Recent Tw-happenings

I've been posting all kinds of nonsense to twitter lately. Here are some of the more informative tweets:

1) Lineup stuff:

•  J.E. has released the first public Adjusted Lineup data. 
• The Big 3 (+2 specific players) is the 3rd worst lineup in the NBA (see above), and Miami used this lineup in the playoffs.
• This is confirmed by the fact that said lineup only scores more points than their opponents 40% of the time.

2) Jason Kidd has played the best defense (of Miami & Dallas players) during the playoffs.

3) Nick Collison is not represented well by box-score or advanced box-score statistics.

4) I put together Lebron James' year-by-year Statistical and Actual Regularized +/- numbers since '08.

Most (& Least) Valuable Players in the 2011 Playoffs, Pre-Finals Edition

EDIT: I have also posted the most & least efficient players to Google Docs (as many of the poor performances don't make it into the bottom 25 as such players lost their series).


Here are the top & bottom 25, respectively. (These are not adjusted for opponent quality).

The numbers are based on my statistical plus-minus numbers, which I convert to wins via the "Pythagorean" formula. (Although I give each player the same baseline "team" value here for simplicity).

	Player	Tm	win% added per game	playoff wins added
1	LeBron James	MIA	18.9%	2.84
2	Kevin Durant	OKC	16.5%	2.80
3	Dirk Nowitzki	DAL	14.5%	2.17
4	Dwyane Wade	MIA	12.3%	1.85
5	Jason Kidd	DAL	10.7%	1.60
6	James Harden	OKC	9.2%	1.56
7	Derrick Rose	CHI	9.6%	1.54
8	Chris Bosh	MIA	8.3%	1.25
9	Joakim Noah	CHI	6.8%	1.09
10	Chris Paul	NOH	17.7%	1.06
11	Zach Randolph	MEM	7.7%	1.00
12	Jason Terry	DAL	6.3%	0.95
13	Kevin Garnett	BOS	10.4%	0.94
14	Ray Allen	BOS	10.2%	0.91
15	Luol Deng	CHI	5.0%	0.80
16	Russell Westbrook	OKC	4.2%	0.71
17	Marc Gasol	MEM	4.8%	0.62
18	Nick Collison	OKC	3.3%	0.57
19	Shawn Marion	DAL	3.4%	0.51
20	Paul Pierce	BOS	5.4%	0.48
21	Rajon Rondo	BOS	5.3%	0.48
22	Marcus Camby	POR	7.2%	0.43
23	Dwight Howard	ORL	7.1%	0.43
24	Danny Granger	IND	8.4%	0.42
25	Tyson Chandler	DAL	2.8%	0.42

And the worst:

	Player	Tm	win% added per game	playoff wins added
1	Mike Bibby	MIA	-6.1%	-0.92
2	Kendrick Perkins	OKC	-5.1%	-0.86
3	DeShawn Stevenson	DAL	-3.9%	-0.58
4	Amare Stoudemire	NYK	-12.8%	-0.51
5	Marco Belinelli	NOH	-8.5%	-0.51
6	Sam Young	MEM	-3.6%	-0.47
7	Richard Jefferson	SAS	-7.1%	-0.43
8	J.J. Redick	ORL	-7.1%	-0.42
9	Joe Johnson	ATL	-3.5%	-0.42
10	Glen Davis	BOS	-4.4%	-0.39
11	Joel Anthony	MIA	-2.4%	-0.36
12	Emeka Okafor	NOH	-5.9%	-0.35
13	Jarrett Jack	NOH	-5.6%	-0.34
14	Tyler Hansbrough	IND	-6.7%	-0.34
15	Wilson Chandler	DEN	-6.6%	-0.33
16	Marvin Williams	ATL	-2.7%	-0.33
17	Landry Fields	NYK	-7.6%	-0.30
18	Jason Collins	ATL	-2.4%	-0.28
19	Raymond Felton	DEN	-5.7%	-0.28
20	Willie Green	NOH	-4.6%	-0.28
21	Arron Afflalo	DEN	-9.2%	-0.28
22	Lamar Odom	LAL	-2.7%	-0.27
23	J.R. Smith	DEN	-5.5%	-0.27
24	Bill Walker	NYK	-6.5%	-0.26
25	Antonio McDyess	SAS	-4.1%	-0.25

NBA PLAYOFF PLAYER RATINGS as of 5/24

Earlier today, I made a prediction formula (regression) that predicts a player's Adjusted Plus-Minus based only on the advanced stats which Basketball-Reference spits out*

The following is their estimated Adjusted Plus-Minus, their total (pace-adjusted) personal point differential, and their total value to their team per 100 possessions (eRAPM * ~Min%).

