Disagreeing with Doug Moe

Doug Moe is synonymous with the Denver Nuggets. No one had a longer tenure as coach of this team. And no coach led this franchise to more victories. So one suspects Moe knows the Nuggets.

Recently the “Big Stiff” (as Moe is known) was asked who is the greatest player in the history in the Nuggets. And here is Moe’s answer (HT: Phil Maymin):

“I’d have to say Melo,” Moe said. “His talent is going to win more games than anybody else. With him, I think the Nuggets are going to have more 50-win seasons than with any other player. And that’s not a knock against any of those players.”

Carmelo Anthony is currently leading the NBA in scoring. But when I look past scoring, I find myself disagreeing with Moe. According to Table One, Melo entered the current season ranked 12^th on the Nuggets’ all-time Wins Produced rankings.

Rank	Name	Seasons	Games	Minutes	Wins	Wins/Season	WP48
1	Fat Lever	6	474	16,867	114.2	19.0	0.325
2	Dikembe Mutombo	5	391	14,411	83.7	16.7	0.279
3	Marcus Camby	6	372	11,776	78.8	13.1	0.321
4	T.R. Dunn	10	734	18,322	75.3	7.5	0.197
5	Alex English	11	837	29,893	60.6	5.5	0.097
6	Dan Issel	8	639	19,835	54.3	6.8	0.131
7	Andre Miller	4	269	9,451	36.1	9.0	0.183
8	David Thompson	5	333	10,800	35.4	7.1	0.157
9	Michael Adams	4	304	10,601	27.8	6.9	0.126
10	Danny Schayes	8	536	11,096	27.7	3.5	0.120
11	Antonio McDyess	6	361	12,271	26.4	4.4	0.103
12	Carmelo Anthony	6	445	16,107	26.3	4.4	0.078
13	Bryant Stith	8	458	13,248	25.3	3.2	0.092
14	Reggie Williams	6	419	12,556	25.2	4.2	0.096
15	Nene Hilario	7	370	10,575	24.1	3.4	0.110
16	Wayne Cooper	5	351	8,433	22.1	4.4	0.126
17	LaPhonso Ellis	6	343	11,352	21.7	3.6	0.092
18	Glen Gondrezick	4	288	4,926	21.2	5.3	0.207
19	Calvin Natt	5	189	5,385	20.4	4.1	0.181
20	James Posey	4	261	7,417	18.4	4.6	0.119
21	Raef LaFrentz	4	222	6,947	17.3	4.3	0.119
22	Kiki Vandeweghe	4	293	9,794	17.0	4.2	0.083
23	George McGinnis	2	121	3,976	16.6	8.3	0.200
24	Bobby Jones	2	100	2,662	14.8	7.4	0.266
25	Nick Van Exel	4	245	9,179	14.7	3.7	0.077
26	Ervin Johnson	1	82	2,599	14.6	14.6	0.269
27	Chris Andersen	4	225	3,658	14.3	3.6	0.188
28	J.R. Smith	3	218	5,139	12.9	4.3	0.120
29	Allen Iverson	3	135	5,671	12.7	4.2	0.107
30	Kenyon Martin	5	265	8,152	12.2	2.4	0.072
31	Jerome Lane	4	192	3,030	11.7	2.9	0.185
32	Chauncey Billups	3	135	4,512	11.2	3.7	0.120
33	Greg Buckner	2	143	3,277	11.0	5.5	0.160
34	Mark Jackson	1	52	2,001	10.1	10.1	0.243
35	Elston Turner	3	232	4,561	9.5	3.2	0.100
36	Ryan Bowen	5	298	4,359	9.5	1.9	0.105
37	Blair Rasmussen	6	429	9,158	9.2	1.5	0.048
38	Anthony Carter	3	150	3,783	9.1	3.0	0.115
39	Reggie Evans	2	92	1,733	8.7	4.4	0.241
40	Danny Fortson	2	130	3,228	8.3	4.1	0.123
41	Greg Anderson	2	123	3,452	8.3	4.1	0.115
42	Jalen Rose	2	161	3,932	8.2	4.1	0.100
43	Robert Pack	4	259	5,365	7.4	1.8	0.066
44	Earl Boykins	4	255	6,433	7.3	1.8	0.054
45	Donnell Harvey	2	106	2,292	7.1	3.6	0.149
46	Bison Dele	2	143	2,768	6.9	3.4	0.119
47	George Johnson	1	75	1,938	6.8	6.8	0.168
48	Tom Boswell	2	97	2,723	6.7	3.4	0.119
49	Eduardo Najera	4	243	5,348	6.6	1.6	0.059
50	Billy McKinney	3	198	4,656	5.7	1.9	0.059
51	Kim Hughes	3	159	2,453	5.6	1.9	0.109
52	George McCloud	3	223	5,955	4.9	1.6	0.040
53	Renaldo Balkman	1	53	780	4.9	4.9	0.299
54	Keon Clark	3	144	3,011	4.8	1.6	0.076
55	Bill Hanzlik	8	593	12,301	4.8	0.6	0.019
56	Darrell Walker	1	81	2,020	4.7	4.7	0.111
57	Juwan Howard	3	108	3,728	4.5	1.5	0.058
58	Jon Barry	1	57	1,101	4.0	4.0	0.174
59	Dean Garrett	1	82	2,632	3.8	3.8	0.070
60	Jim Price	1	49	1,090	3.4	3.4	0.148
61	John Roche	3	147	3,398	3.3	1.1	0.047
62	Bobby Jackson	1	68	2,042	3.2	3.2	0.074
63	Cory Alexander	3	88	1,904	3.1	1.0	0.078
64	Robert Smith	2	127	1,857	2.9	1.4	0.074
65	Avery Johnson	2	72	1,417	2.8	1.4	0.096
66	Linas Kleiza	4	301	5,724	2.7	0.7	0.023
