All-Overrated and All-Underrated NBA Teams, 2013

All-Underrated Squad
Nate Robinson, PG
Andre Iguodala, SG
Thaddeus Young, SF
Nick Collison, PF
Kevin Garnett, C

All-Overrated Squad
Deron Williams, PG
JR Smith, SG
Klay Thompson, SF
Earl Clark, PF
Javale McGee, C

BOOM.

Players of the Month: December (so far)

Welcome, all! Here I will be grading players according to their estimated offensive and defensive impacts (using my per-100-possession stat, SimplePlayerRating) via Month-of-December-State. I'm rolling out my Defensive SPR here, finally. Formula at the bottom.

EDIT: Fixed the per-game numbers.

Surprises of the month go to: Andray Blatche (#6 #7), Paul George (#9 #10), Kemba Walker (#10 #11) and JJ Hickson (#17 #21!!!).

Without further ado, here are your top and bottom 26.

The Top 26

	Player	Season	OSPR	DSPR	Total SPR	SPR per Game
1	Carmelo Anthony	2012-13	10.6	-0.7	9.9	7.5
2	LeBron James	2012-13	6.9	1.3	8.1	6.5
3	Blake Griffin	2012-13	7.1	2.5	9.6	6.4
4	Kevin Durant	2012-13	6.2	0.4	6.6	5.4
5	Chris Paul	2012-13	6.3	2.1	8.4	5.4
6	Kobe Bryant	2012-13	6.4	-0.5	5.9	5.0
7	Andray Blatche	2012-13	4.7	3.6	8.2	4.8
8	Ryan Anderson	2012-13	7.9	-1.4	6.5	4.6
9	Tony Parker	2012-13	7.3	-0.6	6.6	4.6
10	Paul George	2012-13	4.7	1.0	5.7	4.5
11	Kemba Walker	2012-13	6.4	-0.1	6.3	4.4
12	Chris Copeland	2012-13	12.7	1.2	13.9	4.3
13	Russell Westbrook	2012-13	4.9	0.8	5.8	4.2
15	Stephen Curry	2012-13	5.0	-0.3	4.7	3.9
16	Paul Millsap	2012-13	4.1	1.3	5.4	3.7
17	James Harden	2012-13	4.0	0.5	4.5	3.7
18	Tyson Chandler	2012-13	3.7	1.3	5.0	3.5
19	Andrei Kirilenko	2012-13	1.1	3.5	4.6	3.5
21	J.J. Hickson	2012-13	5.4	1.0	6.4	3.4
20	Matt Barnes	2012-13	4.5	1.9	6.4	3.4
22	Ed Davis	2012-13	4.4	2.4	6.8	3.4
23	Dwyane Wade	2012-13	5.3	-0.4	4.8	3.2
24	David Lee	2012-13	3.8	0.2	4.0	3.2
27	Kyrie Irving	2012-13	5.9	-1.6	4.3	3.2
26	Eric Bledsoe	2012-13	3.6	4.6	8.2	3.2

The Bottom 26

	Player	Season	OSPR	DSPR	Total SPR	SPR per Game
387	Daniel Gibson	2012-13	-5.5	-1.7	-7.2	-3.9
386	Kyle Singler	2012-13	-4.1	-1.8	-5.9	-3.8
388	Doron Lamb	2012-13	-8.9	-1.6	-10.5	-3.8
382	Mickael Pietrus	2012-13	-4.6	-1.1	-5.7	-3.5
383	Andre Iguodala	2012-13	-5.3	0.7	-4.6	-3.4
379	Chris Singleton	2012-13	-5.9	0.1	-5.8	-3.3
381	J.R. Smith	2012-13	-3.6	-1.5	-5.1	-3.3
378	Andrea Bargnani	2012-13	-3.2	-2.2	-5.4	-3.2
377	Victor Claver	2012-13	-11.3	2.0	-9.3	-3.2
376	Jeff Taylor	2012-13	-3.1	-2.3	-5.4	-3.1
374	Jerry Stackhouse	2012-13	-4.0	-2.6	-6.6	-3.0
375	Bismack Biyombo	2012-13	-5.0	0.5	-4.5	-3.0
372	Alonzo Gee	2012-13	-3.3	-0.8	-4.1	-3.0
373	Gerald Green	2012-13	-4.7	-1.9	-6.7	-2.9
370	Willie Green	2012-13	-5.6	-2.2	-7.8	-2.8
369	Festus Ezeli	2012-13	-7.6	-0.9	-8.5	-2.6
368	Dahntay Jones	2012-13	-4.1	-2.4	-6.5	-2.6
367	Austin Rivers	2012-13	-2.6	-1.7	-4.3	-2.6
365	Keith Bogans	2012-13	-9.1	-2.6	-11.7	-2.6
364	Sebastian Telfair	2012-13	-3.2	-2.4	-5.6	-2.4
363	Aaron Brooks	2012-13	-2.7	-1.6	-4.3	-2.4
361	Martell Webster	2012-13	-2.8	-1.0	-3.8	-2.4
362	John Salmons	2012-13	-1.8	-1.8	-3.6	-2.4
359	Jason Maxiell	2012-13	-5.4	1.6	-3.8	-2.3
360	Tony Allen	2012-13	-6.1	1.5	-4.6	-2.3
358	Nolan Smith	2012-13	-6.5	-2.8	-9.3	-2.3

