NCAA Teams' Bench Impact

Bench Impact = estimated total Bench Rating per 100 possessions x %Minutes - estimated Starters' Rating per 100 possessions x %Minutes

-Wyoming/Colorado's bench impact is low because of true disparity between their starting 5 and their bench.
-Ohio State's bench is actually decent, but their starting 5 is significantly better....
-Grambling suffers immensely by not playing Quincy Roberts more -- EDIT -- this was because of the transfer rules that prevented Q-Rob from playing the first half of the season, so technically he was on their "bench" when I ranked his minutes played back in January. His presence was definitely the most notable discrepancy in minutes played and overall value because of that.



Bottom 25

	team	bench impact
1	Wyoming	-10.35
2	South Carolina Upstate	-10.31
3	Colorado	-9.36
4	Green Bay	-9.14
5	Alabama State	-9.08
6	Florida International	-9.01
7	Ohio State	-8.96
8	Northern Iowa	-8.90
9	Maryland	-8.78
10	Evansville	-8.75
11	Cal Poly	-8.62
12	Monmouth	-8.61
13	Texas State	-8.56
14	Tennessee State	-8.18
15	South Carolina	-7.94
16	Lehigh	-7.87
17	Lamar	-7.85
18	Jackson State	-7.84
19	Southeast Missouri State	-7.78
20	Rider	-7.76
21	Tennessee-Martin	-7.74
22	Xavier	-7.67
23	Pepperdine	-7.63
24	St. Francis (NY)	-7.57
25	Utah	-7.35

Top 25

	team	bench impact
1	Grambling	5.87
2	South Alabama	2.79
3	Vermont	2.54
4	Colorado State	2.50
5	Loyola Marymount	2.46
6	North Carolina-Greensboro	1.75
7	Texas Southern	1.53
8	Oklahoma State	1.39
9	Western Carolina	1.03
10	William & Mary	1.00
11	Cornell	0.97
12	Marist	0.94
13	California-Santa Barbara	0.85
14	Maryland-Eastern Shore	0.68
15	Western Michigan	0.51
16	IPFW	0.49
17	Southern Mississippi	0.41
18	Alcorn State	0.35
19	Southeastern Louisiana	0.35
20	Southern Methodist	0.28
21	Canisius	0.26
22	Rhode Island	0.26
23	Miami (FL)	0.24
24	Toledo	0.15
25	East Tennessee State	0.13

Top 25 Players as of 1/18/2011

I have adjusted minutes% for teammate quality and injuries (I factor back in minutes played above or below what we would expect a coach to play them with a number I call SQZ: how much more or less a coach squeezes out of a player over the course of a game.)

True Impact per Game = Efficiency Impact x (expected min% while healthy, with average teammates)

rank	player	team	ORTG	usage%	DRTG	True Impact per Game
1	Kevin Jones	West Virginia	129.6	24.2	92.5	9.6
2	Jared Sullinger	Ohio State	128.9	26.3	76	9.4
3	Damian Lillard	Weber State	136.1	32.2	99.2	9.4
4	Marcus Denmon	Missouri	140.5	23.2	93.9	8.6
5	Thomas Robinson	Kansas	117.3	28.2	78.7	8.5
6	Anthony Davis	Kentucky	140.5	18	73.6	8.1
7	Doug McDermott	Creighton	127.7	31.6	102	7.9
8	Kenny Boynton	Florida	135.9	24.6	102.8	7.6
9	Dominique Morrison	Oral Roberts	131	25.4	103.1	7.4
10	Will Barton	Memphis	119	26	94.4	7.3
11	Cody Zeller	Indiana	134	22.2	90	7.2
12	J'Covan Brown	Texas	122.6	28.2	100.7	7.1
13	John Shurna	Northwestern	117.5	27.4	102.4	7.0
14	Mike Scott	Virginia	127.1	28.7	82.3	6.9
15	Jae Crowder	Marquette	124.6	23.3	84.6	6.7
16	Isaiah Canaan	Murray State	130.3	26.4	95.8	6.6
17	Deshaun Thomas	Ohio State	124.8	23.8	89.7	6.6
18	Hollis Thompson	Georgetown	129	21.3	93	6.6
19	John Jenkins	Vanderbilt	127.1	26.3	101	6.6
20	Nate Wolters	South Dakota State	121.2	30	101.9	6.4
21	Ricardo Ratliffe	Missouri	138.2	22.4	91.5	6.3
22	Jeremy Lamb	Connecticut	120.9	23.6	100	6.2
23	Drew Crawford	Northwestern	119.1	25.5	104.7	6.2
24	Jordan Taylor	Wisconsin	115.8	24.5	86.7	6.2
25	Jason Clark	Georgetown	117.5	26.1	89.8	6.0