67	Anthony Roberts	4	187	3,517	2.7	0.7	0.037
68	Steve Blake	1	49	1,643	2.7	2.7	0.078
69	Anthony Goldwire	3	129	3,025	2.7	0.9	0.042
70	Winston Garland	1	78	2,209	2.6	2.6	0.056
71	Dale Ellis	3	244	7,562	2.5	0.8	0.016
72	Bryon Russell	2	71	1,029	2.5	1.2	0.116
73	Marcus Liberty	4	232	4,294	2.5	0.6	0.028
74	Anthony Cook	2	80	1,236	2.3	1.1	0.089
75	Richard Anderson	1	78	1,380	2.2	2.2	0.075
76	Todd Lichti	4	224	4,114	2.1	0.5	0.024
77	Francisco Elson	3	201	3,392	2.0	0.7	0.029
78	Voshon Lenard	5	239	6,521	1.9	0.4	0.014
79	Pete Williams	2	58	583	1.9	0.9	0.156
80	Wesley Person	1	25	461	1.9	1.9	0.194
81	Calbert Cheaney	2	77	1,784	1.7	0.9	0.046
82	Dahntay Jones	1	79	1,426	1.7	1.7	0.057
83	Dave Greenwood	1	29	491	1.6	1.6	0.159
84	DerMarr Johnson	3	168	2,575	1.6	0.5	0.030
85	Earl Watson	1	46	980	1.5	1.5	0.076
86	Kenny Higgs	2	148	3,385	1.5	0.8	0.022
87	Orlando Woolridge	1	53	1,823	1.4	1.4	0.036
88	Mack Calvin	1	77	988	1.3	1.3	0.064
89	Darnell Hillman	1	33	746	1.3	1.3	0.085
90	Kenny Battle	1	40	682	1.3	1.3	0.092
91	Rodney White	3	186	3,079	1.1	0.4	0.018
92	Vincent Yarbrough	1	59	1,381	1.1	1.1	0.039
93	Tom Hammonds	5	331	5,207	1.1	0.2	0.010
94	Joe Kopicki	1	42	308	1.0	1.0	0.163
95	Shawnelle Scott	1	21	252	1.0	1.0	0.190
96	Zendon Hamilton	1	54	848	1.0	1.0	0.056
97	Shammond Williams	1	27	712	1.0	1.0	0.066
98	Chris Gatling	1	40	770	1.0	1.0	0.060
99	Brooks Thompson	1	65	1,047	1.0	1.0	0.044
100	Rodney Rogers	2	159	3,548	0.9	0.5	0.012
101	Brian Taylor	1	39	1,222	0.8	0.8	0.033
102	Harold Ellis	1	27	344	0.8	0.8	0.109
103	Ruben Patterson	1	26	736	0.6	0.6	0.041
104	Popeye Jones	1	40	330	0.6	0.6	0.089
105	Jamal Sampson	1	22	124	0.6	0.6	0.228
106	Terry Mills	1	17	279	0.6	0.6	0.099
107	Kevin Willis	1	43	1,059	0.6	0.6	0.026
108	Bo Ellis	3	168	1,984	0.5	0.2	0.013
109	Eric Murdock	1	12	114	0.5	0.5	0.190
110	Carlos Arroyo	1	20	275	0.4	0.4	0.074
111	Isaiah Rider	1	10	173	0.4	0.4	0.114
112	Corey Gaines	1	10	226	0.4	0.4	0.085
113	Tim Hardaway	1	14	325	0.4	0.4	0.054
114	Cedrick Hordges	2	145	2,971	0.4	0.2	0.006
115	Rob Williams	2	153	3,367	0.3	0.2	0.005
116	Gary Garland	1	78	1,106	0.3	0.3	0.014
117	Scott Williams	1	41	737	0.2	0.2	0.016
118	Joe Barry Carroll	1	30	719	0.2	0.2	0.012
119	Jacky Dorsey	1	7	37	0.2	0.2	0.225
120	Darwin Cook	1	30	386	0.1	0.1	0.018
121	Tim Kempton	1	71	1,061	0.1	0.1	0.006
122	George Zidek	2	22	130	0.1	0.1	0.051
123	Michael Brooks	1	16	133	0.1	0.1	0.048
124	Adam Harrington	1	6	74	0.1	0.1	0.080
125	Steve Scheffler	1	7	46	0.1	0.1	0.098
126	Matt Fish	1	16	117	0.1	0.1	0.037
127	Tyson Wheeler	1	1	3	0.1	0.1	1.359
128	Junior Harrington	1	82	2,003	0.1	0.1	0.002
129	Aaron Williams	1	1	10	0.1	0.1	0.260
130	Luis Flores	1	1	4	0.1	0.1	0.603
131	Kelly McCarty	1	2	4	0.0	0.0	0.593
132	Julius Hodge	2	18	71	0.0	0.0	0.032
133	Roy Rogers	1	40	355	0.0	0.0	0.006
134	Taurean Green	1	9	30	0.0	0.0	0.057
135	Anthony Mason	1	3	21	0.0	0.0	0.050
136	Eldridge Recasner	1	3	13	0.0	0.0	0.076
137	John Crotty	1	12	180	0.0	0.0	0.005
138	Rich Kelley	1	38	565	0.0	0.0	0.001
139	Jimmy King	1	2	22	0.0	0.0	0.018
140	Joe Smith	1	11	148	0.0	0.0	0.001
141	Andre Moore	1	7	34	0.0	0.0	0.000
142	Jeff Trepagnier	2	19	193	0.0	0.0	-0.001
143	Jason Hart	1	11	36	0.0	0.0	-0.027
144	Mike Higgins	1	5	32	0.0	0.0	-0.039
145	Jerome Allen	1	25	251	0.0	0.0	-0.006
146	Norm Cook	1	2	10	0.0	0.0	-0.151
147	Chris Herren	1	45	597	0.0	0.0	-0.003
148	Charles Smith	1	1	2	-0.1	-0.1	-1.282
149	Mark Strickland	1	46	517	-0.1	-0.1	-0.006
150	Kenny Smith	1	33	654	-0.1	-0.1	-0.005
151	Wayne Englestad	1	11	50	-0.1	-0.1	-0.078
152	Monty Williams	1	1	6	-0.1	-0.1	-0.680
153	Vincent Askew	1	1	9	-0.1	-0.1	-0.566
154	Dan McClintock	1	6	58	-0.1	-0.1	-0.089
155	Randy Woods	1	8	72	-0.1	-0.1	-0.073
156	Mark Bryant	1	3	14	-0.1	-0.1	-0.382
157	Tracy Murray	1	13	135	-0.1	-0.1	-0.041
158	Cliff Levingston	1	57	469	-0.1	-0.1	-0.012
