For all your fancy-pants statistical needs.

Praise for The Basketball Distribution:

"...confusing." - CBS
"...quite the pun master." - ESPN

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

1Marcus Denmon (Missouri)B1211.40
2Steven Pledger (Oklahoma)B1211.00
3Henry Sims (Georgetown)BigEast9.41
4Kenny Boynton (Florida)SEC9.27
5Jared Sullinger (Ohio St.)B109.21
6Ricardo Ratliffe (Missouri)B129.18
7Tyrus McGee (Iowa St.)B128.55
8Travon Woodall (Pittsburgh)BigEast8.17
9Cody Zeller (Indiana)B108.10
10Kevin Jones (West Virginia)BigEast8.01
11Davante Gardner (Marquette)BigEast7.80
12Jae Crowder (Marquette)BigEast7.79
13Darius Johnson-Odom (Marquette)BigEast7.77
14Mike Scott (Virginia)ACC7.77
15Hollis Thompson (Georgetown)BigEast7.24
16J'Covan Brown (Texas)B127.22
17Arnett Moultrie (Mississippi St.)SEC7.14
18Terrell Stoglin (Maryland)ACC7.12
19Dion Waiters (Syracuse)BigEast7.00
20Pierre Jackson (Baylor)B127.00
21JaMychal Green (Alabama)SEC6.97
22Doron Lamb (Kentucky)SEC6.97
23Jeremy Lamb (Connecticut)BigEast6.76
24Erick Green (Virginia Tech)ACC6.64
25Erving Walker (Florida)SEC6.63
26Tyler Zeller (North Carolina)ACC6.57
27Robbie Hummel (Purdue)B106.38
28Andre Young (Clemson)ACC6.14
29Shabazz Napier (Connecticut)BigEast5.99
30Brandon Young (DePaul)BigEast5.98
31Mike Rosario (Florida)SEC5.96
32Nasir Robinson (Pittsburgh)BigEast5.77
33John Jenkins (Vanderbilt)SEC5.73
34Drew Crawford (Northwestern)B105.71
35Thomas Robinson (Kansas)B125.70
36Ashton Gibbs (Pittsburgh)BigEast5.61
37Maalik Wayns (Villanova)BigEast5.53
38Trae Golden (Tennessee)SEC5.47
39Ryan Kelly (Duke)ACC5.45
40Kris Joseph (Syracuse)BigEast5.41
41Deshaun Thomas (Ohio St.)B105.39
42C.J. Harris (Wake Forest)ACC5.37
43B.J. Young (Arkansas)SEC5.22
44Scott Wood (North Carolina St.)ACC5.11
45Jeronne Maymon (Tennessee)SEC5.10
46Reggie Bullock (North Carolina)ACC5.07
47Trevor Mbakwe (Minnesota)B105.04
48John Shurna (Northwestern)B104.99
49Victor Oladipo (Indiana)B104.96
50Mouphtaou Yarou (Villanova)BigEast4.95

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

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.

teamwin% diffpyth rnk (adj rnk)adj win%
1Gonzaga8.7%34 (20)93.5%
2Murray St.8.7%50 (31)89.1%
3Temple8.6%36 (21)92.7%
4Creighton8.5%55 (33)88.2%
5Indiana8.1%14 (5)98.2%
6Miami FL7.7%63 (55)83.9%
7Lehigh7.6%88 (67)77.9%
8Tulsa7.3%95 (75)74.6%
9Connecticut7.2%23 (12)95.4%
10Long 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% diffpyth rnk (adj rnk)adj win%
1California-2.7%20 (44)86.4%
2Purdue-2.7%15 (40)87.0%
3Virginia-0.4%24 (36)87.8%
4Florida St.-0.1%32 (48)85.8%
5Baylor0.7%13 (25)90.9%
6West Virginia1.2%33 (46)86.3%
7St. Louis1.2%22 (30)89.6%
8Missouri1.2%8 (17)94.0%
9Marquette1.5%6 (13)94.7%
10New Mexico1.8%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:

teamadj win%pythdiff
2Ohio St.0.99070.96470.0259
4North Carolina0.98850.95190.0366
9Michigan St.0.97400.91550.0586
14Brigham Young0.94300.89640.0467
18Wichita St.0.93590.87940.0565
23Nevada Las Vegas0.91240.87420.0382
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 and

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

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

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).