Player	Tm	~poss. played	eRAPM	total value	value per 100
LeBron James	MIA	1183	6.9	82.1	6.2
Kevin Durant	OKC	1308	6.2	81.6	5.4
Dwyane Wade	MIA	1071	5.7	61.5	4.6
Dirk Nowitzki	DAL	1048	5.7	59.7	4.5
Derrick Rose	CHI	1170	4.4	51.9	3.7
Jason Kidd	DAL	937	4.4	41.7	3.2
James Harden	OKC	967	3.9	38.2	2.5
Chris Bosh	MIA	1070	3.1	33.5	2.5
Jason Terry	DAL	883	3.8	33.4	2.5
Chris Paul	NOH	482	6.7	32.1	5.7
Zach Randolph	MEM	992	2.9	29.1	2.4
Kevin Garnett	BOS	632	4.4	27.5	3.2
Joakim Noah	CHI	967	2.8	27.5	1.9
Ray Allen	BOS	696	3.9	26.8	3.2
Luol Deng	CHI	1255	1.9	23.8	1.7
Marc Gasol	MEM	1000	1.8	18.1	1.5
Russell Westbrook	OKC	1145	1.4	15.7	1.0
Paul Pierce	BOS	661	2.1	14.0	1.6
Tyson Chandler	DAL	833	1.7	13.9	1.0
Rajon Rondo	BOS	665	2.1	13.8	1.6
Marcus Camby	POR	322	3.9	12.6	2.2
Dwight Howard	ORL	497	2.5	12.5	2.2
Danny Granger	IND	353	3.5	12.2	2.6
Jameer Nelson	ORL	416	2.9	11.9	2.1
Nick Collison	OKC	723	1.6	11.7	0.8
Manu Ginobili	SAS	335	3.3	11.2	2.4
Gerald Wallace	POR	436	2.4	10.6	1.9
Kobe Bryant	LAL	682	1.5	10.6	1.1
Shawn Marion	DAL	840	1.1	9.2	0.7
Serge Ibaka	OKC	917	1.0	9.1	0.6
LaMarcus Aldridge	POR	497	1.8	9.0	1.6
Ronnie Brewer	CHI	451	1.8	8.0	0.6
Andrew Bynum	LAL	617	1.2	7.6	0.8
Ron Artest	LAL	553	1.2	6.8	0.8
Taj Gibson	CHI	501	1.3	6.6	0.5
Carlos Boozer	CHI	927	0.7	6.6	0.5
Eric Maynor	OKC	383	1.7	6.5	0.4
Ty Lawson	DEN	322	1.8	5.9	1.2
Kirk Hinrich	ATL	333	1.8	5.9	1.0
Jose Barea	DAL	463	1.2	5.7	0.4
Jrue Holiday	PHI	362	1.5	5.6	1.2
Carmelo Anthony	NYK	299	1.6	4.8	1.3
Jeff Teague	ATL	459	1.0	4.6	0.6
Greivis Vasquez	MEM	274	1.7	4.5	0.4
Nene Hilario	DEN	312	1.3	4.2	0.9
Pau Gasol	LAL	690	0.6	4.1	0.4
Trevor Ariza	NOH	464	0.8	3.7	0.6
Andre Miller	POR	374	0.9	3.2	0.6
Brandon Roy	POR	266	1.0	2.5	0.4
James Jones	MIA	524	0.5	2.5	0.2
Elton Brand	PHI	357	0.7	2.5	0.5
Derek Fisher	LAL	626	0.4	2.3	0.2
George Hill	SAS	364	0.5	1.9	0.3
Thabo Sefolosha	OKC	636	0.3	1.8	0.1
Ryan Anderson	ORL	283	0.5	1.5	0.3
Josh Smith	ATL	844	0.1	0.7	0.1
Peja Stojakovic	DAL	607	0.1	0.5	0.0
Al Horford	ATL	902	0.0	0.3	0.0
Matt Barnes	LAL	252	0.1	0.2	0.0
Darrell Arthur	MEM	387	0.0	0.2	0.0
Brendan Haywood	DAL	451	0.0	0.1	0.0
Jermaine O'Neal	BOS	380	0.0	0.1	0.0
Jamal Crawford	ATL	690	-0.1	-0.4	0.0
Matt Bonner	SAS	237	-0.2	-0.5	-0.1
Jason Richardson	ORL	295	-0.2	-0.7	-0.1
Shawne Williams	NYK	195	-0.5	-0.9	-0.2
Shannon Brown	LAL	320	-0.4	-1.3	-0.1
Mario Chalmers	MIA	613	-0.3	-1.6	-0.1
Keith Bogans	CHI	553	-0.3	-1.7	-0.1
Steve Blake	LAL	279	-0.8	-2.2	-0.3
Andre Iguodala	PHI	351	-0.6	-2.2	-0.5
C.J. Watson	CHI	251	-0.9	-2.3	-0.2
O.J. Mayo	MEM	698	-0.4	-2.7	-0.2
Louis Williams	PHI	251	-1.2	-3.1	-0.7
Tony Parker	SAS	426	-0.7	-3.1	-0.6
Brandon Bass	ORL	268	-1.2	-3.3	-0.6
Kenyon Martin	DEN	285	-1.2	-3.3	-0.7
Jodie Meeks	PHI	241	-1.4	-3.3	-0.7
Paul George	IND	256	-1.5	-3.8	-0.8
Darren Collison	IND	281	-1.5	-4.3	-0.9
Daequan Cook	OKC	368	-1.2	-4.4	-0.3
Nazr Mohammed	OKC	285	-1.6	-4.5	-0.3
Nicolas Batum	POR	291	-1.5	-4.5	-0.8
Tony Allen	MEM	674	-0.7	-4.6	-0.4
Carl Landry	NOH	410	-1.2	-4.8	-0.8
Gary Neal	SAS	214	-2.3	-4.8	-0.9
Toney Douglas	NYK	216	-2.3	-4.9	-1.3
Zydrunas Ilgauskas	MIA	200	-2.5	-5.0	-0.6
Danilo Gallinari	DEN	285	-1.8	-5.1	-1.1
Wesley Matthews	POR	389	-1.4	-5.6	-1.0
Mike Conley	MEM	977	-0.6	-5.8	-0.5
Tim Duncan	SAS	409	-1.4	-5.8	-1.0
Shane Battier	MEM	653	-0.9	-5.8	-0.5
Omer Asik	CHI	285	-2.1	-5.9	-0.4
Zaza Pachulia	ATL	376	-1.6	-6.0	-0.6
Thaddeus Young	PHI	245	-2.5	-6.0	-1.3
Kyle Korver	CHI	507	-1.2	-6.1	-0.4
Delonte West	BOS	328	-2.0	-6.4	-0.8
Hedo Turkoglu	ORL	403	-1.6	-6.6	-1.2
Roy Hibbert	IND	254	-2.6	-6.6	-1.4
Jeff Green	BOS	333	-2.1	-7.1	-0.8
Antonio McDyess	SAS	279	-2.6	-7.2	-1.3
Lamar Odom	LAL	551	-1.4	-7.9	-0.8
Raymond Felton	DEN	293	-2.8	-8.2	-1.7
Jason Collins	ATL	304	-2.7	-8.2	-0.7
Marvin Williams	ATL	416	-2.3	-9.5	-0.8
Wilson Chandler	DEN	222	-4.3	-9.5	-2.0
Jarrett Jack	NOH	214	-4.6	-9.8	-1.7
Tyler Hansbrough	IND	316	-3.1	-9.8	-2.1
Emeka Okafor	NOH	362	-2.8	-10.3	-1.8
Glen Davis	BOS	368	-3.1	-11.4	-1.3
Joe Johnson	ATL	958	-1.3	-12.0	-1.1
Joel Anthony	MIA	817	-1.5	-12.1	-0.9
J.J. Redick	ORL	231	-5.3	-12.3	-2.2
Richard Jefferson	SAS	339	-3.7	-12.4	-2.2
Sam Young	MEM	493	-2.7	-13.5	-1.1
DeShawn Stevenson	DAL	395	-3.7	-14.6	-1.1
Marco Belinelli	NOH	333	-4.5	-14.8	-2.6
Amare Stoudemire	NYK	258	-5.9	-15.2	-4.0
Mike Bibby	MIA	601	-4.0	-24.2	-1.8
Kendrick Perkins	OKC	869	-2.9	-25.3	-1.7

