching for Rhythm

Two weeks ago on The Disciples of Clyde Podcast, Bethlehem Shoals and Dan Filowitz spent some time discussing the occasional, infuriating phenomenon of players trying to shoot their way into a rhythm from the outside. The discussion focused on LeBron James and Kobe Bryant, and with their various marked advantages scoring inside, how frustrating it can be to watch them start a game seemingly determined to find a rhythm by shooting 18 ft. jumpers. Towards the end of the podcast they wondered whether there was any data available to track the frequency of this occurence, and if not there was a casual challenge for a listener to put some together. I should note that I don’t have a Synergy Sports membership so it’s possible that this data is available there, making many hours of work on my part completely redundant. That being said, here is my answer to their challenge.

The questions I focused on were these: Do certain players have discernible shot patterns to start a game? How do those differ from their shot patterns for the rest of the game?

To create a sample I began by putting together a list of the Top 20 players from last season in terms of Field Goal Attempts per game. I then used Hoopdata‘s player pages to calculate what percentage of their Overall Field Goal Attempts came from each area of the floor. For their game starting shot patterns I went through and looked at the first 4 Field Goal Attempts of each game for each player during the 2009-2010, what location those shots came from, and turned that into a percentage.

There are admittedly some flaws with the way I choose to gather the data. I did not include Free Throw Attempts, which are an indicator of attacking the rim. Despite being high volume shooters, not all of these players started the game as their team’s primary scoring option. For example Aaron Brooks and Derrick Rose often didn’t take their 4th shot attempt until the 2nd Quarter. However, the technique I used was the easiest and most consistent way for me to assemble this information. While there is more data which factors into this equation, what I have here certainly has a story to tell. Below is a color-coded table of the percentage of each player’s shots which came from each area of the floor.

 This is a lot of data and not the easiest for comparing patterns, so let’s take these numbers and put them into individual charts. For space reasons, I only included the individual charts for LeBron and Kobe. To see the individual charts for the rest of the Top 20 click here. The blue line on each table represents a player’s Overall Shot Percentage for each area. The red line represents the Shot Percentages on each player’s 1st 4 shots.

Here it’s much easier to see the patterns emerging. Kobe appears to have a slight tendency to begin games relying on his mid-range jumpshot. LeBron on the other end appears to hold back his 3PT attempts and take more shots At the Rim to start the game. LeBron’s graph also illustrates the rather large dichotomy in his shot selection. As Shoals and Filowitz alluded to, he doesn’t have a reliable floater and doesn’t seem as comfortable with true mid-range jumpshots. More than 80% of his shots are either taken at the rim, or are long jump shots. Kobe has a much more regular distribution of shots from different areas. (The scales are different for each graph which makes Kobe’s look slightly more irregular.) I don’t know that I will have the time to put it together but I would be very interested to see if similar graphs from the beginning of Kobe’s career would look more similar to LeBron’s.

To look at the data for all 20 players at once, I took each player’s 1st 4 Shot Percentages for each area and subtracted their Overall Shot Percentages. A red value means a player was less likely to shoot from that area at the beginning of the game than overall. A green value means a player was more likely to shoot from that area at the beginning of the game than overall.

Here you can see that only 4 of the 20 players, Tyreke Evans, Dirk Nowitzki, Dwayne Wade and LeBron James, seemed inclined to begin a game by getting to the rim more than they normally would. On the other hand 18 out of 20 began a game by shooting more mid-range jumpers, and 11 of 20 had an increase in long jumpers to start the game. Even primarily post players David Lee, Chris Kaman and Zach Randolph increased their long jumper percentages at the beginning of a game. Across this sample, the occurrence of players starting a game trying to establish a rhythm from the outside seems extremely prevalent. The strange thing is that LeBron and Kobe, the two players who sparked this discussion, seem to fit the pattern less than most.

To cycle back to the case of LeBron and Kobe, the phenomenon of them shooting long jumpers to begin a game seems as much an issue of perception as anything else. Kobe sees an increase in the percentage of shots he takes from the 16-23ft. range to start a game, but it is a modest increase when compared to players like Brandon Roy, Zach Randolph, Kevin Durant and Monta Ellis. The percentages show that LeBron is actually more likely to attack the rim at the beginning of the game.  In going through the play-by-play data I did find 5 or 6 games where LeBron started off with 4 straight long jump shots. Kobe fell into the same trap on occasion. Even though these games are outliers for both players, they stand out because they seem to be such a blatant under-utilization of their skills. These players are so effective at the rim, LeBron off penetration and Kobe in post-ups, that when they neglect these advantages it stands out that much more.