PlayerTeamYearORTG%USGadjusted Offensive Impact
1Marcus DenmonMissouriSr143.324.213.0
2Damian LillardWeber St.Jr125.731.712.8
3Steven PledgerOklahomaJr146.521.711.9
4Darius Johnson-OdomMarquetteSr134.725.111.7
5Ricardo RatliffeMissouriSr142.922.511.6
6Doug McDermottCreightonSo127.228.711.6
7Anthony DrmicBoise St.Fr135.424.211.0
8Trae GoldenTennesseeSo122.630.511.0
9Tahj TateDelaware St.Fr120.831.210.9
10Justin EdwardsMaineFr130.225.910.8

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.

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.


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.


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%ORTGadj offense (on)adj defense(on)adj offense(off)adj defense(off)
Hairston. P.J.
McAdoo, James M.
Bullock, Reggie47.510.
Hubert, Desmond2.
Watts, Justin5.
Strickland, Dexter72.513.
Marshall, Kendall77.542.
Henson, John*
Zeller, Tyler*
Barnes, Harrison*

And here are the results:

Statistical +/- per 100Adj. +/- per 100Efficiency Impact (per 100)Efficiency Impact (Game)
Hairston. P.J.18.812.265.132.09
McAdoo, James M.12.402.233.912.05
Bullock, Reggie-0.581.450.920.51
Hubert, Desmond31.050.185.670.17
Watts, Justin3.98-0.200.510.03
Strickland, Dexter2.00-1.73-1.45-1.23
Marshall, Kendall0.33-1.84-1.87-1.69
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*).

TmAdjusted 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,, 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 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 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) :

RankTeamWins per 34
Losses per 34
2Ohio St.322
2North Carolina322
15Nevada Las Vegas295
15St. Mary's295
15Michigan St.295
15Miami FL295
15West Virginia295
15Florida St.295
15New Mexico295
15Texas A&M295

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:


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?

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:

1Nowitzki, Dirk74.5%63.2%19.4%
2Paul, Chris74.4%80.6%24.7%
3James, LeBron70.2%60.0%19.5%
4Ginobili, Manu60.9%49.4%18.6%
5Anderson, Ryan60.3%51.6%19.6%
6Howard, Dwight60.0%56.2%21.4%
7Jones, James58.3%47.9%18.8%
8Terry, Jason58.3%51.4%20.1%
9Gasol, Pau53.8%60.4%25.7%
10Davis, Baron52.4%45.3%19.7%
11Nash, Steve49.8%54.8%25.1%
12Garnett, Kevin49.4%55.2%25.5%
13Brand, Elton49.4%48.1%22.3%
14Wade, Dwyane48.7%48.4%22.7%
15Blatche, Andray44.8%38.0%19.4%
16Hilario, Nene44.7%38.4%19.6%
17Ibaka, Serge44.3%36.4%18.8%
18Millsap, Paul44.2%37.3%19.3%
19Tolliver, Anthony43.3%42.2%22.3%
20Duncan, Tim43.1%48.3%25.6%
21Hibbert, Roy43.0%60.4%32.1%
22Radmanovic, Vladimir42.7%36.9%19.7%
23Okafor, Emeka42.3%38.0%20.5%
24Fields, Landry41.6%31.5%17.3%
25Harris, Devin41.0%32.5%18.1%
26Wright, Dorell40.3%38.2%21.7%
27Collins, Jason40.1%43.8%25.0%
28Lowry, Kyle40.0%39.5%22.6%
29Calderon, Jose39.7%38.3%22.0%
30Farmar, Jordan39.6%31.7%18.3%
31Crawford, Jamal39.2%30.6%17.8%
32Martin, Kenyon38.4%33.8%20.1%
33Williams, Louis37.1%32.3%19.8%
34Watson, Earl37.0%31.9%19.7%
35Aldridge, LaMarcus36.7%39.0%24.3%
36O'Neal, Shaquille36.4%39.1%24.6%
37Foster, Jeff36.1%53.9%34.1%
38Collison, Nick35.6%32.1%20.6%
39Davis, Glen34.6%29.6%19.5%
40Miles, C.J.34.5%26.8%17.7%
41Jack, Jarrett34.3%27.9%18.6%
42Gordon, Eric34.1%29.2%19.6%
43Harrington, Al34.1%28.7%19.2%
44Wallace, Gerald34.1%23.4%15.7%
45House, Eddie33.9%33.8%22.8%
46Korver, Kyle33.8%33.3%22.5%
47McGee, JaVale33.7%27.0%18.3%
48Sefolosha, Thabo33.7%33.0%22.4%
49Mohammed, Nazr33.5%28.3%19.3%
50Bargnani, Andrea32.7%26.8%18.7%
51Young, Sam31.8%26.8%19.3%
52Chalmers, Mario31.8%29.9%21.5%
53Dunleavy, Mike31.6%33.8%24.5%
54Young, Thaddeus31.4%29.6%21.5%
55Arroyo, Carlos31.3%31.6%23.1%
56Turkoglu, Hedo31.2%22.9%16.8%
57Durant, Kevin31.0%32.3%23.8%
58Thompson, Jason30.8%25.6%19.0%
59Love, Kevin30.6%32.6%24.3%
60Gortat, Marcin30.6%26.2%19.5%
61Griffin, Blake30.6%29.6%22.1%
62Speights, Marreese30.5%34.1%25.6%
63Miller, Andre30.4%33.9%25.4%
64Chandler, Wilson30.0%21.3%16.2%
65Kidd, Jason30.0%34.5%26.3%
66Williams, Mo29.8%27.3%21.0%
67Boykins, Earl29.7%33.8%26.0%
68Ilyasova, Ersan29.6%25.8%19.9%
69Hill, George29.5%23.6%18.3%
70Deng, Luol29.5%33.0%25.6%
71Martin, Cartier28.9%31.9%25.2%
72Bogut, Andrew28.5%26.1%20.9%
73Randolph, Zach28.5%26.4%21.2%
74Jennings, Brandon28.4%30.2%24.3%
75Pierce, Paul28.3%28.9%23.3%
76Dooling, Keyon28.0%24.4%19.9%
77Curry, Stephen27.5%23.3%19.4%
78Johnson, Amir27.4%24.1%20.1%
79Foye, Randy27.2%23.5%19.7%
80Cousins, DeMarcus26.9%23.4%19.8%
81Barbosa, Leandro26.3%21.2%18.5%
82Walker, Bill26.2%25.8%22.5%
83Jeffries, Jared26.2%29.1%25.4%
84George, Paul26.1%30.3%26.5%
85Mayo, O.J.26.1%21.6%18.9%
86Hayes, Chuck25.9%26.5%23.3%
87Johnson, Joe25.9%23.0%20.3%
88Rose, Derrick25.8%43.2%38.3%