I also found the relative team ratings during the playoffs according to this system:

Tm	Est. Efficiency Margin
DAL	11.31
MIA	10.15
OKC	8.59
CHI	7.65
BOS	6.74
POR	4.92
LAL	2.18
MEM	1.59
ORL	0.51
NOH	-0.70
PHI	-1.38
ATL	-1.49
IND	-2.63
SAS	-3.25
DEN	-3.40
NYK	-4.29

MATH:

 *I used Basketball Reference's '07-'11 career advanced stats and Engalmann's Regularized three-year offensive and defensive +/-, for all players with >1000 minutes played. I made a regression (with no intercept), gradually weaning out variables with a P-value of >.05, and the following is the result:

(Offense has an R^2 of ~.6, Defense of ~.4)

coefficient	value
TRB%	3.282424852
ORB%	-1.704972232
DRB%	-1.550130287
STL%	1.12274153
ORTGxUsg	1.062903376
USG%	-0.945778193
TS% out of 100	-0.361792241
BLK%	0.247314245
eFG% out of 100	0.135585541
ORTG	0.085543135
TOV%	0.078829447
DRTG	-0.036343776

Win Shares per 48 Minutes versus Easy Player Ratings

Since Win-Shares per 48 minutes and my Easy Player Ratings use the exact same data (although EPR also adds minutes per game) to calculate player "efficiency," I thought I would compare the two.

I took all NBA players from in the 3-point era who played more than 1000 minutes and looked at their WS/48 and EPR values. From there, I just had a look at the top 100 in Win-Shares per 48.

Most notably, David Robinson (3.36 EPR) beats out Michael Jordan (3.27 EPR), barely -- this is somewhat intuitive, as Jordan's old years in Washington significantly decrease his "average," likely from his poor defensive numbers.




Now here are the 10 players in the top 100 who WS/48 comparatively likes the most:

	Player	From	To	USG%	ORtg	Points Prod Per 100	DRtg	MP		WS/48	(rk)		EPR	(rk)	difference
1	Leon Powe	2007	2011	20.4	115	23.46	103	13.9		0.174	33		1.17	88	-55
2	Steve Mix	1980	1983	21.7	113	24.521	103	17.2		0.168	39		1.18	87	-48
3	Marcin Gortat	2008	2011	15.3	116	17.748	102	17.2		0.162	46		1.17	89	-43
4	Steve Nash	1997	2011	21.2	119	25.228	110	31.3		0.168	39		1.22	80	-41
5	Kevin Love	2009	2011	22.1	117	25.857	109	29.8		0.163	45		1.19	86	-41
6	Mark Price	1987	1998	22.8	116	26.448	109	29.9		0.158	52		1.14	93	-41
7	Ryan Anderson	2009	2011	20.7	115	23.805	105	18.9		0.157	53		1.08	94	-41
8	Amir Johnson	2006	2011	14.3	122	17.446	107	17.8		0.154	61		0.80	99	-38
9	Carl Landry	2008	2011	20.5	117	23.985	108	24.9		0.155	60		1.04	95	-35
10	Todd MacCulloch	2000	2003	17.7	113	20.001	100	15.4		0.166	43		1.32	74	-31

Unfortunately, Steve Nash's lower usage and nearly non-existent defensive stats underrate him in EPR. Personally, I think Nash is in the top 5 offensive players of all time, which his usage and Offensive Rating do not capture well. Not sure where the rest of these guys fall - it could be because of low minutes-played, but that term is not worth all that much.


Here is a look at the players in the top 100 who the EPR system likes more than WS/48:

	Player	From	To	USG%	ORtg	Points Prod Per 100	DRtg	MP		WS/48	(rk)		EPR	(rk)	difference
1	Patrick Ewing*	1986	2002	28	106	29.68	99	34.3		0.15	77		1.83	28	49
2	Jack Sikma	1980	1991	20.9	110	22.99	102	33.7		0.149	82		1.58	44	38
3	Scottie Pippen*	1988	2004	22.5	108	24.3	102	34.9		0.146	94		1.49	57	37
4	Eddie Jones	1995	2008	20.1	111	22.311	103	34.4		0.147	89		1.52	53	36
5	Dennis Rodman*	1987	2000	11.4	114	12.996	100	31.7		0.15	77		1.59	42	35
6	Horace Grant	1988	2004	15.2	117	17.784	104	33.2		0.147	89		1.51	54	35
7	Dikembe Mutombo	1992	2009	15	111	16.65	99	30.8		0.153	69		1.72	35	34
8	Jeff Ruland	1982	1993	22.3	109	24.307	102	33.4		0.149	82		1.53	51	31
9	Dominique Wilkins*	1983	1999	30.3	112	33.936	108	35.5		0.148	85		1.45	60	25
10	Peja Stojakovic	1999	2011	21.5	114	24.51	106	33.5		0.147	89		1.41	66	23

EPR likes moderately high usage at average offensive rating, which explains Ewing near the top. In my regression, Ewing's efficiency + usage is above the threshold of having negative (relative) impact. Not quite sure what to make of the rest.