Top 25 Freshies, as of 1/11/2012

EDIT: Misleading title. This only includes games through 1/11, not 1/13.

By team impact (estimated efficiency margin impact times % of possessions played).

rank	player	team	conf	ORTG	usage%	DRTG	Eff Imp.	Team Imp.
1	Anthony Davis	Kentucky	SEC	134	18	71	9.37	6.79
2	Cody Zeller	Indiana	Big Ten	134	22	86	9.63	6.50
3	Kevin Pangos	Gonzaga	WCC	132	21	97	7.75	5.88
4	Michael Kidd-Gilchrist	Kentucky	SEC	120	21	85	5.96	4.60
5	Sheldon McClellan	Texas	Big 12	132	21	96	7.17	4.58
6	Spencer DinWiddie	Colorado	Pac-12	127	21	96	5.96	3.76
7	Trey Burke	Michigan	Big Ten	110	25	97	4.30	3.67
8	D'Angelo Harrison	St. John's (NY)	Big East	116	24	102	3.81	3.32
9	Austin Rivers	Duke	ACC	108	26	103	4.47	3.32
10	Seth Tuttle	Northern Iowa	MVC	123	19	89	6.14	3.23
11	Kentavious Caldwell-Pope	Georgia	SEC	109	27	99	4.15	3.18
12	Adam Smith	North Carolina-Wilmington	CAA	116	27	111	4.08	3.09
13	Jonathan Holmes	Texas	Big 12	126	19	94	5.53	3.06
14	Anthony Drmic	Boise State	MWC	123	24	98	4.66	3.06
15	Omari Grier	Florida Atlantic	Sun Belt	130	21	105	6.58	3.04
16	Justin Edwards	Maine	AEC	120	26	99	3.97	3.01
17	Otto Porter	Georgetown	Big East	116	16	88	4.19	2.85
18	Jordan Tolbert	Texas Tech	Big 12	113	31	96	4.68	2.76
19	Rodney Hood	Mississippi State	SEC	125	17	100	3.19	2.69
20	Andre Drummond	Connecticut	Big East	112	20	94	3.83	2.59
21	B.J. Young	Arkansas	SEC	117	27	94	4.32	2.56
22	P.J. Hairston	North Carolina	ACC	124	26	93	7.68	2.50
23	Quinn Cook	Duke	ACC	137	20	102	7.87	2.48
24	Quincy Miller	Baylor	Big 12	109	25	88	4.02	2.34
25	Michael Caffey	Long Beach State	Big West	120	15	98	4.34	2.33

Top 51 Overall NCAA Players as of 1/10

Games through 1/10.
Team Impact = (Estimated Efficiency Impact per 100 possessions) x (% of Possessions played in)
Where offensive and defensive efficiency impacts are estimated* by using offensive and defensive rating (& offensive usage%) at sports-reference.com, and strength of schedule from kenpom.com.