159	Reggie Slater	2	29	262	-0.1	-0.1	-0.022
160	Cheik Samb	1	6	24	-0.1	-0.1	-0.244
161	Darvin Ham	1	35	313	-0.1	-0.1	-0.019
162	Jeff McInnis	1	13	117	-0.1	-0.1	-0.052
163	Jelani McCoy	1	6	33	-0.1	-0.1	-0.189
164	Jawann Oldham	1	4	21	-0.1	-0.1	-0.306
165	Mike Wilks	1	8	122	-0.1	-0.1	-0.058
166	Devin Brown	1	3	71	-0.2	-0.2	-0.102
167	Johnny Taylor	2	37	729	-0.2	-0.1	-0.010
168	Walter Davis	4	235	5,277	-0.2	0.0	-0.002
169	Dwight Anderson	1	5	33	-0.2	-0.2	-0.254
170	Roy Marble	1	5	32	-0.2	-0.2	-0.262
171	Craig Neal	1	10	125	-0.2	-0.2	-0.068
172	Ricky Pierce	1	33	600	-0.2	-0.2	-0.014
173	Adonis Jordan	1	6	79	-0.2	-0.2	-0.111
174	Mark Pope	2	13	47	-0.2	-0.1	-0.192
175	Melvin Booker	1	5	21	-0.2	-0.2	-0.435
176	Garth Joseph	1	2	8	-0.2	-0.2	-1.188
177	Keith Edmonson	1	15	101	-0.2	-0.2	-0.096
178	Howard Eisley	1	19	281	-0.2	-0.2	-0.038
179	Brad Wright	1	2	7	-0.2	-0.2	-1.523
180	Willie White	2	82	577	-0.2	-0.1	-0.019
181	Elmore Spencer	1	6	21	-0.2	-0.2	-0.549
182	John Kuester	1	33	212	-0.2	-0.2	-0.054
183	Phil Hicks	1	20	128	-0.2	-0.2	-0.092
184	Scott Hastings	2	116	1,091	-0.2	-0.1	-0.011
185	Don MacLean	1	56	1,107	-0.3	-0.3	-0.011
186	David Burns	1	6	53	-0.3	-0.3	-0.250
187	Terry Davis	1	19	228	-0.3	-0.3	-0.060
188	Arvid Kramer	1	8	45	-0.3	-0.3	-0.308
189	Rastko Cvetkovic	1	14	48	-0.3	-0.3	-0.297
190	Ron Mercer	1	37	1,408	-0.3	-0.3	-0.012
191	Steven Hunter	1	19	120	-0.3	-0.3	-0.139
192	Kiwane Garris	1	28	225	-0.4	-0.4	-0.077
193	Carl Herrera	1	24	265	-0.4	-0.4	-0.072
194	Chucky Atkins	2	38	467	-0.4	-0.2	-0.041
195	Doug Overton	1	55	607	-0.4	-0.4	-0.032
196	Tony Battie	1	65	1,506	-0.4	-0.4	-0.013
197	Chris Whitney	1	29	762	-0.4	-0.4	-0.027
198	Darnell Mee	2	40	293	-0.4	-0.2	-0.071
199	Elmer Bennett	1	5	59	-0.4	-0.4	-0.359
200	Robert Werdann	1	28	149	-0.5	-0.5	-0.147
201	Kenny Dennard	1	43	413	-0.5	-0.5	-0.054
202	Michael Doleac	1	26	344	-0.5	-0.5	-0.064
203	Mark Randall	2	36	194	-0.5	-0.2	-0.118
204	Ralph Simpson	1	32	584	-0.5	-0.5	-0.040
205	LaSalle Thompson	1	17	105	-0.5	-0.5	-0.241
206	Greg Grant	2	24	260	-0.6	-0.3	-0.102
207	Sonny Weems	1	12	55	-0.6	-0.6	-0.513
208	Carl Nicks	1	27	493	-0.6	-0.6	-0.058
209	Geoff Crompton	1	20	88	-0.6	-0.6	-0.342
210	Johan Petro	1	27	218	-0.6	-0.6	-0.141
211	Loren Meyer	1	14	70	-0.7	-0.7	-0.494
212	Von Wafer	1	21	90	-0.7	-0.7	-0.397
213	Tim Legler	1	10	148	-0.7	-0.7	-0.242
214	Predrag Savovic	1	27	256	-0.7	-0.7	-0.140
215	Eddie Hughes	2	86	1,116	-0.8	-0.4	-0.034
216	Tariq Abdul-Wahad	3	64	1,210	-0.9	-0.3	-0.034
217	Jim Farmer	2	29	472	-0.9	-0.4	-0.089
218	Priest Lauderdale	1	39	345	-0.9	-0.9	-0.125
219	Sarunas Marciulionis	1	17	255	-0.9	-0.9	-0.171
220	Ronnie Valentine	1	24	123	-0.9	-0.9	-0.360
221	Otis Smith	2	43	359	-0.9	-0.5	-0.125
222	Howard Carter	1	55	688	-1.0	-1.0	-0.069
223	Eric Washington	2	104	2,300	-1.0	-0.5	-0.021
224	Dave Robisch	4	165	2,853	-1.1	-0.3	-0.018
225	Mark Blount	1	54	885	-1.1	-1.1	-0.061
226	Bob Wilkerson	3	236	7,586	-1.1	-0.4	-0.007
227	Mengke Bateer	1	27	408	-1.1	-1.1	-0.135
228	James Ray	3	103	843	-1.2	-0.4	-0.069
229	Mark Alarie	1	64	1,110	-1.2	-1.2	-0.053
230	Maurice Martin	2	69	422	-1.7	-0.8	-0.192
231	Gary Plummer	1	60	737	-1.7	-1.7	-0.111
232	Kenny Satterfield	2	58	980	-1.8	-0.9	-0.087
233	Eric Williams	2	42	925	-2.0	-1.0	-0.102
234	Jay Vincent	2	78	1,850	-2.3	-1.2	-0.061
235	Kevin Brooks	3	126	1,031	-2.5	-0.8	-0.117
236	Johnny Newman	1	74	2,176	-2.6	-2.6	-0.057
237	Yakhouba Diawara	2	118	1,721	-3.0	-1.5	-0.084
238	Mike Evans	6	419	7,431	-3.2	-0.5	-0.021
239	Tom LaGarde	1	77	868	-3.8	-3.8	-0.208
240	Mahmoud Abdul-Rauf	6	439	12,481	-4.5	-0.8	-0.017
241	Joe Wolf	3	198	3,374	-4.7	-1.6	-0.067
242	Charlie Scott	2	148	4,477	-5.2	-2.6	-0.055
243	Nikoloz Tskitishvili	3	143	1,785	-6.1	-2.0	-0.164
244	Mark Macon	3	131	3,571	-6.2	-2.1	-0.083

