Posts Tagged ‘Win Score’

Arturo injects some reality into NBA Draft night fantasies

June 28, 2012

I love objectively analyzing sports, but it has its downside too.  It basically takes all of the oxygen away fromone of the time honored traditions of fandom, what I will call the “season of stupid hopes”.  (The Ceremony of Innocence is Drowned as someone once wrote).

For instance, its June and, looking at things objectively, I already know the Bucks have at best a decent chance to make the playoffs, but  they have a very slim chance of advancing past the opening round, and they have absolutely ZERO chance of winning an NBA championship.  That’s kind of depressing, but its realistic.  And sobering.

Same goes with NBA draft night.  If you subjectively analyze the prospects through your blue and red NBA glasses, like every ESPN analyst does, then every tall skinny African American is potentially another Kevin Garnett, every three point ace reminds you of Ray Allen, every raw big man could be the next Andrew Bynum, and absolutely every tall white European with an outside shot is certain to be the next Dirk Nowitzki.  But, in most cases, it just isnt so.

Yet, in the face of a mountain of evidence to the contrary, we repeat the same fantastic exercise every June.  We willingly delude ourselves using absurd, largely physical comparisons.  Comparisons that have little basis in statistical reality.   For instance, remember when Kenny Fields was the next Marques Johnson, Bucks fans?  Because he was a 6’7” small forward from UCLA.  Or remember how Todd Day was going to be the next superstar?  (I’m not sure why we thought that)  How dumb do those notions seem now?

Here is the truth.  Arturo shows on his genius draft model over at the Wages of Wins Journal that in fact very few of the players chosen last night will have any positive impact at all.  At most 4 of them look capable of producing above average winning statistics, but almost 80% of them look as though they will be utterly incapable of making any significant impact on their new team’s fortunes (unless it is a negative impact).

That may be depressing, and it may render all the next day “Draft Report Cards” moot, and it may take all the fun out of sports, but its pretty truthful.

I’m all giddy about Henson, the Bucks choice, but the truth is, he won’t turn the Bucks around.  According to Arturo, he’s just on the border of being “draftable”, and Arturo forecasts his rookie statline as decent at best, and probably below average.  In short, Mr Henson aint gonna be Milwaukee’s new Lew Alcindor.  In fact, the only player in the Draft who has ANY shot at having a franchise changing impact is the top pick Anthony Davis.  After that, you’ve got a couple of possibly above average players (one of whom is second rounder Jae Crawford), then you have a handful of probably average players, and after that you’re left with pure junk.

Those are the ugly facts.

But the Bucks should be better, and that’s enough for me.

Hey, the Bucks made a smart move!

June 28, 2012

The Bucks made a pretty astute little move yesterday when they acquired the veteran journeyman C Samuel Dalembert from the Houston Rockets for, essentially, two draft spots.

First, they gave up nothing.  The Bucks administration is just as likely to make a mistake at 14 as they were at 12.  Secondly, they picked up a pretty productive center who fits perfectly in the Scott Skiles defensive regime.

Dalembert’s Win Chart for the last 5 seasons

WS DWS MWS W% W__L W.500 VALUE
2012 14.59 11.95 1.32 0.728 5.3__2.0* 1.6* 6.9*
2011 13.37 11.34 1.01 0.675 5.2__2.5 1.4 6.6
2010 17.22 11.54 2.84 0.984 8.7__0.1 4.3 12.9
2009 14.19 11.33 1.43 0.738 6.2__2.2 2.1 8.2
2008 13.92 11.49 1.21 0.707 7.9__3.4 2.3 10.2
AVER 14.65 11.53 1.56 0.766 6.7__2.0 2.3 9.1

*: numbers projected over a regulation 82 game schedule

As you can see, Dalembert is a consistent +0.500% winner.  In 2010 he even made my 20 MVP List.  Even if you throw out that outlier, he has been above average in every other season.  He basically gives the Bucks exactly what Bogut gave them, with probably a tiny bit more defensive presence in the middle (his penchant for blocking shots is probably the reason why his Defensive Win Score is a little high).

Dalembert is certainly better than the other horror shows the Bucks were rumored to be ready to draft: the big man from Illinois, who did nothing in the Big Ten last season (he has one of those “last name as my first name” names that drive British people nuts and that now escapes me) or Zeller from UNC, who was productive in his last season (but so was Psycho T and he has completely sucked for the Pacers) but who was never that productive in his earlier seasons, and who has the standing reach of a small forward.

NBA teams should stay away from Doc Rivers kid

June 27, 2012

If you are looking for potential NBA underperformers in tonight’s NBA draft, I’d start with Doc Rivers kid, Austin.

First off, he is weak and weighs only 202 pounds, very light for a shooting guard.

Second, his standing reach, which I consider true basketball height, is only 8’0.5”.  That’s short for a point guard, let alone a shooting guard.

Third, and most importantly, Rivers’ collegiate statistics forewarn possible NBA ineffectiveness.  Rivers was a terribly inefficient player in his lone collegiate season.  His Win Score, projected out over 48 minutes, would be 4.21, which is over 2.0 points below the NBA average.  Sometimes players will outperform their NCAA stats when they reach the NBA, but not often.

Finally, shooting guard is a position filled with busts, mainly because a player’s effectiveness at this position is largely a measure of his shooting efficiency.  Shooting efficiency is highly volatile.

I would stay away from Austin Rivers, but some dumb NBA GM will waste a high pick on him tonight.

NBA Power Rankings by “Ty Rating”: the rising Heat and the sinking Celts

February 17, 2012

Using the same formula, and the same gambling website (Statfox Sports), that I used to power rank the likely NCAA tournament field, I power ranked the National Basketball Association.

