For all your fancy-pants statistical needs.

Praise for The Basketball Distribution:

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

Fourteen Scorers Remain (Sort of)

Assuming that the final four teams in the NBA playoffs are Miami, Indiana, San Antonio, and Memphis, (a big assumption) there are only 14 players with at least 100 minutes played who are taking at least 20% of their team's true-shooting attempts.

Sorted by points per 48 in the playoffs (LeBron has probably been hurt here by slower play, Parker vice versa).



PlayerPosTmTS%SA%Pts/48TS% - Avg
Tony ParkerPGSAS54.0%30.5%23.40.5%
LeBron JamesPFMIA62.5%28.1%22.39.0%
Tim DuncanCSAS50.3%27.1%19.4-3.2%
Manu GinobiliSGSAS51.2%24.8%18.1-2.2%
Jerryd BaylessPGMEM50.3%24.3%17.1-3.2%
Zach RandolphPFMEM56.0%24.2%19.02.6%
Paul GeorgeSFIND51.5%23.3%16.1-1.9%
Mike ConleyPGMEM53.2%22.8%16.9-0.2%
Dwyane WadeSGMIA47.4%22.6%13.6-6.1%
David WestPFIND54.3%21.9%16.00.8%
Ray AllenSGMIA65.4%21.8%18.211.9%
Marc GasolCMEM57.0%21.5%17.13.6%
George HillPGIND55.5%21.3%15.82.0%
Chris BoshCMIA56.8%20.6%14.93.3%



Also, check out Miami...

PlayerPosTmTS%SA%Pts/48TS% - Avg
LeBron JamesPFMIA62.5%28.1%22.39.0%
Chris AndersenCMIA78.0%18.8%18.724.5%
Ray AllenSGMIA65.4%21.8%18.211.9%
Norris ColePGMIA77.3%15.2%15.023.9%
Chris BoshCMIA56.8%20.6%14.93.3%
Dwyane WadeSGMIA47.4%22.6%13.6-6.1%
Udonis HaslemPFMIA59.7%16.0%12.26.3%
Mario ChalmersPGMIA52.4%14.5%9.7-1.0%
Shane BattierSFMIA43.2%14.9%8.2-10.2%

The Sweet 16 and Beyond


Here are the results of my latest 10,000 simulations of the tournament.

Notable Notes:
Expected number of double-digit seeds in the Sweet 16:   2.5
Odds of all four 1-seeds making it to the Sweet 16:   27%
Odds of La Salle making it to the Sweet 16:   37%               

regsds16e8f4title gameChampexp. Wins
E1Indiana90%69%55%36%23%3.74
S3Florida78%65%44%26%16%3.28
W1Gonzaga78%63%41%25%14%3.20
MW1Louisville67%52%34%20%10%2.83
W2Ohio St.73%51%28%15%7%2.74
S4Michigan69%42%22%11%6%2.50
MW2Duke64%41%22%11%5%2.43
E2Miami FL70%46%16%7%3%2.42
W6Arizona82%33%13%5%1%2.34
MW3Michigan St.68%31%14%6%2%2.21
E4Syracuse76%24%13%5%2%2.21
S7San Diego St.82%21%7%2%1%2.12
S1Kansas58%27%12%5%2%2.05
MW4St. Louis65%20%9%3%1%1.98
E3Marquette58%25%7%2%1%1.92
W12Mississippi63%18%7%2%1%1.92
MW8Colorado St.33%20%10%4%2%1.69
S8North Carolina42%17%6%2%1%1.68
MW7Creighton36%19%8%3%1%1.68
E6Butler42%15%3%1%0%1.62
S5Virginia Commonwealth31%13%4%1%0%1.51
E7Illinois30%13%3%1%0%1.47
W10Iowa St.27%14%4%1%0%1.47
W13La Salle37%7%2%0%0%1.46
MW12Oregon35%7%2%0%0%1.45
MW6Memphis32%9%2%1%0%1.43
S11Minnesota22%13%5%1%0%1.42
W9Wichita St.22%12%4%1%0%1.40
E12California24%3%1%0%0%1.28
W14Harvard18%3%0%0%0%1.21
S15Florida Gulf Coast18%1%0%0%0%1.19
E9Temple10%3%1%0%0%1.15