-The formula for DSPR is
DSPR = (1.3xSteals - 0.1xMissedFG + 0.2xDRB + 0.5xBLK)x100/Possessions Played - 3

-OSPR can be found here.

An Apology for my Quietude: EZ Score

SORRY FOR THE DELAY. I have been hired by the Losangephoenix Spurockets to do basketballysis!


Just kidding.
I've actually been pretty busy doing other church-music related things. Not my bball-twitter-peeps kind of material, I know.

I've had ten or so blog posts in the works, none of which ever reached fruition.
Displeased with my blog production level ( < 20%), I decided to post what I think could probably have made me the most money (I don't know, a couple dollars?) had I decided to streamline and sell it.

Yes, my compassion and eagerness outweighs my entrepreneurial sense. And yes, I did have to spell-check "entrepreneurial."

With sincere apologies to Evan Zamir who owns a 51% market share on the term "EZ" in the basketball-stats world, I present "EZ Score." EZ Score is a game charting system that takes into account every possession (so it requires a bit of rewinding your recorded video), and every player on your team. It is pretty simple to describe, but a little bit open to interpretation. If anyone cares, I can post the Excel-specific nitty gritty on how to accomplish it, but here are the main basic details:


THE RULES OF EZ SCORE:

Introduction:

This is a system that imitates Dean Oliver's offensive and defensive rating system, although it is a little bit more intensive in that every possession (both offensive and defensive) must be charted. Credit is only given to whomever directly contributes to the possession result. This is pretty wide-open to interpretation, but generally I follow it like so:

Everything past #1 for each data point could be optional if you want, but it will give you less-refined results.

Each possession must be entered manually, simply by entering the player's jersey number like so:

A dream-team including MJ, Hansbrough, and Penny.

Where
P1=Most responsible / directly responsible for the possession result
P2=Less responsible than player #1
P3=Less responsible than player #1

i.e. 
If only one player truly deserves credit, only enter one player. My excel sheet distributes credit accordingly to the between 1 and 3 players(weights are noted at the end of this section).

THE SPECIFICS:

Offense:
Good possession (2+ points):
P1) Whoever scores
optional:
P2) Pass or screen or offensive rebound leading to score
P3) Pass or screen or offensive rebound leading to #2

Normative plays (1 point):
P1) Whoever scores. Optionally, the assister/etc can receive credit as P2 and/or P3, but this depends on your philosophy (is it the passer's "fault" that the player misses a free throw?, etc)

Bad plays (0 points):
P1) Turnover, missed field goal
optional:
P2) Not boxing out/missing easily available rebound


Defense:
Good possessions (0 points):
P1) Forced field-goal miss or defensive rebound (if more causal than #2), fouls, forced turnovers
optional:
P2) Forced field-goal miss or defensive rebound (if less causal than #1), fouls, forced turnovers, help defense
P3) Same as #2

Normative possessions (1 point):
P1) fouler gets 100% credit

Bad possessions (2+ points):
P1) Your man or your zone scores / fail to switch / etc
P2) If a man is wide open due to #1, whomever helps, etc receives #2.
P3) Same as #2