At any rate, here is the entire list of the top 100 players by WS/48

Player	From	To	USG%	ORtg	Points Prod Per 100	DRtg	MP		WS/48	(rk)		EPR	(rk)
David Robinson*	1990	2003	26.2	116	30.39	96	34.7		0.25	1		3.36	1
Michael Jordan*	1985	2003	33.3	118	39.29	103	38.3		0.25	1		3.27	2
LeBron James	2004	2011	31.8	115	36.57	102	40.1		0.227	4		2.94	3
Tim Duncan	1998	2011	27.8	110	30.58	95	35.8		0.216	6		2.92	4
Larry Bird*	1980	1992	26.5	115	30.48	101	38.4		0.203	13		2.69	5
Chris Paul	2006	2011	23.8	121	28.80	104	37.1		0.233	3		2.67	6
Dirk Nowitzki	1999	2011	27	117	31.59	103	36.5		0.214	8		2.61	7
Karl Malone*	1986	2004	29.4	113	33.22	101	37.2		0.205	12		2.58	8
Magic Johnson*	1980	1996	22.3	121	26.98	104	36.7		0.225	5		2.53	9
Manu Ginobili	2003	2011	25.1	115	28.87	99	28.1		0.214	8		2.52	10
Shaquille O'Neal	1993	2011	29.5	113	33.34	101	34.7		0.208	11		2.49	11
Yao Ming	2003	2011	26.8	112	30.02	99	32.5		0.2	14		2.43	12
Dwight Howard	2005	2011	23.2	111	25.75	98	36		0.188	19		2.42	13
Charles Barkley*	1985	2000	24.8	119	29.51	105	36.7		0.216	6		2.39	14
Kevin Garnett	1996	2011	25.2	111	27.97	99	36.7		0.191	18		2.39	15
Hakeem Olajuwon*	1985	2002	27.1	108	29.27	98	35.7		0.177	30		2.23	16
Arvydas Sabonis*	1996	2003	22.9	112	25.65	97	24.2		0.2	14		2.23	17
Julius Erving*	1980	1987	26.9	112	30.13	102	34.1		0.182	22		2.08	18
Dwyane Wade	2004	2011	32.5	111	36.08	104	37.6		0.193	16		2.06	19
John Stockton*	1985	2003	18.9	121	22.87	104	31.8		0.209	10		2.05	20
Player	From	To	USG%	ORtg	Points Prod Per 100	DRtg	MP		WS/48	(rk)		EPR	(rk)
Kareem Abdul-Jabbar*	1980	1989	24.2	116	28.07	104	32.6		0.182	22		2.03	21
Pau Gasol	2002	2011	23.6	115	27.14	104	35.9		0.179	26		2.00	22
Kobe Bryant	1997	2011	31.5	112	35.28	105	36.4		0.187	20		1.97	23
Adrian Dantley*	1980	1991	26.8	120	32.16	110	36		0.192	17		1.95	24
Shawn Marion	2000	2011	21.1	111	23.42	101	36.3		0.162	46		1.91	25
Paul Pierce	1999	2011	27.8	110	30.58	103	37		0.167	41		1.84	26
Sidney Moncrief	1980	1991	20.6	119	24.51	105	30.2		0.187	20		1.83	27
Patrick Ewing*	1986	2002	28	106	29.68	99	34.3		0.15	77		1.83	28
Clyde Drexler*	1984	1998	25.4	114	28.96	105	34.6		0.173	34		1.82	29
Moses Malone*	1980	1995	26	114	29.64	105	33.5		0.179	26		1.82	30
Larry Nance	1982	1994	20.6	116	23.90	104	33.4		0.171	36		1.81	31
Amare Stoudemire	2003	2011	26.9	114	30.67	106	34.6		0.177	30		1.77	32
Kevin McHale*	1981	1993	22.4	118	26.43	106	31		0.18	24		1.77	33
Alonzo Mourning	1993	2008	25.6	108	27.65	100	31		0.166	43		1.74	34
Dikembe Mutombo	1992	2009	15	111	16.65	99	30.8		0.153	69		1.72	35
Brad Daugherty	1987	1994	22.2	114	25.31	105	36.5		0.156	56		1.70	36
Reggie Miller	1988	2005	21.6	121	26.14	109	34.3		0.176	32		1.68	37
Elton Brand	2000	2011	24	111	26.64	104	36.9		0.156	56		1.64	38
Chauncey Billups	1998	2011	22	118	25.96	107	32.3		0.179	26		1.64	39
Carlos Boozer	2003	2011	23.9	111	26.53	103	32.6		0.159	51		1.63	40
Player	From	To	USG%	ORtg	Points Prod Per 100	DRtg	MP		WS/48	(rk)		EPR	(rk)
Chris Bosh	2004	2011	24.8	113	28.02	106	36.9		0.16	49		1.62	41
Dennis Rodman*	1987	2000	11.4	114	13.00	100	31.7		0.15	77		1.59	42
Greg Oden	2009	2010	19.7	117	23.05	103	22.1		0.18	24		1.58	43
Jack Sikma	1980	1991	20.9	110	22.99	102	33.7		0.149	82		1.58	44
Andrew Bynum	2006	2011	18.5	117	21.65	103	24.3		0.172	35		1.57	45
Bob Lanier*	1980	1984	21	112	23.52	102	28.1		0.161	48		1.57	46
Robert Parish*	1980	1997	21.2	113	23.96	103	29		0.157	53		1.56	47
Andrei Kirilenko	2002	2011	18.9	112	21.17	102	30.8		0.156	56		1.56	48
Artis Gilmore*	1980	1988	18.4	119	21.90	106	30.9		0.167	41		1.54	49