Simulated Tournament, Luck-Adjusted Style

Alright...so I did another 10,000 simulations, but I used a combination of my luck-adjustments and Ken Pomeroy's strength of schedule to come up with some decent results. Notably, turnovers (offensive and defensive) are far more important here.



ro32s16e8f4title gameChampexp. Wins
1Indiana100%85%68%58%38%26%3.74
2Florida99%81%64%43%24%15%3.26
3Louisville100%71%59%39%23%12%3.03
4Gonzaga100%67%49%33%21%11%2.81
5Ohio St.97%70%49%25%13%6%2.60
6Michigan92%73%48%25%13%7%2.58
7Duke99%69%46%24%12%5%2.55
8Miami FL95%67%47%15%6%2%2.32
9Syracuse96%72%21%13%5%2%2.09
10Kansas99%62%29%11%4%2%2.07
11Georgetown97%63%19%8%3%1%1.90
12Michigan St.84%56%26%11%5%2%1.83
13Wisconsin73%56%24%13%7%3%1.76
14New Mexico88%51%19%6%2%1%1.67
15Marquette71%46%21%5%1%0%1.44
16Arizona73%39%15%5%2%0%1.34
17Pittsburgh73%28%17%10%5%2%1.34
18St. Louis75%43%11%4%1%0%1.34
19North Carolina75%33%12%4%1%1%1.26
20Creighton65%23%12%5%2%1%1.08
21Oklahoma St.61%32%7%3%1%0%1.04
22North Carolina St.74%13%7%4%1%0%0.99
23Colorado St.57%18%13%7%3%1%0.99
24Virginia Commonwealth67%19%8%2%1%0%0.97
25Butler56%25%9%1%0%0%0.91
26San Diego St.57%23%5%1%0%0%0.87
27Kansas St.60%20%4%1%0%0%0.86
28Notre Dame54%17%9%3%1%0%0.83
29Nevada Las Vegas59%18%3%1%0%0%0.81
30Minnesota58%12%6%2%1%0%0.79
31Colorado51%16%8%1%0%0%0.77
32St. Mary's44%21%8%3%1%0%0.76
33Illinois49%15%7%1%0%0%0.73
34Iowa St.46%12%6%2%1%0%0.67
35Bucknell44%17%5%1%0%0%0.67
36Missouri43%11%6%3%1%0%0.64
37Memphis42%15%4%1%0%0%0.62
38Oklahoma43%14%2%1%0%0%0.60
39Oregon39%17%3%1%0%0%0.59
40UCLA42%7%3%1%0%0%0.54
41California41%10%1%0%0%0%0.51
42Cincinnati35%8%3%1%0%0%0.46
43Mississippi27%15%3%1%0%0%0.46
44Davidson29%13%3%0%0%0%0.46
45Akron33%6%1%0%0%0%0.41
46Belmont27%8%2%0%0%0%0.37
47Wichita St.27%5%2%1%0%0%0.35
48New Mexico St.25%8%1%0%0%0%0.34
49Villanova25%5%1%0%0%0%0.32
50Boise St.23%6%1%0%0%0%0.31
51Temple26%2%0%0%0%0%0.29
52Valparaiso16%5%1%0%0%0%0.22
53La Salle16%3%0%0%0%0%0.20
54Middle Tennessee14%4%1%0%0%0%0.19
55Harvard12%2%0%0%0%0%0.14
56South Dakota St.8%2%0%0%0%0%0.10
57Pacific5%1%0%0%0%0%0.06
58Montana4%1%0%0%0%0%0.04
59Florida Gulf Coast3%1%0%0%0%0%0.04
60Iona3%0%0%0%0%0%0.04
61Northwestern St.1%0%0%0%0%0%0.01
62Western Kentucky1%0%0%0%0%0%0.01
63Albany1%0%0%0%0%0%0.01
64Southern0%0%0%0%0%0%0.00
65Long Island0%0%0%0%0%0%0.00
66North Carolina A&T0%0%0%0%0%0%0.00
67Liberty0%0%0%0%0%0%0.00