rank	player	team	conf	Offensive Impact (100)	Defensive Impact (100)	Total Impact (100)	Team Impact	Min%
1	Damian Lillard	Weber State	Big Sky	13.5	-0.4	13.1	11.0	84%
2	Kevin Jones	West Virginia	Big East	7.9	2.2	10.1	9.1	90%
3	Doug McDermott	Creighton	MVC	11.1	-0.6	10.5	8.1	77%
4	Dominique Morrison	Oral Roberts	Summit	9.0	-0.1	8.9	7.9	89%
5	Jared Sullinger	Ohio State	Big Ten	8.0	4.8	12.8	7.8	61%
6	Marcus Denmon	Missouri	Big 12	9.1	0.7	9.8	7.8	80%
7	Isaiah Canaan	Murray State	OVC	8.9	0.6	9.5	7.5	79%
8	Will Barton	Memphis	CUSA	6.7	1.7	8.4	7.3	87%
9	Thomas Robinson	Kansas	Big 12	4.8	4.5	9.3	7.2	77%
10	Kenny Boynton	Florida	SEC	10.0	-0.8	9.2	7.2	78%
11	Mike Scott	Virginia	ACC	7.5	2.1	9.6	7.0	73%
12	Jae Crowder	Marquette	Big East	6.7	2.6	9.3	7.0	75%
13	Anthony Davis	Kentucky	SEC	4.2	5.2	9.4	6.8	73%
14	Cody Zeller	Indiana	Big Ten	7.1	2.5	9.6	6.5	68%
15	Nate Wolters	South Dakota State	Summit	8.2	-0.6	7.6	6.4	85%
16	Jeremy Lamb	Connecticut	Big East	6.1	0.9	7.0	6.3	90%
17	Noah Hartsock	Brigham Young	WCC	5.5	2.4	7.9	6.3	79%
18	Colt Ryan	Evansville	MVC	6.8	0.1	6.9	6.2	90%
19	Jarrod Jones	Ball State	MAC	6.8	1.2	7.9	6.2	78%
20	Larry Anderson	Long Beach State	Big West	5.0	2.0	7.1	6.2	87%
21	John Jenkins	Vanderbilt	SEC	7.7	0.4	8.1	6.2	76%
22	Doron Lamb	Kentucky	SEC	7.0	1.1	8.1	6.1	76%
23	Ryan Broekhoff	Valparaiso	Horizon	7.0	0.6	7.6	6.1	80%
24	Ryan Kelly	Duke	ACC	7.8	1.9	9.6	6.0	62%
25	Hollis Thompson	Georgetown	Big East	6.4	1.5	7.9	5.9	76%
26	Deshaun Thomas	Ohio State	Big Ten	6.0	2.4	8.4	5.9	70%
27	Kevin Pangos	Gonzaga	WCC	6.7	1.0	7.7	5.9	76%
28	Drew Crawford	Northwestern	Big Ten	6.9	-0.1	6.8	5.7	85%
29	Steven Pledger	Oklahoma	Big 12	7.4	0.3	7.7	5.6	74%
30	Kris Joseph	Syracuse	Big East	5.6	1.9	7.5	5.6	74%
31	C.J. McCollum	Lehigh	Patriot	6.9	0.3	7.2	5.6	77%
32	Jordan Theodore	Seton Hall	Big East	4.7	1.5	6.2	5.5	90%
33	Brian Conklin	Saint Louis	A-10	6.3	1.4	7.7	5.5	71%
34	J'Covan Brown	Texas	Big 12	5.9	0.8	6.7	5.5	82%
35	Reggie Hamilton	Oakland	Summit	7.8	-1.2	6.6	5.5	84%
36	Chase Tapley	San Diego State	MWC	5.3	1.6	6.9	5.5	80%
37	Langston Galloway	Saint Joseph's	A-10	5.6	0.8	6.4	5.5	85%
38	Allen Crabbe	California	Pac-12	5.4	1.2	6.6	5.4	82%
39	John Shurna	Northwestern	Big Ten	6.0	0.1	6.1	5.4	88%
40	Deonte Burton	Nevada	WAC	6.5	0.5	7.0	5.4	77%
41	Zack Rosen	Pennsylvania	Ivy	6.1	-0.3	5.7	5.4	94%
42	Erick Green	Virginia Tech	ACC	6.2	0.7	6.9	5.3	77%
43	Julian Mavunga	Miami (OH)	MAC	4.7	1.0	5.6	5.3	94%
44	Draymond Green	Michigan State	Big Ten	3.2	3.4	6.6	5.3	80%
45	Chace Stanback	Nevada-Las Vegas	MWC	6.5	1.5	8.0	5.3	66%
46	Anthony Raffa	Coastal Carolina	Big South	4.7	1.8	6.5	5.2	80%
47	Robbie Hummel	Purdue	Big Ten	5.4	1.4	6.8	5.1	76%
48	Trevor Releford	Alabama	SEC	4.5	2.5	7.0	5.1	73%
49	Ricardo Ratliffe	Missouri	Big 12	7.3	1.4	8.7	5.1	59%
50	Robert Covington	Tennessee State	OVC	6.8	0.2	7.0	5.1	73%
51	Tyler Zeller	North Carolina	ACC	4.9	3.0	7.8	5.1	65%

