The Major-Conference Offensive Impact Leaders

Pretty self-explanatory. I've made a few important corrections to the formula recently.*
Must have played 40% of minutes to qualify.
Offensive rating and team SOS data from kenpom.com

	player	Conf	adjOI
1	Marcus Denmon (Missouri)	B12	11.40
2	Steven Pledger (Oklahoma)	B12	11.00
3	Henry Sims (Georgetown)	BigEast	9.41
4	Kenny Boynton (Florida)	SEC	9.27
5	Jared Sullinger (Ohio St.)	B10	9.21
6	Ricardo Ratliffe (Missouri)	B12	9.18
7	Tyrus McGee (Iowa St.)	B12	8.55
8	Travon Woodall (Pittsburgh)	BigEast	8.17
9	Cody Zeller (Indiana)	B10	8.10
10	Kevin Jones (West Virginia)	BigEast	8.01
11	Davante Gardner (Marquette)	BigEast	7.80
12	Jae Crowder (Marquette)	BigEast	7.79
13	Darius Johnson-Odom (Marquette)	BigEast	7.77
14	Mike Scott (Virginia)	ACC	7.77
15	Hollis Thompson (Georgetown)	BigEast	7.24
16	J'Covan Brown (Texas)	B12	7.22
17	Arnett Moultrie (Mississippi St.)	SEC	7.14
18	Terrell Stoglin (Maryland)	ACC	7.12
19	Dion Waiters (Syracuse)	BigEast	7.00
20	Pierre Jackson (Baylor)	B12	7.00
21	JaMychal Green (Alabama)	SEC	6.97
22	Doron Lamb (Kentucky)	SEC	6.97
23	Jeremy Lamb (Connecticut)	BigEast	6.76
24	Erick Green (Virginia Tech)	ACC	6.64
25	Erving Walker (Florida)	SEC	6.63
26	Tyler Zeller (North Carolina)	ACC	6.57
27	Robbie Hummel (Purdue)	B10	6.38
28	Andre Young (Clemson)	ACC	6.14
29	Shabazz Napier (Connecticut)	BigEast	5.99
30	Brandon Young (DePaul)	BigEast	5.98
31	Mike Rosario (Florida)	SEC	5.96
32	Nasir Robinson (Pittsburgh)	BigEast	5.77
33	John Jenkins (Vanderbilt)	SEC	5.73
34	Drew Crawford (Northwestern)	B10	5.71
35	Thomas Robinson (Kansas)	B12	5.70
36	Ashton Gibbs (Pittsburgh)	BigEast	5.61
37	Maalik Wayns (Villanova)	BigEast	5.53
38	Trae Golden (Tennessee)	SEC	5.47
39	Ryan Kelly (Duke)	ACC	5.45
40	Kris Joseph (Syracuse)	BigEast	5.41
41	Deshaun Thomas (Ohio St.)	B10	5.39
42	C.J. Harris (Wake Forest)	ACC	5.37
43	B.J. Young (Arkansas)	SEC	5.22
44	Scott Wood (North Carolina St.)	ACC	5.11
45	Jeronne Maymon (Tennessee)	SEC	5.10
46	Reggie Bullock (North Carolina)	ACC	5.07
47	Trevor Mbakwe (Minnesota)	B10	5.04
48	John Shurna (Northwestern)	B10	4.99
49	Victor Oladipo (Indiana)	B10	4.96
50	Mouphtaou Yarou (Villanova)	BigEast	4.95

* I adjusted the overall formula slightly** to more evenly adjust for competition, and to more accurately portray players (R^2 is about 5% better than my prior formula)

**
adjDsos = (% of games against D-I * Adj Def.SOS) + (% games against non D-I * 114)
PointsProducedPer100=ORTG*PercentPoss/100

adjOI = (PointsProducedPer100)/(100.3*0.2)*n1 + PercentPoss/20*n2 + ORTG/100.3*n3 + 0.2*(100.3-adjD.SOS)

where:
n1= 26.97

n2= -22.38

n3= -4.73

Indiana great. Gonzaga good. Cal mediocre.

This is my original method for rating teams after which my blog is named, although I've never done league-wide rankings.The normal distribution states that each team's chance of winning is dependent on their strength (efficiency or point differential) and the consistency of that strength (the standard deviation of efficiency differential).

I adjust standard deviation for competition by simply using Actual minus Expected point margin.*


Let's look at the ten teams who gain the most in terms of win% when we adjust for consistency.

	team	win% diff	pyth rnk (adj rnk)	adj win%
1	Gonzaga	8.7%	34 (20)	93.5%
2	Murray St.	8.7%	50 (31)	89.1%
3	Temple	8.6%	36 (21)	92.7%
4	Creighton	8.5%	55 (33)	88.2%
5	Indiana	8.1%	14 (5)	98.2%
6	Miami FL	7.7%	63 (55)	83.9%
7	Lehigh	7.6%	88 (67)	77.9%
8	Tulsa	7.3%	95 (75)	74.6%
9	Connecticut	7.2%	23 (12)	95.4%
10	Long Beach St.	7.0%	47 (35)	87.8%

Gonzaga's actual versus expected point margin only has a standard deviation of 6.1 - this is significantly low.

Next, the ten teams in Pomeroy's top-100++ who drop the most.
(++The list was mostly bottom-feeders anyways, so we'll just look at teams we care more about).

		win% diff	pyth rnk (adj rnk)	adj win%
1	California	-2.7%	20 (44)	86.4%
2	Purdue	-2.7%	15 (40)	87.0%
3	Virginia	-0.4%	24 (36)	87.8%
4	Florida St.	-0.1%	32 (48)	85.8%
5	Baylor	0.7%	13 (25)	90.9%
6	West Virginia	1.2%	33 (46)	86.3%
7	St. Louis	1.2%	22 (30)	89.6%
8	Missouri	1.2%	8 (17)	94.0%
9	Marquette	1.5%	6 (13)	94.7%
10	New Mexico	1.8%	37 (50)	85.3%

Cal plays very strongly on average, but has an adjusted standard deviation of 15.7. Quite inconsistent.


And here are the top 25 overall:

	team	adj win%	pyth	diff
1	Kentucky	0.9913	0.9686	0.0227
2	Ohio St.	0.9907	0.9647	0.0259
3	Syracuse	0.9890	0.9511	0.0379
4	North Carolina	0.9885	0.9519	0.0366
5	Indiana	0.9817	0.9011	0.0806
6	Kansas	0.9813	0.9310	0.0503
7	Wisconsin	0.9812	0.9733	0.0080
8	Duke	0.9789	0.9264	0.0525
9	Michigan St.	0.9740	0.9155	0.0586
10	Florida	0.9710	0.9158	0.0552
11	Louisville	0.9690	0.9237	0.0452
12	Connecticut	0.9539	0.8816	0.0723
13	Marquette	0.9471	0.9321	0.0149
14	Brigham Young	0.9430	0.8964	0.0467
15	Xavier	0.9406	0.8957	0.0448
16	Georgetown	0.9400	0.8933	0.0467
17	Missouri	0.9397	0.9272	0.0124
18	Wichita St.	0.9359	0.8794	0.0565
19	Stanford	0.9355	0.8967	0.0388
20	Gonzaga	0.9353	0.8485	0.0868
21	Temple	0.9269	0.8408	0.0861
22	Texas	0.9234	0.8812	0.0422
23	Nevada Las Vegas	0.9124	0.8742	0.0382
24	Alabama	0.9119	0.8851	0.0268
25	Baylor	0.9086	0.9012	0.0074

 
Yes, yes. The Hoosiers are doing quite well....





*- While explaining individual parts of a team or player in "tempo-free" language is beneficial, describing a team by "efficiency margin" rather than "point margin" is not particularly beneficial. In the NBA, point margin usually predicts win-percentage only slightly worse than efficiency margin (although last year, point margin did better). However, for numerous statistical and tactical reasons, pace is important to consider, in my opinion, when rating teams. It is much easier for a team to put away opponents if they are more efficient at a much quicker pace; statistically this makes sense: the more samples we have, the better chance the final output is near our expected output. Furthermore, I don't have access to game-by-game efficiency margin, so the only way I can measure standard deviations/variance/consistency is by measuring Pomeroy-Ratings's expected point margin versus the actual game point margin.

A Terrible Blog Post About Murray St.