As noted yesterday, Anthony is on pace to produce about nine wins this season. So when the 2009-10 season ends, Melo might catch David Thompson. But Anthony is going to have produce at his curren rate until 2018-19 (when he will be 35 years of age) to catch the number one ranked player in history. Yes, it’s possible that can happen. But given what happens to players once they pass the age of 30, it seems unlikely.

If Anthony does maintain his current pace for another decade — again, this is unlikely — he will eventually pass Fat Lever on the list. Lever– who played for Denver and Doug Moe – produced 114.2 wins in just six seasons in the Mile High City. In addition to leading Denver in Wins Produced, Lever’s 0.325 WP48 [Wins Produced per 48 minutes] also ranks number one in Denver history (Anthony has never posted a WP48 close to this mark) . Lever, though, never scored 20 points per game. So it’s understandable that people might think Carmelo Anthony is doing more today.

I should add… Lever is also not the only Moe player to surpass Anthony. T.R. Dunn, Alex English and Dan Issel produced more wins than Anthony in a Denver uniform. And Moe coached all these players. But apparently he thinks all fall short of Melo.

Again, Anthony scores. And as Moe notes, he plays for some of the most successful teams in Denver history. But I think Denver’s success is more about Carmelo’s teammates (i.e. Marcus Camby, Andre Miller, Chauncey Billups, Nene Hilario, and Chris Andersen) than it’s about Denver’s lone current All-Star. At least, these players — despite not being great scorers — appear to produce more wins than Anthony.

Now I am not saying Anthony isn’t a good player. But I do not think he is as productive as other top players today (i.e. LeBron James, Chris Paul, etc…). And he is not the most productive player in the history of the Nuggets.

Let me close by noting that this is the fourth time I have reported every player in the history of a team. Here are the previous three teams I have examined.

Ranking Every Player in the History of the Utah Jazz

Ranking Every Player for the Boston Celtics since 1977

Ranking Every Player for the LA Lakers since 1977

Perhaps this next summer – after the 2009-10 season ends – I will look at a few more teams.

- DJ

The WoW Journal Comments Policy

Our research on the NBA was summarized HERE.

The Technical Notes at wagesofwins.com provides substantially more information on the published research behind Wins Produced and Win Score

Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:

Simple Models of Player Performance

Wins Produced vs. Win Score

What Wins Produced Says and What It Does Not Say

Introducing PAWSmin — and a Defense of Box Score Statistics

Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.