My NBA chart is set up a bit differently because I condensed three steps.  Instead of posting each team’s Win Score and Defensive Win Score, followed by the expected winning percentage and then the winning percentage the rest of the league is posting against the same schedule, and then the “Ty Rating” based upon that, instead I post below the “Comparative Win Score” the “Comparative Defensive Win Score” and the Ty Rating based upon the same.  Let me provide a quick example.

Example using the #20 Milwaukee Bucks

Below on the chart, the 20th ranked team is the Milwaukee Bucks.  Under “WS” the Bucks post a “-1.1″.  That means the Bucks Team Win Score is 1.1 points below the Win Score the rest of the NBA is posting against the same schedule.  Under “DWS” it says “-2.1″.  That means that the Bucks are allowing their Opponents to post Win Scores that are 2.1 points higher than the same teams have been able to post against the rest of the NBA.  (Defensive Win Scores that are indicated as negative mean a below average performance).  If you add the two numbers together, you arrive at “-3.2″.  You then divide that by 10 to arrive at “-0.32″.  This is the Bucks “absolute” Marginal Win Score, from which I can calculate their absolute winning percentage, which is their “Ty Rating”.  Essentially, it is the difference between the winning percentage the team has achieved versus the winning percentage the rest of the NBA has achieved against the same schedule plus 0.500.  So, while the Bucks expected winning percentage is 0.404% (11.7 wins and 17.3 losses — the team is actually 12-17), because the rest of the NBA is only playing 0.455% basketball against the same schedule the Bucks have played, the Bucks “absolute” winning percentage, or their “Ty Rating” is 0.449%, so its a little better.

Here is the chart:

NBA WS DWS Ty Rating
1 Miami 7.6 6.1 0.733
2 Chic 7.1 6.4 0.731
3 OKC 5.5 5.1 0.682
4 LA Lakers 3.4 5.5 0.653
5 Denv 6.8 1.1 0.636
6 LA Clip 4.9 2.7 0.631
7 Orlando 2.6 3.7 0.609
8 Dallas 3.1 2.9 0.606
9 Phila 0.4 4.8 0.591
10 Atl 3.4 1.1 0.579
11 San An 3.1 0.8 0.569
12 Port 0.8 2.6 0.557
13 Memp -1.5 2.8 0.524
14 Hous -1.3 1.7 0.509
15 Ind -1.8 1.9 0.504
16 Minn -1.1 1.1 0.501
17 Bost -4.9 4.8 0.499
18 Utah -0.6 -0.7 0.479
19 NOH  -4.9 1.9 0.451
20 Milw -1.1 -2.1 0.449
21 NY Knicks -4.6 1.1 0.441
22 Phoenix -0.8 -2.8 0.441
23 Clev -1.6 -2.2 0.438
24 Gold St 1.8 -6.1 0.429
25 Sacra -3.5 -5.9 0.343
26 Tor -7.1 -3.1 0.329
27 NJ Nets -5.7 -6.6 0.294
28 Detroit -8.7 -4.2 0.283
29 Wash -6.6 -7.4 0.265
30 Char -9.9 -9.2 0.181

NBA Ty Ratings

Heat and Bulls clearly the NBA elite

Its neck-and-neck between the Miami Heat and the Chicago Bulls for best team in the NBA.  The two teams also rank #1 and #2 in overall offensive efficiency (by which I mean relative Win Score), and they invert that order for #1 and #2 in overall defensive teams in the NBA as well (by which I mean relative Defensive Win Score).

Three teams surprised me with their placement.  The Lakers are a lot higher than I anticipated.  They may have some fight left in the Purple and Gold.  And on the other side, the Boston Celtics placed much lower than I expected at #17.  The Celtics still play top 10 defense, but without Kendrick Perkins, the team is really struggling on the boards, and that is costing them games.  The other team who placed much lower than I anticipated was the New York Knickerbockers.  However, as I discussed two posts ago, the addition of world famous PG Jeremy Lin, the Knicks have shored up a major weakness and may begin to ascend the rankings.

Another surprise was the Minnesota Timberwolves.  I knew they were playing much better this season, but it is actually their defense that is propelling them more so than their offense.  That is surprising.  The aforementioned Bucks seem to have been stuck in the #18-#21 power ranking range throughout the entire Scott Skiles/John Hammond administration.  That is disappointing, to say the least.

Finally, we have the putrid Charlotte Bobcats and almost-as-putrid Washington Wizards.  What is the thread that runs between each organization?  Michael Jeffrey Jordan was in a management position for each.  Bucks fans, we cannot be thankful for much, but we can be thankful for this:  Herb Kohl prevented Michael Jordan from bringing his eye for talent to Milwaukee.  Jordan makes Isiah Thomas look like Branch Rickey.

Finally, has anyone heard from PG John Wall?  I thought he was supposed to be such a game changer for the Wizards when they selected him number one overall last season.  He certainly has not been.  His career is heading toward oblivion, just as many of us predicted when he was drafted.

Power Ranking the likely 2012 NCAA Basketball Tournament Field by “Ty Rating”

February 15, 2012

I’m getting a head start on handicapping the likely 2012 NCAA Basketball Tournament.  I have taken all of the teams mentioned in the various “bracketology” sites, minus the low seed automatics, and I have power ranked the top 60 teams using something I call the “Ty Rating”.