89Lewis, Rashard25.7%19.0%16.9%
90Odom, Lamar25.1%23.2%21.1%
91Jianlian, Yi24.8%22.6%20.8%
92Kirilenko, Andrei24.7%20.5%18.9%
93Lopez, Brook24.7%26.8%24.8%
94Fernandez, Rudy24.5%19.4%18.1%
95Bynum, Andrew24.3%24.4%22.9%
96Parker, Anthony24.1%22.0%20.8%
97Williams, Deron23.8%19.7%19.0%
98Harden, James23.7%23.2%22.4%
99Battier, Shane23.4%19.1%18.7%
100Turiaf, Ronny22.6%18.5%18.7%
101Budinger, Chase22.6%20.6%20.9%
102Henderson, Gerald22.6%17.9%18.1%
103Johnson, Wesley22.5%21.6%22.0%
104Andersen, Chris22.2%23.0%23.7%
105Greene, Donte22.2%20.6%21.3%
106Kaman, Chris22.1%22.4%23.2%
107Prince, Tayshaun22.0%19.7%20.4%
108Hawes, Spencer21.9%20.8%21.7%
109Dalembert, Samuel21.6%18.3%19.3%
110Anthony, Carmelo21.3%16.6%17.8%
111Morrow, Anthony21.3%18.5%19.9%
112Smith, Josh21.1%18.0%19.5%
113Bonner, Matt20.9%19.5%21.4%
114Williams, Marvin20.8%15.4%16.9%
115Beasley, Michael20.8%18.2%20.1%
116Monroe, Greg20.4%19.4%21.7%
117Matthews, Wes20.2%17.3%19.6%
118Bosh, Chris20.0%20.7%23.7%
119Williams, Shawne20.0%16.5%18.8%
120Bibby, Mike19.8%15.1%17.4%
121Smith, J.R.19.6%15.7%18.3%
122Thomas, Tyrus19.5%19.2%22.6%
123O'Neal, Jermaine19.3%25.1%29.7%
124Frye, Channing19.2%16.3%19.3%
125Miller, Brad19.1%20.3%24.2%
126Asik, Omer18.5%23.0%28.4%
127Allen, Tony18.5%15.9%19.6%
128Augustin, D.J.18.4%20.4%25.3%
129Felton, Raymond18.2%14.8%18.5%
130Gay, Rudy18.2%16.9%21.2%
131Gibson, Daniel18.2%15.6%19.6%
132Udrih, Beno18.2%16.1%20.3%
133Lee, Courtney18.0%17.5%22.2%
134Green, Jeff17.8%12.8%16.5%
135Fisher, Derek17.6%29.6%38.4%
136West, David17.2%16.0%21.3%
137Conley, Mike17.1%22.8%30.5%
138Udoh, Ekpe17.1%16.0%21.3%
139Bayless, Jerryd17.0%15.5%20.9%
140Gallinari, Danilo16.5%12.8%17.7%
141Stoudemire, Amare16.5%13.6%18.9%
142Granger, Danny16.3%18.5%25.9%
143Stojakovic, Peja16.1%16.9%23.9%
144Gordon, Ben16.1%14.4%20.4%
145Ellington, Wayne16.0%16.7%23.9%
146Jones, Dahntay15.9%22.2%32.0%
147Diaw, Boris15.8%14.1%20.4%
148Maxiell, Jason15.8%16.2%23.5%
149Daniels, Marquis15.5%16.2%23.9%
150McRoberts, Josh15.3%17.4%25.9%
151Lee, David15.2%12.9%19.4%
152Horford, Al15.0%13.5%20.5%
153Rush, Brandon14.9%16.3%25.0%
154Williams, Shelden14.9%13.8%21.3%
155Roy, Brandon14.7%12.8%19.9%
156Neal, Gary14.2%12.6%20.2%
157Wilcox, Chris14.0%14.1%23.0%
158Jones, Solomon13.9%23.3%38.4%
159White, D.J.13.8%14.7%24.3%
160Bledsoe, Eric13.8%12.4%20.6%
161Douglas-Roberts, Chris13.7%13.6%22.6%
162Hollins, Ryan13.5%13.8%23.3%
163Smith, Jason13.2%12.8%22.1%
164Villanueva, Charlie13.2%11.9%20.6%
165Gasol, Marc13.2%11.2%19.5%
166Meeks, Jodie12.9%10.3%18.2%
167Williams, Reggie12.7%9.7%17.4%
168Samuels, Samardo12.7%15.3%27.6%
169Livingston, Shaun12.6%13.3%24.0%
170Delfino, Carlos12.3%10.1%18.8%
171Hickson, J.J.12.3%11.5%21.5%
172Casspi, Omri11.9%10.3%19.7%
173Barea, Jose11.8%11.3%22.0%
