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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.



About Me

I wish my heart were as often large as my hands.