In going through this information I wondered several times whether these same patterns hold true for the playoffs as well. If this NBA postseason has taught us anything it’s that regular season samples can be an extremely poor indicator of what will happen in the playoffs. I will be working on those numbers and hopefully will have them together by the end of next week.

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Pacers and Wins Produced

Recently, Dave Berri, at the Wages of Wins Journal, put out a call looking for bloggers to use his statistical model, Wins Produced, to cover each NBA team. I volunteered to cover the Indiana Pacers for Berri’s “Wages of Wins Network.” My first post went up last week, and I thought it might require more explanation than was appropriate for that forum.

I have been reading Berri’s blog for over a year now. I am in the process of re-reading his book, The Wages of Wins, and look forward to immersing myself in his second book, Stumbling on Wins. His work is controversial and has caused a great deal of discussion across the internet. This FreeDarko post, and the accompanying commentary, is one of the most comprehensive discussions of his work I could find on the internet (although it falls mostly on the negative side). Like many basketball fans, many of his conclusions don’t fit with other pieces of my basketball knowledge. Some of what he writes makes complete sense to me, and other ideas challenge my conceptions. Over the past year I have come to a comfortable accord with his work: I see it as a valid statistical tool, one of many that I would like to keep in my analytical tool box. Like any other statistic, Wins Produced has strengths and flaws, answers some questions and raises others. It is one lens with which to examine the nature of basketball in an attempt to understand it better. In part, I volunteered to write with his blog as a chance to work with his statistical model, and hopefully understand it better.

After writing my first Pacers post for the Wages of Wins, I wanted to include some additional information. It seems that most of his blog readers have a working knowledge and a general acceptance of his ideas, methods and conclusions. I know that there are many others, Pacers fans included, who are much more skeptical. I don’t have the mathematical skill to defend or criticize his methods, but I would like to talk about some of the things that make sense to me when using the Wins Produced model to examine the Pacers. Again, I don’t advocate Wins Produced as a perfect or singular statistical model. But looking at the Pacers, some pieces of Berri’s model should make sense to even the most skeptical readers.

Let’s start with some basics about Wins Produced. Wins Produced and its more commonly used sister stat, Wins Produced per 48 (WP48), are formulas which use a player’s 2 Point Field Goals Made, 3 Point Field Goals Made, Free Throws Made, Offensive Rebounds, Defensive Rebounds, Missed Free Throws, Missed Field Goals, Turnovers, Steals, Assists, Blocks and Personal Fouls, to calculate how much they contribute to their team’s wins. How Wins Produced is calculated is less important to this discussion here than the statistical categories which are used, but if you are more curious here is a lengthy description of the calculation process

The impetus for the formation of this new “Wages of Wins Network” was the work of a blog reader Andres Alvarez, who created an automation for Berri’s Wins Produced. Previously each calculation was done by hand, by Berri for each post, meaning that league and team wide data was available only when Berri chose to cover that team for a post. The sticking point was a numeric adjustment for the position a player spent minutes at. This had been done by Berri’s subjective estimation, but Alvarez created a formula using a player’s height, weight, body-mass index, listed position and assist rate to automate a player’s position designation. While this formula is not perfect, it did allow for a database to be created showing Wins Produced and WP48 for each player in the league over the past few seasons. The position designation is important because the each player’s raw WP48 needs to be adjusted to account for the expected production from their position. To quote Berri:

As noted in The Wages of Wins, centers and power forwards get rebounds and tend not to commit turnovers.  Guards are the opposite.  The nature of basketball is that teams need guards, small forward, and big men.  Given nature of the game, players have to be compared to their position averages.

When calculating WP48 using the position adjustments, every minut a team played needs to be accounted for by all five positions. The Pacers played 19,705 minutes this season. Which means 3,941 minutes were available for each position. If you consider that some of Danny Granger’s minutes were spent at power forward, then you need to account for who was playing small forward during those minutes. When looking at Alvarez’s site you will see the position designations as assigned by his formula expressed as a percentage. For example his formula has Mike Dunleavy spending 52% of his minutes at shooting guard and 47% of his minutes at small forward.