Now, the weighting. In excel, I weight every possession depending on the # of contributing players
If there is 1 player, they receive 100% of the score & possessions
p1=(100% * points, 1 possession)

If there are 2 players, the first receives 66.67% of the score and possession, the second receives 33.33%.
p1=(66% * points, 0.66 possessions), p2=(33% * points, 0.33 possessions)

If there are 3 players, the first receives 50%, and the second two receive 25% apiece.
p3=(50%*points, 0.5 poss), (25%*points, 0.25 poss)


So, there are many obvious small changes one could make. Many of these would increase the work of the charter and might not necessarily be necessary; it's a balancing act. The most obvious to me is the ability to choose in the two or three-player scenarios between ranked and equal weights for player 2 & 3 (i.e. the ability to say that a scorer-screener-assister are weighted something like 50-30-20 rather than 50-25-25) But I would love to hear your suggestions.


So..here are my results for USA v. France in the Olympics this year. This took maybe 30 minutes more than it would have, had it been a regular game-watching experience.

I'll try to do this for a few games this year as my free time permits. Use responsibly!

Introducing SPR (Offense)

If you're like me, and you run regressions of statistics against multi-year regularized-adjusted plus-minus in your spare time, you'll note that there are numerous ways to come up with good estimates. Wanting to create a system that requires as little math as possible, I have found combinations that can estimate offensive value well, while not requiring people to utilize long strings of decimals.


So I've decided to finally release my Simple Player Rating (for offense) to the public. Here it go!
"SPR: Offense" represents an estimate of how much a player boosted their team's offensive rating above average.


SPR: Offense =
(Points - FGmade - Turnovers + 0.5 x (Oreb + Assists - FGmissed - Free Throw Attempts))*100/Possessions Played - 6.5

a)To come up with possessions played, you can find this on any basketball-reference.com box score:
Possessions Played = Pace x (Minutes Played) / (Game Minutes, Usually 48)

b) Also, if you want to convert this to a per-game statistic, simply multiply by:
(Minutes Played) / (Game Minutes)




Here is how this stat correlates with 8-year-RAPM:

In case you were wondering, with the raw values that my regression gave me the R^2 is only 0.01 higher.









And here are the per-game and per-100 SPR Offense from Miami vs Boston Game 6, aka "BRON-BRON GOES NUTS."

Defense is coming soon. Enjoy responsibly!

New Stat: SLEASY%

SLEASY% (Super-Lazy-Estimate-of-Assisted-Shots,Yo%)

Based on Dean Oliver's realization that a player's % of shots assisted on is extremely close to:
=1.14*(Team Assists - Player Assists)/(Team Field Goals Made)

I plugged this into a large NBA player dataset, where:
Team Assists per 100 = 5*Average(Assists per 100)
Team Field Goals Made per 100= 4*Average(Field-Goals-Made-Per-100)+playerFGMper100

The logic is a little hazy, especially considering that the "team" assists are made up of 5 average players, but we are subtracting a non-average player. So it's lazy.

OK, from here, we can get a linear regression. The correlation is EXTREMELY high, but definitely misses the mark on a few players with very high field-goals made or assists dished per 100:

SLEASY% = 0.73 - 0.03xAssistsPer100 - 0.01xFieldGoalsMadePer100

In my 8-year dataset for the NBA through 2011, we find the following top and bottom 10:

Pretty straightforward...low usage players up-top, frequent shooting PGs at the bottom. Can't assist yourself! Or can you ??

Condensed Tweets

Sweeter than condensed milk.

Jibber-Jabber:
I don't hate Calipari a lot this year...
Hoos
Your Simple March Madness Playlist

Numbers:
MLSUOTFR
TSHT9TWGF
Low-Seeded Teams With Depth
High-Seeded Teams With...Shallowness
Cuse isn't much of a one-seed (Although this isn't because of Melo)
Parity!
Four-Factors goes 3 and 0.
Final Cheat Sheet (Updated)
Memphis v St Louis is unfortunate, I agree


Over and out.

2012 Bracket Cheat Sheet

Why yes, it is the most wonderful time of the year.