Bobby Jones	1980	1986	17.1	116	19.84	102	24.2		0.17	37		1.54	50
Jeff Ruland	1982	1993	22.3	109	24.31	102	33.4		0.149	82		1.53	51
Kevin Durant	2008	2011	29.8	111	33.08	107	38		0.154	61		1.53	52
Eddie Jones	1995	2008	20.1	111	22.31	103	34.4		0.147	89		1.52	53
Horace Grant	1988	2004	15.2	117	17.78	104	33.2		0.147	89		1.51	54
Dan Issel*	1980	1985	24.9	118	29.38	109	29.9		0.17	37		1.50	55
Kermit Washington	1980	1988	15.9	114	18.13	102	29.3		0.154	61		1.50	56
Scottie Pippen*	1988	2004	22.5	108	24.30	102	34.9		0.146	94		1.49	57
Al Horford	2008	2011	17.5	116	20.30	105	33.8		0.154	61		1.48	58
Kevin Johnson	1988	2000	22.6	118	26.67	109	34.1		0.178	29		1.47	59
Dominique Wilkins*	1983	1999	30.3	112	33.94	108	35.5		0.148	85		1.45	60
Player	From	To	USG%	ORtg	Points Prod Per 100	DRtg	MP		WS/48	(rk)		EPR	(rk)
Bill Laimbeer	1981	1994	16.9	115	19.44	104	31.8		0.149	82		1.43	61
Ray Allen	1997	2011	24.5	114	27.93	108	37		0.154	61		1.43	62
Joakim Noah	2008	2011	15.4	115	17.71	102	26.2		0.16	49		1.42	63
Nene Hilario	2003	2011	18.1	113	20.45	103	29.6		0.152	72		1.42	64
Blake Griffin	2011	2011	27.3	111	30.30	107	38		0.152	72		1.41	65
Peja Stojakovic	1999	2011	21.5	114	24.51	106	33.5		0.147	89		1.41	66
Tracy McGrady	1998	2011	29.6	108	31.97	104	33.7		0.154	61		1.40	67
Shawn Kemp	1990	2003	25	106	26.50	100	27.9		0.147	89		1.39	68
Brandon Roy	2007	2011	25.4	116	29.46	110	35.6		0.157	53		1.37	69
Marques Johnson	1980	1990	24	112	26.88	106	34		0.15	77		1.37	70
Player	From	To	USG%	ORtg	Points Prod Per 100	DRtg	MP		WS/48	(rk)		EPR	(rk)
Detlef Schrempf	1986	2001	20.3	117	23.75	107	29.6		0.156	56		1.33	71
Paul Millsap	2007	2011	19.7	114	22.46	104	26		0.151	74		1.32	72
Vince Carter	1999	2011	29	109	31.61	106	36.4		0.146	94		1.32	73
Todd MacCulloch	2000	2003	17.7	113	20.00	100	15.4		0.166	43		1.32	74
Mehmet Okur	2003	2011	21.6	113	24.41	105	29.1		0.146	94		1.31	75
Scot Pollard	1998	2008	12.5	114	14.25	99	16.5		0.153	69		1.27	76
Cedric Maxwell	1980	1988	16.9	118	19.94	106	28.7		0.154	61		1.27	77
Anderson Varejao	2005	2011	13.7	115	15.76	102	24.7		0.151	74		1.26	78
Gary Payton	1991	2007	22.5	111	24.98	106	35.3		0.148	85		1.24	79
Steve Nash	1997	2011	21.2	119	25.23	110	31.3		0.168	39		1.22	80
Player	From	To	USG%	ORtg	Points Prod Per 100	DRtg	MP		WS/48	(rk)		EPR	(rk)
Jeff Hornacek	1987	2000	19.7	117	23.05	108	31.5		0.154	61		1.21	81
Brad Miller	1999	2011	18.4	114	20.98	105	28.6		0.15	77		1.20	82
Serge Ibaka	2010	2011	15.9	115	18.29	103	22.8		0.153	69		1.20	83
Maurice Cheeks	1980	1993	15.4	117	18.02	106	31.8		0.146	94		1.19	84
Calvin Natt	1980	1990	21.3	114	24.28	107	31.4		0.146	94		1.19	85
Kevin Love	2009	2011	22.1	117	25.86	109	29.8		0.163	45		1.19	86
Steve Mix	1980	1983	21.7	113	24.52	103	17.2		0.168	39		1.18	87
Leon Powe	2007	2011	20.4	115	23.46	103	13.9		0.174	33		1.17	88
Marcin Gortat	2008	2011	15.3	116	17.75	102	17.2		0.162	46		1.17	89
Terrell Brandon	1992	2002	23.9	110	26.29	105	29.8		0.147	89		1.15	90
David Lee	2006	2011	19	117	22.23	108	31		0.148	85		1.14	91
Kevin Martin	2005	2011	24.7	117	28.90	111	31.2		0.151	74		1.14	92
Mark Price	1987	1998	22.8	116	26.45	109	29.9		0.158	52		1.14	93
Ryan Anderson	2009	2011	20.7	115	23.81	105	18.9		0.157	53		1.08	94
Carl Landry	2008	2011	20.5	117	23.99	108	24.9		0.155	60		1.04	95
Terry Porter	1986	2002	19.1	115	21.97	107	27.8		0.15	77		1.01	96
Ricky Pierce	1983	1998	25	116	29.00	110	24.4		0.146	94		0.96	97
Kiki Vandeweghe	1981	1993	23.6	119	28.08	113	30.3		0.148	85		0.96	98
Amir Johnson	2006	2011	14.3	122	17.45	107	17.8		0.154	61		0.80	99
Ty Lawson	2010	2011	19	118	22.42	110	23.6		0.146	94		0.69	100