174McGrady, Tracy11.6%10.3%20.1%
175Krstic, Nenad11.5%9.7%19.2%
176Petro, Johan11.4%13.1%26.3%
177Pietrus, Mickael11.4%9.9%20.0%
178Brown, Shannon11.3%12.9%26.2%
179Chandler, Tyson11.2%14.1%28.9%
180Teague, Jeff11.1%11.2%22.9%
181Brockman, Jon11.1%13.2%27.3%
182Splitter, Tiago10.8%11.7%24.7%
183Lawson, Ty10.8%8.5%18.0%
184Carter, Vince10.6%8.5%18.3%
185Booker, Trevor10.5%9.6%20.9%
186Sessions, Ramon10.2%9.6%21.4%
187Butler, Caron10.1%10.3%23.3%
188Lopez, Robin9.9%10.0%23.1%
189Richardson, Quentin9.8%9.4%21.9%
190Jefferson, Richard9.7%8.5%20.2%
191Pachulia, Zaza9.5%8.7%20.9%
192Parker, Tony9.5%9.6%23.1%
193Iguodala, Andre9.3%8.3%20.2%
194Daye, Austin9.3%7.5%18.5%
195Scola, Luis9.1%9.8%24.6%
196Perkins, Kendrick9.1%9.4%23.5%
197Noah, Joakim9.1%11.0%27.6%
198Afflalo, Arron9.1%7.2%18.0%
199Childress, Josh9.1%8.9%22.5%
200Ellis, Monta8.5%8.3%22.2%
201Harris, Manny8.5%8.7%23.4%
202Bass, Brandon8.4%6.7%18.1%
203Brewer, Ronnie8.4%8.3%22.6%
204McDyess, Antonio8.4%8.3%22.6%
205Hill, Grant8.3%8.0%21.9%
206Gibson, Taj8.3%7.9%21.8%
207Allen, Ray8.2%8.1%22.8%
208Brewer, Corey8.1%6.9%19.3%
209Stuckey, Rodney7.8%7.0%20.5%
210Holiday, Jrue7.5%6.8%20.7%
211Patterson, Patrick7.4%8.8%27.1%
212Watson, C.J.7.3%11.1%34.6%
213Warrick, Hakim7.2%6.0%19.3%
214Thornton, Al6.8%5.2%17.3%
215Cunningham, Dante6.5%5.0%17.9%
216Landry, Carl6.1%4.8%17.9%
217Vujacic, Sasha6.1%4.9%18.3%
218Posey, James5.9%7.6%29.6%
219Brown, Kwame5.8%4.9%19.4%
220Graham, Joey5.7%6.3%25.1%
221Martin, Kevin5.3%5.7%24.7%
222Garcia, Francisco5.1%4.5%20.1%
223Thomas, Kurt5.1%6.2%27.6%
224Salmons, John4.7%4.2%20.4%
225Richardson, Jason4.6%3.5%17.5%
226Blake, Steve4.5%6.8%34.5%
227Smith, Craig4.1%4.8%26.9%
228Douglas, Toney3.6%2.8%18.2%
229Bogans, Keith3.5%4.1%26.9%
230Arthur, Darrell3.3%3.0%20.8%
231Artest, Ron3.3%3.5%24.7%
232Evans, Maurice3.3%2.5%17.3%
233Billups, Chauncey3.0%2.6%20.1%
234Varejao, Anderson2.8%3.5%28.0%
235Jefferson, Al2.7%2.7%22.4%
236Dragic, Goran2.6%2.5%21.9%
237Jordan, DeAndre2.5%2.0%18.7%
238Jackson, Stephen2.3%2.0%19.6%
239Williams, Jason2.2%3.4%34.8%
240Price, Ronnie2.1%2.2%23.9%
241Evans, Tyreke1.8%1.4%18.0%
242Butler, Rasual1.4%1.4%22.6%
243Arenas, Gilbert1.2%0.9%17.3%
244Boozer, Carlos1.0%1.0%23.0%
245Johnson, James0.3%0.3%22.2%
246Mills, Patrick0.2%0.3%26.7%
247Green, Willie0.2%0.1%19.0%
248Belinelli, Marco0.1%0.1%20.3%
249Miller, Mike-0.1%-0.1%21.9%
250Pekovic, Nikola-0.9%-0.9%24.3%
251Gooden, Drew-1.0%-1.1%23.4%
252Harangody, Luke-1.1%-1.3%26.2%
253Hamilton, Richard-1.3%-1.2%21.2%
254Crawford, Jordan-1.4%-1.4%23.5%
255Thornton, Marcus-1.5%-1.1%17.0%
256Randolph, Anthony-1.5%-1.7%26.9%
257Derozan, DeMar-1.8%-1.6%20.0%
258Rondo, Rajon-1.8%-1.8%22.9%
259Batum, Nicolas-2.2%-1.8%18.6%
260Maynor, Eric-2.5%-3.9%34.8%
261Jamison, Antawn-2.6%-2.5%22.1%
