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2013 NBA ASPM Team Projections

October 29, 2012
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Here are my 2013 NBA Team Projections, based on my ASPM player projections. For playing time, I roughly used ESPN’s Fantasy Projections, tweaked so each team totaled 475% of the minutes (I assumed the rest of the minutes would go to replacement level (-3.25) players).

Here are my regular season projections:

I have updated these to deal with some issues with the ESPN Fantasy playing time projections (such as discussed below with Memphis).

TeamConf.Div.EfficiencySoSEff. Mar.Pyth Wins
Miami HeatEastSE5.93-0.426.3657
San Antonio SpursWestSW4.84-0.024.8654
Oklahoma City ThunderWestNW4.08-0.014.0952
New York KnicksEastAtlantic3.43-0.253.6951
Atlanta HawksEastSE2.86-0.303.1650
Denver NuggetsWestNW3.130.053.0949
Los Angeles LakersWestPacific2.81-0.052.8749
Memphis GrizzliesWestSW2.770.002.7749
Los Angeles ClippersWestPacific2.610.002.6148
Indiana PacersEastCentral1.64-0.321.9646
Minnesota TimberwolvesWestNW1.520.111.4145
Boston CelticsEastAtlantic0.40-0.140.5442
Dallas MavericksWestSW0.610.140.4742
Chicago BullsEastCentral0.09-0.130.2242
Philadelphia 76ersEastAtlantic0.09-0.100.2042
Milwaukee BucksEastCentral-0.09-0.160.0841
Brooklyn NetsEastAtlantic-0.12-0.140.0141
Utah JazzWestNW0.000.14-0.1341
Washington WizardsEastSE-1.17-0.08-1.0938
Phoenix SunsWestPacific-1.220.22-1.4437
Toronto RaptorsEastAtlantic-2.23-0.20-2.0335
Orlando MagicEastSE-2.33-0.11-2.2335
Houston RocketsWestSW-2.060.23-2.2935
New Orleans HornetsWestSW-2.310.35-2.6634
Sacramento KingsWestPacific-2.680.31-3.0033
Portland Trail BlazersWestNW-2.750.36-3.1133
Golden State WarriorsWestPacific-3.280.35-3.6331
Detroit PistonsEastCentral-4.59-0.09-4.5029
Cleveland CavaliersEastCentral-5.220.11-5.3427
Charlotte BobcatsEastSE-6.780.16-6.9323

(The original table:)

TeamConf.Div.EfficiencySoSEff. Mar.Pyth Wins
Miami HeatEastSE5.35-0.405.7556
San Antonio SpursWestSW4.72-0.014.7354
Oklahoma City ThunderWestNW4.14-0.014.1552
Memphis GrizzliesWestSW3.85-0.053.9051
New York KnicksEastAtlantic3.35-0.263.6051
Atlanta HawksEastSE2.78-0.303.0949
Denver NuggetsWestNW3.090.063.0349
Los Angeles LakersWestPacific2.81-0.052.8649
Los Angeles ClippersWestPacific2.250.032.2347
Indiana PacersEastCentral1.73-0.322.0547
Minnesota TimberwolvesWestNW1.670.101.5645
Boston CelticsEastAtlantic0.72-0.160.8743
Chicago BullsEastCentral0.20-0.140.3442
Philadelphia 76ersEastAtlantic0.20-0.110.3142
Dallas MavericksWestSW0.310.170.1441
Milwaukee BucksEastCentral-0.05-0.170.1241
Brooklyn NetsEastAtlantic-0.33-0.13-0.2040
Utah JazzWestNW-0.450.17-0.6239
Washington WizardsEastSE-1.13-0.09-1.0338
Phoenix SunsWestPacific-1.270.22-1.4937
Toronto RaptorsEastAtlantic-2.09-0.20-1.8836
Portland Trail BlazersWestNW-2.170.33-2.5034
Houston RocketsWestSW-2.340.25-2.5934
New Orleans HornetsWestSW-2.280.35-2.6434
Orlando MagicEastSE-2.81-0.10-2.7234
Sacramento KingsWestPacific-2.780.32-3.1033
Golden State WarriorsWestPacific-3.230.34-3.5731
Detroit PistonsEastCentral-4.45-0.10-4.3629
Cleveland CavaliersEastCentral-5.230.11-5.3427
Charlotte BobcatsEastSE-6.570.14-6.7124

  • The Harden trade dropped OKC from around +5.0 to +4.14, or about 3 wins.
  • Boston’s aging adjustments are killer
  • ASPM likes the Knicks–it has them with 9(!) players that are at or above average this year, led by Tyson Chandler at +2.40.
  • Wait-a-second… the Griz are equal to the Lakers?  Let’s see:
Team Player ASPM %Min
LAL Dwight Howard 4.48 53.4%
LAL Kobe Bryant 3.25 64.9%
LAL Pau Gasol 1.89 63.4%
LAL Steve Nash 0.42 57.8%
LAL Jodie Meeks -0.13 38.9%
LAL Metta World Peace -0.76 50.8%
LAL Jordan Hill -1.03 32.6%
LAL Antawn Jamison -1.39 49.0%
LAL Chris Douglas-Roberts -2.07 11.6%
LAL Steve Blake -2.23 40.3%
LAL Earl Clark -2.42 12.3%
Mem Mike Conley 1.98 77.4%
Mem Marc Gasol 1.90 78.3%
Mem Rudy Gay 1.26 79.0%
Mem Tony Allen 1.15 56.7%
Mem Zach Randolph 0.55 63.9%
Mem Jerryd Bayless -0.37 48.5%
Mem Darrell Arthur -0.75 31.6%
Mem Marreese Speights -1.24 39.6%
  • Looks like a lot of that particular projection has to do with giving heavy minutes to the relatively young Memphis core, and light minutes to the older LAL core. EDIT: I have fixed this issue with the minutes on Memphis; see updated table at the top.
  • Playoffs: Miami, NY, ATL, Indiana, Boston, Chicago, Philly, and Milwaukee in the East (with Brooklyn 1 game back), and San Antonio, OKC, Memphis, Denver, LAL, LAC, Minnesota, and Dallas in the West, (with Utah 2 games back).
  • Isn’t it unfair for one team to have 7 of the top 100 players in the NBA?  That’s what the Spurs have, and the worst of those 7 is 76th!