31 Responses to "Disagreeing with Doug Moe"

Chicago Tim says:

January 22, 2010 at 10:15 pm

Is it fair to compare players who play different positions? A long time ago, you noted that it is harder for guards to produce wins because there are more people capable of playing that position. A good center, on the other hand, can produce many wins because there are so few people capable of playing that position.

http://dberri.wordpress.com/2006/12/04/

Of course, all of that makes 6′ 3″ Fat Lever even more remarkable. I found this old SI article written while Fat Lever was playing, and theorizing about why he hadn’t received more recognition. Apparently he deliberately kept a low profile, and had a somewhat boringly efficient game. The anti-Allen Iverson, if you will.

http://sportsillustrated.cnn.com/vault/article/magazine/MAG1066964/index.htm
Don Taylor says:

January 23, 2010 at 11:26 am

Carmello Anthony, David Lee and Dwayne Wade are my favorite active players (I’m into footwork) even with that being the case, I immediately thought, “Lafayette Lever” as soon as I heard the question. Shame on Doug Moe. Fat played his hart out for him.
todd2 says:

January 23, 2010 at 12:32 pm

Knew Fat could play. Alex English would have been at the top of my list, or Dan Issel. Their WP48′s are surprisingly low. Bobby Jones’ numbers are surprisingly high—he had a rep for being a stopper. Thanks, Prof! Some great players and teams came and went in the Western Conference during Earvin Johnson’s time with the Lakers; nine times in the finals!
mrparker says:

January 23, 2010 at 8:19 pm

Prof,
Can you comment on what made Fat Lever so productive. I’m looking at his bref page and I don’t see anything indicitive of a productive player besides an absurd steals percentage.

Thanks
Robert says:

January 23, 2010 at 8:21 pm

id assume it was the fact he was technically a PG but put up very gaudy rebounding totals.
mrparker says:

January 23, 2010 at 9:50 pm

oh yeah,
I didn’t pay attention to those numbers. I was just looking at 51% true shooting percentage and got lost.
benamery21 says:

January 24, 2010 at 12:06 am

Doug Moe’s response highlights a WOW premise: that even very experienced basketball tacticians have a hard time experientially evaluating the relative contributions of players to wins given the distractions of high scoring and entertaining balletics by some players. This goes a long way to explain why few coaches improve their teams winning percentage appreciably over the average coach–they are subject to the same errors of perception as most of us.

Case in point: Phoenix tonight (home win against GSW)
Alvin Gentry’s massive mistake (from a WOW perspective) with regard to his change in starting line-up (dropping Jason Richardson in favor of Barbosa and Channing Frye in favor of Robin Lopez) was partially ameliorated tonight by Barbosa’s return to the injured list (for about 4 weeks due to wrist surgery). Jason Richardson returned to his starting slot and Barbosa (whose productivity has been abysmal of late due to significantly below career level shooting stats) will no longer be hurting the Suns by playing at less than 100%.

Lopez, so far, has been productive in a starting role, but I contend that he reduces “all-star” Amare Stoudemire’s productivity by more than Frye (whose Win Score stats have been better than Lopez over the course of the full season). Lopez, Frye, and Louis Amundson (who has also lost minutes to Lopez’ new role) all outproduced Stoudemire tonight in fewer minutes.

Gentry also persists in giving (developmental) minutes to (highly unproductive) Earl Clark in close games. This has resulted in several unnecessary losses. Clark appeared tonight for 11 minutes, apparently in an attempt to keep the contest with Golden State sporting by spotting them Clark’s negative Win score.
benamery21 says:

January 24, 2010 at 12:24 am

I would have said Deke Mutumbo if asked the same question. Ex-Sun Devil Lever definitely ranks as one of the most underappreciated players ever. I wonder if Moe’s response might have been more political than forthright, given that he’s still associated with the franchise and public opinion has Melo first on the current team?
benamery21 says:

January 24, 2010 at 1:45 am

dberri: Where would Fat’s ’84-’85 Nuggets rank on your “all-time best starting squad” list (they didn’t make the top 25 you published in Nov ’09)? The ’84-’85 Lakers (who they lost to in the Western conference finals) ranked 4th on your all time list. I calc’d a 0.207 WP48 for Denver (Lever, Dunn, English, Natt, Cooper) which would put them about 15th (but is based on the PAWSmin conversion and doesn’t account for their torrid #1 in the NBA pace that season). They took a 1-1 split back home from LA to game 3 (even though Fat was injured and didn’t play in the series), but wouldn’t win again with 3 injured starters (Fat out whole series, English out from gm 3 on, and Natt playing with a sprained ankle).
Jimbo says:

January 24, 2010 at 9:16 am

Have you seen Ariza this year? His PER has fallen off dramatically and I’m sure his WP has also which really goes to show that when a player moves from a team where he is a secondary option and doesn’t have to hoist a ton of shots to a team where he does have to, his ‘overall’ stats will diminish. This observation goes to show that your model undervalues scorers b/c you think efficient scorers who don’t score a lot would score more at the same efficiency level if given more shots, which just isn’t true as Ariza’s experience demonstrates.

Something to think about…
dberri says:

January 24, 2010 at 10:06 am

Jimbo,
As I noted a few weeks ago, a number of other players on the Rockets have seen their shot attempts increase and their productivity was not changed. I guess that is something to think about also…
khandor says:

January 24, 2010 at 12:24 pm

With all due respect …

What is quite hilarious is that some here actually think/believe that a statistical based formula for determining the relative value of a real life player’s actual level of “production” is a more accurate indicator of “the best” player in Denver Nuggets history, compared with the judgment of a relatively successful former NBA head coach/”tactician” by the name of Doug Moe.