Ty Rating is simply each team’s expected Winning Percentage (derived from the difference between the team’s Win Score average and its Defensive Win Score) subtracted from the expected Winning Percentage the rest of the country would have against the very same schedule.  In other words, it first evaluates each team’s performance, and then adjusts it for the strength of the schedule the team faced.  All of the calculations are based upon numbers I found at this nifty gambling site called “StatFox Sports” (Sidenote:  While I love the site, if they are not affiliated with Fox Sports, or with the old site StatFox, they are creeping very close to two trademark violations).

StatFox Sports makes the Ty Rating possible because it not only lists each team’s “Team” and “Opponent” statistics, it also lists the averages yielded and produced by those opponents.  By doing so, it allows me to precisely adjust each team’s success according to the strength of its schedule.  SOS adjustment is an absolute must when it comes to college sports analysis because of the widely different competition faced by the different schools.  Before now, I would have had to calculate each school’s opponent strength manually.  That’s way too much work.  With StatFox Sports its all done for me.  That’s why I’ve been looking for a site like StatFox Sports for quite a while.  I basically stumbled on this beauty, and now I’m back in the college basketball business, big time.

“Ty Rating” Calculation Example using #23 Virginia Cavaliers

Tony Bennett’s Virginia Cavaliers have a team Win Score Average of 35.1 — not that great, just above the BCS average (based on my opponent strength calculations, I peg the upper Division I Win Score average at 31.98, and the Defensive Win Score average at 28.01, the difference is borne by the 200 or so lower Division I schools the team’s feast on).  However the Cavaliers Defensive Win Score average is a phenomenal 12.6, way way above average.  You subtract the difference and divide by ten and you get a Team Marginal Win Score of +2.25, which translates into an expected Winning Percentage of about 0.884, or about 20.3 wins in their 23 games played.  Their actual record is 19-4, so MWS estimates extremely well.  But, that does not give a necessarily accurate portrait of Virginia’s relative strength as a basketball team, because they could have been playing the Washington Generals every night for all we know.

So to adjust my power rating of Virginia to account for the strength of the opponent’s Virginia has faced, I take the collective Win Score average produced by Virginia’s opponents’ opponents, and that happens to be 28.6, pretty high.  Then I calculate the collective Defensive Win Score average yielded by Virginia’s opponents’ opponents and that happens to be 27.9.  If you put those two numbers together, you get an Opponent’s Opponent MWS of 0.07, which means Virginia has played a relatively weak schedule, because the rest of the country would be expected to play 0.514% basketball — or winning basketball — against the same schedule.  For comparison, the NCAA Tournament field Opponent’s Opponent expected winning percentage average is 0.435%.

So, while Virginia has an impressive raw Marginal Win Score and winning percentage of 0.884%, when you adjust for their weak schedule, by subtracting the generic opponent expected Winning Percentage of 0.514%, you get a more modest “Ty Rating” for Virginia of 0.369, which is just above the field average “Ty Rating” of 0.354.  Thus the Ty Rating levels the field and provides an opponent neutral evaluation of each team’s relative strength as we enter “Bracket Season”.

How to read the Chart

The Chart below features a ranking of the 60 most likely qualifiers and bubble teams for this season’s NCAA Tournament as presented by ESPN’s Bracketologists.  The ranking is based on each team’s Ty Rating, as explained above.  The first column marked “WS” is the team’s Win Score average.  Win Score is an efficiency score based on a weighting of box score statistics based according to how each statistic correlates with winning.  The column marked “DWS” is each team’s Opponents Win Score average.  The third column is the expected Winning Percentage for a team with a Win Score/Defensive Win Score differential equal to the one posted by the given team.  The fourth column, marked “SOS” for strength of schedule, is the very same evaluation, except done on the Opponent’s opponents.  In other words, it is the expected winning percentage the rest of the country would post against the very same schedule of opponents.  Finally, there is the “Ty Rating” which is an expression of each team’s relative strength by comparing the difference between the expected winning percentage each team has achieved against the expected winning percentage the rest of the country has achieved.

I have analysis of the field below that.