Data from Statsheet.com and Kenpom.com


Don't have time to write more at the moment.
This was made using the formula from Holy Grail part II, except I took out the plus-minus component (not good enough data). This means that these rankings formula is biased towards offense (+/- improves the defensive component heavily).

Also, I fit the "impact per game" to Murray State's expected efficiency margin against an average team (13.2, according to Kenpom.com).

Short, but sweet. Tells us what we already know, I suppose:

1) Canaan rules on offense.
2) The Racers are pretty dern good at stealing the ball (#39 in Stl% as a team).

Top 10 Offensive Impact Players, as of 12/2

I've updated the formula slightly* since I wrote the essay for the College Basketball Prospectus preview this year. Offensive Ratings, usage, and strength of schedule all from kenpom.com.

Offensive impact estimates how much a player's contributions (by usage & efficiency) impact his team's offense above average (i.e. a +13 player would make an average team score 1.13 PPP, if the league average was 1 PPP).

	Player	Team	Year	ORTG	%USG	adjusted Offensive Impact
1	Marcus Denmon	Missouri	Sr	143.3	24.2	13.0
2	Damian Lillard	Weber St.	Jr	125.7	31.7	12.8
3	Steven Pledger	Oklahoma	Jr	146.5	21.7	11.9
4	Darius Johnson-Odom	Marquette	Sr	134.7	25.1	11.7
5	Ricardo Ratliffe	Missouri	Sr	142.9	22.5	11.6
6	Doug McDermott	Creighton	So	127.2	28.7	11.6
7	Anthony Drmic	Boise St.	Fr	135.4	24.2	11.0
8	Trae Golden	Tennessee	So	122.6	30.5	11.0
9	Tahj Tate	Delaware St.	Fr	120.8	31.2	10.9
10	Justin Edwards	Maine	Fr	130.2	25.9	10.8

I expect more Major-Conference heavy hitters to top this list soon. We've all taken the oath of small sample sizes here, right?

I don't feel comfortable sharing more than the top-10 because many players with Top-50 Offensive Impact numbers are cut out by not having high enough Offensive Ratings (for example: Jordan Taylor, at least at the moment) to be grouped on Pomeroy's site. But I don't have the time to input every NCAA player manually.



*Math:
The coefficients for ORTG, Usage, and ORTG*Usage are still the same, but I used a more theory-based adjustment for strength of schedule. Also I added a term that helps predict team ORTG better (the numbers were too small so each player gets boosted by 1ish).

Why North Carolina Will Win The National Championship

Because John Henson is magic.

"Whhhhhhhhh"
"OKAY! YOU CAN HAVE IT!"

Heels vs. Badgers, Predicted Four-Factors

Hey, that title kinda rhymes.


I've been trying to create adjusted four-factors for quite a while now, and I've finally settled on a method that is somewhat sound. First, I adjusted each team's four-factors for strength of schedule. Then I adjusted for home-court advantage using a similar method. Here are my predicted results for UNC v Wisconsin.

	eFG%	TO%	OR%	FTR	Efficiency
UNC	48.2	17.4	31.8	39.5	101.9
WISC	49.7	19.1	33.4	9.9	100.4

All the margins here are very slight, except for Free-Throw-Rate, which seems about right considering the Heels' and Badgers' status quo.*

Caveat: If we adjust Free-Throw-Rate for FT%, UNC's efficiency would most certainly drop in this formula. The four factors explain 95-99% of a team's efficiency. The remainder mostly comes from OR% being overrated when eFG% is "artificially" boosted by 3-pointers (this is why FG% is a necessary evil...), and free throw percentage.




*-What is the plural of quo? "Quotient"? "Quos"?? I sure hope it's not "quos."

Introducing The Holy Grail II: Offense & Defense (featuring UNC v. UNLV)

Accurate +/- data thanks to Adrian Atkinson (@freeportkid on twitter), editor of the Tar Heel Tip-off.

In this year's College Basketball Prospectus, I introduced "The Holy Grail" - a very simple formula for estimating true player offensive impact per possession based on Offensive Rating and Possessions Used. Not only does this describe player production (R^2 value of .65 against "true" player production), but it only requires a couple of inputs.

Unfortunately, this does not give us the whole picture: no defense, and no admission that "intangibles" could be in effect that are described by traditional plus-minus. This is especially useful when trying to analyze one game: the more causal/correlative statistics we have to offset any issues of sample size, the better**. Enter my new NCAA stat, which we will simply call "Efficiency Impact."

By using statistics to predict "true" player production, we can estimate a player's overall impact (offense + defense) rather than just offense. For an idea of the depth of this formula, these are the main parts of its tabulation:

• Offensive Rating & Possessions Used
• Defensive Rebound Percentage
• Steal Percentage (Steals/Opponent Poss)
• Block Percentage (Blocks / Opponent 2FGA)
• Assist Rate (Assists / Team FGM)
• Offensive & Defensive Efficiencies, both On & Off-Court
  and more...

READER'S NOTE: I have adjusted the +/- to be taken into account based on the number of possessions played; for NBA players, I estimated the amount of "noise" (inaccuracy) introduced based on low sample size, and adjusted accordingly. The same methodology is used here.

Here are the main inputs from the UNC in the UNLV game:
(In the "adjusted" categories, positive is always good, negative is always bad).

	MIN%	AST%	STL%	BLK%	DR%	POS%	ORTG	adj offense (on)	adj defense(on)	adj offense(off)	adj defense(off)
Hairston. P.J.	35.0	0.0	0.0	7.3	0.0	18.0	204.9	1.6	-0.6	0.7	0.1
McAdoo, James M.	45.0	17.2	8.4	0.0	15.8	16.8	114.9	1.4	-0.3	0.6	0.2
Bullock, Reggie	47.5	10.7	2.6	0.0	15.0	15.9	110.9	0.9	-0.3	0.5	0.2
Hubert, Desmond	2.5	0.0	0.0	0.0	0.0	50.6	151.5	0.0	0.0	-0.1	0.2
Watts, Justin	5.0	0.0	0.0	0.0	47.6	0.0	0.0	-0.2	-0.1	-0.1	0.2
Strickland, Dexter	72.5	13.0	1.7	0.0	6.5	15.7	122.0	-0.4	-0.7	-0.4	0.0
Marshall, Kendall	77.5	42.7	0.0	0.0	12.2	14.6	106.8	-0.5	-0.6	-0.5	0.1
Henson, John*	80.0	0.0	0.0	3.2	17.8	22.1	75.3	-0.2	-0.5	-0.3	0.1
Zeller, Tyler*	60.0	0.0	0.0	0.0	27.7	14.7	63.9	-0.7	-0.8	-0.5	0.0
Barnes, Harrison*	75.0	6.6	1.6	0.0	6.3	28.6	87.2	-1.5	-0.4	-1.2	0.2

And here are the results:

	Statistical +/- per 100	Adj. +/- per 100	Efficiency Impact (per 100)	Efficiency Impact (Game)
Hairston. P.J.	18.81	2.26	5.13	2.09
McAdoo, James M.	12.40	2.23	3.91	2.05
Bullock, Reggie	-0.58	1.45	0.92	0.51
Hubert, Desmond	31.05	0.18	5.67	0.17
Watts, Justin	3.98	-0.20	0.51	0.03
Strickland, Dexter	2.00	-1.73	-1.45	-1.23
Marshall, Kendall	0.33	-1.84	-1.87	-1.69
Henson, John*	-7.06	-0.89	-2.38	-2.22
Zeller, Tyler*	-8.64	-2.24	-3.73	-2.61
Barnes, Harrison*	-3.84	-3.36	-3.90	-3.41

Hairston's insanely high offensive rating (204.9) on nearly 20% of UNC's possessions during his minutes, in addition to UNC's overall improvement in efficiency leads to Hairston leading the Heels for the game. The big 3 boys*, on the other hand, didn't even break 90 in terms of ORTG, and played for much of the game.

Desmond Hubert only really played in one possession, but grabbed an offensive board and made a free throw, thus the high usage/high ORTG and impact per 100.

I will continue posting these, especially for Carolina games as Adrian's +/- data is more reliable than StatSheet's, but I am willing to analyze more games.

---

* - I don't think anyone calls them that, but I just did.
** - In this prediction formula, I have largely canceled out statistics that covariate heavily, leading to coefficients that have very low p-values. Each of these in tandem lead to a rating that is well-adjusted (for example, I have found time and time again that Offensive Rating overrates players' shooting efficiency, so this formula inserts a negative term against True Shooting Percentage). Using plus-minus data is similar: why should we trust a player's box-score rating if their team did considerably worse while they were on the floor?