When look at WP48 there are some general benchmarks to keep in mind. Anyone with a negative WP48 would be considered to have hurt his team more than they helped. Essentially, their play was producing losses instead of wins. A WP48 of 0.100 is considered average, anything beyond that is obviously above average. A WP48 of 0.200 or higher is considered “star production.” A WP48 of 0.300 or higher is considered “super-star production.”

These benchmarks create much of the skepticism from Pacers fans. In his time in Indiana, Troy Murphy has consistently posted WP48 numbers above 0.200, while Danny Granger has hovered around 0.100. “There is now way Murphy is our best player, let alone a star. There is no way Granger is merely average,” are common refrains. What I want to do is look at some numbers and see where these WP48 numbers come from when looking at the Pacers.

In the analysis below I compared each player on the Pacers roster to an average player at their position, using the statistics which are components of Wins Produced and WP48. For the sake of simplicity I have just used a player’s listed position for comparison and calculation. This explains why some of the WP48 numbers here won’t match with the ones on The Wages of Wins or Andres Alvarez’s Wins Produced Automation. My hope is that by looking at the WP48 numbers for the Pacers not just in isolation, but as a reflection of how they compare to average players in the component categories, they will make a little more sense and connect more closely to our subjective observations.

*Below Average Numbers in Red
*Numbers are per 48 minutes
*Points-per-shot = [PTS-FTM]/FGA
*Adjusted Field Goal Percentage = PPS/2
*Net Possessions = Rebounds + Steals – Turnovers
*Win Score = PTS + REB + STL + ½*BLK + ½*AST – FGA – ½*FTA – TO – ½*PF

  • Roy Hibbert is classified as a below-average center by Wins Produced, and looking at these statistical categories, it’s easy to see why. Hibbert is significantly below-average in the possession categories, specifically rebounding, steals and turnovers. Despite a dramatic improvement last season he is still below-average in Personal Fouls per 48 as well.
  • Jeff Foster was injured most of last season, but performed reasonably well in his limited minutes. His terrific rebounding ability helped to offset some of his below-average production in the scoring categories.
  • According to WP48, Solomon Jones was the worst player on the Pacers roster last season, and here it is easy to see why. He offers significantly below-average production in almost every statistical category used to calculate Wins Produced. 

 

  • When using only the power forward position adjustment for Troy Murphy he comes out at 0.321, which classifies him as a super-star by Wins Produced. This is confusing for many people, but when looking at these statistical categories it makes more sense.  His 3PT shooting ability makes him significantly above-average in terms of Points Per Shot and Adjusted Field Goal Percentage. He is also significantly above-average as a rebounder. In fact the only categories where Murphy’s production is below-average for a power forward is in the areas of Free Throw Attempts and Blocked Shots. When you factor in a position adjustment for the minutes Murphy has played at center his WP48 comes down a little bit, below 0.300.
  • Tyler Hansbrough had a surprisingly good rookie season, when he was able to make it onto the floor. He played with a frenetic energy which allowed him to draw a lot of fouls and do an unexpectedly good job of rebounding. Unfortunately, this frenetic energy made it difficult for him to finish effectively and he shot only 36.0% from the field. This number is responsible for his extremely low Points Per Shot and Adjusted Field Goal Percentage numbers, and in turn his very low WP48.
  • Josh McRoberts was very productive especially towards the end of the season when he finally started receiving consistent minutes. McRoberts was below average in a few categories, but prove to be a very efficient scorer, and above-average in essentially all the possession categories. He has definitely proven he deserves more minutes next season.

  • Danny Granger is considerably above average in all of the scoring categories. He is below average with respect to Rebounds, Turnovers and Win Score. His relatively modest performance in these possession categories is why Wins Produced views him as a simply above-average performer as opposed to the top-tier star many fans believe him to be.  When you factor in the minutes he plays at power forward in small lineups, his WP48 decreases a little more as his performance in the possession categories is even further from average.
  • Mike Dunleavy was just slightly below-average in a few categories. When you factor in the minutes he played at shooting guard his WP48 increases slightly to an essentially average player.