TEAM WS DWS exW% SOS Ty Rating
1 Kentucky 48.2 12.7 1.105 0.423 0.682
2 Ohio St 41.2 12.6 1.005 0.375 0.631
3 Mich St 42.2 14.1 0.979 0.392 0.587
4 New Mex 44.1 13.2 1.027 0.455 0.572
5 Syracuse 46.1 19.8 0.949 0.383 0.565
6 Missouri 46.5 18.3 0.981 0.443 0.538
7 Kansas 40.6 18.5 0.877 0.345 0.532
8 UNC 46.1 20.5 0.937 0.406 0.531
9 Wisconsin 36.6 11.8 0.923 0.404 0.519
10 UNLV 47.5 18.8 0.989 0.482 0.507
11 Indiana 41.5 21.7 0.838 0.349 0.444
12 Duke 39.5 23.2 0.779 0.343 0.436
13 Baylor 41.8 20.3 0.867 0.436 0.431
14 Texas 35.4 19.2 0.777 0.361 0.416
15 Uconn 38.2 22.6 0.767 0.355 0.412
16 Witch St 43.9 18.4 0.935 0.524 0.411
17 Gonzaga 39.7 20.3 0.832 0.429 0.402
18 Florida 44.2 26.9 0.796 0.407 0.388
19 Louisville  36.5 19.3 0.794 0.407 0.386
20 California 36.9 17.9 0.825 0.441 0.383
21 Flor St 34.5 17.9 0.784 0.409 0.375
22 St Marys 43.8 19.9 0.908 0.535 0.373
23 Virginia 35.1 12.6 0.884 0.514 0.369
24 Creighton 45.1 25.8 0.829 0.465 0.365
25 Memphis 39.1 24.1 0.757 0.392 0.365
26 Arizona 35.2 19.7 0.765 0.407 0.358
27 Kan St 30.9 15.7 0.759 0.406 0.353
28 Marquette 39.4 23.1 0.779 0.429 0.349
29 St Louis 34.3 16.5 0.804 0.455 0.349
30 Iowa St 38.8 25.1 0.735 0.387 0.348
31 NC State 40.4 26.3 0.742 0.391 0.351
32 BYU 43.7 21.9 0.872 0.529 0.342
33 Miss State 39.4 26.6 0.719 0.384 0.335
34 W Virg 36.1 23.8 0.711 0.384 0.327
35 Gtown 37.1 16.8 0.762 0.438 0.324
36 Vanderbilt 37.3 25.7 0.699 0.377 0.322
37 Alabama 32.6 19.8 0.719 0.404 0.315
38 Wyoming 31.1 13.3 0.804 0.501 0.303
39 Lng Be St 37.5 21.9 0.767 0.465 0.302
40 Wash 35.1 24.3 0.686 0.392 0.294
41 Midd Tenn 35.8 17.2 0.818 0.526 0.292
42 Akron 35.8 20.8 0.757 0.479 0.278
43 San D St 35.5 19.2 0.779 0.507 0.271
44 Minnesota 35.9 23.1 0.719 0.449 0.269
45 Xavier 34.3 24.2 0.674 0.409 0.265
46 Michigan 31.8 23.4 0.635 0.367 0.268
47 Ntr Dame 34.7 25.7 0.655 0.391 0.264
48 Harvard 34.8 15.5 0.829 0.569 0.261
49 Murray St 38.4 18.2 0.845 0.611 0.234
50 Oregon 31.3 24.3 0.621 0.389 0.232
51 Miami 35.1 26.9 0.641 0.409 0.232
52 Purdue 32.5 26.1 0.613 0.383 0.229
53 Belmont 44.1 25.76 0.813 0.585 0.228
54 Oral Rbts 34.3 23.4 0.687 0.472 0.215
55 Nthwstern 36.5 32.4 0.572 0.363 0.209
56 Cinn 35.1 24.1 0.689 0.485 0.203
57 Seton Hall 32.7 25.7 0.621 0.424 0.196
58 Temple 38.1 27.2 0.595 0.414 0.181
59 South Miss 32.6 22.1 0.681 0.513 0.168
60 Drexel 30.2 17.9 0.711 0.545 0.166
AVERAGE 38.1 20.9 0.791 0.435 0.354

Kentucky and Ohio State are this season’s War Machines

If you are looking for the favorites in this year’s NCAA field, it has to be Kentucky and Ohio State.  First of all, Defensive Win Score, combined with a decent +40 Win Score, is usually the mark of a champion.  Both Kentucky and OSU have those qualities, and they are the only two teams in the entire expected field that have Ty Ratings above 0.600.  They have to be the prohibitive favorites.  Look at Kentucky’s expected Winning Percentage — the team should not have lost a single game!  (An expected winning percentage above 1.000% is a function of the uneven distribution of statistics).

Last Two Champions “Ty Ratings”

With all of that said about how strong Kentucky and OSU are, last season’s champion, UConn, had a Ty Rating of only 0.303, which would have been good for #38 in this season’s initial poll.  Two years ago, the champion, Duke, had a Ty Rating of 0.509, which would be good for #9 in this season’s initial poll.

Underrated and Overrated

No matter what kind of analytical Power System you use, you will always have a head scratcher.  This season’s is New Mexico, a team that is #4 in the initial Ty Ratings, ahead of UNC, Kansas, Missouri and Duke.  New Mexico is not as highly rated by others as they are by me, but those are the breaks.  I have to maintain the integrity of the system.  New Mexico is my early sleeper.  Another two teams who may be underrated are the battling Wisconsin Badgers, and the UNLV Running Rebels (who lost last night).  Others in the list of underrated would be the Big Ten’s Michigan State and Indiana, each of whom grade out better than the respect they are currently being afforded by national polls.

The overrated seem to live in the ACC.  Virginia, as I mentioned, has not played a strong schedule, but they certainly play winning defense.  UNC is not as strong as reputed.  Neither is Duke.

One team that is vastly overrated is Murray State.  Murray State has a gaudy record, but they have been very lucky, and they have not played a very strong schedule at all.  They could be an overseed that you would look to eliminate early in your bracket.

My ratings do not like the Georgetown Hoyas, either.  But alot of of others, including Ken Pom, have them much higher rated

Bubblicious

Let’s look at Joe Lunardi’s  “Last 4 In” (Minnesota, NC State Cincinnati, and Miami) compared to some of his “Last Outs” (Xavier, Washington, Belmont, Wyoming, Oregon, and Northwestern).  Of those ten teams, which do the Ty Ratings favor?

The Ty Ratings favor in reverse order: Minnesota (0.269); Washington (0.294); Wyoming (0.303); and NC State (o.351).  Obviously, the Ty Ratings disagree with Lunardi heavily on the worthiness of Cincinnati (0.203) and Miami (0.232).  It also sees NC State as more of a lock, and Wyoming as a deserving of much more respect (Lunardi has them in his “Second Four Out” — Ty Ratings have them all the way in).

More to Come

I will be keeping up the Ty Ratings on a separate page of this blog, and commenting on them all the way up to bracket picking time.  Stay tuned.  I will also be analyzing, retroactively, how the Ty Ratings would have fared in past tournaments.  Stay tuned.

Ranking the 2012 NBA All-Stars according to Value Added

February 10, 2012

The NBA released its selections for the 2012 All-Star team.  Needless to say there are a lot of snubs, and some real headscratchers.  How the hell did Deron Williams get nominated ahead of Rajon Rondo?  Williams is having an AWFUL season; Rondo is having his normal brilliant season.