Tidy Text: Top Teams' Toughness Tabulation, 11/28/2011

Here's a quick look at how the top-ten-Pomeroy teams are faring early in the season.

I took each team's wins, and adjusted them for strength of opponent, home-court-advantage, and most notably, diminishing returns (for example, winning by 40 then by 20 makes you look like a +23 team, rather than a +30 team*).

Tm	Adjusted Win%
Wisconsin (2)	0.991
Ohio St. (3)	0.985
Kentucky (1)	0.977
Syracuse (5)	0.948
Alabama (10)	0.947
Florida (8)	0.941
Duke (6)	0.937
Louisville (7)	0.926
Missouri (9)	0.924
North Carolina (5)	0.921

The top 3 are playing like the top 3 (ish), Bama is playing quite well, and UNC has been lagging behind.




*This is a pretty simple excel calculation. Each game returns an adjusted efficiency margin by home-court-advantage/opponent strength, which I assume has a game-standard deviation of 16. I plug this number into the NormDist function in excel like so

=NormDist(x=Adj.Margin, mean=0, st.dev=16, cumulative=TRUE)

So when we average a 40-point win and a 20-point win (assuming a pace of 72 possessions), we get the following:

40-pt-win = .99974 win%
20-pt-win = .95873 win%

Average these two win%, and you get .97923.
The NormDist function regresses in a pretty intuitive way (theoretically, we could say that it estimate's the team's "real" win%). And, intuitively, plugging this Win% back in reverse does NOT give us +30 points, but rather +23.5 points. And so on.

The Media Audit 11/16/11: "Commodores," "Bruins," and other words one would rarely use outside discussing NCAA sport.

Stuck inside with a terrible cold, so we get a blog post!
Today we are going to look at the following three 'claims' by media or popular convention, and support or refute based on the hard data:

Claim #1: Coach K has won 903 games.

Okay, just kidding. I wish him only luck as a human being, and I wish him only ill in basketball. Moving on to the real...

Claim #1: Vanderbilt is a top-caliber team.
There are a few obvious reasons I bring this up.

1. The Commodores were #7 in the preseason polls
2. The Commodores were #9 in Ken Pomeroy's preseason rankings (buy the book!)
3. The Commodores have dropped to #18 in the polls and #19 in the Pomeroy rankings after losing to Cleveland State...at home...by 13.

Couple thoughts:

Obviously, we don't have a lot of information on Vanderbilt's true ability, as they have only played three games. However, with a tough schedule ahead (Kentucky 2x, Florida 2x, Marquette, Louisvile), Vandy needs to shape up if they want to keep their losses to a minimum. Shape up, you say? Yes:

1. Vandy shot 39.2% in effective FG% against a Cleveland St. in the post-Jarvis Varnardo era.
2. Vandy turned the ball over on over 30% of their possessions in the same game.
3. Biggest consistent issue: field goal defense. Vandy allowed 54.7% eFG against Oregon, 54.6% against Cleveland St, and 51% against Bucknell. For comparison, the worst average eFG defense last year was Central Arkansas, who allowed 56.2% against even weaker competition.

Vandy is starting off much worse than average at forcing misses. The best team last year with below-average eFG defense was Marquette, who did so against the ridiculously stacked Big East. Suffice to say, they gotta step things up. 

Claim #2: Belmont is not a top-caliber team.

The Bruins received votes on Monday, but did not make it into the top 25. Suffering from Davidson syndrome, Belmont decided to schedule against super-tough Duke, and somewhat-less-tough Memphis, and lost both times. As I mused earlier, Belmont's one-point loss against Duke was honestly very very impressive. The sixteen point loss at Memphis was perhaps less impressive, but Belmont had pulled within seven at the 4:30 mark in the 2nd half, on the road no less.

ESPN wrote that Belmont "put up a good fight," so perhaps the media still has their eye on the Bruins, although they will be hard to follow until March, when their fate is determined by their RPI...

Claim #3: Ben Howland should be fired.

I am a very conservative statistician, when I am being honest. To that degree, I find it very difficult to say that any one coach should be fired after couple crappy seasons. Let's look at the facts, by UCLA's Pomeroy rankings:

2004: 125

2005: 66

2006: 3 (lost to nat'l champs Florida in...the National Championship)

2007: 6 (lost to nat'l champs Florida in the Final Four)

2008: 3 (lost to nat'l runner-ups Memphis in the Final Four)

2009: 12 (crushed by Nova in the round of 32)

2010: 109

2011: 54

UCLA has most definitely had a down couple of years after having three of their best seasons in a long, long time. So let's look at this year:

2012 (so far): 

• #93 Pomeroy 
• Lost to #171 Loyola Marymount at home by 11
• Lost to #112 Middle Tennessee at home by 20
• Allowed 66.7% eFG against these teams

Allowing 66.7% from the field is in the bottom 20 in the country, and honestly is probably somewhat due to luck on Loyola/MTSU's parts. 

But with a moderately tough conference schedule and a home game against Texas soon, UCLA is in danger of starting out 1 and 3 against D-I opponents. Pomeroy's projections have the Bruins at under .500 in conference and under .500 overall. Scary times for Howland's squad, but given the talent I'm not all-too surprised. 

Bonus Claim #4: J'Covan Brown played an amazing game last night.

One of my roommates (who I will refer to as "Mur-Dog") just asked, "How good is J'Covan Brown?" So now I will tell you, offensively, how good his game was last night:

With an offensive rating of 157 on 29.5% of his team's possessions while on the court, against a slightly-better-than-average defense, J'Covan Brown's estimated offensive impact per 100 possessions was +20.7. Which on its own is better than any offense in the country (the highest adjusted offense is Ohio State, whose offense is roughly +18.8 better than average). Over the course of the game, he played 90% of the game, which boosted their offense by a total of +18.7 points per 100 possessions. DANG.

----

As soon as I reformat my hard drive (got my first PC virus in two years...), I will start discussing players by their overall (defensive + offensive) impacts. Hint: John Henson is sort of the best player in the country in his first two games. Color me surprised, offensively.

"My Team Should be #4, not #10!"

Howdy, folks.

Just thought you should know that we can demonstrate, mathematically, that there is little observable difference between teams in the top 25...er...top 32...er....top 15.

If the top 32 teams played .500 teams for 34 games, this would be their expected record (according to Ken Pomeroy's preseason numbers) :

Rank	Team	Wins per 34	Losses per 34
1	Kentucky	33	1
2	Ohio St.	32	2
2	North Carolina	32	2
2	Duke	32	2
5	Syracuse	31	3
5	Connecticut	31	3
5	Pittsburgh	31	3
5	Louisville	31	3
9	Vanderbilt	30	4
9	Wisconsin	30	4
9	Kansas	30	4
9	Florida	30	4
9	Temple	30	4
9	Missouri	30	4
15	Baylor	29	5
15	Xavier	29	5
15	Gonzaga	29	5
15	Nevada Las Vegas	29	5
15	Purdue	29	5
15	Memphis	29	5
15	Marquette	29	5
15	Michigan	29	5
15	St. Mary's	29	5
15	Michigan St.	29	5
15	Miami FL	29	5
15	West Virginia	29	5
15	Belmont	29	5
15	Florida St.	29	5
15	New Mexico	29	5
15	Texas	29	5
15	Texas A&M	29	5

True, Texas A&M would very likely lose to Kentucky (giving Pomeroy's preseason ratings the benefit of the doubt here), but over the course of a season, there is little observable difference between the two (at least in terms of wins and losses). But given college games usually deviate from 10 to 12 points from the predicted point margin, it is even true that over the course of time, there is little observable difference between the 25th and 1st teams -- even to algorithmic ranking systems.

I say all this not to oppose ranking -- I love ranking teams and players. It's what I do. But to get up in a tizzy about who is #1 and who is #5 in rankings, especially pollster rankings, is honestly pointless (and honestly unknowable). Furthermore, as you get further and further from #1, teams become more and more close in skill level -- i.e. given accuracy of ratings, the difference between UK and OSU is significantly greater than the difference between UT and TA&M (a consequence proven by the central limit theorem).

So next time you want to bicker about the pollsters, take a step back and say: "The difference between team #4 and team #10 is actually quite small," over and over.

College Basketball Prospectus 2011-2012

It's out! I am very proud to be listed as a contributor on this book alongside some of my favorite sportswriters/statisticians/sportswrititicians.