Easy Player Rating System!

As a basketball statistician, I am thoroughly obsessed with Jeremias Engalmann's Adjusted Plus-Minus numbers.
As a basketball fan, I realize that most people are hesitant to even attempt to understand what's going on in Adjusted Plus-Minus.

To combine the two worlds, I have looked at the latest four individual seasons of data, and I have compared advanced basketball stats with the Adjusted Plus-Minus numbers. By using four advanced stats (easily found at Basketball-Reference.com: per game, per season, or per-career), we can estimate how much a player impacts their team, per possession.

Here are the four statistics:

-Minutes per Game
-Offensive Rating (ORTG)
-Usage% (Possessions used)
-Defensive Rating (DRTG)

I made a regression of values to predict the Adjusted Plus-Minus, and averaged each season from 2008-2011 (with players having 500 minutes or more). The result is the following:

=(0.035 x Minutes/Game) + (0.43 x (Usage%/100) x Offensive Rating) - (0.14 x Defensive Rating) - (0.44 x Usage%) + 13.8

For Dwight Howard, this season, we get:
37.6 Minutes per game
27.2% Usage
113 Offensive Rating
94 Defensive Rating

=(0.035 x 37.6) + (0.43 x 0.272 x 113) - (0.14 x 94) - (0.44 x 27.2) + 13.8
=1.316 + 13.21648 - 13.16 - 11.968 + 13.8
=3.2        --- (although it's 3.45 if you use the more precise numbers below)

[For stat-heads, the actual coefficients are:  Min/G: 0.034853, ORTG*Usg/100: 0.434141, DRTG: -0.13995, USG%: -0.43613]


So for the 2010-2011 regular season, I have calculated the predicted "Net Efficiency." This is simply our estimate of how much a player adds to his team's point differential per 100 possessions of offense & 100 possessions of defense. Also, I have calculated the estimated "Wins Added" by multiplying their efficiency by the % of minutes they played, and converting that number into a wins estimate (how many wins an average team would have gained from their performance).

Here are the results for 2010-2011 (Regular Season - players with 500 or more minutes).

Updated Playoff Odds, 4/29

All first-round series except San Antonio/Memphis have been decided.

Serieses?


Easiest battle is Chicago over Atlanta in 5.
Closest series is the Los Angeles over Dallas in 6.

Not Exactly On Hiatus

Hello all!

I am currently finishing up my 8th semester at Appalachian State, and graduating in a couple of weeks.
I've been busy with non-basketball...or at least I've been attempting to be busy with non-basketball.

I'll be writing a bit for The Coolest Basketball Site (TM) this summer, which I am extremely excited about.


Anyways, here are my silly opinions on basketball as the playoffs begin:

• Kevin Love is NOT the MIP. He's not even in the 100 best players in the league. (Individual rebounds and Rebound% are not all that telling). And he is most DEFINITELY not the MVP *shudder*.
• Derrick Rose is not the MVP. Even if you're supposed to choose "Best player on the Best team," he does not appear to be that. He is a close overall candidate by some agreeable measures, however.
• Oh, and, with Harrison Barnes staying in Chapel Hill, the pre-season rankings will look something like this:
  
• #1: North Carolina
• #2: Ohio St.
• ...
• ...
• #1,000: Eventual National Champion

-Nathan

Playoff Odds as of April 8th

I simulated the NBA playoffs, under a few assumptions.

1) Memphis makes the playoffs
2) There are no tie-breakers and teams are seeded according to conference standings
2) The regularized +/- 2011 numbers are accurate

(5:38p EST) - NEW RESULTS, updated with estimated playoff minutes

EDIT: A bit on the method.

I took all players who aren't out for the season, and converted their minutes into estimated playoff minutes (per DSMok1's suggestion), which I then had to adjust to force each team's game minutes to sum to 240 total minutes.

I then looked at each player's regularized +/- numbers, and gave them a contribution of Projected Minutes% * (Offense+Defense).

I then set the league average to zero, and each team was given their own "RAPM" rating. This was used to simulate the playoffs via a spreadsheet made by DSMok1 which I modified.

Dirk for MVP!

The boys over at the APBR metrics board are asking who should be MVP. I think that if we use the ridge-adjusted plus minus ratings, we can get a good answer.

I converted each player's worth into a total efficiency differential (Offensive RAPM times Minutes% for Offense, etc), then looked at how much worse we would expect their team to be without the player. I converted efficiency ratings into "pWins" or "pythagorean wins" by doing Offensive Rating^14/(ORTG^14 + DRTG^14).

HERE ARE THE RESULTS

My Lazy Lazy Lazy Lazy RPI

EDIT/UPDATE: I'm not very happy with the math here.

I have constructed a very lazy RPI, that is more theoretically sound than the RPI.


Considering the linear version of the Log5 win-percentage formula, we could (lazily) assume the following:


Season Win%= 0.5 + Real Win% - Opponent's Real Win%


Therefore:
1) Real Win% = Season Win% + Opponent's Real Win% - 0.5


and


2) Opponent's Real Win% = Opponent's Season Win% + Opponent's Opponent's Real Win% - 0.5


If we assume (lazily) that "opponent's opponents" play average (.500) teams, we can then also assume that their Season Win% equals their Season Win%, like so:


3) OOSeasonWin% = 0.5 + OORealWin% - .5
4) OOSeasonWin%=OORealWin%
So we can replace "Opponent's Opponent's Real Win%" in #2 with "Opponent's Opponent's Season Win%"

When we replace Opponent's Real Win% with #2, we get


5) Real Win% = Season Win% + (Opponent's Season Win% + Opponent's Opponent's Season Win% - 0.5 )-0.5
6) Real Win% = Season Win% + Opponent's Season Win% + Opponent's Opponent's Season Win% - 1


or as the RPI would have it,
RPI = WP + OWP + OOWP - 1




So in theory, Win%, Opponent Win%, and Opponent's Opponent Win% should all be weighted 'equally.'

Here We Go!!! (Into overtime!)

The (real) NCAA tournament starts in about one hour. To kick it off, I present my Overtime% for the first round.

Each game is ranked by the probability that is going into overtime*:

Here are the results




*The theoretical odds of going into overtime are lower than that of actually going into overtime**; there are a few specific things that have to happen to make the game sum to zero - the rankings are still valid though.