In an effort to assess the overall strength of both the fan and coaches selections, I have ranked each of the All-Stars by VALUE.  The rankings are presented below (to understand each column, consult the Courtside Glossary for a simple explanation of each).  Based on my calculations, LeBron James is the best All Star, but some of the players voted in by the fans (Carmelo, for instance) and nominated by the coaches (Chris Bosh and Deron Williams) do not belong anywhere near an All Star game roster.

Value Ranking of the 2012 NBA All-Stars

ALL STARS WS DWS MWS W% W__L W>0.5% VALUE
L James 17.75 4.49 6.63 1.627 6.0__(-2.3) 4.2 10.2
K Love 17.78 6.48 5.65 1.461 5.7__(-1.8) 3.7 9.4
K Durant 14.14 4.93 4.59 1.284 5.0__(-1.1) 3.1 8.1
D Howard 19.39 10.35 4.52 1.269 4.9__(-1.0) 2.9 7.9
A Iguodala 12.67 4.33 4.17 1.211 4.3__(-0.7) 2.5 6.8
B Griffin 14.57 8.32 3.12 1.033 3.6__(-0.1) 1.8 5.4
S Nash 11.86 4.84 3.51 1.098 3.3__(-0.3) 1.8 5.1
D Rose 9.05 3.89 2.58 0.941 3.3__0.2 1.6 4.9
P Pierce 10.29 4.29 2.99 1.011 3.2__(-0.1) 1.6 4.8
L Deng 9.74 4.21 2.76 0.971 3.2__0.1 1.6 4.8
A Bynum 18.08 12.07 3.01 1.012 3.0__0.0 1.5 4.5
C Paul 13.09 6.32 3.38 1.077 2.9__(-0.2) 1.6 4.5
M Gasol 14.39 11.34 1.52 0.761 3.1__0.9 1.1 4.2
K Bryant 8.22 5.15 1.53 0.763 3.0__1.0 1.1 4.1
L Alridge 12.64 9.97 1.33 0.729 2.9__1.1 0.9 3.8
J Johnson 6.91 4.03 1.44 0.747 2.8__1.0 0.9 3.7
D Wade 9.22 3.79 2.71 0.963 2.3__0.1 1.1 3.4
R Westbrook 7.46 5.67 0.89 0.655 2.5__1.3 0.6 3.1
R Hibbert 14.26 11.72 1.27 0.718 2.2__0.9 0.7 2.9
T Parker 7.09 5.81 0.64 0.611 2.3__1.5 0.4 2.7
C Bosh 11.51 10.54 0.49 0.585 2.2__1.6 0.3 2.5
D Nowitzki 10.12 9.36 0.38 0.567 1.6__1.3 0.2 1.8
C Anthony 6.86 6.88 -0.01 0.501 1.6__1.5 0.1 1.6
D Williams 5.82 8.82 -1.49 0.248 1.0__3.0 -1.1 -0.1
AVERAGES 11.79 6.98 2.39 0.911 1.4 4.6

LeBron, Love and Durant are the top All-Stars

As I mentioned above, the very sensitive LeBron James is having a magnificent season.   Behind him, Kevin Love (the man everyone assured me would be the next “Big Country Reeves”) is having another spectacular season, despite his recent suspension for his Suh Stomp on the Houston Rocket PF Luis Scola.  And, in the top 3 we also have SF Kevin Durant.   With the most recent setback to Portland C Greg Oden, it appears Durant was the better choice in that draft.

Behind those three stands Dwight Howard, the three time winner of the Marginal Win Score MVP.  Howard is having a down season by his standards, and it is mainly because his defensive effort has declined.

After that you have the grossly underappreciated Andre Iguodala, the highly productive 76er SF.  He is having a great season.

The only other player I will mention is PG Derrick Rose.  Last season he was named the MVP, although most analytical NBA writers argued that he was not close to being the Association’s Most Valuable Player.  But, give him his due.  He has really blossomed under Chicago’s new regime, and his value keeps going up.

The Biggest Snubs left off this year’s All Star Rosters

The biggest snubs in the All Star nomination process were

C Tyson Chandler (Value 5.9)

SG James Harden (Value 4.2)

PF/C Pau Gasol (Value 5.4)

PG Rajon Rondo (Value 4.1)

SF Gerald Wallace (Value 4.9)

PF/C Carlos Boozer (Value 4.9)

Huge night for LRBMAM

January 16, 2010

I have updated the Milwaukee Bucks Win Chart to reflect the productivity in last night’s win over Golden State.  Luc Moute and Andrew Bogut and Brandon Jennings all had big nights.

If you notice on the Win Chart I have eliminated positional data.  Two reasons for that move.

Popcorn Love (but its more than that to me)

One, as you know, I’ve detached the Bucks Win Chart from 82games.com reliance.  I now do a straight Marginal Win Score myself off the awesome Play-by-Play data “flow chart” provided on Popcornmachine.net.  Truly awesome feature.  It makes straight Marginal Win Score so much easier to provide.

And with true straight Marginal Win Score, position is irrelevant.  It’s all about matchups.  For instance, if Jennings is on Monte Ellis, who cares if I call the pair “point guards” or “shooting guards”?  Its Jennings vs. Ellis, and that’s all.  That’s the important thing.

Second, eliminating positional data makes updating the chart so much faster and easier to do.

Popcorn’s “Quarter-by-Quarter” chart does the same.  Now I can discern, without having to read the unreadable NBA transcript, exactly who is on the floor at any given moment.