So, Lillard is the primary reason that an otherwise-extremely-mediocre offensive team (Pomeroy subscribers only) is #57th in the Pomeroys for offense. Weber's second-best offensive player (Scott Bamforth) uses 10% usage less, and is 10 points per 100 less efficient.

When I sum up all of Weber State's offensive contributions, we get +14.4. Their adjusted offensive rating is only +8 or so on kenpom.com, so it's my guess that (but if we just fit players' performances to their team ratings, we get some extremely ugly results, so I don't do that...I am basing it on last year's fit for Strength of Schedule, however). Even if we multiply each player's offensive value by 0.6 (which is roughly the same as 8/14.4), Lillard is still a top-25 player.



Mike Scott is still also an extremely good player as far as the eye can see, and is even underrated by his minutes (like Sullinger).





* Not going to reveal the regression equation just yet. Or ever :-)

No Apologies, Just Numbers

Okay, some apologies.


My prior post has sparked a little bit of interest/intrigue/outrage/fear among college basketball fans.
Never fear! Happier numbers are on the way.

First, let me reiterate that these are based on advanced stats only, such as offensive rating and defensive rating, from Sports-Reference.com. There are no plus-minus stats involved.

I realized that there were some pretty important adjustments that needed to be made. First, that players with extremely high numbers in any one category were being skewed.* Second, that the strength of schedule adjustment was a little counter-intuitive, and a little too strong. I adjusted my ratings thusly.

Unfortunately, the sample size for this season does not produce very intuitive results at the moment, which I should have mentioned in my last post (so please don't destroy me for anything that looks strange...just chew on it). For example, that Weber State's Damian Lillard is number one in the system. While I am a huge fan of Lillard and would have no problem listing him up there at the end of the year if things remain the same, I am wary of the fact that his team impact is two full points higher than the next-highest (and his per-possession impact is higher than Sullinger's). As major-conference teams improve their strength of schedules during conference play and players get more minutes, things will even out.

As proof that things will even out, here is last year's top-10 in terms of overall Team Impact (efficiency impact times minutes-on-the-floor%). We can call this the Holy Grail 3.0

Player	School	Impact(100)	Team Eff. Impact
Kemba Walker	Connecticut	13.1	12.1
Jordan Taylor	Wisconsin	12.4	11.2
Talor Battle	Penn State	10.3	9.8
Jimmer Fredette	Brigham Young	11.0	9.7
Jared Sullinger	Ohio State	12.2	9.6
JaJuan Johnson	Purdue	10.4	9.2
Derrick Williams	Arizona	11.8	8.7
Jon Leuer	Wisconsin	10.4	8.7
Ben Hansbrough	Notre Dame	9.7	8.4
Jon Diebler	Ohio State	9.3	8.3

I hope most people wouldn't have a problem with this list.

So here are the current numbers, which are now split into offense and defense...separated by the Top-26 Major Conference Players and the Top 26 not-so-Major Conferences. (Had to include the older Zeller. It's the law.)

Major:

Not-So-Major:

Notice I refrained from ranking players by their per-possession numbers...I think these are more intuitive and represent Player-of-the-Year candidates (if your coach didn't play you enough, you unfortunately didn't impact your team enough).

And look, Hummel is on a list! Now everyone can be happy. Even me!



* My method involved dividing advanced statistics such as Steal%, Assist%, or Offensive Rating by their league average. My NBA numbers had extremely high sample sizes and didn't have any problem with skewed numbers...here we have that problem a lot more, especially in smaller categories (such as Steal%) rather than larger categories (the more all-encompassing "Offensive Rating").

The Holy Grail 2.5: NCAA Player Ratings (1/4/2012)

EDIT: I have updated my methodology and made things a little easier to understand, I hope: http://www.thebasketballdistribution.com/2012/01/no-apologies-just-numbers.html

Now that Sports-Reference.com uses the same advanced stats for NBA and College players, it has become very simple and straightforward to create a statistical +/- based on advanced stats for college players.

BORING STUFF:

In order to adjust for college, I simply use each advanced stat* divided by its league average. Then, applying the same to college works well. I fit the 2010-2011 season's worth of raw statistical +/- data to each team's efficiency margin, and then to their adjusted efficiency margin. I then built a model that combines raw statistical +/- and team strength of schedule (simply Adjusted Efficiency Margin mins Raw Efficiency Margin, via kenpom.com) to give us an "adjusted Statistical +/-" that we will call (as we have before) Efficiency Impact.

/END BORING STUFF

For each player, we can look at Efficiency Impact per 100-possessions, and Team Efficiency Impact(their efficiency impact times the % of their team's possessions they played in).

Here are the top 100 players in both of those categories. First, efficiency impact per 100 possessions (25% of teams' minutes played to qualify)

rank	player	team	Efficiency Impact/100
1	Jared Sullinger	Ohio State	19.4
2	Russ Smith	Louisville	16.8
3	Thomas Robinson	Kansas	15.8
4	Damian Lillard	Weber State	15.8
5	Mike Scott	Virginia	14.9
6	Dion Waiters	Syracuse	14.7
7	James Southerland	Syracuse	14.0
8	JaMychal Green	Alabama	13.9
9	Cody Zeller	Indiana	13.7
10	C.J. McCollum	Lehigh	13.6
11	Marcus Denmon	Missouri	13.4
12	Anthony Davis	Kentucky	13.2
13	Jae Crowder	Marquette	13.2
14	Jared Berggren	Wisconsin	13.1
15	Isaiah Canaan	Murray State	12.9
16	Jarrod Jones	Ball State	12.8
17	Brian Conklin	Saint Louis	12.5
18	Jamaal Franklin	San Diego State	12.4
19	Ryan Pearson	George Mason	12.1
20	Herb Pope	Seton Hall	11.7
21	Doug McDermott	Creighton	11.5
22	Arsalan Kazemi	Rice	11.4
23	Kevin Jones	West Virginia	11.4
24	Anthony Raffa	Coastal Carolina	11.3
25	Miguel Paul	East Carolina	11.3
26	Chase Tapley	San Diego State	11.2
27	Ricardo Ratliffe	Missouri	11.2
28	Ryan Evans	Wisconsin	11.1
29	Henry Sims	Georgetown	11.1
30	Draymond Green	Michigan State	10.9
31	Cody Ellis	Saint Louis	10.8
32	Luke Martinez	Wyoming	10.6
33	Jereal Scott	Stephen F. Austin	10.6
34	Noah Hartsock	Brigham Young	10.5
35	Mike Moser	Nevada-Las Vegas	10.5
36	Tyler Zeller	North Carolina	10.4
37	Carlos Lopez	Nevada-Las Vegas	10.4
38	Quinn Cook	Duke	10.3
39	Will Barton	Memphis	10.3
40	Kris Joseph	Syracuse	10.3
41	Rob Jones	Saint Mary's (CA)	10.2
42	Dominique Sutton	North Carolina Central	10.2
43	Jordan Taylor	Wisconsin	10.2
44	Jason Clark	Georgetown	10.2
45	Ian Hummer	Princeton	10.2
46	Steven Werner	Sam Houston State	10.1
47	Leonard Washington	Wyoming	10.1
48	Robert Covington	Tennessee State	10.1
49	Drew Gordon	New Mexico	10.0
50	P.J. Hairston	North Carolina	10.0
51	Trevor Releford	Alabama	9.9
52	Tony Mitchell	Alabama	9.9
53	Ryan Kelly	Duke	9.8
54	William Buford	Ohio State	9.8
55	Terrell Holloway	Xavier	9.7
56	Kenny Boynton	Florida	9.7
57	Harrison Barnes	North Carolina	9.7
58	John Henson	North Carolina	9.7
59	Terell Parks	Western Illinois	9.6
60	Victor Oladipo	Indiana	9.6
61	Carl Hall	Wichita State	9.5
62	Deshaun Thomas	Ohio State	9.4
63	Davante Gardner	Marquette	9.4
64	Ken Horton	Central Connecticut State	9.4
65	Javon McCrea	Buffalo	9.4
66	Joe Ragland	Wichita State	9.4
67	D'Aundray Brown	Cleveland State	9.4
68	Michael Kidd-Gilchrist	Kentucky	9.3
69	Steven Pledger	Oklahoma	9.3
70	Donte Poole	Murray State	9.3
71	Robbie Hummel	Purdue	9.3
72	Brandon Fortenberry	Southeastern Louisiana	9.3
73	Doron Lamb	Kentucky	9.3
74	Khris Middleton	Texas A&M	9.2
75	Evan Smotrycz	Michigan	9.1
76	Jackie Carmichael	Illinois State	9.1
77	Jorge Gutierrez	California	9.0
78	Chace Stanback	Nevada-Las Vegas	9.0
79	Velton Jones	Robert Morris	9.0
80	Jaquon Parker	Cincinnati	9.0
81	Kenton Walker	Saint Mary's (CA)	8.9
82	Kevin Pangos	Gonzaga	8.9
83	Erick Green	Virginia Tech	8.9
84	Quincy Acy	Baylor	8.8
85	Nate Wolters	South Dakota State	8.8
86	Mark Lyons	Xavier	8.8
87	Arnett Moultrie	Mississippi State	8.8
88	D.J. Cooper	Ohio	8.7
89	Sheldon McClellan	Texas	8.7
90	Jamar Samuels	Kansas State	8.7
91	Hollis Thompson	Georgetown	8.7
92	Trevor Mbakwe	Minnesota	8.7
93	Reggie Bullock	North Carolina	8.7
94	Tony Snell	New Mexico	8.7
95	Jamal Fenton	New Mexico	8.7
96	Brad Waldow	Saint Mary's (CA)	8.6
97	Scott Saunders	Belmont	8.6
98	Mike Dixon,	Missouri	8.6
99	Maurice Kemp	East Carolina	8.5
100	Jamar Gulley	Missouri State	8.5

rank	player	team	Team Efficiency Impact	Efficiency Impact/100	Min%
1	Thomas Robinson	Kansas	12.3	15.8	77.7%
2	Damian Lillard	Weber State	12.1	15.8	76.8%
3	Jared Sullinger	Ohio State	11.8	19.4	61.0%
4	Jarrod Jones	Ball State	11.3	12.8	88.6%
5	Mike Scott	Virginia	10.7	14.9	72.2%
6	Marcus Denmon	Missouri	10.5	13.4	78.6%
7	C.J. McCollum	Lehigh	10.4	13.6	76.3%
8	Kevin Jones	West Virginia	10.1	11.4	88.8%
9	Isaiah Canaan	Murray State	9.8	12.9	76.1%
10	Herb Pope	Seton Hall	9.7	11.7	83.2%
11	Anthony Davis	Kentucky	9.5	13.2	72.3%
12	Anthony Raffa	Coastal Carolina	9.5	11.3	84.1%
13	Cody Zeller	Indiana	9.2	13.7	67.1%
14	Chase Tapley	San Diego State	9.0	11.2	80.7%
15	Jae Crowder	Marquette	9.0	13.2	68.2%
16	Draymond Green	Michigan State	8.8	10.9	80.9%
17	Jordan Taylor	Wisconsin	8.8	10.2	86.4%
18	Ryan Pearson	George Mason	8.8	12.1	72.9%
19	Brian Conklin	Saint Louis	8.7	12.5	69.6%
20	Jared Berggren	Wisconsin	8.7	13.1	66.7%
21	Ian Hummer	Princeton	8.6	10.2	84.5%
22	Will Barton	Memphis	8.6	10.3	83.2%
23	Miguel Paul	East Carolina	8.5	11.3	74.7%
24	Rob Jones	Saint Mary's (CA)	8.4	10.2	82.2%
25	Noah Hartsock	Brigham Young	8.3	10.5	78.6%
26	Doug McDermott	Creighton	8.2	11.5	71.1%
27	William Buford	Ohio State	8.2	9.8	83.5%
28	Russ Smith	Louisville	8.2	16.8	48.4%
29	Dominique Sutton	North Carolina Central	8.1	10.2	78.9%
30	Tony Mitchell	Alabama	7.9	9.9	80.0%
31	Dion Waiters	Syracuse	7.8	14.7	53.2%
32	Nate Wolters	South Dakota State	7.8	8.8	88.6%
33	Ryan Evans	Wisconsin	7.7	11.1	68.8%
34	Robert Covington	Tennessee State	7.6	10.1	75.5%
35	Mike Moser	Nevada-Las Vegas	7.6	10.5	72.5%
36	Kenny Boynton	Florida	7.6	9.7	78.0%
37	Ken Horton	Central Connecticut State	7.6	9.4	80.4%
38	JaMychal Green	Alabama	7.5	13.9	54.0%
39	D'Aundray Brown	Cleveland State	7.5	9.4	80.4%
40	Jason Clark	Georgetown	7.5	10.2	73.6%
41	Arsalan Kazemi	Rice	7.5	11.4	65.2%
42	Drew Gordon	New Mexico	7.4	10.0	74.0%
43	Kris Joseph	Syracuse	7.4	10.3	72.2%
44	Terrell Holloway	Xavier	7.4	9.7	76.0%
45	Jordan Theodore	Seton Hall	7.4	7.7	95.9%
46	Jereal Scott	Stephen F. Austin	7.4	10.6	69.8%
47	Jamaal Franklin	San Diego State	7.2	12.4	57.7%
48	Trevor Releford	Alabama	7.1	9.9	71.9%
49	Michael Kidd-Gilchrist	Kentucky	7.1	9.3	76.2%
50	Doron Lamb	Kentucky	7.1	9.3	76.2%
51	Robbie Hummel	Purdue	7.0	9.3	75.5%
52	Deshaun Thomas	Ohio State	7.0	9.4	74.2%
53	Larry Anderson	Long Beach State	6.9	7.9	87.1%
54	Tim Frazier	Penn State	6.9	7.7	89.2%
55	Terell Parks	Western Illinois	6.9	9.6	71.8%
56	D.J. Cooper	Ohio	6.8	8.7	78.2%
57	Jeremy Lamb	Connecticut	6.8	8.1	84.1%
58	Kevin Pangos	Gonzaga	6.8	8.9	76.7%
59	Langston Galloway	Saint Joseph's	6.8	7.9	85.8%
60	Edwin Fuquan	Seton Hall	6.8	7.6	89.7%
61	Greg Mangano	Yale	6.8	8.0	85.2%
62	J'Covan Brown	Texas	6.8	8.0	84.6%
63	Ricardo Ratliffe	Missouri	6.8	11.2	60.5%
64	Henry Sims	Georgetown	6.8	11.1	61.0%
65	Ryan Broekhoff	Valparaiso	6.7	8.5	79.2%
66	Erick Green	Virginia Tech	6.7	8.9	75.4%
67	Tyler Zeller	North Carolina	6.7	10.4	64.3%
68	Velton Jones	Robert Morris	6.7	9.0	74.0%
69	John Henson	North Carolina	6.6	9.7	68.8%
70	Luke Martinez	Wyoming	6.6	10.6	62.9%
71	Donte Poole	Murray State	6.6	9.3	71.3%
72	Isaiah Wilkerson	NJIT	6.6	7.8	84.6%
73	Jared Cunningham	Oregon State	6.5	7.7	84.5%
74	Dominique Morrison	Oral Roberts	6.5	7.3	88.6%
75	Julian Mavunga	Miami (OH)	6.4	5.9	108.9%
76	Steven Pledger	Oklahoma	6.4	9.3	68.7%
77	Drew Crawford	Northwestern	6.4	7.6	83.8%
78	Quincy Acy	Baylor	6.3	8.8	71.9%
79	Javon McCrea	Buffalo	6.3	9.4	67.4%
80	Bradford Burgess	Virginia Commonwealth	6.3	7.9	80.0%
81	Hollis Thompson	Georgetown	6.3	8.7	72.5%
82	Leonard Washington	Wyoming	6.3	10.1	62.3%
83	Harrison Barnes	North Carolina	6.3	9.7	65.0%
84	Jorge Gutierrez	California	6.2	9.0	68.8%
85	John Jenkins	Vanderbilt	6.2	8.2	76.0%
86	Ryan Kelly	Duke	6.2	9.8	62.9%
87	Aaron Craft	Ohio State	6.2	8.0	77.7%
88	Victor Oladipo	Indiana	6.2	9.6	64.6%
89	Matthew Dellavedova	Saint Mary's (CA)	6.2	6.8	91.1%
90	Reggie Hamilton	Oakland	6.1	7.2	85.9%
91	Andre Roberson	Colorado	6.1	8.5	71.8%
92	Joe Harris	Virginia	6.1	8.0	75.9%
93	Colt Ryan	Evansville	6.0	7.5	80.7%
94	Sean Kilpatrick	Cincinnati	6.0	7.1	84.4%
95	Mike Muscala	Bucknell	6.0	7.6	78.8%
96	Kerron Johnson	Belmont	6.0	8.4	71.4%
97	Tony Snell	New Mexico	5.9	8.7	68.3%
98	Chace Stanback	Nevada-Las Vegas	5.9	9.0	65.8%
99	Jeffery Taylor	Vanderbilt	5.9	7.7	77.0%
100	DeAndre Kane	Marshall	5.9	7.2	81.9%

Quite a few mid-majors at the top here, but the strength-of-schedule adjustment is well-calibrated. These will probably match somewhat closely to Pomeroy's "KPOY."





* - My raw regression involves Defensive Rating, Offensive Rating, Usage%, Assist%, and Steal%.

NCAA Power Ratings

I now have my own usually-updated NCAA power ratings. (Located at the top of the page).

These are simply based on point margin, and I adjust for consistency, recency, and which teams appear to play up or down to their opponents (i.e. fixing the issue of cupcake-killers), in the spirit of the work of DSMok1 aka Daniel.

"Original Rating" is basically SRS, simply adjusting a team's point differential by their opponent's point differential several layers down.

"Quality Rating" weighs each opponent/game by their "original rating" and whether or not a game was at home or away. The final result of a very difficult game is worth the most in this average, and the final result of a very easy game impacts this the least.

"Cupcake Rating" does the exact opposite of "quality rating."

Finally, each team is ranked by using my expected outcome (from Quality/Cupcake/Original ratings) against the top-25 "Original" teams. This gives us a basic idea of how teams might fare come March.

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!!!