Buy it here:
http://www.basketballprospectus.com/products/cbp2011/

!

Why John Henson Scares Me

EDIT: Hello, people from The Devil's Den. Aptly-titled! Just an FYI, I ran my NBA-style regression on these numbers (which I used to get extremely accurate numbers in the 2011 NBA finals) and Henson's "Roland Rating" here on offense honestly looks more like -1.5 than -17.5 when we adjust for randomness.

This is going to be a very short blog post. As a Tar Heel fan, John Henson scares the heck out of me.


Some of you who read this will scream out "PLUS MINUS IS FALLIBLE." And true, correlation does NOT imply causation. But this discrepancy appears to be very significant. Around 90% of Henson's on-court/off-court performance was tracked by StatSheet last season, and there are some interesting results.


The Heels seem to be about 3.2 points per 100 possessions better on defense with Henson on the floor.
Offense, on the other hand, is a very scary story, that I saw with my own eyes all season long:


(These are slightly estimated & rounded, based on UNC's average pace)

The Heels are nearly EIGHTEEN POINTS worse (per 100) on offense with Henson on the floor. Poor foul-shooting and over-shooting from inside the lane without passing to more efficient teammates are what appear to be the cause, just from watching.


Now, with this in mind, it might seem like the Heels would be worse with Henson (offensively) in every game, but that figure is more like 68% of the time. So this data at least suggests that there is maybe more to the picture than meets the eye.




I saw that Henson played miserably in his 11 minutes against William & Mary (who UNC ended up clobbering). So I removed unranked and non-ACC teams, and got the following:

So. Yeah. Any ideas?

Hope everyone's having a great summer...

Down with the RPI!!!

Adjusted Player "dWin%"

This is, as far as I know, the first publicly available Adjusted "Win%" metric for NBA players.

For a quick refresher, here's the theory:

1) Adjusted plus-minus data uses a regression equation to determine which player impacts team point margin the most, etc.
2) We can determine the probability of any team winning a game based on location (home or away), time left, and point margin. (I'm using Kupfer's old estimate).
3) According to this (and Ken Pomeroy, NCAA guru), there are certain points in the game with much more leverage than others.
4) Increasing your lead by 1 point is much more valuable to your team's win with 1 second left while the game is tied than at the beginning of the game when it is tied (i.e. clutch points>average points>"garbage time" points).

Using this, I have given each game substitution from the 2011 season play-by-play a "delta Win% per 100," showing the degree that each team's win% has changed. Using this instead of Efficiency Margin, I simply followed the steps in Eli Witus' golden blog post of old for the 2011 regular season.

I penalize high standard errors and reward low standard errors (by converting value+SE into a NormDist function then back into a value), thus making the system look slightly more like rAPM than APM.

You'll notice that some players have extremely high values that don't necessarily make sense; remember that in-game-Win-probability has a limit (it cannot exceed 100%). These rankings do not share that, (i.e. putting Dirk on an average team for a whole game would not win>120% of their games) since per-100 Win% can have extreme highs and lows. If you'd rather, simply move the decimal place over two spots to the left in your mind and consider that "delta Win% per possession."

Without further ado, here are the rankings:

	name	ReΔW%/100	ΔW%/100	SE
1	Nowitzki, Dirk	74.5%	63.2%	19.4%
2	Paul, Chris	74.4%	80.6%	24.7%
3	James, LeBron	70.2%	60.0%	19.5%
4	Ginobili, Manu	60.9%	49.4%	18.6%
5	Anderson, Ryan	60.3%	51.6%	19.6%
6	Howard, Dwight	60.0%	56.2%	21.4%
7	Jones, James	58.3%	47.9%	18.8%
8	Terry, Jason	58.3%	51.4%	20.1%
9	Gasol, Pau	53.8%	60.4%	25.7%
10	Davis, Baron	52.4%	45.3%	19.7%
11	Nash, Steve	49.8%	54.8%	25.1%
12	Garnett, Kevin	49.4%	55.2%	25.5%
13	Brand, Elton	49.4%	48.1%	22.3%
14	Wade, Dwyane	48.7%	48.4%	22.7%
15	Blatche, Andray	44.8%	38.0%	19.4%
16	Hilario, Nene	44.7%	38.4%	19.6%
17	Ibaka, Serge	44.3%	36.4%	18.8%
18	Millsap, Paul	44.2%	37.3%	19.3%
19	Tolliver, Anthony	43.3%	42.2%	22.3%
20	Duncan, Tim	43.1%	48.3%	25.6%
21	Hibbert, Roy	43.0%	60.4%	32.1%
22	Radmanovic, Vladimir	42.7%	36.9%	19.7%
23	Okafor, Emeka	42.3%	38.0%	20.5%
24	Fields, Landry	41.6%	31.5%	17.3%
25	Harris, Devin	41.0%	32.5%	18.1%
26	Wright, Dorell	40.3%	38.2%	21.7%
27	Collins, Jason	40.1%	43.8%	25.0%
28	Lowry, Kyle	40.0%	39.5%	22.6%
29	Calderon, Jose	39.7%	38.3%	22.0%
30	Farmar, Jordan	39.6%	31.7%	18.3%
31	Crawford, Jamal	39.2%	30.6%	17.8%
32	Martin, Kenyon	38.4%	33.8%	20.1%
33	Williams, Louis	37.1%	32.3%	19.8%
34	Watson, Earl	37.0%	31.9%	19.7%
35	Aldridge, LaMarcus	36.7%	39.0%	24.3%
36	O'Neal, Shaquille	36.4%	39.1%	24.6%
37	Foster, Jeff	36.1%	53.9%	34.1%
38	Collison, Nick	35.6%	32.1%	20.6%
39	Davis, Glen	34.6%	29.6%	19.5%
40	Miles, C.J.	34.5%	26.8%	17.7%
41	Jack, Jarrett	34.3%	27.9%	18.6%
42	Gordon, Eric	34.1%	29.2%	19.6%
43	Harrington, Al	34.1%	28.7%	19.2%
44	Wallace, Gerald	34.1%	23.4%	15.7%
45	House, Eddie	33.9%	33.8%	22.8%
46	Korver, Kyle	33.8%	33.3%	22.5%
47	McGee, JaVale	33.7%	27.0%	18.3%
48	Sefolosha, Thabo	33.7%	33.0%	22.4%
49	Mohammed, Nazr	33.5%	28.3%	19.3%
50	Bargnani, Andrea	32.7%	26.8%	18.7%
51	Young, Sam	31.8%	26.8%	19.3%
52	Chalmers, Mario	31.8%	29.9%	21.5%
53	Dunleavy, Mike	31.6%	33.8%	24.5%
54	Young, Thaddeus	31.4%	29.6%	21.5%
55	Arroyo, Carlos	31.3%	31.6%	23.1%
56	Turkoglu, Hedo	31.2%	22.9%	16.8%
57	Durant, Kevin	31.0%	32.3%	23.8%
58	Thompson, Jason	30.8%	25.6%	19.0%
59	Love, Kevin	30.6%	32.6%	24.3%
60	Gortat, Marcin	30.6%	26.2%	19.5%
61	Griffin, Blake	30.6%	29.6%	22.1%
62	Speights, Marreese	30.5%	34.1%	25.6%
63	Miller, Andre	30.4%	33.9%	25.4%
64	Chandler, Wilson	30.0%	21.3%	16.2%
65	Kidd, Jason	30.0%	34.5%	26.3%
66	Williams, Mo	29.8%	27.3%	21.0%
67	Boykins, Earl	29.7%	33.8%	26.0%
68	Ilyasova, Ersan	29.6%	25.8%	19.9%
69	Hill, George	29.5%	23.6%	18.3%
70	Deng, Luol	29.5%	33.0%	25.6%
71	Martin, Cartier	28.9%	31.9%	25.2%
72	Bogut, Andrew	28.5%	26.1%	20.9%
73	Randolph, Zach	28.5%	26.4%	21.2%
74	Jennings, Brandon	28.4%	30.2%	24.3%
75	Pierce, Paul	28.3%	28.9%	23.3%
76	Dooling, Keyon	28.0%	24.4%	19.9%
77	Curry, Stephen	27.5%	23.3%	19.4%
78	Johnson, Amir	27.4%	24.1%	20.1%
79	Foye, Randy	27.2%	23.5%	19.7%
80	Cousins, DeMarcus	26.9%	23.4%	19.8%
81	Barbosa, Leandro	26.3%	21.2%	18.5%
82	Walker, Bill	26.2%	25.8%	22.5%
83	Jeffries, Jared	26.2%	29.1%	25.4%
84	George, Paul	26.1%	30.3%	26.5%
85	Mayo, O.J.	26.1%	21.6%	18.9%
86	Hayes, Chuck	25.9%	26.5%	23.3%
87	Johnson, Joe	25.9%	23.0%	20.3%
88	Rose, Derrick	25.8%	43.2%	38.3%
89	Lewis, Rashard	25.7%	19.0%	16.9%
90	Odom, Lamar	25.1%	23.2%	21.1%
91	Jianlian, Yi	24.8%	22.6%	20.8%
92	Kirilenko, Andrei	24.7%	20.5%	18.9%
93	Lopez, Brook	24.7%	26.8%	24.8%
94	Fernandez, Rudy	24.5%	19.4%	18.1%
95	Bynum, Andrew	24.3%	24.4%	22.9%
96	Parker, Anthony	24.1%	22.0%	20.8%
97	Williams, Deron	23.8%	19.7%	19.0%
98	Harden, James	23.7%	23.2%	22.4%
99	Battier, Shane	23.4%	19.1%	18.7%
100	Turiaf, Ronny	22.6%	18.5%	18.7%
101	Budinger, Chase	22.6%	20.6%	20.9%
102	Henderson, Gerald	22.6%	17.9%	18.1%
103	Johnson, Wesley	22.5%	21.6%	22.0%
104	Andersen, Chris	22.2%	23.0%	23.7%
105	Greene, Donte	22.2%	20.6%	21.3%
106	Kaman, Chris	22.1%	22.4%	23.2%
107	Prince, Tayshaun	22.0%	19.7%	20.4%
108	Hawes, Spencer	21.9%	20.8%	21.7%
109	Dalembert, Samuel	21.6%	18.3%	19.3%
110	Anthony, Carmelo	21.3%	16.6%	17.8%
111	Morrow, Anthony	21.3%	18.5%	19.9%
112	Smith, Josh	21.1%	18.0%	19.5%
113	Bonner, Matt	20.9%	19.5%	21.4%
114	Williams, Marvin	20.8%	15.4%	16.9%
115	Beasley, Michael	20.8%	18.2%	20.1%
116	Monroe, Greg	20.4%	19.4%	21.7%
117	Matthews, Wes	20.2%	17.3%	19.6%
118	Bosh, Chris	20.0%	20.7%	23.7%
119	Williams, Shawne	20.0%	16.5%	18.8%
120	Bibby, Mike	19.8%	15.1%	17.4%
121	Smith, J.R.	19.6%	15.7%	18.3%
122	Thomas, Tyrus	19.5%	19.2%	22.6%
123	O'Neal, Jermaine	19.3%	25.1%	29.7%
124	Frye, Channing	19.2%	16.3%	19.3%
125	Miller, Brad	19.1%	20.3%	24.2%
126	Asik, Omer	18.5%	23.0%	28.4%
127	Allen, Tony	18.5%	15.9%	19.6%
128	Augustin, D.J.	18.4%	20.4%	25.3%
129	Felton, Raymond	18.2%	14.8%	18.5%
130	Gay, Rudy	18.2%	16.9%	21.2%
131	Gibson, Daniel	18.2%	15.6%	19.6%
132	Udrih, Beno	18.2%	16.1%	20.3%
133	Lee, Courtney	18.0%	17.5%	22.2%
134	Green, Jeff	17.8%	12.8%	16.5%
135	Fisher, Derek	17.6%	29.6%	38.4%
136	West, David	17.2%	16.0%	21.3%
137	Conley, Mike	17.1%	22.8%	30.5%
138	Udoh, Ekpe	17.1%	16.0%	21.3%
139	Bayless, Jerryd	17.0%	15.5%	20.9%
140	Gallinari, Danilo	16.5%	12.8%	17.7%
141	Stoudemire, Amare	16.5%	13.6%	18.9%
142	Granger, Danny	16.3%	18.5%	25.9%
143	Stojakovic, Peja	16.1%	16.9%	23.9%
144	Gordon, Ben	16.1%	14.4%	20.4%
145	Ellington, Wayne	16.0%	16.7%	23.9%
146	Jones, Dahntay	15.9%	22.2%	32.0%
147	Diaw, Boris	15.8%	14.1%	20.4%
148	Maxiell, Jason	15.8%	16.2%	23.5%
149	Daniels, Marquis	15.5%	16.2%	23.9%
150	McRoberts, Josh	15.3%	17.4%	25.9%
151	Lee, David	15.2%	12.9%	19.4%
152	Horford, Al	15.0%	13.5%	20.5%
153	Rush, Brandon	14.9%	16.3%	25.0%
154	Williams, Shelden	14.9%	13.8%	21.3%
155	Roy, Brandon	14.7%	12.8%	19.9%
156	Neal, Gary	14.2%	12.6%	20.2%
157	Wilcox, Chris	14.0%	14.1%	23.0%
158	Jones, Solomon	13.9%	23.3%	38.4%
159	White, D.J.	13.8%	14.7%	24.3%
160	Bledsoe, Eric	13.8%	12.4%	20.6%
161	Douglas-Roberts, Chris	13.7%	13.6%	22.6%
162	Hollins, Ryan	13.5%	13.8%	23.3%
163	Smith, Jason	13.2%	12.8%	22.1%
164	Villanueva, Charlie	13.2%	11.9%	20.6%
165	Gasol, Marc	13.2%	11.2%	19.5%
166	Meeks, Jodie	12.9%	10.3%	18.2%
167	Williams, Reggie	12.7%	9.7%	17.4%
168	Samuels, Samardo	12.7%	15.3%	27.6%
169	Livingston, Shaun	12.6%	13.3%	24.0%
170	Delfino, Carlos	12.3%	10.1%	18.8%
171	Hickson, J.J.	12.3%	11.5%	21.5%