IMO, Carmelo Anthony is so much better at what it is he just happens to do as a pro basketball player [i.e. be a scorer], compared to what it was that Fat Lever just happened to do as a pro basketball player [i.e. be an efficient multi-purpose guard], that it’s not even funny.
benamery21 says:

January 24, 2010 at 7:47 pm

khandor:
Almost as funny as the fact that you and I are both still here talking when you apparently think he’s an idiot, and he apparently thinks I’m one.
Chicago Tim says:

January 24, 2010 at 10:52 pm

Khandor, prefacing your remark with “With all due respect” does not automatically make your remark respectful.
khandor says:

January 25, 2010 at 12:15 pm

Chicago Tim,

Part One

1. Thanks for posting that link in your initial comment.

When I go back and read what was actually said there it becomes even more clear that David has some difficulty when it comes to distinguishing between:

i. The “Most Productive Player” … according to a specific mathematical-based metric; and,

ii. The “best player” [or, for that matter, the League MVP] within a specific environment and, therefore, a set of players.

2. It is fundamentally flawed to try to assert that:

“It is easier for a front-court player to produce wins than it is for a back-court player,”

based on the premise that,

“There is a short supply of tall people.”

———————————————————-
re: What does all this mean? One step in calculating Wins Produced is to compare a player’s performance relative to the average at his position. Because there is a “Short Supply of Tall People” (and I still like that phrase), frontcourt players consist of productive players – like Shaq, Robinson, and Rodman – and players that are quite unproductive (insert name of big stiff here). Hence a quality frontcourt player can produce at a level far above the average at his position. In contrast, guards tend to be shorter, so the supply of these players is much larger. Consequently Jordan was being compared to a population of players with relatively more talented athletes, and hence it was harder for Jordan to perform far above the average at his position.
———————————————————-

In fact, this premise is dependent on the poor judgment of many [some?] in the basketball community who insist on using “taller” people at the front-court positions, who happen not to be very good basketball players, in the first place, not the fact that there is a “Short Supply of Tall People”.

Although others might believe the answers to be straight-forward, when the following list of items is discussed, regarding the goings-on in the NBA:

- “MPP”
- “the best player”
- what a player’s specific “position” is
- what a player’s specific “height” is
- what a player’s specific “skill-set” is
- what constitutes a “short” person
- what constitutes a “tall” person
- what a player’s relative level of production is compared to the average player at a specific position
- how many back-court players there are in the NBA
- how many front-court players there are in the NBA
- how “qualitative” distinctions are made best amongst a specific set group of players
- the reason certain players are used most frequently at certain positions in the NBA

I do not share this same perspective.

Without clearly distinguishing between these different concepts and ideas, it is all too easy to think that one is proving something when, in fact, one is actually proving nothing of the sort.

Part Two

In what I’ve written here, thus far, if there is anything which you believe is disrespectful, then, please point it out. It is not my intention to be disrespectful, in any way.

Thanks, in advance.
feltbot says:

January 25, 2010 at 6:04 pm

The fact that this stat has even a shred of currency is mind-boggling to me. A player like Carmelo Anthony is double and triple teamed regularly, something that lowers his own offensive efficiency, but raises that of his teammates and of his team immeasurably.
brgulker says:

January 26, 2010 at 2:21 pm

What is quite hilarious is that some here actually think/believe that a statistical based formula for determining the relative value of a real life player’s actual level of “production” is a more accurate indicator of “the best” player in Denver Nuggets history, compared with the judgment of a relatively successful former NBA head coach/”tactician” by the name of Doug Moe.

Here’s the reason that people “believe” in Dr. Berri’s work.

1) We’ve read his book, not just his blog.

2) Teams that employ “productive” players are consistently among the top teams in the NBA.

3) I’ve yet to read a refutation of his metric that holds any substantive, empirical weight.

4) Dr. Berri readily admits the limitations of his metrics.

5) It bears repeating, teams that employ “productive” players are consistently among the top teams in the NBA.

And to me, that’s the clincher. At the end of the day, winning is what matters. Dr. Berri’s work describes player performance with respect to win consistently and accurately, more so than anyone else who does this type of work.
brgulker says:

January 26, 2010 at 2:22 pm

The fact that this stat has even a shred of currency is mind-boggling to me. A player like Carmelo Anthony is double and triple teamed regularly, something that lowers his own offensive efficiency, but raises that of his teammates and of his team immeasurably.

Um, Dr. Berri has addressed this repeatedly, actually. Time and time again, he shows how players do not get any better when paired with a “superstar.”
benamery21 says:

January 27, 2010 at 1:33 am

Yep. Spot on.

1) We’ve read his book, not just his blog.

2) Teams that employ “productive” players are consistently among the top teams in the NBA.
khandor says:

January 27, 2010 at 6:27 am

re: “2) Teams that employ “productive” players are consistently among the top teams in the NBA.”

Classic “chicken and egg”, right there.

re: “1) We’ve read his book, not just his blog.”

In a sense …

If the blog … which is based on the book … has specific difficulties and, therefore, doesn’t “work/function” properly, then, unfortunately, neither can the book, when placed under the appropriate type of scrutiny from a “basketball” perspective.
khandor says:

January 27, 2010 at 6:38 am

re: “Time and time again, he shows how players do not get any better when paired with a ‘superstar.’”

This supposed fact, according to the work of Dr. Berri, might not actually be fact at all, depending on how one actually decides to “measure” what constitutes [i.] “better” and [ii.] a “superstar”, in the first place.

Inter-dependency is a significant component of basketball and playing with the “right fit” teammate[s], and/or with the right fit “environment”, is a critical factor in determining someone’s actual level of success.
khandor says:

January 27, 2010 at 7:01 am

brgulker,

re: “3) I’ve yet to read a refutation of his metric that holds any substantive, empirical weight.”

What’s the reason you are searching for “substantive, empirical weight”, in the first place? … other than the sort of indisputable evidence which is available through a set of specific data like this.
re: “At the end of the day, winning is what matters.”