The net result will be a Marginal Win Score that is even more reliable.

Why?  Because if I can see clearly who is on the court I can cross check their heights and weights.  If I have that information I have a system that almost always gets the defensive matchups right, and that’s the key.

So, back to the game.  Last night everyone on the Bucks had “positive” MWS games except Charlie Bell, who is sinking like a ship, and Jodie Meeks, who was rising before last night.

When you read the Win Chart, pay attention to the last column.  It tells you which way a player has been trending this month.

Notice how Bogut, who we criticize for being up-and-down, is almost right at even?  Ironic.

Charlie Bell is one downward facing dog.  Don’t be fooled though by Ridnour.  Bear in mind he is falling from virtual Mount Everest.  Bell most certainly is not.

Luc Moute just completely changed his numbers around last night.  Dominant.

Pay attention to how these numbers trend now that I have a bullet proof method of pairing Buck vs. Opponent.  I’m interested to see what happens.

Why was I so wrong about the Bucks?

January 11, 2010

Its time to call myself to task.  Remember back in the days preceding the current Milwaukee Bucks season when I made my final Bucks win projection?

Remember the hubris?  Based on preseason performance, and other variables, I claimed the Milwaukee Bucks would win 40 games, and I provided an estimated win chart to back up my prediction.  At this moment, nearly half way into the season, the team projects closer to 33 wins.  How, or rather “why”, did I get it so wrong?

An Examination of My Errors

Lets examine.  Because MVN.com went down, the post itself is no longer accessible, but I still have the google document with the original win chart.  Here it is.  I have added two columns to the chart to show how far off I was on my original prediction based upon each player’s win contribution (because that takes into consideration not only the player’s performance but also his availability).

Initially, it looks as though I was way off.  Oddly enough, the one guy I got right was Ersan Ilyasova, and that was based upon a guesstimate more than anything.  But more often I got players production numbers pretty wrong.

The players I got really wrong were Ridnour, Bogut, Redd, Moute, and Thomas.  Four of them I was too optimistic about, and Ridnour I was too pessimistic about. (Remember I wrote Meeks off altogether based on his poor preseason, but for the purposes of the chart Ukic’s numbers serve as the generic “3rd string SG” numbers).  You could also say I got Jennings wrong, but everyday he is working very hard to make my numbers right.

Reexamining based on average Bucks seasons

But actually, upon further examination, I didn’t do too badly at all.  The one area that is supposed to be the most reliable, each player’s offensive Win Score statistics, turned out to be the area that mucked me up.  There’s no way to account for that.  And the area that should be most difficult to predict — each player’s “defensive” Win Score statistics — I actually got almost exactly right.

A second chart

I did a follow up chart that compares each player’s numbers to date versus the numbers they would be producing if they merely met their average career offensive Win Score numbers.  Meaning, I held their current defensive (or “Opponent”) Win Score per 48 averages constant, and then plugged in their career offensive (or “Individual”) Win Score numbers and then I calculated what their new MWS48 averages and win production would be.  Here is that new chart.

As you can see, if every Milwaukee Bucks player who had NBA experience coming into this season played the exact same defense and merely produced their personal average offensive numbers (save for Ilyasova who clearly had improved in Europe over the NBA numbers he put up as an 18 year old, so I didn’t include him), the Bucks would actually be exactly where projected them to be.  Remember, that isn’t asking for their best performance, just their run-of-the-mill performance.

Is this good news going forward?  Maybe, but probably not.  As you can see, the one player who was most underperforming himself was, of course, Michael Redd.  He was a whopping 1.1 wins under average.  And now he’s gone.  The next biggest underperformer has been Luc Moute.  Since he was a rookie last season its hard to tell if this season is the aberration or if last season was, or if the numbers he’s producing this season are actually his norm.

Players who might provide for some improvement are Charlie Bell, Hakim Warrick, and Carlos Delfino.  If each of them can continue with their respective defensive numbers, and then can merely add their average offensive numbers to that, the Bucks could improve.

But actually, the one player Bucks fans may legitimately place some hope in is a surprise.  It’s Jodie Meeks (the player I was so low on I didn’t even include him in my preseason win chart!).  He’s actually having a pretty good offensive season, much better than Michael Redd was having.  What he needs to do is clean up his defense.  If he can improve there, he could — surprisingly — be an upgrade from Redd.

Bogut’s having “Above Average” Season?

One other point.  Andrew Bogut is actually performing above his career numbers at the moment.  As you recall coming into this season I did a career win resume for Andrew that showed that for most of his career he’s been just an average center.

The disappointment for me is that Bogut is performing well below the numbers he produced last season.  I was hoping (probably against logic) that Bogut had established a new norm for himself last season.  It looks instead like last season was a bit of an outlier, as they say.

Also, it seems as though he is a really situational center.  Meaning, it seems as though he can produce well against poor centers, but if you put someone decent in front of him he really doesn’t do much.   I have an idea how I can check that theory and I will report back with my findings.

FOOTNOTE TO THE POST

I just reexamined my original win chart once again.  Actually, if I had to do it all over, I probably wouldn’t change anything.  None of the numbers are ridiculous.  Most of them were actually fairly conservative — except for the playing minutes, I guess, and I relied on BasketballProspectus for those.

Otherwise, nothing was really out-of-whack.  Bogut was coming off a +2.40 MWS48 season and I projected him at +1.80.  I projected Redd under his career average, and the same goes for Moute.  In fact, every one of the projections was made under each of the player’s demonstrated best season.