172	Casspi, Omri	11.9%	10.3%	19.7%
173	Barea, Jose	11.8%	11.3%	22.0%
174	McGrady, Tracy	11.6%	10.3%	20.1%
175	Krstic, Nenad	11.5%	9.7%	19.2%
176	Petro, Johan	11.4%	13.1%	26.3%
177	Pietrus, Mickael	11.4%	9.9%	20.0%
178	Brown, Shannon	11.3%	12.9%	26.2%
179	Chandler, Tyson	11.2%	14.1%	28.9%
180	Teague, Jeff	11.1%	11.2%	22.9%
181	Brockman, Jon	11.1%	13.2%	27.3%
182	Splitter, Tiago	10.8%	11.7%	24.7%
183	Lawson, Ty	10.8%	8.5%	18.0%
184	Carter, Vince	10.6%	8.5%	18.3%
185	Booker, Trevor	10.5%	9.6%	20.9%
186	Sessions, Ramon	10.2%	9.6%	21.4%
187	Butler, Caron	10.1%	10.3%	23.3%
188	Lopez, Robin	9.9%	10.0%	23.1%
189	Richardson, Quentin	9.8%	9.4%	21.9%
190	Jefferson, Richard	9.7%	8.5%	20.2%
191	Pachulia, Zaza	9.5%	8.7%	20.9%
192	Parker, Tony	9.5%	9.6%	23.1%
193	Iguodala, Andre	9.3%	8.3%	20.2%
194	Daye, Austin	9.3%	7.5%	18.5%
195	Scola, Luis	9.1%	9.8%	24.6%
196	Perkins, Kendrick	9.1%	9.4%	23.5%
197	Noah, Joakim	9.1%	11.0%	27.6%
198	Afflalo, Arron	9.1%	7.2%	18.0%
199	Childress, Josh	9.1%	8.9%	22.5%
200	Ellis, Monta	8.5%	8.3%	22.2%
201	Harris, Manny	8.5%	8.7%	23.4%
202	Bass, Brandon	8.4%	6.7%	18.1%
203	Brewer, Ronnie	8.4%	8.3%	22.6%
204	McDyess, Antonio	8.4%	8.3%	22.6%
205	Hill, Grant	8.3%	8.0%	21.9%
206	Gibson, Taj	8.3%	7.9%	21.8%
207	Allen, Ray	8.2%	8.1%	22.8%
208	Brewer, Corey	8.1%	6.9%	19.3%
209	Stuckey, Rodney	7.8%	7.0%	20.5%
210	Holiday, Jrue	7.5%	6.8%	20.7%
211	Patterson, Patrick	7.4%	8.8%	27.1%
212	Watson, C.J.	7.3%	11.1%	34.6%
213	Warrick, Hakim	7.2%	6.0%	19.3%
214	Thornton, Al	6.8%	5.2%	17.3%
215	Cunningham, Dante	6.5%	5.0%	17.9%
216	Landry, Carl	6.1%	4.8%	17.9%
217	Vujacic, Sasha	6.1%	4.9%	18.3%
218	Posey, James	5.9%	7.6%	29.6%
219	Brown, Kwame	5.8%	4.9%	19.4%
220	Graham, Joey	5.7%	6.3%	25.1%
221	Martin, Kevin	5.3%	5.7%	24.7%
222	Garcia, Francisco	5.1%	4.5%	20.1%
223	Thomas, Kurt	5.1%	6.2%	27.6%
224	Salmons, John	4.7%	4.2%	20.4%
225	Richardson, Jason	4.6%	3.5%	17.5%
226	Blake, Steve	4.5%	6.8%	34.5%
227	Smith, Craig	4.1%	4.8%	26.9%
228	Douglas, Toney	3.6%	2.8%	18.2%
229	Bogans, Keith	3.5%	4.1%	26.9%
230	Arthur, Darrell	3.3%	3.0%	20.8%
231	Artest, Ron	3.3%	3.5%	24.7%
232	Evans, Maurice	3.3%	2.5%	17.3%
233	Billups, Chauncey	3.0%	2.6%	20.1%
234	Varejao, Anderson	2.8%	3.5%	28.0%
235	Jefferson, Al	2.7%	2.7%	22.4%
236	Dragic, Goran	2.6%	2.5%	21.9%
237	Jordan, DeAndre	2.5%	2.0%	18.7%
238	Jackson, Stephen	2.3%	2.0%	19.6%
239	Williams, Jason	2.2%	3.4%	34.8%
240	Price, Ronnie	2.1%	2.2%	23.9%
241	Evans, Tyreke	1.8%	1.4%	18.0%
242	Butler, Rasual	1.4%	1.4%	22.6%
243	Arenas, Gilbert	1.2%	0.9%	17.3%
244	Boozer, Carlos	1.0%	1.0%	23.0%
245	Johnson, James	0.3%	0.3%	22.2%
246	Mills, Patrick	0.2%	0.3%	26.7%
247	Green, Willie	0.2%	0.1%	19.0%
248	Belinelli, Marco	0.1%	0.1%	20.3%
249	Miller, Mike	-0.1%	-0.1%	21.9%
250	Pekovic, Nikola	-0.9%	-0.9%	24.3%
251	Gooden, Drew	-1.0%	-1.1%	23.4%
252	Harangody, Luke	-1.1%	-1.3%	26.2%
253	Hamilton, Richard	-1.3%	-1.2%	21.2%
254	Crawford, Jordan	-1.4%	-1.4%	23.5%
255	Thornton, Marcus	-1.5%	-1.1%	17.0%
256	Randolph, Anthony	-1.5%	-1.7%	26.9%
257	Derozan, DeMar	-1.8%	-1.6%	20.0%
258	Rondo, Rajon	-1.8%	-1.8%	22.9%
259	Batum, Nicolas	-2.2%	-1.8%	18.6%
260	Maynor, Eric	-2.5%	-3.9%	34.8%
261	Jamison, Antawn	-2.6%	-2.5%	22.1%
262	Cook, Daequan	-3.0%	-3.6%	27.5%
263	Head, Luther	-3.3%	-3.7%	25.8%
264	Howard, Juwan	-3.6%	-4.4%	28.2%
265	Elson, Francisco	-3.7%	-4.4%	27.1%
266	Kleiza, Linas	-3.7%	-3.3%	20.3%
267	Young, Nick	-3.9%	-3.1%	18.4%
268	Wallace, Ben	-3.9%	-4.2%	24.7%
269	Nelson, Jameer	-4.0%	-4.2%	23.8%
270	Robinson, Nate	-4.4%	-4.7%	24.0%
271	Beaubois, Rodrigue	-4.5%	-5.8%	29.5%
272	Jeter, Eugene	-4.8%	-4.9%	23.6%
273	Wright, Julian	-4.8%	-4.7%	22.0%
274	Bryant, Kobe	-4.9%	-6.1%	28.5%
275	Cardinal, Brian	-5.0%	-5.9%	27.0%
276	Davis, Ed	-5.1%	-4.6%	20.6%
277	Ridnour, Luke	-5.2%	-10.6%	47.0%
278	Wilkins, Damien	-5.4%	-5.8%	24.4%
279	Brooks, Aaron	-5.4%	-4.8%	20.1%
280	Stevenson, DeShawn	-5.9%	-5.4%	21.0%
281	Amundson, Louis	-6.2%	-6.4%	23.6%
282	Telfair, Sebastian	-6.7%	-13.7%	47.0%
283	Westbrook, Russell	-6.7%	-10.7%	36.6%
284	Vasquez, Greivis	-6.9%	-9.1%	30.4%
285	Flynn, Jonny	-6.9%	-14.4%	47.5%
286	Seraphin, Kevin	-7.1%	-7.6%	24.5%
287	Mbah a Moute, Luc	-7.4%	-5.9%	18.2%
288	Humphries, Kris	-7.8%	-6.5%	19.1%
289	Evans, Reggie	-8.2%	-8.7%	24.2%
290	Barnes, Matt	-8.2%	-8.4%	23.4%
291	Carter, Anthony	-8.8%	-10.7%	27.8%
292	Camby, Marcus	-8.8%	-7.8%	20.2%
293	Outlaw, Travis	-9.3%	-7.0%	17.4%
294	Duhon, Chris	-9.5%	-10.9%	26.1%
295	Blair, DeJuan	-9.8%	-8.9%	20.8%
296	Przybilla, Joel	-10.6%	-12.6%	27.3%
297	Favors, Derrick	-10.7%	-8.5%	18.1%
298	Milicic, Darko	-10.7%	-10.1%	21.5%
299	Williams, Terrence	-10.8%	-15.9%	33.5%
300	Dudley, Jared	-11.6%	-9.8%	19.3%
301	Wall, John	-11.7%	-9.7%	19.0%
302	Powell, Josh	-12.0%	-13.8%	26.2%
303	Maggette, Corey	-12.5%	-10.3%	18.9%
304	Eyenga, Christian	-12.8%	-12.6%	22.4%
305	Hansbrough, Tyler	-13.1%	-15.0%	26.2%
306	Hinrich, Kirk	-13.1%	-9.2%	16.1%
307	Bynum, Will	-13.3%	-14.2%	24.3%
308	Ford, T.J.	-13.4%	-31.5%	53.6%
309	Forbes, Gary	-13.6%	-12.8%	21.4%
310	McGuire, Dominic	-13.6%	-13.5%	22.7%
311	Bell, Raja	-13.9%	-11.7%	19.3%
312	Sanders, Larry	-14.5%	-15.0%	23.7%
313	Pondexter, Quincy	-14.6%	-15.7%	24.6%
314	Hayward, Gordon	-15.3%	-13.4%	20.1%
315	Marion, Shawn	-15.4%	-13.4%	19.9%
316	Mason, Roger	-15.4%	-22.0%	32.7%
317	Redick, J.J.	-15.5%	-12.3%	18.1%
318	Gee, Alonzo	-16.2%	-15.1%	21.4%
319	Brown, Derrick	-16.2%	-17.8%	25.0%
320	Hill, Jordan	-16.9%	-17.0%	23.0%
321	Collison, Darren	-17.4%	-39.4%	51.9%
322	Murphy, Troy	-17.4%	-20.9%	27.3%
323	Price, A.J.	-17.8%	-40.8%	52.3%
324	Gomes, Ryan	-19.8%	-17.2%	19.9%
325	Aminu, Al-Farouq	-20.0%	-18.2%	20.8%
326	Haywood, Brendan	-20.1%	-23.1%	26.3%
327	Biedrins, Andris	-20.5%	-19.0%	21.2%
328	Nocioni, Andres	-21.1%	-19.5%	21.1%
329	Webster, Martell	-24.6%	-24.9%	23.1%
330	West, Delonte	-26.6%	-34.3%	29.5%
331	Moon, Jamario	-31.3%	-27.5%	20.1%
332	Law, Acie	-32.8%	-34.5%	24.1%
333	Ariza, Trevor	-34.2%	-31.9%	21.4%
334	Graham, Stephen	-34.8%	-33.6%	22.1%
335	Haslem, Udonis	-35.0%	-52.8%	34.5%
336	Weems, Sonny	-36.2%	-30.7%	19.3%
337	Dampier, Erick	-37.5%	-46.5%	28.3%
338	Ilgauskas, Zydrunas	-37.6%	-42.3%	25.7%
339	Turner, Evan	-46.6%	-39.4%	19.3%
340	Anthony, Joel	-54.3%	-58.5%	24.6%