I've simulated the NCAA tournament 10,000 times with the following specifications

-Brigham Young out. Iona is out. I mistyped.
-Miss. Valley State out.
-Injured/disqualified players out.
-Minutes adjusted (better players getting more and vice versa).
-Home-court advantage adjusted for.
-Lucky efficiency accounted for (somewhat)..i.e. TO% and Defensive Rebound% are worth more than "usual," for example.

Using all of this, I created a bracket cheat sheet. Guaranteed to beat Pomeroy, Sagarin, and the LRMC!*

http://dl.dropbox.com/u/241759/TournamentCheatSheet.xlsx



Enjoy, and use responsibly. Don't gamble with this stuff.


*-OK, not really guaranteed.

The Orange Are Okay.

Before we begin: credit to the numbers here go out to Daniel Myers @DSMok1 and his NCAA statistical plus-minus sheet. I have my own version (which I made about a day before his :), but it didnt' respond to Sports-Reference's reformatting very well.

If we assume that Melo's minutes get replaced by Christmas, and also slightly replaced by James Southerland, we see the following:

Efficiency Margin per 100 (with Melo having high Minutes%):  31.7
Efficiency Margin per 100 (with Melo's minutes replaced by Christmas, then Southerland): 30.4

This is a difference of -1.3 points per 100 possessions, or just -0.82 points per game, in total point margin. So Syracuse moves from a (rough statistical estimate:) #6 team to a #9 team.

Why does this happen?
I will show you.

I am just making some guesses here, but:

-Melo was playing 58% of Cuse's minutes beforehand...he missed a few games. So I bump this up to 74% to compensate/guesstimate.
-Also to compensate, I bump underrated James Southerland's minutes down by 0.1%, and Christmas' minutes down by 16%.


So we then get the following:

Orange are okay! Just probably underrated by impressionable bracketeers.

All points are not created equally...

...therefore not all points predict the future equally.

The True Value of the Four Factors

Dean Oliver's four-factors are well-understood in how they impact the game.

To figure this out, people usually run a regression of game or team four-factors versus efficiency.

We find the following, roughly:

	raw coefficients
OeFG%	1.32
OTO%	-1.19
OOR%	0.63
OFTM/FGA	0.17
DeFG%	-1.32
DTO%	1.19
DOR%	-0.63
DFTR	-0.11

However, to say that these are the proper weights for each of these is to assume that each of these is just as controlled by the offense as the defense. Ken Pomeroy has been blogging on the subject and it got me thinking: there is no way that these can be the true (relative) PREDICTIVE values for four-factors.

I ran a LOOCV test for each 2010-11 NCAA team, for each game they played (for both teams).

In layman's terms: I looked at each game, then averaged all the four factors for the other games a team played.

The results look may only slightly different. The difference is, in fact, huge.

	predictive coef
OeFG%	1.27
OTO%	-1.71
OOR%	0.73
OFTM/FGA	0.17
DeFG%	-1.11
DTO%	0.99
DOR%	-0.62
DFTR	-0.19

The differences can be outlined here (x = predictive / raw) :

	x
OeFG%	96%
OTO%	144%
OOR%	116%
OFTM/FGA	103%
DeFG%	84%
DTO%	83%
DOR%	99%
DFTR	174%

This is now the basis for my ratings. The strength of schedule part is a bit of a guess, but the results are so different no matter what I do, I can't really tell if it's working :)

All this to say: Murray State is actually more overrated than we thought (#227 in offensive TO%, somehow).

Conference Bias in the Polls: No Mountain-West Love

"The Nation's Best Point Guard" apparently.

I did a very quick study on how conferences/teams are underrated or overrated in the polls.

First, I combined the ESPN and AP Polls from Monday.

Here's a simple formula based on regression to add the vote totals from both polls for NCAA basketball:

Total Adjusted Votes= AP votes + (2*ESPN Votes+26)

The top-27 in this method is shown left.

All teams worse than #27 were given a simple "#40" as a placeholder . I then compared each of these team's rankings to my own ranking system, which is very similar to Ken Pomeroy's...I can just fiddle with it as I please :). From this, it's pretty easy to see that teams that have won a lot of close games are overrated and vice versa (if you trust us stat-geeks on the principle of 'luck').