The problem is, everyone except Ridnour is well below his best season, never mind his “average” season.  And Kurt Thomas, a player I really relied on despite the fact that he showed some wear on his tires in the preseason, has simply dropped off the cliff.  In retrospect the team would have been much better served had they kept Amir Johnson and given the backup center minutes to him.  He’s having a productive season in Toronto.

Correcting the ABA-NBA “equality” myth

January 9, 2010

Since reading Bill Simmons views on the history of the ABA and how the ABA favorably compared to the “too white” NBA when both were in existence (all of this, of course, written in his Book of Basketball) I wondered “How did the NBA actually compare to the ABA?” and “Were the NBA and ABA ever truly equal?”

A lot of people — not just Simmons — believe that in the last waning years of its existence the red-white-and-blue ball of the ABA caught up to, and perhaps even dribble drove right past, the traditional NBA orange ball we all grew up with and love.  Was that possible?  Was the NBA the inferior league?

Those that believe that point as evidence to the ABA’s growing dominance over the NBA when the rival Association’s faced off during the 1971-75 exhibition seasons.   The two leagues played a total of 155 times, yielding increasingly positive results for the upstart as the series went on.

Sure the ABA got their asses kicked pretty badly in the first couple of years, but the ABA and its supporters could legitimately brag that they indeed won the majority of the exhibitions in each of the last three years and actually won the overall series 79 games to 75.

Does that mean the ABA was actually better than the senior NBA?  Not so fast.

You can’t compare two leagues by simply pointing to a series of games and saying the bare results hold the comparative truth.  The truth does not arrive until you adjust the results so that one’s apples are being compared to the other’s apples, not its Buffalo Braves.

What nearly all who point to the ABA-NBA exhibition results as proof of ABA equality fail to mention is the location of the games and the matchups.  Since the NBA did not want to “legitimize” the ABA with a lot of games in NBA arenas, an overwhelming majority of the games were played in ABA gyms and were therefore officiated by ABA refs.  Thus to have any comparative value whatsoever the scores must first be adjusted to account for homecourt advantage.

Moreover, to get a true feel for the relative strength of each Association, you have to “neutralize” the two teams playing so that each game can stand as a reliable comparison.  The games weren’t match ups of relative equals like the Big Ten-ACC challenge in college basketball.  The NBA often used bottom feeder clubs in the exhibitions (the Lakers didn’t play in a single NBA-ABA game) while the ABA kept its bottom feeders at home and instead sent its marquee clubs.

So you must account for those disparities when considering the exhibition results and I below I did that.  And the new outcomes I came up with put the lie to any notion of ABA equality until the very last days of ABA basketball (1976 the ABA drew even after it contracted itself down to its “cream” six teams).

Here’s the method  I used.

Utilizing Country Club Scoring to Settle the Issue

To give a clear accounting of each Association’s strength with relation to its rival, I just applied golfers logic to all of the exhibition results.

When two golfers are of uneven strength the pair “handicap”  the score to create the illusion of even competition.

Similarly, I used Basketball-Reference’s “Simple Rating System” to adjust the exhibition outcomes so that, to the extent possible, every single game matched a fictitious “Average ABA team” versus a fictitious “Average NBA team” on even footing.  (So for example, in 1971-72 the Bucks were something like +10.0 points above NBA average on the Simple Rating System, so for every Bucks exhibition the Bucks had to give ten points and so on).

After that I gave +3.4 points, the standard Vegas homecourt adjustment, to the visiting team.  If the game was a “semi-home” game I gave +1.0 point to the visitor, and for seemingly “neutral” site games I gave nothing.  Given the passion that reports say the games brought to ABA arenas, that more than probably understates the value of homecourt advantage, but its close enough.  (Note that I threw out several games to keep the comparison legitimate.  For instance I threw out all of the Atlanta Hawks games in which they suited up Dr. J, and all of the Virginia Squires from that exhibition season that did not feature the Doctor).

The “New” Results

Here are my new adjusted results season-by-season:

1971-1975ABA-NBA Exhibition

Handicapped Results

ActualNBA

wins

ActualABA

wins

NBAPoint

Spread

With

handicap

ABAPoint Spread

With

Handicap

NBARelative Winning% ABARelative Winning%
1971-72 (21 games) 14 7 +5.1 -5.1 .648%(13.6 wins) .351% (7.4 wins)
1972-73 (30 games) 22 8 +8.4 -8.4 .745%(22.3 wins) .255% (7.7 wins)
1973-74 (24 games) 9 15 +4.6 -4.6 .633%(15.2 wins) .367% (8.8 wins)
1974-75 (22 games) 7 15 +4.7 -4.7 .637%(14.0 wins) .363% (8.0 wins)
1975-76 (48 games) 18 30 +0.5 -0.5 .513% (24.6 wins) .487% (23.4 wins)
OVERALL (145 games) 70 75 +4.1 -4.1 .620% (89.9 wins) .380% (55.1 wins)

ABA was about 80% of the NBA

As  you can see, the adjusted results paint a different picture than the one propagated by ABA enthusiasts, one of more prolonged and consistent NBA dominance.   At no point prior to 1976 was the ABA anywhere near the NBA’s equal. (note:  “Relative Winning %s” for each Association were determined according to each season’s adjusted average point differential — which I for some reason referred to as “point spread”… sorry — and the number of games a fictitious team would likely win with such an average point differential — using this formula).

The results, I think, need to be read in two year increments.

It seems in the first two exhibition seasons the NBA thoroughly dominated, both actually and in adjusted terms.  Then in the next two seasons the ABA leveraged the matchups and locations but nevertheless the adjusted point spreads — which ended up being remarkably similar despite the sundry adjustments — painted a continuing picture of dominance, albeit adjusted dominance.