  • When Brandon Rush is compared to the average shooting guard his statistics look very reasonable. He is below average with regards to Free Throw Percentage, Field Goal Attempts, Free Throw Attempts, Points Scored, Steals and Assists. He is above average with regards to Points per Shot, Adj. Field Goal Percentage, Rebounds, Turnovers, Net Possessions, Blocked Shots, Personal Fouls and Wins Score. All of this balances out to an average performer at the shooting guard position. Considering Brandon Rush to be an average shooting guard is a leap for many Pacers fans, but when focusing on these statistical categories it seems more reasonable.
  • Dahntay Jones’ performance is strikingly below-average, almost across the board.
  • Luther Head is above average in most of the shooting/scoring categories, but is below-average with regards to most of the possession categories. 

 

  • Wins Produced viewed Earl Watson as the Pacers best point guard last season, and looking at these statistics it’s easy to see why. While he didn’t score as much as Ford or Price, he was above-average with respect to Rebounds, Steals, as well as being the Pacers best assist man.
  • Price was above-average with respect to the scoring categories, and very close to average in most others. His paltry 5.9 Assists per 48 minutes almost completely explains his below average showing in WP48.
  • I think most Pacers fans would disagree with the idea the T.J. Ford was essentially just as productive as A.J. Price this season. But again . . . look at the numbers here. While Price was above-average in the scoring categories, Ford proved to be above-average when looking at Rebounds, Points Scored, and Free Throw Attempts. While still below-average, Ford was also much more effective distributing the ball than Price.

To avoid confusion, let me restate the argument here. I am not saying that Wins Produced is the best or only statisical model with which to view basketball performance. I would like to point out that, with respect to the Pacers, the numbers make a lot more sense than we usually give them credit for. When you break them out into their component categories and compare each players production to an average NBA player, it is easier to see why Troy Murphy looks like a star, Danny Granger looks closer to average, and Roy Hibbert looks woefully underproductive. Whether these statisical categories accurately or completely reflect a player’s true worth is a discussion for another time and place.

I would encourage Pacers fans with even a passing interest in statistics to read The Wages of Wins and sample a few posts at the accompanying blog. Ask some questions in the commments there and at other basketball stats sites you enjoy, dig a little deeper, and look at some more of the numbers before you decide whether Wins Produced can help you to a better understanding of the NBA.

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Crazy about Colors and Correlations, pt. 2

This post is a follow-up to Crazy about Colors and Correlations, Pt. 1 in which I examined the correlations between Offensive Efficiency and some key offensive categories. These posts are less about me discovering some untold revelation of statistics and more about my excitement at figuring out the correlation function in Excel, and how to color code tables.

For this analysis I included the Defensive Efficiency of each team and compared the correlations with the defensive categories of Pace, Opponent’s Turnover Rate, Defensive Rebound Rate, Block Rate, Opponent’s Assist Rate, Opponent’s Free Throw Rate, Opponents At Rim FG%, Opponents 3PT%, and Opponents 3PT Rate. The idea is to see if there are certain characteristics that strong defensive teams often share. For Opponent’s Turnover Rate, Defensive Rebound Rate and Block Rate a strong correlation will actually be indicated by a negative result. (When those categories increase Defensive Efficiency should decrease.)

Here are the results:

Strong Correlations –

  • Correlation between O At Rim FG% and Defensive Efficiency: 0.7580
  • Correlation between Opponent’s Assist Rate and Defensive Efficiency: 0.7105
  • Correlation between Opponent’s 3PT% and Defensive Efficiency: 0.7078
  • Correlation between Defensive Rebound Rate and Defensive Efficiency: -0.6797

Low/Moderate Correlations –

  • Correlation between Block Rate and Defensive Efficiency: -0.5934
  • Correlation between Pace and Defensive Efficiency: 0.3899
  • Correlation between Opponent’s Turnover Rate: -0.1679

(Essentially) No Correlation –

  • Correlation between Opponent’s 3PT Rate and Defensive Efficiency: 0.0958
  • Correlation between Opponent’s Free Throw Rate and Defensive Efficiency: 0.0850

When looking at the results, several categories jump off the page. Most of the best defenses in the league do a good job of defending at the rim and defending the three point line. They rebound their opponent’s misses and limit ball movement, forcing them into isolation shots. There was a moderate correlation between shot blocking and playing at a slower pace; which in theory limits transition opportunities. The three smallest correlations were pretty surprising to me. The best defenses don’t necessarily force a lot of turnovers or limit their opponents free throws and three point shots. This seems to be counter to the common wisdom of getting steals and avoiding fouls.

Again, nothing earth shattering here, but enjoy all the colors and decimal places!

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