Simulated Tournament, LRMC Style

I simulated the NCAA tournament 10,000 times based on the LRMC ratings. Just a pure-point-margin version of Ken Pomeroy's table. Use responsibly!



ro32s16e8f4title gamechampExp. Wins
1Indiana100%87%70%55%31%19%3.62
2Florida96%85%72%52%34%22%3.61
3Gonzaga100%78%63%48%31%18%3.37
4Louisville100%77%63%41%23%11%3.16
5Kansas99%83%53%24%12%6%2.77
6Miami (FL)95%73%54%21%8%3%2.55
7Ohio St.94%71%48%21%11%4%2.50
8Duke96%57%39%21%10%4%2.28
9Michigan87%60%30%11%5%2%1.95
10Syracuse90%62%19%10%3%1%1.85
11Georgetown90%56%14%5%2%1%1.67
12Creighton77%37%23%11%5%2%1.55
13Michigan St.79%45%18%7%3%1%1.52
14Wisconsin68%49%16%9%4%1%1.46
15New Mexico81%41%17%5%2%0%1.45
16Saint Louis73%38%10%3%1%0%1.26
17Oklahoma St.64%38%11%4%1%0%1.18
18Marquette56%33%13%3%1%0%1.05
19VCU65%27%9%3%1%0%1.05
20UNLV67%27%5%2%1%0%1.02
21Arizona53%30%12%3%1%0%1.01
22Bucknell58%26%9%2%0%0%0.95
23Kansas St.64%24%4%2%1%0%0.95
24Pittsburgh59%15%8%4%2%1%0.89
25San Diego St.57%25%5%1%0%0%0.88
26Iowa St.58%18%9%2%1%0%0.87
27N.C. State68%11%5%2%1%0%0.86
28Belmont47%25%9%2%1%0%0.84
29North Carolina64%13%4%1%0%0%0.81
30Davidson44%24%8%2%0%0%0.78
31Minnesota61%10%5%1%0%0%0.78
32Colorado53%15%7%1%0%0%0.76
33Colorado St.52%12%7%3%1%0%0.75
34Middle Tenn. St.35%20%8%3%1%0%0.68
35Missouri48%10%6%2%1%0%0.67
36Memphis40%17%5%1%0%0%0.64
37Butler42%16%4%1%0%0%0.64
38Illinois47%11%5%1%0%0%0.63
39Oklahoma43%16%2%1%0%0%0.62
40Notre Dame42%10%4%1%0%0%0.56
41Oregon36%16%3%1%0%0%0.55
42Wichita St.41%7%4%2%1%0%0.55
43Mississippi32%17%3%1%0%0%0.54
44Akron35%10%2%0%0%0%0.48
45UCLA39%4%2%0%0%0%0.46
46St. Mary's25%12%4%1%0%0%0.43
47California33%9%1%0%0%0%0.43
48Villanova36%4%1%0%0%0%0.42
49New Mexico St.27%8%1%0%0%0%0.36
50Temple32%2%1%0%0%0%0.35
51Cincinnati23%5%2%0%0%0%0.31
52Boise St.21%6%1%0%0%0%0.28
53Valparaiso21%6%1%0%0%0%0.28
54Harvard19%4%0%0%0%0%0.23
55La Salle15%4%0%0%0%0%0.19
56South Dakota St.13%3%0%0%0%0%0.17
57Fla Gulf Coast10%3%0%0%0%0%0.13
58Montana10%2%0%0%0%0%0.13
59Iona6%2%0%0%0%0%0.08
60Pacific5%1%0%0%0%0%0.06
61Northwestern St.4%1%0%0%0%0%0.05
62Albany4%1%0%0%0%0%0.04
63West. Kentucky1%0%0%0%0%0%0.01
64Southern0%0%0%0%0%0%0.00
65LIU Brooklyn0%0%0%0%0%0%0.00
66Liberty0%0%0%0%0%0%0.00
67N.C. A&T0%0%0%0%0%0%0.00

Exactly How Good Is R. Kelly?