**End-of-40-minutes Game-scores fall under the normal distribution, but there is a significant dip around zero.

Updated Bracket Picks (Expected Wins)

I've updated the LRMC simulation to only include Clemson, and weaken BYU & Belmont (by the equivalent of 1 point per 100 possessions).

Here's the results.

For a bit of math - this method is pretty arbitrary in terms of assigning values - I just assumed this year's distribution of teams. I weakened BYU to better account for Davies' recent dismissal, and weakened Belmont, because....well....it just doesn't fit for them to be #4.

Top Final Fours

The majority of huge-pool winners accurately predict the Final Four...here is a sampling of the top (seven) possible final fours that my LRMC simulation predicts.

E	W	SW	SE	Probability
Ohio St.	Duke	Kansas	Pittsburgh	4.04%
Ohio St.	Duke	Kansas	BYU	3.42%
Ohio St.	Duke	Kansas	Belmont	2.10%
Ohio St.	SDSU	Kansas	Pittsburgh	1.81%
Ohio St.	Duke	Notre Dame	Pittsburgh	1.51%
Washington	Duke	Kansas	Pittsburgh	0.97%
Ohio St.	Duke	Purdue	Pittsburgh	0.88%

If you're doing a multiple-bracket pool, this might be useful...updated simulation results coming soon.

Official Tournament Picks, CONTINUED....

Here are some more numbers to help you make your picks:

"EW" means expected wins ('opening' round not included).
-Overall odds in the original efficiency simulation

-Overall odds in the LRMC simulation

-Consistency values (for each tournament team) - higher means more inconsistent

I am currently running a new efficiency simulation that uses a different formula than before to predict Percent Chance of Win.

Also, here is a quick rundown by Dean Oliver on why slow-paced teams like Wisconsin increase their likelihood of losing to less efficient teams.

OFFICIAL Tournament Picks, VERSION ONE

This post is dedicated to my Mother, who wants my tournament picks :-D.

I have looked at every pick and noted its overall value in ESPN's tournament challenge.
Ohio St. winning the championship is the highest value not because they have a great chance of winning the championship, but putting them there

Here's the Spreadsheet (version 1, efficiency ratings)

For the mathematically inclined:

I used DSMok1's adjusted ratings and ran ~10,000 simulations of the bracket to find each team's odds of getting to each round.

Each round value = Sum of all Previous (Round Value * Odds of Winning That Round) + Round Value * Odds of Winning That Round.

Of note: these values did NOT include the probable home-court advantage (for example, UNC plays in Charlotte). I will add this soon if I feel its warranted.

S-Curve Madness: The Good, The Bad, and The Ugly

Wow, what a bracket. I'll be very quick here (forgive me if I miss something important), as I've got to get moving on my official predictions.

The good:

-Notre Dame didn't get a 1-seed (they didn't deserve one).
-St. Johns was a 6-seed (still a bit too over-seeded, but I'll take it).

The bad:
-Utah St, Clemson, Missouri, Gonzaga, Marquette are significantly better than their seeds suggest.

-Obviously, Virginia Tech not making the tournament is pretty ridiculous.
-Duke has the easiest path in the world until they will likely meet (#4 Pomeroy) Texas in the Sweet 16.

The ugly:

-As I disdainfully remarked on twitter, Belmont is seeded worse than ~THIRTY FOUR teams that have played worse than them, according to Ken Pomeroy's very reliable ratings. By adjusted efficiency, Belmont is #18 and Wisconsin #9. By adjusted margin of victory, they are #20 and #11. By genius-math-hyper-accurate-point-margin, they are #4 and #12. Ugh.

-Carolina vs. Washington is a very similar matchup, if they both go to the round of 32. Pomeroy has UNC at #14, Washington at #15. LRMC has UNC at #15 and Washington at #8. And Raymond has UNC at #14, Wash. at #7.

Alright, I'll keep everyone posted. Let's go Georgia!

The Humans are a Little Too Impressed...

As it gets down to crunch time for the teams who are on the bubble, and as the 68-team-field will be together in under 100 hours, I present to you a quick summary of the teams that impress pollsters, but not computer ratings.

"The Computers Aren't As Impressed"

Notre Dame: #4 AP, #4 Coaches, #15 Pomeroy

Now statistically, we can't really be sure of where a team's true value lies any better than +/- ten places or so, but currently Notre Dame's good record comes from winning close games (13th in 'Luck'). I would be relatively surprised if Notre Dame made it to the final four, although they have a decent chance of getting a #1 seed.

Arizona: #16 AP, #15 Coaches, #28 Pomeroy
Again, Arizona is good, but they're not that good. Many of their wins include single-digit-victory home games.

St. Johns: #17 AP, #18 Coaches, #34 Pomeroy
Inconsistency is the key here. Pollsters (and everyone) is impressed with wins against West Virginia, Georgetown, Notre Dame, Duke, Connecticut, Cincinnati, Marquette, Pittsburgh, and Villanova. While many of those wins came closely at home, this still is a huge sample of good performance against good teams. This St. John's squad has also lost to St. Bonaventure at home, Fordham on the road, Seton Hall on the road (by 14!). Their six Big East losses have come at an average of 14.7 points. Ouch.

Xavier: #18 AP, #20 Coaches, #35 Pomeroy
Xavier has definitely improved. After only beating Western Michigan by 3 at home, going into overtime with IUPUI Ft. Wayne at home, and losing to Miami OH on the road by eleven, they have won 16 of their last 17. The computers are not impressed, however, by the mediocre schedule those 16 wins came against. The one loss in that stretch? To #207-Pomeroy Charlotte.

Temple: #24 AP, #25 Coaches, #38 Pomeroy
Again, the pollsters just felt like boosting up a mid-major with lots of wins. While Temple beat Maryland and Georgetown, they don't have anything particularly great on their resume (the Georgetown win came at home, and was only by 3). Temple has mostly been feeding off their weak conference to get here.

That's all for now. Selection Sunday and bracket simulations coming soon!

Unranked Lurkers

There are several teams who simultaneously impress statistical rankings, but do not impress poll-rankings.