And here are the projected top 100 players in the NBA, by ASPM:

RankPlayerTeamAgeASPM
1LeBron JamesMIA287.85
2Chris PaulLAC276.52
3Dwyane WadeMIA315.15
4Derrick RoseCHI244.54
5Dwight HowardLAL274.48
6Kevin DurantOKC244.45
7Russell WestbrookOKC244.04
8Kevin LoveMIN243.37
9Kobe BryantLAL343.25
10Kawhi LeonardSAS212.89
11Blake GriffinLAC232.84
12Tony ParkerSAS302.76
13Deron WilliamsBKN282.72
14Josh SmithATL272.70
15Manu GinobiliSAS352.68
16Paul MillsapUTA272.69
17Kyrie IrvingCLE202.65
18Rajon RondoBOS262.52
19Joakim NoahCHI272.47
20Ryan AndersonNOR242.45
21Greg MonroeDET222.39
22Tyson ChandlerNYK302.40
23Andrew BynumPHI252.38
24Carmelo AnthonyNYK282.27
25Paul GeorgeIND222.28
26Kenneth FariedDEN232.22
27Al HorfordATL262.15
28James HardenHOU232.13
29Jeremy LinHOU242.11
30Kevin GarnettBOS362.09
31Marcin GortatPHO282.07
32Brandon JenningsMIL232.06
33LaMarcus AldridgePOR272.04
34Ty LawsonDEN252.03
35Dirk NowitzkiDAL342.02
36Mike ConleyMEM251.98
37Andre IguodalaDEN291.93
38Marc GasolMEM281.90
39Pau GasolLAL321.89
40Al JeffersonUTA281.88
41Carlos BoozerCHI311.88
42Serge IbakaOKC231.88
43Stephen CurryGSW241.77
44Kyle LowryTOR261.74
45Paul PierceBOS351.75
46Tim DuncanSAS361.72
47Nene HilarioWSH301.70
48DeAndre JordanLAC241.54
49Greg StiemsmaMIN271.52
50Ricky RubioMIN221.46
51Louis WilliamsATL261.44
52Brandan WrightFree Agent251.38
53Thaddeus YoungPHI241.39
54DeJuan BlairSAS231.33
55Gustavo AyonORL271.28
56Rudy GayMEM261.26
57Chris BoshMIA281.25
58Jeff TeagueATL241.25
59Danny GrangerIND291.22
60Ersan IlyasovaMIL251.20
61Danny GreenSAS251.15
62Jrue HolidayPHI221.16
63Tony AllenMEM311.15
64Monta EllisMIL271.09
65John WallWSH221.05
66Nicolas BatumPOR241.06
67Gerald WallaceBKN301.03
68Chris AndersenFree Agent341.01
69Ronnie BrewerNYK271.01
70Luol DengCHI270.95
71Eric GordonNOR240.93
72Ed DavisTOR230.89
73George HillIND260.89
74Jared DudleyPHO270.89
75Amir JohnsonTOR250.86
76Tiago SplitterSAS280.86
77Anderson VarejaoCLE300.83
78Danilo GallinariDEN240.84
79DeMarcus CousinsSAC220.78
80Elton BrandDAL330.76
81Amare StoudemireNYK300.75
82J.R. SmithNYK270.74
83Kevin MartinOKC290.75
84Andrew BogutGSW280.69
85JaVale McGeeDEN250.70
86David WestIND320.65
87Andris BiedrinsGSW260.61
88Tyreke EvansSAC230.61
89Wesley MatthewsPOR260.60
90Goran DragicPHO260.59
91Joe JohnsonBKN310.58
92Zach RandolphMEM310.55
93Chandler ParsonsHOU240.52
94Kosta KoufosDEN230.53
95Brandon RoyMIN280.50
96Roy HibbertIND260.50
97Trevor ArizaWSH270.49
98Brook LopezBKN240.42
99Marvin WilliamsUTA260.41
100Steve NashLAL380.42

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To-Do List

  1. Salary and contract value discussions and charts
  2. Multi-year APM/RAPM with aging incorporated
  3. Revise ASPM based on multi-year RAPM with aging
  4. ASPM within-year stability/cross validation
  5. Historical ASPM Tableau visualizations
  6. Create Excel VBA recursive web scraping tutorial
  7. Comparison of residual exponents for rankings
  8. Comparison of various "value metrics" ability to "explain" wins
  9. Publication of spreadsheets used
  10. Work on using Bayesian priors in Adjusted +/-
  11. Work on K-Means clustering for player categorization
  12. Learn ridge regression
  13. Temporally locally-weighted rankings
  14. WOWY as validation of replacement level
  15. Revise ASPM with latest RAPM data
  16. Conversion of ASPM to" wins"
  17. Lineup Bayesian APM
  18. Lineup RAPM
  19. Learn SQL