YOU SEE ME RUNNIN' THROUGH THAT OPEN DOOOOR.
Before I begin, I must honor the fact that I am a die-hard Carolina fan. I bleed Carolina blue and I hate Duke the instant I wake up each morning. But that cannot change the impact of the beardy white man, R. Kelly.

You might remember last season that I tweeted LeHigh's praises in terms of their ability to possibly beat Duke, *even before the brackets came out*...but my statistics pushed LeHigh's odds way up when we learned that Kelly wouldn't make the LeHigh game...because in my system, he was definitely their best player.

I've heard a lot of claims from all sorts of people on how good R. Kelly is...the boys over at 99.9 The Fan (Raleigh represent...) seem to think that he has made Duke's defense impeccably better. I'm not so sure...but only because Ken Pomeroy mentioned that in a blog post.


By my SPR measure that estimates per-100-possession impact, he is the best player in the ACC (same as Daniel Myers' ASPM. But I am more interested in how much worse their defense got with him out. Let us investigate.

I looked at Duke's expected efficiency differential based on kenpom.com efficiency stats (and home-court advantage), versus how they actually played, and here's the difference we see:




So despite Kelly's immense impact on offense, we can tell that at least Duke's defense looks better with him on the floor. Sixty-four places better.



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


PlayerSeasonOSPRDSPRTotal SPRSPR per Game
1Carmelo Anthony2012-1310.6-0.79.97.5
2LeBron James2012-136.91.38.16.5
3Blake Griffin2012-137.12.59.66.4
4Kevin Durant2012-136.20.46.65.4
5Chris Paul2012-136.32.18.45.4
6Kobe Bryant2012-136.4-0.55.95.0
7Andray Blatche2012-134.73.68.24.8
8Ryan Anderson2012-137.9-1.46.54.6
9Tony Parker2012-137.3-0.66.64.6
10Paul George2012-134.71.05.74.5
11Kemba Walker2012-136.4-0.16.34.4
12Chris Copeland2012-1312.71.213.94.3
13Russell Westbrook2012-134.90.85.84.2
15Stephen Curry2012-135.0-0.34.73.9
16Paul Millsap2012-134.11.35.43.7
17James Harden2012-134.00.54.53.7
18Tyson Chandler2012-133.71.35.03.5
19Andrei Kirilenko2012-131.13.54.63.5
21J.J. Hickson2012-135.41.06.43.4
20Matt Barnes2012-134.51.96.43.4
22Ed Davis2012-134.42.46.83.4
23Dwyane Wade2012-135.3-0.44.83.2
24David Lee2012-133.80.24.03.2
27Kyrie Irving2012-135.9-1.64.33.2
26Eric Bledsoe2012-133.64.68.23.2

The Bottom 26


PlayerSeasonOSPRDSPRTotal SPRSPR per Game
387Daniel Gibson2012-13-5.5-1.7-7.2-3.9
386Kyle Singler2012-13-4.1-1.8-5.9-3.8
388Doron Lamb2012-13-8.9-1.6-10.5-3.8
382Mickael Pietrus2012-13-4.6-1.1-5.7-3.5
383Andre Iguodala2012-13-5.30.7-4.6-3.4
379Chris Singleton2012-13-5.90.1-5.8-3.3
381J.R. Smith2012-13-3.6-1.5-5.1-3.3
378Andrea Bargnani2012-13-3.2-2.2-5.4-3.2
377Victor Claver2012-13-11.32.0-9.3-3.2
376Jeff Taylor2012-13-3.1-2.3-5.4-3.1
374Jerry Stackhouse2012-13-4.0-2.6-6.6-3.0
375Bismack Biyombo2012-13-5.00.5-4.5-3.0
372Alonzo Gee2012-13-3.3-0.8-4.1-3.0
373Gerald Green2012-13-4.7-1.9-6.7-2.9
370Willie Green2012-13-5.6-2.2-7.8-2.8
369Festus Ezeli2012-13-7.6-0.9-8.5-2.6
368Dahntay Jones2012-13-4.1-2.4-6.5-2.6
367Austin Rivers2012-13-2.6-1.7-4.3-2.6
365Keith Bogans2012-13-9.1-2.6-11.7-2.6
364Sebastian Telfair2012-13-3.2-2.4-5.6-2.4
363Aaron Brooks2012-13-2.7-1.6-4.3-2.4
361Martell Webster2012-13-2.8-1.0-3.8-2.4
362John Salmons2012-13-1.8-1.8-3.6-2.4
359Jason Maxiell2012-13-5.41.6-3.8-2.3
360Tony Allen2012-13-6.11.5-4.6-2.3
358Nolan Smith2012-13-6.5-2.8-9.3-2.3



-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 ??

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/FGA0.17
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/FGA0.17
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/FGA103%
DeFG%84%
DTO%83%
DOR%99%
DFTR174%

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 State10.138
A1020.016
WCC30.009
MVC20.004
Other Mid-Majors5-0.002
BE9-0.002
MWC2-0.007
B126-0.012
ACC5-0.014
P122-0.033
B106-0.040
SEC4-0.048

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


#ExpRkDiff
Murray State118.9
A1021.9
WCC30.9
MVC20.1
Other Mid-Majors5-0.7
BE9-0.7
MWC2-1.4
B126-2.0
ACC5-2.4
P122-5.0
B106-6.0
SEC4-7.1


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 LuckExpRkDiffActualRkDiffBias
Murray State10.13818.933.014.1
B106-0.040-6.0-2.04.0
BE9-0.002-0.73.03.7
ACC5-0.014-2.41.23.6
SEC4-0.048-7.1-7.00.1
WCC30.0090.9-0.7-1.6
MVC20.0040.1-1.5-1.6
B126-0.012-2.0-4.3-2.3
P122-0.033-5.0-8.0-3.0
Other Mid-Majors5-0.002-0.7-5.4-4.7
A1020.0161.9-6.5-8.4
MWC2-0.007-1.4-12.0-10.6

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

The Journey of Jeremy Lin
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


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


Top 25

teambench impact
1Grambling5.87
2South Alabama2.79
3Vermont2.54
4Colorado State2.50
5Loyola Marymount2.46
6North Carolina-Greensboro1.75
7Texas Southern1.53
8Oklahoma State1.39
9Western Carolina1.03
10William & Mary1.00
11Cornell0.97
12Marist0.94
13California-Santa Barbara0.85
14Maryland-Eastern Shore0.68
15Western Michigan0.51
16IPFW0.49
17Southern Mississippi0.41
18Alcorn State0.35
19Southeastern Louisiana0.35
20Southern Methodist0.28
21Canisius0.26
22Rhode Island0.26
23Miami (FL)0.24
24Toledo0.15
25East Tennessee State0.13

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)

 rankplayerteamORTGusage%DRTGTrue Impact per Game
1Kevin JonesWest Virginia129.624.292.59.6
2Jared SullingerOhio State128.926.3769.4
3Damian LillardWeber State136.132.299.29.4
4Marcus DenmonMissouri140.523.293.98.6
5Thomas RobinsonKansas117.328.278.78.5
6Anthony DavisKentucky140.51873.68.1
7Doug McDermottCreighton127.731.61027.9
8Kenny BoyntonFlorida135.924.6102.87.6
9Dominique MorrisonOral Roberts13125.4103.17.4
10Will BartonMemphis1192694.47.3
11Cody ZellerIndiana13422.2907.2
12J'Covan BrownTexas122.628.2100.77.1
13John ShurnaNorthwestern117.527.4102.47.0
14Mike ScottVirginia127.128.782.36.9
15Jae CrowderMarquette124.623.384.66.7
16Isaiah CanaanMurray State130.326.495.86.6
17Deshaun ThomasOhio State124.823.889.76.6
18Hollis ThompsonGeorgetown12921.3936.6
19John JenkinsVanderbilt127.126.31016.6
20Nate WoltersSouth Dakota State121.230101.96.4
21Ricardo RatliffeMissouri138.222.491.56.3
22Jeremy LambConnecticut120.923.61006.2
23Drew CrawfordNorthwestern119.125.5104.76.2
24Jordan TaylorWisconsin115.824.586.76.2
25Jason ClarkGeorgetown117.526.189.86.0

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