Here are all the teams who are in the top-25 of at least one of my three favorite systems --and not ranked in either major poll--: by Ken Pomeroy's efficiency margin, Raymond's Ratings (basically SRS, if you're familiar), and GA Tech's Bayesian Logistic Regression Markov Chain.

Top-25, The-Humans-Are-Unimpressed
(bold indicates a computer system being similarly unimpressed, italics indicate top-10 computer rating)

-Washington (P10, 19-9) - #13 Pomeroy, #8 Raymond, #6 LRMC

Washington has had a particularly tough time away from home, losing two of three on neutral courts (by single digits to Kentucky and Michigan State), and losing six of eleven on the road (only once by double-digits). At home, on the other hand, the Huskies won their first thirteen before putting up a non-characteristic eFG% of 39.7 against Washington St. on Sunday, losing by eleven. Their blowout wins prove a marked inconsistency, as they absolutely beat up on decent teams. Washington beat top-100 Pomeroy teams Oregon, Long Beach St, Arizona, UCLA, USC, Cal (twice), and Stanford, by an average of 18.4 points. I won't give up on them (as Pomeroy similarly encourages you to do) because of this, and their demolishing of Arizona and their quality road performance at UCLA.

-Maryland (ACC, 18-11) - #16 Pomeroy, #16 Raymond, #32 LRMC

Four of these losses include: An 8-point road loss at Duke, an 11-point road loss at Carolina, a 9-point neutral loss to Pittsburgh, and 8-point road loss to Villanova. Their overall strength of schedule is low since 1/3 of their season has been against sub-200 teams. This is evident in the extremely flawed RPI, who knocks Maryland down to #83. Ouch. If these guys win the ACC tourney, I would expect them to win a couple in the big dance...but the odds are not in their favor to get that first step.

-West Virginia (BE, 18-10) - #18 Pomeroy, #18 Raymond, #19 LRMC

Exactly half of WVU's ten losses have come against top-25 Pomeroy teams, and they have wins against Vanderbilt, Cincinnati, Purdue, and Notre Dame. Their Pomeroy strength of schedule, as one might expect, is 2nd in the country.

-Belmont (ASun, 27-4) - #20 Pomeroy, #24 Raymond, #8 LRMC

Belmont is an interesting case. We have a very low sample size of decent competition with these guys - they've only played three games against tougher opponents than #106-Pomeroy East Tennessee St. In fact, only eleven of their thirty-one games have been against top-200 Pomeroy teams. At any rate, their only losses have come against decent opponents (save Lipscomb), on the road, by an average of only seven points.

-Illinois (B10, 18-11) - #21 Pomeroy, #22 Raymond, #20 LRMC

The Illini have been extremely inconsistent this season. While they have lost to Illinois-Chicago on a neutral court, they beat tough opponents in Maryland, Wisconsin, Gonzaga, and North Carolina. Also, they've had to deal with the nation's 6th-highest string of opponents (by Pomeroy's metric).

-Cincinnati (BE, 22-7) - #24 Pomeroy, #28 Raymond, #34 LRMC

Cinci is at least somewhat recognized by humans, as they received the 32nd most votes in the AP poll this week. Their seven losses have come from teams with an average ranking of 19-Pomeroy.

-Marquette (BE, 18-11) - #25 Pomeroy, #26 Raymond, #27 LRMC

Marquette has had an unfortunate schedule. They've lost to neutral-court Duke by 5, road-Pittsburgh by 8, road-Lousville by 1, road-Nova by 5, road-Notre Dame by 5, and road Georgetown by 9. While the humans might not be impressed, just from those numbers alone, a statistical-minded Nathan Walker would call Marquette a top-25 team.

-St. Mary's (WCC, 23-7) - #45 Pomeroy, #32 Raymond, #23 LRMC

St. Mary's inconsistency is marked by a high-high and a very-low-low. They only lost to BYU by 1 on a Neutral court, but lost to #302 Pomeroy San Diego. In between has come a few losses to good teams and beating up every other average team they encountered.

Unfortunately, some of these squads are going to be Nationally Invited, if you know what I mean. For those that make the big dance, there's a good chance they'll beat their better-seeded opponent.

Offensive Decision% version 2

I've updated my Offensive Decision% formula to include Offensive Rebounds. A field goal attempt is now only considered one 'decision' if the player does not rebound the attempt. So the denominator now subtracts Offensive Rebounds x Shot% in an estimation of how many offensive rebounds a player makes on their own missed field goals. Here's the entire formula:




Here are the current results for the NBA, with those playing 500 minutes or more.

NCAA team offensive efficiency impacts

I have previously done work on estimating how much statistics (specifically, the Four Factors + 2 more) impact efficiency. My prior method was lazy and inaccurate at adjusting for Strength of Schedule. The new method adjusts each factor rating differently. For the math, scroll to the bottom* EDIT: Yes, the total numbers do not EXACTLY equal (Adjusted Offensive Rating - League Average Offensive Rating), but they are close (R^2 of .99, to be concise).


But here's what you really want.
NCAA adjusted offensive four factors



*The original method took (Deductive Efficiency - Deduced efficiency with league average stat) and multiplied this by (Adjusted Efficiency / Raw Efficiency). The new method is a little more complex. I found out that each stat didn't impact efficiency as much as I thought, since each factor interacts with one another. I found the following:

While predicting change in efficiency (minus average), the following weights occur: eFG&FG+=1.065833, TO%+=1.088916, OR%+=0.935664, FTR&FT+=0.38507

Each individual output would have to be multiplied by these coefficients. However, I still needed to adjust for strength of schedule. To do this, I subtracted Adjusted - Raw Offense for each team to get their Schedule Adjustment Factor. I then weighed each of the four factors so that they would sum to one (fg=0.306672, to=0.313314, or=0.269218,ft= 0.110796). Here's an example of how eFG%&FG% look:

eFG&FG+=1.065833 * [(Deduced Efficiency - Deduced efficiency with average eFG% and FG%) + .306672*Schedule Adjustment ]