262Cook, Daequan-3.0%-3.6%27.5%
263Head, Luther-3.3%-3.7%25.8%
264Howard, Juwan-3.6%-4.4%28.2%
265Elson, Francisco-3.7%-4.4%27.1%
266Kleiza, Linas-3.7%-3.3%20.3%
267Young, Nick-3.9%-3.1%18.4%
268Wallace, Ben-3.9%-4.2%24.7%
269Nelson, Jameer-4.0%-4.2%23.8%
270Robinson, Nate-4.4%-4.7%24.0%
271Beaubois, Rodrigue-4.5%-5.8%29.5%
272Jeter, Eugene-4.8%-4.9%23.6%
273Wright, Julian-4.8%-4.7%22.0%
274Bryant, Kobe-4.9%-6.1%28.5%
275Cardinal, Brian-5.0%-5.9%27.0%
276Davis, Ed-5.1%-4.6%20.6%
277Ridnour, Luke-5.2%-10.6%47.0%
278Wilkins, Damien-5.4%-5.8%24.4%
279Brooks, Aaron-5.4%-4.8%20.1%
280Stevenson, DeShawn-5.9%-5.4%21.0%
281Amundson, Louis-6.2%-6.4%23.6%
282Telfair, Sebastian-6.7%-13.7%47.0%
283Westbrook, Russell-6.7%-10.7%36.6%
284Vasquez, Greivis-6.9%-9.1%30.4%
285Flynn, Jonny-6.9%-14.4%47.5%
286Seraphin, Kevin-7.1%-7.6%24.5%
287Mbah a Moute, Luc-7.4%-5.9%18.2%
288Humphries, Kris-7.8%-6.5%19.1%
289Evans, Reggie-8.2%-8.7%24.2%
290Barnes, Matt-8.2%-8.4%23.4%
291Carter, Anthony-8.8%-10.7%27.8%
292Camby, Marcus-8.8%-7.8%20.2%
293Outlaw, Travis-9.3%-7.0%17.4%
294Duhon, Chris-9.5%-10.9%26.1%
295Blair, DeJuan-9.8%-8.9%20.8%
296Przybilla, Joel-10.6%-12.6%27.3%
297Favors, Derrick-10.7%-8.5%18.1%
298Milicic, Darko-10.7%-10.1%21.5%
299Williams, Terrence-10.8%-15.9%33.5%
300Dudley, Jared-11.6%-9.8%19.3%
301Wall, John-11.7%-9.7%19.0%
302Powell, Josh-12.0%-13.8%26.2%
303Maggette, Corey-12.5%-10.3%18.9%
304Eyenga, Christian-12.8%-12.6%22.4%
305Hansbrough, Tyler-13.1%-15.0%26.2%
306Hinrich, Kirk-13.1%-9.2%16.1%
307Bynum, Will-13.3%-14.2%24.3%
308Ford, T.J.-13.4%-31.5%53.6%
309Forbes, Gary-13.6%-12.8%21.4%
310McGuire, Dominic-13.6%-13.5%22.7%
311Bell, Raja-13.9%-11.7%19.3%
312Sanders, Larry-14.5%-15.0%23.7%
313Pondexter, Quincy-14.6%-15.7%24.6%
314Hayward, Gordon-15.3%-13.4%20.1%
315Marion, Shawn-15.4%-13.4%19.9%
316Mason, Roger-15.4%-22.0%32.7%
317Redick, J.J.-15.5%-12.3%18.1%
318Gee, Alonzo-16.2%-15.1%21.4%
319Brown, Derrick-16.2%-17.8%25.0%
320Hill, Jordan-16.9%-17.0%23.0%
321Collison, Darren-17.4%-39.4%51.9%
322Murphy, Troy-17.4%-20.9%27.3%
323Price, A.J.-17.8%-40.8%52.3%
324Gomes, Ryan-19.8%-17.2%19.9%
325Aminu, Al-Farouq-20.0%-18.2%20.8%
326Haywood, Brendan-20.1%-23.1%26.3%
327Biedrins, Andris-20.5%-19.0%21.2%
328Nocioni, Andres-21.1%-19.5%21.1%
329Webster, Martell-24.6%-24.9%23.1%
330West, Delonte-26.6%-34.3%29.5%
331Moon, Jamario-31.3%-27.5%20.1%
332Law, Acie-32.8%-34.5%24.1%
333Ariza, Trevor-34.2%-31.9%21.4%
334Graham, Stephen-34.8%-33.6%22.1%
335Haslem, Udonis-35.0%-52.8%34.5%
336Weems, Sonny-36.2%-30.7%19.3%
337Dampier, Erick-37.5%-46.5%28.3%
338Ilgauskas, Zydrunas-37.6%-42.3%25.7%
339Turner, Evan-46.6%-39.4%19.3%
340Anthony, Joel-54.3%-58.5%24.6% 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 for college players).

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

rkexpPlayerTeamYrNCAA OffenseSize+Ht"Wt
11Kyrie IrvingDukeFr11.8522%74180
28Kemba WalkerConnecticutJr11.5416%73172
32Derrick WilliamsArizonaSo9.8686%80240
413Jimmer FredetteBrigham YoungSr9.4742%74195
524Nolan SmithDukeSr8.9828%74185
623Darius MorrisMichiganSo8.9319%76180
710Alec BurksColoradoSo8.2020%78185
811Marcus MorrisKansasJr7.7071%80225
914Jordan HamiltonTexasSo7.5174%79226
103Brandon KnightKentuckyFr6.8226%75185
1120Marshon BrooksProvidenceSr6.7128%77190
1216Markieff MorrisKansasJr6.6077%81232
1318Tobias HarrisTennesseeFr5.7164%80220
149Tristan ThompsonTexasFr5.7072%80226
157Kawhi LeonardSan Diego St.So5.2473%79225
1615Klay ThompsonWashington St.Jr3.9339%78200
1725Trey ThompkinsGeorgiaJr3.8590%81247
1817Chris SingletonFlorida St.Jr3.7771%81227
1922Tyler HoneycuttUCLASo2.3415%80183
2021Kenneth FariedMorehead St.Sr1.9274%80228

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

teamStartersMPTOV%ORtgDRtg+/-Raw Est APM(100)Adj Est +/- (G)
DALJason Kidd36.325.8143108185.323.25
DALJason Terry33.914.5130104133.591.82
DALShawn Marion35.413.7100105183.211.61
MIAChris Bosh38.97.614411341.631.52
MIAMario Chalmers38.515.811011151.021.01
DALBrian Cardinal12.250131111183.740.69
MIADwyane Wade41.120.89011230.400.54
DALTyson Chandler30.1171089831.430.26
DALIan Mahinmi11.0015496-10.17-0.19
DALDeShawn Stevenson12.60174104-8-0.51-0.40
MIAEddie House21.10131112-1-1.27-0.45
DALJose Juan Barea29.620117103-7-0.05-0.66
MIAJuwan Howard7.034.70122-11-7.84-1.10
DALDirk Nowitzki38.96.77898-4-0.43-1.17
MIAJoel Anthony10.9063120-3-5.58-1.22
MIAUdonis Haslem33.90119117-7-1.98-1.23
MIAMike Miller8.300119-16-10.02-1.69
MIALeBron James40.426.497115-24-3.31-2.58

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 ('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

teamStartersMPTOV%ORtgDRtg+/-Raw est APM(100)Adj est +/- (G)
DALJason Kidd39.8730.4139115134.273.17
DALTyson Chandler38.670150117143.032.08
DALDirk Nowitzki39.628.2125119142.461.66
DALShawn Marion34.0315.480117211.850.99
MIAMario Chalmers23.4512175128-10.530.55
MIAMike Miller23.2016.71431280-0.290.15
MIAJuwan Howard5.8202001346-0.020.07
DALJason Terry30.4813.414612310.440.00
MIAUdonis Haslem33.0001401310-0.78-0.13
MIAEddie House3.4733.30953-3.85-0.23
DALJose Juan Barea25.65814612640.00-0.24
MIAJoel Anthony16.200200135-9-1.55-0.32
DALBrian Cardinal9.6234.7106114-7-2.42-0.57
DALIan Mahinmi8.10123123-3-3.70-0.70
MIADwyane Wade34.4518.8126124-13-1.74-0.82
MIAMike Bibby15.4033.362134-7-4.74-1.33
MIAChris Bosh39.3820113131-13-2.24-1.35
DALDeShawn Stevenson13.970133127-12-4.44-1.42
MIALeBron James45.6316.898131-11-2.23-1.56

(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)

teamPlayersMPTOV%ORtgDRtg+/-raw est APM(100)adj est +/-(G)
MIALeBron James45.320.210410814.113.73
DALDirk Nowitzki42.010.712899124.993.52
MIADwyane Wade38.60137101-13.612.79
MIAMario Chalmers28.811.113111063.141.79
DALTyson Chandler40.0013710442.981.68
DALJason Kidd35.531.110010952.471.11
MIAUdonis Haslem29.012.58610551.700.94
DALDeShawn Stevenson14.20278113-73.060.62
MIAJoel Anthony23.1010810230.990.41
MIAMike Miller12.250268640.11-0.01
MIAJuwan Howard6.4014610960.05-0.01
DALBrian Cardinal0.100116-3-9.17-0.02
DALShawn Marion43.113.489113-10.84-0.11
MIAChris Bosh37.5989114-10-0.13-0.22
DALIan Mahinmi8.00145115-6-2.64-0.60
DALPeja Stojakovic6.133.36098-11-4.01-0.63
MIAMike Bibby19.116.760101-4-1.65-0.72
DALJose Juan Barea19.031.1521123-1.67-1.05
DALJason Terry32.10112109-6-0.75-1.15

(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.

DALDirk Nowitzki128991287.4%4.543.97
DALTyson Chandler137104483.3%3.352.79
MIADwyane Wade137101-180.5%2.191.76
MIALeBron James104108194.3%1.801.70
DALJason Kidd100109573.9%2.301.70
MIAMario Chalmers131110660.1%2.501.50
DALShawn Marion89113-189.9%1.501.35
DALDeShawn Stevenson278113-729.6%3.421.01
MIAUdonis Haslem86105560.5%1.440.87
MIAJoel Anthony108102348.1%1.490.72
DALJason Terry112109-666.8%0.800.53
MIAJuwan Howard146109613.4%1.870.25
DALIan Mahinmi145115-616.7%0.150.03
DALJose Juan Barea52112339.7%0.020.01
DALBrian Cardinal116-30.2%-3.69-0.01
MIAMike Miller2686425.3%-0.38-0.10
DALPeja Stojakovic6098-1112.6%-2.22-0.28
MIAMike Bibby60101-439.8%-0.87-0.35
MIAChris Bosh89114-1078.1%-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)

teamStartersmin%ORtgDRtg+/-Adj +/- per 100Total +/- Contrib.
MIADwyane Wade87.8%14810753.793.33
DALDirk Nowitzki87.6%97106133.282.88
DALTyson Chandler79.4%15410843.092.45
DALJason Terry64.8%124105133.542.29
DALShawn Marion85.2%12310612.131.81
MIAMike Bibby46.0%1459873.341.54
MIAChris Bosh82.9%7111231.491.23
MIALeBron James82.5%99101-51.271.05
DALJason Kidd78.2%7210610.820.65
DALDeShawn Stevenson46.1%13195-50.820.38
DALBrendan Haywood17.0%651125-0.15-0.02
DALBrian Cardinal2.1%108-4-3.30-0.07
MIAMario Chalmers51.9%98116-7-0.33-0.17
DALPeja Stojakovic10.3%0114-7-3.53-0.36
DALJose Barea29.3%82109-11-1.69-0.50
MIAJoel Anthony55.9%1142-0.94-0.52
MIAMike Miller31.6%099-3-1.97-0.62
MIAUdonis Haslem61.3%52114-12-1.76-1.08


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

Playerst. ORAPMst. DRAPMNet Plus-MinusStatistical RTGAdj RTGMin%Contrib
DALDirk Nowitzki
MIAChris Bosh
MIALeBron James
DALTyson Chandler
DALJason Terry
MIAMario Chalmers
MIAUdonis Haslem
MIADwyane Wade
MIAJuwan Howard
DALBrendan Haywood
DALPeja Stojakovic
DALDeShawn Stevenson
MIAMike Miller
MIAMike Bibby
DALJason Kidd
DALJose Barea
DALShawn Marion
MIAJoel Anthony

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

Net per 1000.253406036


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