To convert Luck (Win% - Expected Win%) to ranking deviation (Poll rank - my rank), I found the following easy equation:
Expected Ranking Deviation = 140 * Luck% - 0.38

So for each conference, I found the average luck of my top 49 (I stopped at 49 because that's where Murray is ranked) and found the following. I split the rankings into three categories

-Murray State (the only OVC team here, and the most overrated team in the country)
-Conferences (with at least 2 listed teams in the poll)
-Other Conferences (the total of each of the conferences with one bid)
 

Like so:

	#	Top 40 Avg Luck
Murray State	1	0.138
A10	2	0.016
WCC	3	0.009
MVC	2	0.004
Other Mid-Majors	5	-0.002
BE	9	-0.002
MWC	2	-0.007
B12	6	-0.012
ACC	5	-0.014
P12	2	-0.033
B10	6	-0.040
SEC	4	-0.048

Then I used the Expected Ranking Deviation formula to come up with each 'conference's average expected ranking deviation:

	#	ExpRkDiff
Murray State	1	18.9
A10	2	1.9
WCC	3	0.9
MVC	2	0.1
Other Mid-Majors	5	-0.7
BE	9	-0.7
MWC	2	-1.4
B12	6	-2.0
ACC	5	-2.4
P12	2	-5.0
B10	6	-6.0
SEC	4	-7.1

Then I simply subtracted each team's Expected Ranking Difference from their Actual Ranking Difference to give us an estimate of conference bias. The results are pretty intuitive:

	#	Top 40 Luck	ExpRkDiff	ActualRkDiff	Bias
Murray State	1	0.138	18.9	33.0	14.1
B10	6	-0.040	-6.0	-2.0	4.0
BE	9	-0.002	-0.7	3.0	3.7
ACC	5	-0.014	-2.4	1.2	3.6
SEC	4	-0.048	-7.1	-7.0	0.1
WCC	3	0.009	0.9	-0.7	-1.6
MVC	2	0.004	0.1	-1.5	-1.6
B12	6	-0.012	-2.0	-4.3	-2.3
P12	2	-0.033	-5.0	-8.0	-3.0
Other Mid-Majors	5	-0.002	-0.7	-5.4	-4.7
A10	2	0.016	1.9	-6.5	-8.4
MWC	2	-0.007	-1.4	-12.0	-10.6

The only real surprise to my eyes is the Big 12 having a bias of -2.3. Murray State is overrated by their ridiculously easy schedule which they haven't beaten to a pulp. The only four other overrateds are the rest of the power conferences minus the Pac-12, who is still in "recovery mode."

The Journey of Jeremy Lin

Via InfographicWorld

Click image to enlarge

Source: Infographic World

How To Adjust Game Results for Garbage Time (NCAA)

UNC's Blue Steel scrubs. Photo 100% stolen from ESPN.com

There is a fairly straightforward, rule-of-thumb way that I have used in the past to adjust for "garbage time" - basically an effective way of smoothing out the end of games where:

a) the scrubs begin playing
b) teams try and make a comeback despite a mighty deficit...tons of free throw shooting that could skew the point margin in either team's favor
c) the winning team doesn't care about playing anymore, really...
d) etc

Bill James (of baseball stats fame) introduced his own simple metric that StatSheet.com uses, and succeeds rather invariably: Lead "Safeness." Bill James also refers to Coach K as "the human typo" in that article. I love him.





Anyways, the math is pretty straightforward (I remove the bit about who has the ball to make it easier to calculate, as it averages to zero):

     (Point Margin - 3)^2  /  Seconds Remaining = % Safe


So we do a little algebra, and we can quickly estimate point margin when the lead became 100% safe:

     Point Margin =Sqrt(seconds remaining when safe) + 3


Which we then use to forecast the rest of the game, ignoring how it actually played out:

     Adjusted Final Margin = [sqrt(Seconds Remaining when safe)+3] x 40 / [40-(Seconds Remaining when safe/60)]


You can easily find "seconds remaining when safe" via StatSheet.com. For example, last night's UNC-Wake Forest Game was "statistically over" with 3:48 left to go. Plus this in to my handy-dandy spreadsheet, and you get:

Point Margin When Safe: ~18
Adjusted Final Point Margin: 20

So while the Heels only won by 15, they had plenty of room to let the game slide in the final minutes - if they had played with the same quality all the way through they would have won by around 20.

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.