Did “substantive empirical weight” make the correct assessment, in advance, that:

- the Miami Heat were going to beat the Dallas Mavericks for the 2005-2006 NBA Championship?

- the San Antonio Spurs were going to prevail as the 2006-2007 champions over the Cleveland Cavaliers?

- the Boston Celtics were going to capture the 2007-2008 title over the LA Lakers?

- the LA Lakers were going to be crowned NBA champions last season, at the expense of the Orlando Magic?

While a mathematical based formula can certainly do a terrific job of:

* Assigning specific values to different statistical categories associated with the game of basketball and, therefore, account retroactively for the successes and failures of specific teams/players in a given set of data; or,

* Predicting, in a general sense, how many “overall wins” a specific team is “likely” to achieve in an upcoming season, based on past production values for those same participants and mathematical probability;

IMO, it would be a mistake in basketball judgment to believe that such a metric/formula and, therefore, an associated “cookie-cutter” way of thinking about the game, itself, based on said formula, is capable of forming wholly accurate assessments of which players, teams and coaches, etc., are actually superior to their peers, when it comes to being able to win specific games and championships against specific opponents, in the real life NBA … which is REALLY what the bottom line is all about when it comes to properly understanding how this game actually works.

Basketball is not the same game as baseball … e.g. is there any doubt, whatsoever, as to who the New York Yankees started at 3rd base this past season, while they captured their 27th World Series title? As opposed to, let’s say, what position Tim Duncan actually plays for the San Antonio Spurs, both, this year and for the 2006-2007 NBA World Champions [not what position 82games.com has him listed at] … and, understanding it properly involves a great deal more than simply knowing the mathematics/economics of the game.

When Dr. Berri works within the “limits” of what his metrics can actually describe with accuracy, then, I’m a big proponent of his work.
brgulker says:

January 27, 2010 at 8:04 am

IMO, it would be a mistake in basketball judgment to believe that such a metric/formula and, therefore, an associated “cookie-cutter” way of thinking about the game, itself, based on said formula, is capable of forming wholly accurate assessments of which players, teams and coaches, etc., are actually superior to their peers, when it comes to being able to win specific games and championships against specific opponents, in the real life NBA … which is REALLY what the bottom line is all about when it comes to properly understanding how this game actually works.

Either read the book or stop making these types of claims. If you do read the book, not just the blog, you’ll see how far from a “cookie cutter” this model actually is. You will also see how readily the authors admit the limitations of their own work.

But if you don’t read the book, your flawed assumptions will remain. No commenter here has the time to restate every single story and argument that appears in the book — that’s what the book is there for in the first place?

One last comment on your above paragraph:

wholly accurate assessments of which players, teams and coaches, etc., are actually superior to their peers, when it comes to being able to win specific games and championships against specific opponents,

No one, including Dr. Berri, claims any of that. You’re the only one who is.

What’s the reason you are searching for “substantive, empirical weight”, in the first place? …

His book makes an empirical argument. It is natural to seek out counterarguments that are also empirical.

re: “2) Teams that employ “productive” players are consistently among the top teams in the NBA.”

Classic “chicken and egg”, right there.

No it’s not. When teams collect a bunch of productive players, they win games. You can’t get wins without players who produce them. Productive players cause wins.

I can’t think of any other ways to state it …

Did “substantive empirical weight” make the correct assessment, in advance, that:

- the Miami Heat were going to beat the Dallas Mavericks for the 2005-2006 NBA Championship?

- the San Antonio Spurs were going to prevail as the 2006-2007 champions over the Cleveland Cavaliers?

- the Boston Celtics were going to capture the 2007-2008 title over the LA Lakers?

- the LA Lakers were going to be crowned NBA champions last season, at the expense of the Orlando Magic?

No person or model can predict who is going to win four 7-game series in a row. No one on this blog claims to be able to do that perfectly.

IIRC, Dr. Berri predicted that LA would be the best team during 08-09, and it turned out that the best team actually did win the championship.

But predictions are not what WoW is about. WoW is about evaluation. Evaluation is certainly used when people are making predictions, but that’s not the point.

Again, if you read the book, this becomes patently obvious.
khandor says:

January 27, 2010 at 10:50 am

brgulker,

1. re: “Productive players cause wins.”

Although I could well be wrong about this … I have yet to see any type of “substantive, empirical weight” which outlines a causal relationship that exists between “productive” players and “wins”.

2. I’m not saying that Dr. Berri is making an erroneous claim that his methodology is capable of “predicting” the outcome of specific future games with any degree of accuracy, or is capable of making wholly accurate assessments of specific players or teams.

What I’m saying is that:

- when it comes to “evaluating” properly which is actually “better”, using a mathematical-based formula to distinguish between the “quality” of a Rembrandt and a Monet, or a Picasso and a Mozart, or a Socrates and a Frank Lloyd Wright, or a Descartes and a MLK, or a Jordan vs Russell, or a Lakers vs Cavs, etc., is far from being the correct way to go

- when you start shifting ground from saying that,

“Player X was the Most Productive Player during the 2009-2010 NBA season, based on this specific metric,”

to something like,

“Player X should, therefore, have been awarded the MVP Award during the 2009-2010 NBA season, as the best player,”

you are taking a leap of faith and, in the process, making a fundamental mistake in basketball judgment.

Here’s a question for you and others to consider:

Is it possible to lose a game in which your team is outperformed in each of the following categories?

i.e. FG%, 3FG%, FT%, Rebound Differential, Assists, Turnovers, Steals, Blocked Shots, Personal Fouls, Charges Drawn, either FTAs or FGAs, and either FTMs or FGMs

If not, then, how come?
If so, then, how come?