Then in 1975-76, the year before the merger, the ABA placed itself basically on equal footing with the NBA.  Based upon the results I would go so far as to say all six of the ABA teams that were then on-going concerns were in fact NBA worthy teams — not just the “ABA Four” that were ultimately allowed to merge and that continue to this day.  The Kentucky Colonels and the Spirits of St. Louis could have been very respectable NBA franchises, and their fan bases were deprived of that chance (although the owners of the Spirit cut one of the most famous and best “buy-out” deals in the history of contract law).

What happened in Year Two?

The one result that makes no logical sense is the second exhibition season.  Even though it was the second longest exhibition schedule, the results were out of whack with the rest of the series.  The NBA simply whooped up on the ABA.  In fact the adjusted result would have been even worse had I not thrown out five NBA wins that were tainted by Dr. J’s weird decision to sign with — and play two exhibitions for — an Atlanta Hawks team that had no write to sign him — notwithstanding that he was still under contract to the ABA Squires (your Milwaukee Bucks owned his NBA rights.  How on Earth his agent advised him that signing with the Hawks would somehow have a constructive result no one has ever explained to my satisfaction.  If I were the Bucks or the Squires I would have hit the Hawks with a tortious interference action, not just an injunction.  They had no colorable right to Julius Erving… they just decided to sign him and play him!)

I really don’t know what happened or why the NBA delivered such a beatdown.  I think it may be an aberration.

Or perhaps it has something to do with the strength or waning strength of the homecourt advantage enjoyed by the ABA that season.  At any rate, if you adjust the 1973-74 results and put them in line with the (+5.1) point advantage held by the average NBA team over the average ABA team in the preceding season, then the final result would favor the NBA by about +3.7 relative point advantage.  That would mean over the entire existence of the ABA we could conclude that the average NBA team was about +3.7 points better than the average ABA team.

No big deal?  Actually that’s pretty substantial.  That translates into a winning percentage advantage that would be about +20.2% on average.

This I think is the correct ABA equivalency number — somewhere in the neighborhood of 20% less than the NBA.  I think that way because it would comport exactly with another study I did of the Win Score production of 25 random ABA-NBA performers which I outline below.

ABA-NBA Win Score comparison

I used Professor Berri’s basketball analytic known as Win Score to compare how 25 of the biggest ABA stars did when they played in the NBA.  The group of 25 I came up with either were named to the “All-Time ABA team”, are well known, or were mentioned by Bill Simmons as prominent ABA players in his Book of Basketball.

ABA-NBAPerformance Comparisons

using Win Score / 48

ABA NBA
Julius Erving, F 18.39 14.08
-23.4%
Rick Barry, F/G 10.65 9.15
-14.0%
Billy Cunningham, F 15.06 12.73
-15.5%
Spencer Haywood, C/F 21.30 13.18
-30.1%
Connie Hawkins, F/C 17.89 12.86
-28.1%
David Thompson, G/F 11.23 9.78
-13.0%
Zelmo Beatty, C 17.40 14.02
-19.0%
George McGinnis, PF 16.18 12.92
-20.1%
Artis Gilmore, C 21.21 16.78
-20.8%
Charlie Scott, G 4.49 2.12
-52.7%
Dan Issel, C/F 12.94 14.06
+8.6%
Bobby Jones, F 17.28 13.41
-22.4%
Billy Knight, SG 12.21 8.72
-28.5%
Maurice Lucas, PF 11.64 11.66
+0.0%
George Gervin, SG 10.64 8.49
-20.2%
Jim Chones, PF 14.72 10.15
-31.0%
Swen Nater, C 19.45 15.87
-18.4%
Super John Williamson, SG 2.13 1.69
-20.6%
Ron Boone, G 5.98 4.17
-30.0%

Marvin Barnes, F

15.56 7.92
-49.0%

Caldwell Jones, F/C

16.29 11.37
-30.0%

ML Carr, F/G

10.21 8.04
-21.2%
Larry Kenon, F 12.45 11.69
-6.1%
Don Buse, G 10.18 8.11
-20.4%

Tom Owens, C

13.85 10.94
-21.0%

20.2% Rule

As you can see, nearly every one of the 25, save for Dan Issel, saw a decline in his productivity when he brought his game to the NBA, with the average decline being 20.2%, and the median being 20.8%.

So everything seems to come up “20% reduction” when evaluating the strength of the ABA visavis the NBA.  Even Dr. J took a 20% haircut when he made his famed jump to the senior circuit.

But none of this should be read to diminish the achievements of the ABA.  Frankly, I’m stunned at what they were able to accomplish, given the fact that they were somehow able to run a respectable “Shadow NBA”, paying top dollar for talent, when they had little attendance money and no television money to draw upon.  Frankly, they had some balls to give it a go.

And give it a go they did.  And bare this in mind Bucks fans.  Without the ABA, there’s probably no Milwaukee Bucks.  The Bucks improbable bid for franchise came as part of the “panic response” sudden expansionist movement the NBA undertook after the ABA tugged at its contented tail.

So thank you ABA.  But just don’t try to say you were equal to the NBA.  You weren’t.  Not till closing time you weren’t.

Footnote: I just found an eerily similar but much better written piece comparing the NFL to the AFL using virtually the same techniques and some of the same kind of data. (my writings getting so sloppy lately.  Like I told you, I have this weird habit of mirroring the linguistic patterns of the author I’m reading at the moment and at the moment I’m reading “Mr. Everything’s a Digression”.  I gotta throw that fucking book away now. But it has given me a lot of ideas for posts that don’t begin with “The Bucks STILL can’t shoot” so I owe it that much.)


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