NBAdraft.net Top 20, By NCAA Offense

NBAdraft.Net's top 20 College Picks
ranked by Offensive Efficiency and Effective Offensive Size

Based on Adjusted Offensive Plus Minus as predicted by Offensive Rating and Usage% (via kenpom.com for college players).

Size+ is based on how Height + Weight predict offensive performance (at least, in the NCAA).

rk	exp	Player	Team	Yr	NCAA Offense		Size+	Ht"	Wt
1	1	Kyrie Irving	Duke	Fr	11.85		22%	74	180
2	8	Kemba Walker	Connecticut	Jr	11.54		16%	73	172
3	2	Derrick Williams	Arizona	So	9.86		86%	80	240
4	13	Jimmer Fredette	Brigham Young	Sr	9.47		42%	74	195
5	24	Nolan Smith	Duke	Sr	8.98		28%	74	185
6	23	Darius Morris	Michigan	So	8.93		19%	76	180
7	10	Alec Burks	Colorado	So	8.20		20%	78	185
8	11	Marcus Morris	Kansas	Jr	7.70		71%	80	225
9	14	Jordan Hamilton	Texas	So	7.51		74%	79	226
10	3	Brandon Knight	Kentucky	Fr	6.82		26%	75	185
11	20	Marshon Brooks	Providence	Sr	6.71		28%	77	190
12	16	Markieff Morris	Kansas	Jr	6.60		77%	81	232
13	18	Tobias Harris	Tennessee	Fr	5.71		64%	80	220
14	9	Tristan Thompson	Texas	Fr	5.70		72%	80	226
15	7	Kawhi Leonard	San Diego St.	So	5.24		73%	79	225
16	15	Klay Thompson	Washington St.	Jr	3.93		39%	78	200
17	25	Trey Thompkins	Georgia	Jr	3.85		90%	81	247
18	17	Chris Singleton	Florida St.	Jr	3.77		71%	81	227
19	22	Tyler Honeycutt	UCLA	So	2.34		15%	80	183
20	21	Kenneth Faried	Morehead St.	Sr	1.92		74%	80	228

I adjusted these for schedule, but did no NBA->NCAA translation or vice versa. These are based on the following regression:

predicted_Offense = Usage%*(0.92*ORTG - 0.82) - 0.029*ORTG

The Supporting Cast picks up The German's Slack - Game 6

LeBron had a bad day - but so did Dirk.

Bosh, Chalmers, and Wade are the only Miami players who appear to have a positive impact.

Raw Est APM(100) = Estimate of per-100-possessions impact
Adj Est +/- (G) = Estimate of point margin impact during the game

team	Starters	MP	TOV%	ORtg	DRtg	+/-	Raw Est APM(100)	Adj Est +/- (G)
DAL	Jason Kidd	36.3	25.8	143	108	18	5.32	3.25
DAL	Jason Terry	33.9	14.5	130	104	13	3.59	1.82
DAL	Shawn Marion	35.4	13.7	100	105	18	3.21	1.61
MIA	Chris Bosh	38.9	7.6	144	113	4	1.63	1.52
MIA	Mario Chalmers	38.5	15.8	110	111	5	1.02	1.01
DAL	Brian Cardinal	12.2	50	131	111	18	3.74	0.69
MIA	Dwyane Wade	41.1	20.8	90	112	3	0.40	0.54
DAL	Tyson Chandler	30.1	17	108	98	3	1.43	0.26
DAL	Ian Mahinmi	11.0	0	154	96	-1	0.17	-0.19
DAL	DeShawn Stevenson	12.6	0	174	104	-8	-0.51	-0.40
MIA	Eddie House	21.1	0	131	112	-1	-1.27	-0.45
DAL	Jose Juan Barea	29.6	20	117	103	-7	-0.05	-0.66
MIA	Juwan Howard	7.0	34.7	0	122	-11	-7.84	-1.10
DAL	Dirk Nowitzki	38.9	6.7	78	98	-4	-0.43	-1.17
MIA	Joel Anthony	10.9	0	63	120	-3	-5.58	-1.22
MIA	Udonis Haslem	33.9	0	119	117	-7	-1.98	-1.23
MIA	Mike Miller	8.3	0	0	119	-16	-10.02	-1.69
MIA	LeBron James	40.4	26.4	97	115	-24	-3.31	-2.58

The Biggest Game of Lebron James' life...was awful.

Dallas won by 9 points, which is 10.4 points per 100 possessions.

I split credit of the team's point margin in half, adjusted for pace, quality of opponent, and home-court-advantage.

Dallas played like a +4.98 team
Miami played like a -4.98 team

TOV%=percent of a player's possessions that ended in a turnover (basketball-reference.com's boxscore).
ORTG=offensive rating. very complex measure for efficiency
DRTG=defensive rating, similar to the above.
+/- = Plus-Minus rating, as tabulated by ESPN's box score. It shows the change in point margin while a player is on the court.

Raw est APM(100)= an estimate (using the above) of how much a player impacted his team's point margin per 100 possessions.

Adj est +/- (G) = an estimate of how much a player impacted his team's point margin per 100 possessions

team	Starters	MP	TOV%	ORtg	DRtg	+/-	Raw est APM(100)	Adj est +/- (G)
DAL	Jason Kidd	39.87	30.4	139	115	13	4.27	3.17
DAL	Tyson Chandler	38.67	0	150	117	14	3.03	2.08
DAL	Dirk Nowitzki	39.62	8.2	125	119	14	2.46	1.66
DAL	Shawn Marion	34.03	15.4	80	117	21	1.85	0.99
MIA	Mario Chalmers	23.45	12	175	128	-1	0.53	0.55
MIA	Mike Miller	23.20	16.7	143	128	0	-0.29	0.15
MIA	Juwan Howard	5.82	0	200	134	6	-0.02	0.07
DAL	Jason Terry	30.48	13.4	146	123	1	0.44	0.00
MIA	Udonis Haslem	33.00	0	140	131	0	-0.78	-0.13
MIA	Eddie House	3.47	33.3	0	95	3	-3.85	-0.23
DAL	Jose Juan Barea	25.65	8	146	126	4	0.00	-0.24
MIA	Joel Anthony	16.20	0	200	135	-9	-1.55	-0.32
DAL	Brian Cardinal	9.62	34.7	106	114	-7	-2.42	-0.57
DAL	Ian Mahinmi	8.1	0	123	123	-3	-3.70	-0.70
MIA	Dwyane Wade	34.45	18.8	126	124	-13	-1.74	-0.82
MIA	Mike Bibby	15.40	33.3	62	134	-7	-4.74	-1.33
MIA	Chris Bosh	39.38	20	113	131	-13	-2.24	-1.35
DAL	DeShawn Stevenson	13.97	0	133	127	-12	-4.44	-1.42
MIA	LeBron James	45.63	16.8	98	131	-11	-2.23	-1.56

(Here is a quick spreadsheet that explains how I currently estimate APM.)

James played nearly every minute of this game, and didn't really succeed. Per-possession, Bibby had a much worse game, however.

EDIT: By the way - these numbers are based on a prediction of these ratings.

Another Look @ Game 3

based on Engalmann's latest adjustement to Regularized Adjusted Plus-Minus

raw est APM(100)=Estimate of adjusted +/- per 100
adj est +/- (G)= estimated contribution to team point margin during the game (adjusted for % of minutes played, quality of opponent, location)

team	Players	MP	TOV%	ORtg	DRtg	+/-	raw est APM(100)	adj est +/-(G)
MIA	LeBron James	45.3	20.2	104	108	1	4.11	3.73
DAL	Dirk Nowitzki	42.0	10.7	128	99	12	4.99	3.52
MIA	Dwyane Wade	38.6	0	137	101	-1	3.61	2.79
MIA	Mario Chalmers	28.8	11.1	131	110	6	3.14	1.79
DAL	Tyson Chandler	40.0	0	137	104	4	2.98	1.68
DAL	Jason Kidd	35.5	31.1	100	109	5	2.47	1.11
MIA	Udonis Haslem	29.0	12.5	86	105	5	1.70	0.94
DAL	DeShawn Stevenson	14.2	0	278	113	-7	3.06	0.62
MIA	Joel Anthony	23.1	0	108	102	3	0.99	0.41
MIA	Mike Miller	12.2	50	26	86	4	0.11	-0.01
MIA	Juwan Howard	6.4	0	146	109	6	0.05	-0.01
DAL	Brian Cardinal	0.1	0	0	116	-3	-9.17	-0.02
DAL	Shawn Marion	43.1	13.4	89	113	-1	0.84	-0.11
MIA	Chris Bosh	37.5	9	89	114	-10	-0.13	-0.22
DAL	Ian Mahinmi	8.0	0	145	115	-6	-2.64	-0.60
DAL	Peja Stojakovic	6.1	33.3	60	98	-11	-4.01	-0.63
MIA	Mike Bibby	19.1	16.7	60	101	-4	-1.65	-0.72
DAL	Jose Juan Barea	19.0	31.1	52	112	3	-1.67	-1.05
DAL	Jason Terry	32.1	0	112	109	-6	-0.75	-1.15

(Plus-minus number also gives us estimates for OnCourt +/- per 100 minus OffCourt +/- per 100, and teammate OnCourt minus OffCourt, both of which adjust the Offensive Rating, Defensive Rating, and Turnover% values)

Game 3! (Somewhat late...)

The formula is confused as to how Dallas lost. (Total contrib of around 11 versus Miami's 6).

Miami leads, 2-1.

team	Players	ORtg	DRtg	'+/-	min%	eff	contrib
DAL	Dirk Nowitzki	128	99	12	87.4%	4.54	3.97
DAL	Tyson Chandler	137	104	4	83.3%	3.35	2.79
MIA	Dwyane Wade	137	101	-1	80.5%	2.19	1.76
MIA	LeBron James	104	108	1	94.3%	1.80	1.70
DAL	Jason Kidd	100	109	5	73.9%	2.30	1.70
MIA	Mario Chalmers	131	110	6	60.1%	2.50	1.50
DAL	Shawn Marion	89	113	-1	89.9%	1.50	1.35
DAL	DeShawn Stevenson	278	113	-7	29.6%	3.42	1.01
MIA	Udonis Haslem	86	105	5	60.5%	1.44	0.87
MIA	Joel Anthony	108	102	3	48.1%	1.49	0.72
DAL	Jason Terry	112	109	-6	66.8%	0.80	0.53
MIA	Juwan Howard	146	109	6	13.4%	1.87	0.25
DAL	Ian Mahinmi	145	115	-6	16.7%	0.15	0.03
DAL	Jose Juan Barea	52	112	3	39.7%	0.02	0.01
DAL	Brian Cardinal		116	-3	0.2%	-3.69	-0.01
MIA	Mike Miller	26	86	4	25.3%	-0.38	-0.10
DAL	Peja Stojakovic	60	98	-11	12.6%	-2.22	-0.28
MIA	Mike Bibby	60	101	-4	39.8%	-0.87	-0.35
MIA	Chris Bosh	89	114	-10	78.1%	-0.53	-0.42

Game 2! (Very late...)

I finally came up with a formula that helps regress crazy values (i.e. Dirk off the court was worth -100 points per 100 possessions in game 2).


"Adjusted" +/- per 100 is a simple combination of Net +/- (ON - OFF), offensive rating, and defensive rating

DALLAS by 2, (tying the series at 1-1)

team	Starters	min%	ORtg	DRtg	+/-	Adj +/- per 100	Total +/- Contrib.
MIA	Dwyane Wade	87.8%	148	107	5	3.79	3.33
DAL	Dirk Nowitzki	87.6%	97	106	13	3.28	2.88
DAL	Tyson Chandler	79.4%	154	108	4	3.09	2.45
DAL	Jason Terry	64.8%	124	105	13	3.54	2.29
DAL	Shawn Marion	85.2%	123	106	1	2.13	1.81
MIA	Mike Bibby	46.0%	145	98	7	3.34	1.54
MIA	Chris Bosh	82.9%	71	112	3	1.49	1.23
MIA	LeBron James	82.5%	99	101	-5	1.27	1.05
DAL	Jason Kidd	78.2%	72	106	1	0.82	0.65
DAL	DeShawn Stevenson	46.1%	131	95	-5	0.82	0.38
DAL	Brendan Haywood	17.0%	65	112	5	-0.15	-0.02
DAL	Brian Cardinal	2.1%		108	-4	-3.30	-0.07
MIA	Mario Chalmers	51.9%	98	116	-7	-0.33	-0.17
DAL	Peja Stojakovic	10.3%	0	114	-7	-3.53	-0.36
DAL	Jose Barea	29.3%	82	109	-11	-1.69	-0.50
MIA	Joel Anthony	55.9%		114	2	-0.94	-0.52
MIA	Mike Miller	31.6%	0	99	-3	-1.97	-0.62
MIA	Udonis Haslem	61.3%	52	114	-12	-1.76	-1.08

GAME 1, NBA FINALS

Wayne Winston & more recently Kevin Pelton like to use +/- in their discussions on player performance. But Statistical plus-minus geeks like myself are dissatisfied with simple Net +/- ratings. It should be noted that I have not implemented any system that regresses towards the mean for small sample sizes. I will incorporate this later.

Here is how the Heat & Mavericks stack up in my combined system.

Adj. Rating = estimate of how much the player contributed to his team's point margin, per 100 possessions*
Contrib = estimate of a player's total contributions to their team's point margin during play

st. ORAPM = advanced stats prediction of offensive Regularized Adjusted Plus-Minus
st. DRAPM = same^, except defensive RAPM
Statistical RTG = st. ORAPM + st. DRAPM

Net Plus-Minus = On-Court Team Efficiency Margin (per 100) minus Off-court Team Efficiency Margin

	Player		st. ORAPM	st. DRAPM	Net Plus-Minus	Statistical RTG	Adj RTG	Min%	Contrib
DAL	Dirk Nowitzki		1.76	3.60	40.63	5.35	11.84	84.3%	9.98
MIA	Chris Bosh		1.87	0.73	35.72	2.59	9.54	81.1%	7.74
MIA	LeBron James		6.25	-3.31	30.96	2.94	7.53	94.3%	7.10
DAL	Tyson Chandler		-0.99	1.66	21.46	0.67	5.82	70.4%	4.10
DAL	Jason Terry		-2.43	-4.25	30.96	-6.67	5.79	67.7%	3.92
MIA	Mario Chalmers		0.44	-0.66	16.58	-0.22	3.95	59.2%	2.33
MIA	Udonis Haslem		-3.87	-2.47	15.48	-6.34	2.30	61.6%	1.41
MIA	Dwyane Wade		2.82	-1.42	4.89	1.40	1.08	79.3%	0.86
MIA	Juwan Howard		-2.05	8.13	5.80	6.07	4.10	15.9%	0.65
DAL	Brendan Haywood		-3.68	13.72	-21.46	10.04	-0.98	29.6%	-0.29
DAL	Peja Stojakovic		-6.77	1.79	-2.81	-4.97	-1.21	30.7%	-0.37
DAL	DeShawn Stevenson		0.77	1.15	-15.61	1.92	-3.48	30.1%	-1.05
MIA	Mike Miller		0.04	-2.57	-6.63	-2.52	-2.72	42.1%	-1.14
MIA	Mike Bibby		-2.31	6.67	-25.36	4.36	-4.39	29.4%	-1.29
DAL	Jason Kidd		-2.75	0.35	-6.45	-2.40	-2.03	75.9%	-1.54
DAL	Jose Barea		-9.95	0.22	-15.48	-9.73	-5.52	37.8%	-2.09
DAL	Shawn Marion		0.84	2.59	-19.39	3.44	-3.89	73.6%	-2.86
MIA	Joel Anthony		-1.37	1.23	-41.27	-0.14	-10.30	37.2%	-3.83

*In case you were wondering, for the 2011 season, this is how statistical & Net plus-minuses predict (explain) RAPM ratings:

	Coefficients
Intercept	-0.081370214
s_ORAPM	0.160596108
s_DRAPM	0.373897645
Net per 100	0.253406036