Inter-dependency in the game of basketball is one of the fundamental things which define its character and provide its overall artistic beauty.
brgulker says:

January 27, 2010 at 11:17 am

1. re: “Productive players cause wins.”

Although I could well be wrong about this … I have yet to see any type of “substantive, empirical weight” which outlines a causal relationship that exists between “productive” players and “wins”.

Productive players do the following: score more points than their opponents (by scoring efficiently), get more rebounds than their opponents, get more steals than their opponents, get more assists than their opponents, commit fewer fouls than their opponents, turn the ball over less than their opponents, et al.

Productive players — players who do those things — create wins for their team.

Regardless of one’s position on any particular statistical model, I don’t see how this can be disputed. IMO, you might as well dispute that the team who scores the most points wins.

Is it possible to lose a game in which your team is outperformed in each of the following categories?

i.e. FG%, 3FG%, FT%, Rebound Differential, Assists, Turnovers, Steals, Blocked Shots, Personal Fouls, Charges Drawn, either FTAs or FGAs, and either FTMs or FGMs

You’re fundamentally missing the point. You’re using a micro-level case study to try to argue against macro-level analysis. It simply doesn’t work.

The object of WoW is not a random, specific game but rather large sets of data across large amounts of time. In any given game, just about anything can happen. But over the course of 82 games, 164 games, and so on randomness is mitigated.
khandor says:

January 27, 2010 at 1:00 pm

burgulker,

1. When Allen Iverson’s Philadelphia 76ers went to the NBA Finals, was he a particularly efficienct basketball player?

2. re: “Productive players do the following: score more points than their opponents (by scoring efficiently), get more rebounds than their opponents, get more steals than their opponents, get more assists than their opponents, commit fewer fouls than their opponents, turn the ball over less than their opponents, et al.

Productive players — players who do those things — create wins for their team.

Regardless of one’s position on any particular statistical model, I don’t see how this can be disputed. IMO, you might as well dispute that the team who scores the most points wins.”

i. What about the players who do some of those things but not others? Are they, therefore, properly classified as being “unproductive”?

ii. Undoubtably, “productive players” who do some or all of those things can “be a part” of a team that wins a great many games in an NBA season.

Unfortunately, it is a different thing entirely to then try to assert that it’s the productive players, as designated by a specific statistical-based metric, who are the cause of their team winning X number of games in a NBA season.

3. I think that you and, perhaps, many others are essentially missing “the point”.

Basketball, properly understood, is an idiosyncratic micro-level game, best understood and evaluated in anecdotal terms, not a generic macro-level one, best understood and evaluated strictly by an examination of “substantive, empirically weighted” evidence … even though this specific type of “data analysis” can be useful to promote a further and more comprehensive understanding of how the game, itself, actually works.

IMO, doing this specific type of academic research, and then implementing its associated strategies for success will consistently fail to produce a real life NBA title-winning team.

Real life … and, the game of basketball … unfortunately, does not happen to work this way.
benamery21 says:

January 27, 2010 at 11:23 pm

khandor:
Can you predict accurately which side of a die will show up on a given throw? No. But you know what the probability of a given side showing up is. If you bet me you could throw ’7′ s with a pair of dice, we agree on what the odds are, but what actually happens on a given night is subject to chance. dberri’s work doesn’t create that level of precision with respect to the odds but it significantly improves the estimation of what those odds are. Randomness is a factor just as when throwing dice: Did you predict the Clippers beating the Celtics even once this season? But, the odds are that over a large sample size, the model will be fairly accurate in predicting a team’s season wins even when composed of a changed cast of players (if adjusted for minutes played). Is it perfect? No, of course not. There are a number of admitted limitations, and I feel, a few unadmitted ones. But it’s better than anything else I know about.
Some of the limitations could be addressed by improved box score data collection (tracking more and different things). Some are inherent assumptions that there is no easy way around. The limitations are where an analyst/coach using the tool has room to use informed intuition.
benamery21 says:

January 27, 2010 at 11:26 pm

khandor: Anybody who believes ANYTHING is ‘best evaluated in anecedotal terms’ isn’t going to find a lot of common ground with folks here.
khandor says:

January 28, 2010 at 9:44 am

benamery21,

re: khandor: Anybody who believes ANYTHING is ‘best evaluated in anecedotal terms’ isn’t going to find a lot of common ground with folks here.

No disrespect intended, but …

IMO, finding “common ground” isn’t necessarily what this web site is about.

The question, in this specific instance, is:

If the goal is, in fact, to be able to create a real-life championship-winning NBA team …

Is the best way to evaluate individual players, teams and coaches, etc., accurately based on

i. A “substantive, empirical weighted macro-level ‘quantitative’ analysis of the readily available statistical data,”

or,

ii. An “idiosyncratic, micro-level ‘qualitative’ analysis, predicated on anecdotal case studies and the fundamental interdependency inherent in the game.”

No one who reads what I have to say about the game of basketball should ever think that I am someone who fails to recognize the value of Dr. Berri’s work, and its actual limitations.
Is Melo Max? | The Wages of Wins Journal says:

August 9, 2010 at 11:26 pm

[...] Disagreeing with Doug Moe [...]
Trade deadline thoughts, individual defense and Fat Lever | The Wages of Wins Journal says:

March 17, 2012 at 3:46 pm

[...] Doug Moe is wrong, Melo is not the best all-time Nugget [...]

January 2010
M	T	W	T	F	S	S
« Dec				Feb »
				1	2	3
4	5	6	7	8	9	10
11	12	13	14	15	16	17
18	19	20	21	22	23	24
25	26	27	28	29	30	31

Share this: