I decided to dig a bit into the numbers via SynergySports.

This year, in 77 post-up attempts, Patterson is shooting 40% and scoring 44% of the time.

Needing a frame of reference, I deliberated for some time on who to choose.  Carl Landry?  Luis Scola would be pointless as I figured his numbers would eclipse Patterson’s.  I decided to look regardless.

In 214 attempts, Scola has shot 41% and converted 38.8% of the time, numbers very similar to Patterson.

Granted, Scola’s is a) a much larger sample size and b) comes against much better defenders.  So it would be absurd to argue that the two are on equal level of effectiveness.  But what the numbers show, I think, is that with the team short-handed and already without an isolation star, Patterson might be good enough to at the very least test out in the post, as was done tonight.

For NBA fans all across the country, this summer—a two to three month period that normally sprints forward like a roller coaster’s free fall—has felt really, really long. Not to make matters worse, but if no progress is made at the NBA’s next Board of Governors meeting, reportedly slated for September 15, the immediate months ahead figure to resemble a sloth making his molasses sandwich; the situation is stagnant, boring, and beyond the point where words like frustration are able to describe the collective feeling.

The article you’re about to read is my brazen attempt at turning things around—at least while you’re engrossed for the next five minutes. Much like the labor negotiations this is more bullet pointed occurrences than neatly formed narrative. I’ll be taking a look at some interesting team stats that the Houston Rockets put together last season, and analyzing them in an attempt to see what we’ll have moving forward, in a hypothetically complete 2011-12 regular season. All stats are supplied by some usual invaluable NBA resources: Basketball-Reference, 82 games, Hoopdata, and Basketball Value. Some of this you may already know, and some may be new. Regardless, here’s to hoping these five arbitrary facts temporarily turn your frown into a creased brow.

1) Probably the most important intangible people bring up when discussing successful basketball teams—apart from talent—is chemistry. Do the five teammates on the court know each other’s natural instincts? Is Player A aware Player C likes the ball when he’s trailing on the left wing and isn’t as comfortable getting fed on the right? Do player’s D and E know the precise moment to help and switch after Player B is beat off the dribble?

Last season the Rockets produced 14 different starting lineups in 82 games. They didn’t make the playoffs, and all that turnover might be a good reason why. The Los Angeles Lakers, one of the more consistent “good” teams in basketball produced just two starting lineups last season. Their consistency shows two things:  They didn’t have many injuries, and their best players weren’t screwing up alongside one another.

So there you have it. Whoever throws the same five guys on the court the most times wins the championship. Done and d…wait a minute. It looks like the Miami Heat had 14 different starting lineups last year—same as Houston! Hold up, this can’t be right. According to official documentation, the Dallas Mavericks, hoister of the 2011 Larry O’Brien Trophy, had a whopping 22 different combinations. Talk about skewing the numbers; that’s what you call dropping atom bombs on a theory.

2) Rockets offensive rating leapt from 16th in 2009-10 to fourth in 2010-11. Points per game also took a noticeable step up, from eighth to third.

Several things were in play here: Their point guard became more efficient and consistent (because it was a different person), and their midseason transactions favoring youth made them a faster and more dynamic. Throw in a healthy Kevin Martin teetering on his induction into the Underrated Hall of Fame and a more-than-steady Luis Scola, and what you have is a balanced, relentless offensive attack. The roster was flooded with unselfish guys who know how to score, a scary combination and one that should only evolve for the better as the guys grow and gel together in the coming season or two.

Points per game is hardly a valued metric in judging who has the most potent offense, but making their mark at fourth in offensive rating is a truer and more valid statement. When Houston is viewed through a national prism most everyone doesn’t see them as a run and gun carefree offense, which makes their points per 100 possession numbers all the more impressive. With no All-Stars on the team last season, the Rockets showed their offense is a serious beast to reckon with.

3) The Rockets were 17-8 after the All-Star break. This you probably already knew. And if you didn’t then you probably don’t care. What you might find interesting, however, is the records both Dallas and Miami shared during that same post-All-Star break time frame: 17-9.

4) Houston’s lineup of Kyle Lowry, Kevin Martin, Shane Battier, Luis Scola, and Chuck Hayes scored the second most points of any five man group in the entire league, behind only the Laker’s Lamar Odom, Derek Fisher, Kobe Bryant, Pau Gasol, and Ron Artest.

This tidbit is like a bad Wednesday Night Dollar Beer Trivia Question. It might be had on someone’s 3457273 guess, but by then the game’s ended and all the lights have gone dim over the bar. Seriously, how is this true? How does a lineup with Shane Battier and Chuck Hayes score more than ANY with LeBron James, Chris Bosh, and Dwyane Wade? Best not to think about it.

5) The team ranks in the top 10 (on the offensive end) in all of the major four factors (TOV% 2nd , ORB% 10th, eFG% 10th, FT/FGA 6th)

Apart from final score these four statistics are widely accepted as being the most vital in deciding who wins and loses the most basketball games over an extended stretch of time. To rank in the top 10 in all four is obviously a good thing, and a proven recipe for creating a winning brand, but in the end there’s only so far these statistics can take you when that unquestioned superstar is missing. The Dallas Mavericks ranked in the bottom half of the entire league for three of the four stats (they were third in eFG%) but they have Dirk Nowitzki. Numbers on a computer mean very little when you have a player good enough to create his own orbit.

Also, keep in mind that this is strictly on the offensive end. Keeping your opponents four factors at poor rates is equally, if not more, important, but the team’s offensive efficiency is there, which is something to build on.

Bonus: Aaron Brooks had a plus/minus of -106. Kyle Lowry was +312. If simple physics allowed it, Jonny Flynn would be a +∞ next season. I’m sure of it.

Follow Michael Pina on Twitter @ShakyAnkles

ESPN’s John Hollinger already made somewhat accurate trade grades (insider only) – though I believe he underrated Brooks’ upside – so I’ll avoid any type of “grading” here with a “stats-light” rundown of the deals last week:

Brooks Trade

Aaron Brooks will recover from this injury (the Suns have an excellent training staff), and when he does, Pheonix will be very happy with this trade.   Much like the Battier trade, the Rockets exchanged a rental for future assets.  We weren’t willing to pay Brooks and probably aren’t going to make the playoffs, so it makes sense to get something in return that will carry into next season.

As for Dragic, he’s the type of player that will impact the game one way or another.  Much like Lowry, he pushes the tempo and has both a high assist ratio (32.25% this season) and high turnover ratio (20.68%).   As he matures, the hope is that he will sharpen his decision making and cut down the turnovers.  My biggest concern, however, is that he has been largely more successful the past two seasons as a shooting guard.   Here are a few notes:

1.       Last season (considered by some as his mini-breakout year), while shooting a 52.7 eFG%, he was assisted on 39.6% of his shots.  That is a high number for typical point guards, which means that others were creating a lot of his shots (ie. Nash).  This season, his shooting has dropped to a 46.8 eFG%, and only 28.6% of his made shots are assisted.

2.       His net PER* this season is -3.7 at point guard, and +2.0 as a shooting guard.

3.       His most effective 5-man unit** this year had Nash at point guard.  However, it was his 9^th most common combination (by minutes played) in Phoenix, and the only unit in the top 10 most common with Nash playing beside him.

Battier Trade

We did not follow through with the Battier trade so that we could snag an underperforming Hasheem Thabeet and somehow turn him into a respectable player.  It was possibly for the pick, but a late first-rounder several years from now doesn’t have much value now.  The other day Clyde Drexler fittingly said this trade was addition by subtraction.  By allowing Budinger to start and Terrence Williams to enter the rotation, the Rockets will start getting more out of its prospects.

In his two games as a starter, Budinger is averaging 17.5 points per game, but expect that number to fall a little bit.  He’s shooting 58% from the field and 50% on 3-pointers which, while encouraging, is more likely due to a boost in confidence than something to rely upon moving forward.  His per-48 minute scoring has increased since becoming a starter (27.5 vs. 21.6), but his attempts per-48 minutes have actually decreased (15.0 vs. 19.1).   That can be explained by his new role as a starter, as he now has to find his shots after Scola, Martin, and Lowry.  Moving forward, expect him to put up modest, but efficient scoring numbers.

Conventional wisdom says that adding Budinger and subtracting Battier will hurt the Rockets defensively.  Against my better judgment, there are indications that Budinger has actually been more effective defensively than Battier this season.  According to 82games.com, Battier has allowed opponent small forwards to shoot 52.1 eFG% and 16.0 PER, while Budinger has held opponent small forwards to 45.4 eFG% and 12.9 PER.   A similar metric is the On-court, Off-court opponent eFG% (a team stat instead of individual).  Opponents have shot 48.2 eFG% with Budinger on the court this season, and 51.1% when off.  When Battier was on the court, opponents shot 51.0%, and 48.6% when off.  Battier is getting older.  Since 2005-2006, Battier has never had a negative on-court/off-court net opponent eFG% for a season until this year.

As for Terrence Williams, I am not overly optimistic.  Despite the constant comparisons to T-Mac (which I still do not understand), the other T will never be a star (see what I did there?).  Here are a few reasons:

1.        No stats:  He’s finally getting playing time.  What has he done so far in just over 17 total minutes?  He’s made 4 of 12 shots, turned the ball over twice, gotten blocked once, and committed two fouls – all of which can be summarized with a -13 plus/minus, which he managed in two Rockets wins.

2.       Body language:  He doesn’t put his hands up when playing on-ball defense.  He pouts when things don’t go his way.  He doesn’t always hustle, especially after making a bad play.

3.       Defense:  I’ve seen him miss two rotations badly in the past two games (one of which almost got Brad Miller posterized).  He’s also not very consistent on the ball or in boxing out.

Here’s a simple, but perfectly appropriate quote from former NBA Finals MVP Chauncey Billups on Sunday, “Most of defense is just effort and being willing to do it.”  By the time a player gets to the NBA (with a few exceptions), defense is no longer x’s and o’s, but merely a confrontation between human nature and willpower.  T-Will knows what he’s supposed to do, but he doesn’t do it.  He’s the kind of guy that thinks everything is just happening to him, while throwing up his hands and making excuses.  For the sake of the Rockets, I hope he decides to start taking ownership and becomes the player he thinks he already is.

* own PER minus opponent counterpart PER, courtesy of 82games.com

**1.49 Off vs. .74 Def with a plus 19 +/-, 82games.com

Written by Ben Heller, ‘Rocketscience’ is a column devoted to basketball analytics.  Ben Heller can be contacted at heller.benjaminj@gmail.com.

I ran some regressions comparing win% (and just for fun, ESPN.com’s Hollinger Power Rankings) to each of the “Four Factors” to see which are most correlated to winning.  Guess what?  It was eFG% by a landslide.  Here is a summary table:*The neat thing about looking at differentials is it completely takes into account both offense and defense.   When taking this information and trying to apply it to individual players, it’s more difficult to find accurate defensive numbers (82games.com is perhaps the best that I have found), but looking at offensive numbers is quite easy.

On that note, what are some things that Shane Battier and Chuck Hayes have in common?  For starters, they both have “defense-first” mentalities, having excellent reputations as stoppers.  Also, they are both phenomenal locker room presences, and could possibly become future coaches under the right circumstances.  Something you may not have considered?  They are the most efficient scorers by eFG% on the Rockets, and rank near the top of the league.  Hayes ranks 16th in the league (56.3%), and Battier 19th (55.9%).**  Take those two players off the Rockets, who are averaging a 49.81 eFG% on the season, and the Rockets would go from ranking 12th in the league in eFG% to 20th.***

Sure, Hayes gets blocked over 16% of the time, probably because of his height and playing style.  But one player we may be seeking out of a Battier-to-Celtics trade? Kendrick Perkins, the league leader in getting stuffed, with an incredible 20.4% of his shots getting rejected.

It is likely that we feel pressure to pull the trigger on our expiring contracts to get at least something for their rental value for the rest of the season.   Battier has immense value for a team like Boston, but parting with any of their contributing players is not likely.  Something like Jermaine O’Neal, Avery Bradley, and a pick for Battier may something along the lines of what they are considering.

Whatever happens, I firmly believe that this team will crumble down the stretch if both players are dealt, and will suffer a worse second-half record down the stretch if either player is traded.

*A couple notes:  The “Diff” that you are seeing is the differential between own and opponent averages.  For example, Miami had a 51.56 eFG%, with an opponent 46.56 eFG%, yielding a positive 5.3% diff.  Also, the Hollinger number used for the regressions was actually 31 minus the Hollinger power ranking, just to flip the sign and be consistent with win%.

**25+ min/game minimum

***eFG% drops to 49.005% if you remove Battier and Hayes’ shooting.  I know this is an inexact science, but it is interesting nonetheless.  One potential flaw in this consideration is that Battier is assisted on 76% of his shots and Hayes is assisted on 52% of his shots.  Those are somewhat high numbers, speaking to their roles on the team offensively, and the type of shots they are attempting.  Namely, Hayes gets a lot of open layups and Battier gets a lot of open three-pointers.

Written by Ben Heller, ‘Rocketscience’ is a column devoted to basketball analytics.  Ben Heller can be contacted at heller.benjaminj@gmail.com.

The answer to that question is beyond this post, but we can at least take a look at the team-level for wins and losses.  According to nbastuffer.com, there are 18 teams in the league with an analytics department, or at least affiliation with analytics consultants.  Without delving too deeply into the depth of each team’s analytical reliance, I compared the records of “analytics” teams vs. “non-analytics” teams with regard to above .500 and below .500 opponents.  Here is the full breakdown:

And here are the winning percentages:

I was hoping for a defining relationship that would be similar to the Houston/Portland problem, but found a somewhat different trend.*  It looks like teams that incorporate some form of advanced analytics have better records – both against good teams and bad teams.  On top of that, I think an argument could be made that the top teams are winning despite that handicap.  The Bulls are loaded with talent and have excellent coaching in Thibodeau.  New Orleans would be nowhere without Chris Paul.   Finally, the Jazz have one of the best coaches in the league and the hardest offense to guard in the league.

Of course, you could make the coaching/talent argument with some of the top analytical teams too, but of San Antonio, Boston, LA, and Dallas, how much of their talent was a result of their draft position and how much was a result of a keener eye?  Besides Duncan, the Spurs’ best players are Ginobili (57^th overall), Parker (28^th overall), and Hill (26^th overall).   The Celtics found Rondo at 21^st overall, LA got Kobe at 13^th, and Dallas traded a Tractor Trailer for Dirk Nowitzki.

I’m not sure what else to conclude from this, other than 1) the Rockets’ woes against above .500 teams should not be attributed to the team’s penchant for advanced analytics, and 2) getting poor draft position is no excuse for losing.

*One way to take a better look at these records would be to expand the data back to include a couple more seasons, and to make some adjustment for the team’s overall record (because, for example, the Spurs are going to have a good record against both good and bad teams).

Written by Ben Heller, ‘Rocketscience’ is a column devoted to basketball analytics.  Ben Heller can be contacted at heller.benjaminj@gmail.com.

I did two things with this analysis.  First, I compared totals of certain minutes-adjusted statistics (we’ll broadly call it “productivity”) from prior seasons to this season.  Those are already aggregated at a number of different websites, and this time I went with Hoopdata.  From that data I can conclude if there is a trend in productivity from season to season.  Next, I broke down the past couple of seasons on a per-game basis, paying no attention to whatever particular season in which the games were played.  That way I could see if there were differences in productivity due to the “role” or “mindset” of Lowry from season to season, or if it is simply a product of minutes played.

Per Season Productivity – Data

Click to Enlarge

First things first, a few of Lowry’s productivity metrics by season are actually teetering on career-worst levels.  Career lows include TS% (51.8%), DRR (9.7%), And1% (1.4%), FTR (.38), and charges (took his first of the season vs. Orlando…his first!  Through 41 games last year he had already taken 32 charges).   Other stats that are below his average are ORR (4.3%), FT% (72%), FG% (40.6%, though his eFG% has not been bothered due to his taking and making more threes), and from ESPN.com, Points/40min (13.4), and Rebounds/40min (4.8).

It should be noted that he has made improvements in three-point shooting and assists, but I think those improvements only reinforce my analysis:

Per Season Productivity – Analysis

Before I begin, let me say that there are an incredible number of variables that are not controlled in this sample, including injuries, coaching strategy, supporting cast, time of year (only the first half of this season), shoe brand, etc.  Understanding that all NBA stats (especially ones you see from me) are noisy, let’s pretend these are somewhat reliable and can actually allow us to draw conclusions.

From what I see, there is a significant drop-off in all of Lowry’s “hustle” stats, which can loosely be defined as stats accumulated through a combination of effort and a willingness to put your body in harm’s way.  On offense, driving to the basket and drawing a foul requires more effort, and puts a player in more dangerous situations, than passing or settling for a three-point shot.  On defense, the ultimate hustle stat is perhaps charges taken,* as it requires both effort and incredible exposure to injury (or at least pain).   Well, is it really just a coincidence that immediately after signing a fat contract, Lowry’s hustle-related productivity has plummeted?  He is taking (settling for) more threes, taking less free throws, grabbing less rebounds (I’m especially concerned with his ORR, which has really been his bread and butter), and taking significantly less charges.  If that is not a concern, I don’t know what is.   In my mind, a big part of what differentiates Lowry from other point guards in the league is his success in the aforementioned categories.

One possible explanation for this drop off in productivity is that, given his increased minutes and importance to the team, he is both conserving energy so that he can play the whole game and limiting his risk of injury so that he will be there for us every game.  The first point is what I’m about to test, but think for a minute about the second point:  in his young career he has witnessed first-hand two superstar teammates suffer career-destroying injuries, and then the starting point guard of his team lose his job after an injury (interpret cause-effect however you want).  From that angle, he is not only protecting his longevity in the league, but also perhaps looking out for our best interests by limiting the chances of another heart-breaking Rockets injury.**

Game Log Productivity – Inputs

The only way to debunk the “fat contract” and “self preservation” theories that I have just proposed is to show that Lowry’s productivity is more a product of minutes played in that particular game, rather than expected minutes (or average minutes) of that entire season.  In an attempt to control for Lowry’s progress as a player,*** I only included game logs from the past three seasons in the data set.   I compared minutes played to some selected “hustle” stats – TRR, Charges, and FTA.  I’m going to do a simple linear regression for each variable independently, with minutes played being the independent variable and TRR, Charges/48min, and FTA/48min being the dependent variables.  For each, I looked to see if there is a significant relationship between minutes played.  The idea is that if there is a significant (negative) relationship, it’s just a coincidence that Lowry’s hustle numbers are down this year, because his productivity numbers are a result of playing time (something that has increased significantly this year).  If there is no relationship, however, we must conclude that his fall-off must be due to some other reason – maybe even one of my theories.

Game Log Productivity – Analysis

After looking at several different angles with his data, it looks like there is no significant relationship between minutes played and hustle productivity.  Correlation numbers between minutes and the three stats were all insignificant.  I did some more statistics-based calculations and did not find any significant relationship.  Here is a simplified chart showing Lowry’s productivity in banded ranges of minutes:

As you can see, there is nothing to look at, but the lack of relationship is just as informative.  Regardless of Lowry’s playing time, he is generally consistent in his productivity.  The argument that he is conserving energy when he plays big minutes clearly is not valid.   Instead, Lowry’s dip in productivity this year – especially in those stats related to hustle – must be attributable to something other than his playing time.

* On a side note, I believe taking a charge to be the best defensive play in basketball (with zero research to back up that assertion).  It draws a foul, eliminates a high-percentage attempt, and forces a turnover.  It is criminal that this stat is not part of the box score.  While I’m on the subject, it should actually be two stats – successful charges and attempted charges (that resulted in blocks), and then a ratio.

** I of course am staunchly against any type of self-preservation strategy in basketball, especially when it negates your (Lowry’s) biggest advantages on the court.

*** I lied; Hoopdata only went back three seasons for Lowry, so that’s all I have.

That being said, I was hoping to open the floor to you all regarding the use of statistics – both in the NBA in general and here at Red94.    Clearly there are several different uses for statistics.  Coaches, scouts, and management (and sometimes players) use statistics (at varying levels of sophistication..) to gain any advantage possible, whether it’s on the court or through player transactions.

From the fan’s perspective, the level of statistical depth depends on both the will and sophistication of the observer.   While it’s fun to play “armchair GM” (and I’ll be the first to do it), there comes the sobering reality that whatever conclusions you draw, the actual decision-makers will always be more informed with better information.   So, to the observer whose career does not depend on the accuracy of his conclusions, given limited data and resources, isn’t it almost futile to compete?  Gone are the days of any advantage being drawn from the antiquated box score – even advanced derived metrics like PER.

My question is, what is the biggest reason you all are interested in basketball statistics?  Is it more out of passive interest, the urge to play “armchair GM”,  or to just better understand the game? (or something else?)  Genuine responses are appreciated – I’ll try to cater future posts to your interests.

In an attempt to revisit the topic of shot locations, which I have covered in the past here, here, and here, let’s take a look at where the Rockets stand so far this season.

Rockets Offense:

Close 2-pointers

-26.7% of shot attempts are at the rim, 15^th best in the league (27.2% league average)

-19.8% of shot attempts are  <10 ft, 3^rd best (16.3% league average)

Long 2-pointers (considered by many to be “bad” shots)

-7.6% of shot attempts are from 10-15 ft, 25^th (9.1% league average)

-20.3% of shot attempts are from 16-23 ft, 26^th (25.0% league average)

3-pointers

-25.6% of shot attempts, 8^th best (22.5% average)

FTR

33.3 rating, 5^th best (30.9 average)

Analysis:

The Rockets have an obvious game plan that looks to maximize free throws, at-rim shots, and 3-pointers.  On top of that impressively low number of long 2-pointers (16.8 attempts per game), 4.5 of them are from Luis Scola, who is shooting an impressive 49% from that distance this year.  On paper, the Rockets are a borderline elite team offensively.  I am of the opinion that we usually don’t lose games because of our offense, which leads me to…

Rockets Defense:

Opp-Close 2-pointers

-27.4% of opponent shot attempts are at the rim, 11^th worst

-17.2% of opponent shot attempts are  <10 ft, 7^th worst

Opp-Long 2-pointers (looks good until you look at FG% given up from these spots)

-9.6% of opponent shots 10-15 ft, 8^th best (but 40.3 FG%, 9^th worst)

-27.1% of opponent shots 16-23 ft, 8^th best (but 42.0 FG%, 3^rd worst)

Opp-3-pointers

18.7% of opponent shots, best in the NBA, but 55.7 eFG% allowed, 9^th worst

Opp-FTR

30.6 rating, 16^th worst

Analysis:

These rankings suggest that the Rockets are doing two things.  First, we are chasing opponents off of the 3-point line – I saw Shane Battier do it about 8 times last night against Paul Pierce.  As I have mentioned before, 3-pointers are the highest percentage shot behind “at rim” shots and free throws, with a current league average of 54.1 eFG%.  Second, the Rockets are deliberately forcing opponents to take long 2-pointers, possibly by sagging once the opposing player is inside the arc.  Unfortunately when we leave them more open their FG% will increase, causing that game plan to have diminishing returns.  Furthermore, it seems like a good number of those 2-point attempts are close to the rim, which means that, perhaps at least partially due to our chasing players off the 3-point line, opponents are getting deeper into our defense than we would like.  This causes us to have a slightly below average opponent free throw rate as well.

What are the implications?  Nothing new, I suppose.  Adelman is an offensive mastermind, but puts less emphasis on the defensive end.  One takeaway might be that we are closer to being an excellent team than our record would indicate.  Imagine if we improved our defense just enough to be slightly above average?  With our current game plan of chasing opponents off the 3-point line, we would need to fortify the lane with more active and athletic (and disciplined) bigs.  That could perhaps be achieved through developing our current crop of players (if such development was emphasized…).  We would need to practice rotations and help defense significantly more to prevent easy attempts at the rim – something that is possible but not probable, given our coaching staff.

The alternative to minor changes?  Red fans often argue about whether or not blowing up the roster is a good move.  Fantasies of (purported) offensive wizards like Carmelo Anthony perhaps blind us of the problem that has plagued us for years now.  We are already good at scoring the ball.  Really, really good.  If we were to do something drastic (while keeping coaching the same), my vote would be to go after a group of players that are motivated to be dominant defensively without needing a coach to show them how.  Battier and Hayes are good examples of that mindset, but they lack the overall games to be franchise players, and of course are getting older.  I imagine players like Andre Iguodala, Dwight Howard, and Chris Paul (long shots…) are higher up on Morey’s list right now than ‘Melo.

Here’s a look at Budinger’s TS% and points per 48 minutes this season.  Points per 48 minutes is a good way of normalizing points per game for minutes, removing the statistical boost normally generated from increased playing time.

Click to Enlarge

Click to Enlarge

Let’s go through the major arguments in favor of the trade right now, before anyone blows a gasket:

1. Shane Battier is a winner.  Ignore the mainstream stats, because they are misleading.  He always finds ways to win.
2. He is an excellent person, and will always be a source of leadership in the locker room as well as on the court.
3. We had Yao Ming and Tracy McGrady in their primes.  We had to win now, not develop rookies.
4. We also got to dump Stromile Swift, for what it’s worth.

For the most part, all of those arguments were valid.  Shane has not disappointed, and has generally delivered exactly what we expected.  He was a part of the magical 22 game win streak and has always held himself and his teammates to the highest standards.  My biggest concern, however, is reason #3, which is in many ways a disastrous mindset.  I know, hindsight is 20/20, and it’s a lot easier to say win-now failed after your two superstars get irreparably injured.  However, how else can we form a basis for decision-making if not by learning from the past?

Also, before I continue, I want to clarify that I am not entirely enthralled by Rudy Gay.  His combination of flashy dunks and high per-game numbers make casual observer drool.  However, he is a volume scorer with mediocre scoring efficiency.  Here are some stats on Gay to prove my point, courtesy of HoopData.com:

The league average for usage this year is 18.42, true shooting percentage is 54.1%, and percent assisted is 60.0%.  He also has marginal shot selection, which contributes to his percentages.  Over his career, 58% of his shots have been beyond 10 feet, with 62% of those (37% overall) coming from 10-23 feet (inside the 3-point line).

On the other hand, Shane Battier has been more efficient and has much better advanced statistics.  As my “pseudo-control”, I decided to compare Battier’s first 5 years in the league (with the Grizzlies) to Gay’s first five years (also with the Grizzlies).

Let’s first circle back to the aforementioned shooting statistics.  Now scoring is one of Rudy Gay’s special gifts, right?  I think we can also agree that Battier is a pretty ugly scorer some times.  However, despite different usage, they have had similar efficiency**:

It gets interesting when you compare yet more advanced metrics:

Battier is more active on the court, contributing to a higher percentage of every statistic other than defensive rebounding, while turning the ball over less.   Judging by offensive and defensive ratings, Battier was clearly superior.  If those numbers don’t show you what Dawson and Morey saw five years ago, these will:

Win shares are an estimate of the number of wins contributed by a player as a result of his offensive, defensive, or combined contributions.  The WS/48 number represents a per-48 minute number of wins contributed, with the average sitting at approximately 0.1.   If there was any doubt that Battier is a winner, even when he was on the Grizzlies, I hope that this comparison puts that argument to rest. In a final attempt at empathizing with our management at the time of the trade, I will concede that we were very, very close to being a special team, and Shane Battier was perhaps the perfect role-player for us at the time.  We knew he had a limited shelf-life, but it would be worth it as long as we could make a few pushes for the title.

That being said, here is the part where I am going to lose you:  If I was the GM at the time, I would not have made this trade.  The short-term approach to winning is really what irks me, and while Battier is a remarkable person, we acquired him for the back half of his career.  Wait until he retires and hire him as a coach if you like his “presence.”  Actually, if you like defense so much, don’t fire Jeff Van Gundy.  As demonstrated above, Battier is an excellent player.   What’s not demonstrated above is how Gay may have faired the past five seasons if he had a few years to develop with the Rockets under some of the best coaches and players in the league.  Young players on bad teams develop bad habits, and Gay might forever be a different player because of his perceived role on that Grizzlies team, especially after the Gasol trade.  Then again, maybe he is getting better every year, and last summer proved that he can be an incredible role player on a winning team.  Oh yeah, and he’s still only 24 years old.

*The trade was July 12, 2006; Morey became GM May 10, 2007

**Courtesy of BasketballReference.com

The topic of interest today is the seeming disconnect between his strong pursuit of a “star” and his disbelief in the value of “clutch players.”  According to 82games.com, clutch is defined as “4th quarter or overtime, less than 5 minutes left, neither team ahead by more than 5 points.”  In the 2009-2010 regular season, 172 games, or 13.98% of all games played, were decided by 3 points or less.   That’s a pretty high number of games that come down to clutch situations.  Last season the Rockets were 5-5 in games decided by 3 points or less, and 20-20 in games decided by 4 to 9 points.  For a GM purportedly not interested in clutch players or statistics, it has to be a concern that the Rockets are already 2-4 this year in games decided by 3 points or less, and 4-5 in games decided by 4 to 9 points.

Circling back, we know that Morey has potentially made conflicting assertions.  If a star’s usage increases during clutch time, it seems that the need for a star is concurrently exacerbated during clutch time.  With such marginal clutch performances last year, followed by a downward trend thus far this year, I am starting to believe that Morey may have misled us in his apparent disregard for clutch-time statistics.  After all, actions speak louder than words, and his intentions this past off-season were in clear pursuit of a big-time star and legitimate go-to player in the clutch.  So, before we blindly go with the assumption that Morey actually does look at clutch statistics, let’s take a look at some of the evidence supporting this view.

I wanted to see if there is any difference in wins for a team leaning heavily on one player in the clutch versus a conglomerate of players.  In this mini-study, I chose one player from each team in the ’09-’10 season, courtesy of 82games.  The player chosen had the highest combination of games played, minutes, and FGAs per 48 minutes of their respective teams.  I’ll call each of these players their teams’ “clutch players”.  Last year it was Aaron Brooks for the Rockets.  Here is the complete list:

The “star” versus “committee” difference takes care of itself when you take a single player from each team, because if a player is sharing possessions with someone else in the clutch, his per-48 minute numbers will be lower than a player taking the lion’s share of possessions (ie. LeBron).  In other words, players with lower per-48 minute numbers are on teams with no defined go-to player or “star.”

As usual, I compared a number of different metrics with wins, and looked to see which were the most correlated.*   Here are some of the more interesting results**:

Here are my takeaways from this table:

1) Points scored are surprisingly the most correlated statistic, more so than any other metrics taking assists into account.

2) Having a single player use more FGAs in the clutch, and even more accurately, more possessions, is more important to winning than that player’s scoring efficiency.

3) Assist numbers are not very correlated to wins (r² of .038).  When your clutch player passes more as opposed to shooting, the correlation to winning decreases.  That would help explain why LeBron got so much flack several years ago when he passed to Donyell Marshall in the corner for a missed 3-pointer.

This evidence points towards the need for a go-to scorer in the clutch.  The type of player that, regardless of the results, you know decided your team’s fate in the waning minutes of the game.  Kevin Martin may be developing into that kind of player, but what about when Brooks is healthy again?  With Scola using possessions in the post as well, it seems like we are truly a “clutch by committee” team, which does not bode well for winning.  To the right is the list of the top clutch scorers per-48 minutes last year, and their teams’ wins.  Looks like good company to me.

For a more detailed, albeit ancient, examination of clutch stats, see this article.

*A couple of disclaimers here: Total team wins are used, while these stats are only from clutch games, which means that there are many wins that were not attributable to the clutch statistics (number of clutch games played for each player ranged from 20 to 47, with the average being 35.3). Also, these stats are not adjusted for pace, so a faster-paced team will have slightly inflated numbers.

** “Team points”, “created points”, and “team created points” are metrics I invented on the fly in an attempt to break down a player’s impact on the floor.  “Created points” removes points that a player scored from an assist by another player.  It is essentially points scored in isolation scenarios.  “Team Created Points” is created points plus an estimate of points created from a player’s assists.  It can be viewed as all team points that a player created (but didn’t necessarily finish).

Looking at accomplishments by 5 man units is one way to get an idea of a player’s contributions to winning.  According to 82games.com, here are the top 5 units so far this year:

There are a few interesting bullets from this information:

• The only difference between the number 1 unit and the number 2 unit is substituting Miller for Hayes.  There is a predictable jump in offense, but more striking is the huge improvement in defense.
• Brad Miller is in two of our top 3 lineups
• Our defense is atrocious in the one lineup featuring Yao

I know – this is limited data.  However, coaches pay attention to the relative performance of different 5-man units, especially with new players on a team and endless injuries.  I am not necessarily saying that these are our best lineups, but when trends do start to develop, unit success is a strong dictator of playing time.

The Rockets have started to look like a respectable team lately, but is it for the obvious reasons?  Here is Yao’s individual production on the court this year versus Brad Miller:

While their defense is comparable, it seems that Yao’s biggest hindrance thus far has been his offense.  Is this a pattern that will continue when he returns?

On the other end of the court, one aspect of being “undisciplined” defensively, somewhat synonymous with being “raw”, is the uninhibited impulse to block every shot.   When there aren’t any refs, the risk-reward relationship of attempting to block a shot shifts significantly in favor of going for the block.  Concurrently, the influence of pickup basketball rules changes a player’s perceived risk-reward relationship between driving to the rim and pulling up for a lower-percentage shot.  When the opportunity of free throw attempts is removed, a slight boost in FG% for shots around the rim versus, say, a 10-foot jump shot, does not justify the immense social risk of getting rejected.  My hypothesis is that, despite logic and best efforts of a coaching staff to keep players “disciplined” offensively, many players in the NBA cannot control their aversion to getting blocked, and therefore attempt lower-percentage shots.  These low-percentage shots include, in order of increasing egregiousness: raising the arc on a shot to clear the shot-blocker, shooting a fade-away, altering the shot/shooting form in mid-air, attempting a “floater” when there is a possible lane to the rim, and avoiding the drive all together and jacking up a long 2-pointer.

Opponent Shooting Percentage vs. Team Blocks

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The first step in my analysis was to see if there was a correlation between team block rate and opponent shooting percentage.  The idea is that if your team blocks a bunch of shots, opponents will not only miss those shots that were blocked, but will also have a large number of altered shots (assumed to be of greater difficulty and a lower shooting percentage).   Looking at regular-season data from 1999-2009 (databasebasketball.com), there appears to be a decent relationship, as displayed in the graph.   As you can see, as a team blocks more shots (block rate is blocks divided by opponent’s 2-point FGA), their opponents shoot at a lower percentage (ρ = -41.08%, rsq = 16.9%).

Opponent Shooting Location vs. Team Blocks

So we know that there is a relationship between opponents’ shooting and team shot blocking.  That was the first part of the theory.  The second part was that the FG% is lower because of an aversion to drive all the way to the basket.  The best way to measure that is to compare team shot blocking to opponent shooting locations.  I compared team block rates to two different location attempt percentages defined by hoopdata.com (2006-2009) – “at rim” and “<10 feet” (for example, the “at rim” location attempt percentage would be attempts at rim divided by all 2-point attempts; in other words, the percentage of 2-point attempts at the rim).    To my initial surprise, there was a slightly negative correlation between opponents’ at rim attempt percentage and team block rate (ρ = -12.38%, rsq = 1.5%).  It makes sense, as more chances to block shots will obviously result in more blocks; however the relationship is pretty weak.   I then compared the same team block rate to the “<10 feet” attempt percentage.  The results were even more unimpressive (ρ = -3.2%, rsq = .1%).

What does all that mean?  It means that, despite a team blocking shots at a high rate, opponents do not change their shot locations.  My theory that players will tend to stop short and pull up instead of go all the way to the rim when driving against strong shot blockers was incorrect.  They still will attempt those shots.

But what about the FG% thing?  I showed earlier that block rate has a decently strong relationship with opponents’ FG%.  I just showed that that relationship has little to do with shot location, so what else could it be?  Taking a further look at the breakdown in opponent’s FG% by location, you can see what is happening:

click to enlarge

To my best understanding, the biggest effect of strong team shot blocking is altering shots near the rim.  The closer an attempt is to the rim, the greater effect a team’s block rate has on opponent shooting.  For “at rim” attempts, we see the most drastic dip in FG% (ρ = -46.48%, rsq = 21.67%).  It seems that shot blockers also affect “<10 Feet” shots as well, but not as much (ρ = -32.99%, rsq = 10.88%).  Conventional wisdom preaches the benefits of shot blockers altering shots, but this is the clearest evidence actually supporting that claim.

Opponent Shooting Location vs. Best Shot Blocker (Elite Blocker Theory)

Despite my best efforts to encourage basketball viewership from a team perspective, it is widely more popular (and fun) to look at individual players.   After all, when you’re driving to the rim against the Magic, are you thinking about Orlando’s team block rate or are you thinking about Dwight Howard throwing your shot into the bleachers?   I scanned through the same four seasons, and found the leading shot blocker on each team each year (minimum 65 games), using blocks per game.  I chose blocks per game over block rate to place weight on minutes played.  After all, a player’s “presence” cannot be felt if he isn’t on the floor very much.  I then did the same comparison as the previous section.  Predictably, there is a similar lack of meaningful relationship between the best player’s blocks per game and opponent shot attempt location.  However, just like team block rate, there is a relationship worth considering for FG% at the rim (ρ = -35.99%, rsq = 12.95%) and FG% <10 feet (ρ = -19.46%, rsq = 3.79%):

click to enlarge

As you can see, having an elite shot blocker clearly affects opponents’ shooting, and can explain a large part of a team’s block rate, especially at the rim.   If you read my first post, you know how important shooting efficiency is to winning.   Good teams hold opponents to a low FG%, and good shot blockers hold opponents to a low FG%.  So, exactly how important is it that your team is good at blocking shots?

Block Rate to Wins

To keep this all in perspective, I compared block rate to wins from 1999-2009.   Compared to some of the more prominent categories discussed in past posts, blocks are not very significant.  However, there is a positive correlation, with an r-squared of 6.7%.   Here is a graph showing the relationship between wins and block rate:

click to enlarge

While blocking more shots may make a minor contribution to wins, it is directly related to one of the Rockets’ biggest weaknesses – opponent shooting. That weakness is further compounded by abysmal perimeter defense, allowing opposing wing players to penetrate with ease.

Through 11/17, opponents were shooting 63.7% at the rim against the Rockets, above the league average of 62.4%.   In case you were curious, Howard and the Magic are holding their opponents to 54% at the rim, the lowest rate in the league.

The bottom line is Jordan Hill needs more playing time.  All of this business about him being a liability offensively needs to stop.   He is shooting 70.8% on inside shots, with only 53% of them coming from assists (which means that he is creating about half of his own shots inside, and still shooting 71%; sounds pretty good to me).  As I mentioned last week, one of the biggest reasons he cannot stay on the floor is his poor court awareness and defensive rotations.  As soon as he can work out those kinks, I think he can provide the interior help that we have been lacking most of the season.

After a rough start, the Rockets may be showing signs of life, but we are still sitting at 3-6. The mentality of a Rockets follower this season is quickly shifting from “what are our current prospects?” to “what are our future prospects?” And the progression in the latter thought process immediately becomes “who are our prospects?” After all, having young prospects waiting to break out is a comforting feeling, especially during a season short on optimism.  Of the players I consider prospects on the Rockets, the biggest names right now are Jordan Hill, Chase Budinger, Patrick Patterson, Ish Smith, and Jermaine Taylor (and maybe Lowry and Lee, depending on your definition).   Here is a quick overview of these players*:

Budinger – As far as potential is concerned, his ceiling may be limited.  Last year, 85% of Budinger’s field goals were assisted.  So far this year, he is sitting at 75%. In a nutshell, he either cannot or will not create his own shots.  I believe it is the former, which limits him to a solid offensive role player at best in this league.   There have been many examples of athletic, unselfish shooters helping championship teams, such as Rick Fox, Bruce Bowen, and Trevor Ariza.  The difference is that Budinger has not been able to consistently translate his athleticism to the defensive end.  With a backcourt already short on defense, Chase’s minutes will continue to be limited unless he can prove to be a reliable defender.**

Patterson – He had a respectable summer league, but obviously limited data restrains any statistics-based analysis for him at the NBA-level.  I do think it is interesting that we used a lottery pick on an “NBA-ready” player, only to stash him away in the D-League.

Smith – His scoring efficiency is terrible (31.7 FG%, 35.4 eFG%).  Generally hand size and arm length are negatively correlated with outside shooting and positively correlated with inside scoring (think Rondo vs. Stojakovic), but Smith seems to be both a poor shooter (39.7 eFG% on jump shots) and ineffective around the rim (14.3% on inside shots).  On the other hand, Smith has shown the potential to be an elite passer.  With 18 assists on the year and only one passing turnover (ratio of 18.0), he is among the top passers in the league.  Compared to Chris Paul (17.3), Rajon Rondo (5.4), Jason Kidd (4.2), and Steve Nash (2.7), there is a lot to like and it will be interesting to see if he can stay in the rotation when Brooks comes back.

Taylor – This is a sad story, as he really hasn’t had an extended opportunity to shine (minus one 3-game stretch last March when he averaged 14.3 points in about 30 minutes/game).  Jermaine has the potential to fit the off-the-bench spark mold of Von Wafer, but after almost getting dumped for Dampier, his future as a Rocket is in jeopardy.

Hill – I saved the best for last.  Jordan Hill was selected 8^th overall in the 2009 draft, ahead of budding stars DeMar DeRozan, Brandon Jennings, Jrue Holiday, Ty Lawson, and Darren Collison. With his size, athleticism, and quizzical flashes of brightness, he has perhaps the most potential of these listed players. However, Hill is still learning the game and has some major areas in need of improvement.

Let’s talk offense first.  Hill is the best offensive rebounder on the team, with an ORR of 9.7.  That doesn’t say much, however, as that number is good for 29^th in the league among qualified power forwards.  He has a 45.8 FG% this year, which is poor for a post player.  Looking a little deeper, his shooting percentage from inside is excellent (70.0%), but only 42% of his attempts are inside.  The rest (58%) are jump shots, and he is shooting a shameful 28.6 FG% in that category.  This is about the clearest indication of poor shot selection that you will ever see.  Given his age and basketball experience (he picked up the sport in the 9^th grade), his poor shot selection is most likely a combination of a) the offensive system putting him in bad locations on the floor, and b) the coaching staff encouraging him to expand his game and perhaps complement Yao more effectively.  Either way, for now the coaching staff is responsible for working to make things simple for Hill on offense and putting him in position to play to his strengths.  Jordan, please stick to the dunks and hook shots.

As for defensive rebounding, Hill is considerably less productive than Scola with a DRR of 15.2 (48^th among PF) versus Scola’s 24.0 (12^th among PF).   For Hill’s size and athleticism, this is a cause for concern.  Why is he rebounding at such a low rate?   You could argue that he is being drawn away from the basket to contest shots, but that doesn’t seem to stop Dwight Howard.  You could argue that our defensive rotations leave him away from the rim during shot attempts, but that doesn’t stop Scola.  If there is an easy explanation, I haven’t found it yet.  Generally the propensity to get “50/50 balls” is largely a product of intangible attributes such as hustle, will, and intensity. This may be something to pay attention to in the future, as a player devoid of those attributes ultimately has a limited upside.

On a positive note, Hill’s most positive contribution on the court has been shot blocking.  The 6-10 forward is averaging 4.75 blocks per 48 minutes.  That’s good for 5^th in the league among qualified players, better than Dwight Howard (4.41), Josh Smith (4.35), and Tim Duncan (3.30).  I’ll dive deeper into blocks another time, but his defensive presence cannot be understated.  One of his biggest weaknesses defensively is his awareness on defensive rotations.  He is mentally slow on the court, which is probably a product of his late start in basketball.  If he can get those rotations down, his minutes will increase significantly.  Overall, in terms of current value-add and potential for growth, Hill is the Rockets’ best prospect.  There is little doubt he has the physical abilities to be an impact player, so his ceiling will only be limited by his work ethic and focus.

Looking at this list, I am feeling, at best, mildly optimistic about our young crop of players.  The odds of any of them emerging as stars on in this league are minimal.  The best chance of developing a star in the next few years is probably via our draft picks (assuming they are not traded), but 2011 will be a weak draft class, and there is always the potential for both the Rockets and Knicks to have non-lottery picks.

It may be premature to say that we are rebuilding.  But if we’re not rebuilding, then we’re trying to win now, right?  After 9 games, it sure doesn’t feel that way.   A trade to land another star would cement a “win now” approach, but every day we hold on to those picks, we move a little bit closer towards ”rebuilding” with up to three mid-first round selections in the next two drafts.  At some point, management will have to commit to a direction for this team’s future.  Until that happens, Rockets fans may be in for an insufferable period of prolonged mediocrity.

*Stats updated through Saturday; taken primarily from ESPN.com and 82games.com.  The holdup was 82games, so I didn’t update anything else to keep it consistent.  Hill’s ORR fell to 8.4, DRR rose to 15.8, and BLKP48MIN fell to 4.10 after 16 minutes of action Sunday night.

**After a few more weeks, there will be enough data to start looking more closely at Budinger’s ’10-’11 defensive and other advanced statistics, but for now, take my word for it.

1) Attempt more shots:  This is for a couple reasons.  First, if your team is used to a fast pace and the opponent is not, the opponent generally will make more mistakes (more turnovers).  You dictate the speed of the game.  Second, due to lots of quick, long-range shots before defenses are set, there are more opportunities for offensive rebounds.  Think about it –  if the offensive rebound rate off of three’s is higher than the rate off of two’s, then shooting more three’s will increase the number of possessions for your team.

2) Shoot lots of 3-pointers:  Long range shooters must pull the trigger when open.  The eFG% of 3-pointers is better than any other type of field goal attempt other than those close to the rim (dunks, layups, hook shots, etc.).  Also, three’s are easy and take little effort, allowing for more energy on defense and crashing the boards.

3) Take the first available shot:  Maintain the fast tempo by taking quick shots.  These are not always great looks, but remember – your team will have more attempts.

Historically, there have been very few pure run and gun teams.  I remember back when I played, one team in our conference, Emory and Henry, experimented with it for a few years.  Here is an example of one of their box scores.  They would full-court press the entire game, and subbed 5-man units every 4 minutes to keep the guys fresh.  They would shoot primarily three’s, and through steals and offensive rebounds would attempt about 30% more field goals than their opponents every game.  I couldn’t understand why this formula wouldn’t win every single time (they won some games, but could never win against the good teams).

There are zero teams in the NBA with that drastic of a commitment to the run and gun approach.  I think a major reason is that, as the opponents’ skill level increases, the effect of full-court pressure becomes less beneficial, and is often detrimental to a team’s success.  It takes a lot of energy to run a full-court press, a cost that may be too high for the slim rewards yielded against NBA-caliber back courts (against lesser opponents and in lower levels of play, however, it is still very effective).  In the NBA today, teams instead like to embrace the offense part (push the ball up the court, take quick shots), but not the defense part (force turnovers by pressing).  They instead hope that the other team will take quick shots as well, but at a lower percentage.  If you don’t already have some teams in mind, think of the Suns, the Warriors, and the Knicks.  I don’t think the Rockets fall into this run and gun category, even though a few aspects of our team are eerily close.

Run and gun does not win for 1.5 reasons (1.5 because reason #1 is significantly more important):

1) Free throw differential.  This is the single most important reason why run and gun does not work.  If you are camping out on the 3-point line, you are not drawing contact and getting to the free throw line.  Free throws are the second highest percentage shot in basketball (behind dunks which, coincidentally, are also pretty rare when attempting lots of 3’s), so less free throw attempts means that your team has a lower TS%.  Using the same general equation that I presented last time, I investigated the relationship between two different ratios:  free throw attempt differential and three point attempt differential, and their impact on wins.  I used data for all teams in the NBA from 2002 to 2009 (not selection bias – Excel could only handle 255 rows for regressions…), courtesy of databasebasketball.com.  Just as a quick overview, free throw attempts differential is a team’s weight of free throws per offensive possession divided by the opponents’ weight of free throws per (opponents’) offensive possession.  The formula looks like this:  ((.44*o_fta) / o_pos) / ((.44 * d_fta) / d_pos).  Three point attempts differential is the exact same thing, but insert 3-point attempts as the numerator (instead of .44*fta).  These first two charts show the relationship of attempts differentials to wins.  Note that these ratios are not impacted by shooting efficiency (FT% or 3PT%), and are only indicative of attempts.

So what does this data show us?  Both charts essentially confirm the idea that attempting a greater number of high-percentage shots will increase the likelihood of winning.  However, as I previously mentioned, players do not get fouled very often when they attempt 3-pointers.  A key differentiator between run and gun teams and, more generally, “fast-break” or “up-tempo” teams (think Showtime), is that run and gun focuses more heavily on attempting 3-pointers in any situation, rather than exhausting other options first (ie. getting to the rim and getting a layup or drawing contact).  The best teams, it seems, find a way to attempt a high number of 3-pointers and get to the foul line more than their opponents.  Daryl Morey obviously knows this, which would explain why he went after Kevin Martin (think of Martin’s two biggest strengths – drawing fouls and shooting 3-pointers).  The propensity to draw fouls and shoot a high percentage at the charity stripe is a key differentiator between the Rockets and typical run and gun teams.  Also, considering the Rockets’ commitment to lumbering centers Yao and Brad Miller, the openness to sign a shoot-last point guard like Ish Smith, and the lack of follow-through in trading for volume-shooter Carmelo Anthony, many signs point to our Rockets preferring a more dynamic scoring attack.  I will further discuss the implications of this realization with regards to the Rockets after reason #1.5:

1.5) eFG% is misleading for end-of-game shots.  Being a run and gun team with a reliance on high eFG% 3-point shooting does not help much in crunch time in a close game.  I don’t need to pull out any fancy stats here.  Even though Aaron Brooks had a 59.7 eFG% from three last season, the odds of him making a three-pointer on any given attempt were still 39.8% (his 3-point shooting %).  This is the intuition side of me, but good teams make clutch shots.  If your team relies on 3-point shooting (Rockets), and you need a clutch shot at the end of a game (against New Orleans or San Antonio), the odds of you making that particular shot are lower than a 2-point attempt (given the higher possibility of drawing a foul).  I don’t want this to appear like a contradiction from my last post.  I stand firm that 3-pointers are a crucial ingredient to winning, but an overreliance on them allows for greater variance.  Variance is fine during the course of a season or even a full game, but when your team has one shot at the end of a game, variance is the enemy.

Implications for the Rockets:

No, by most definitions the Rockets are not a run and gun team.  However, after digesting this information and thinking about our team, I am of the opinion that we are not optimizing our personnel.  Last year, the Rockets had a free throw attempts differential of 1.038 and a three point attempts differential of 1.35.  While our team’s FT% has been consistently above the league average the past three seasons (this year included), we are not getting to the free throw line enough.  Having excellent free throw shooters is a major competitive advantage that we are not exploiting.*

Despite not getting to the line very often last season, we allowed our opponents to get to the line even less.  Last season 9.88% of our possessions ended on free throw attempts, while our opponents ended 9.52% of possessions on free throw attempts (compared to a league average of 10.38% from 2002-2009).  Our defense, and more specifically our ability to not foul our opponents, covered up our inferior offensive free throw attempt rates.  Before Sunday’s game**, sitting at 0-5, we had attempted 2.8 less free throws per game than our opponents.  As we have witnessed the steady dilution of Jeff Van Gundy’s defensive impression on the Rockets since his departure, this may be the final straw.  Remove a defense-first coach, add an offense-first backcourt, and throw in a bunch of young, undisciplined defenders, and you have maximum exposure to some of the Rockets’ biggest flaws.

With Rick Adelman as coach and with our current roster, I do not see us magically becoming more disciplined defensively.  It’s just not going to happen.  Our best chance of reversing this trend is to attack the basket – a lot.  No more 19-foot jumpers.  We need to finish our drives, go all the way to the rim and get blocked, get fouled, or make a close shot.  The referees may be to blame for a call here and there, but over the course of a season there are no excuses.  The Rockets are currently built to outscore opponents, but if we shy away from one of our biggest competitive advantages on offense –  the highest-percentage shot in basketball outside of dunks – how can we be expected to overcome our defensive ineptitudes?  I implore you to join me in a collective cringe every time the Rockets settle for a long 2-pointer, appreciate aggressiveness, and by all means, learn to love free throws.

*In 2008, when the Rockets won 52 games, our FT attempts and 3-point attempts differentials were 1.098 and 1.273, respectively.  That slight boost in FT attempts over opponents (combined with some other factors) was enough to propel us into solid seeding in the playoffs.  The results of the Minnesota game Sunday were more of a confirmation than ever that the free throw attempts differential is especially important for the Rockets.  In that game, we attempted 47 free throws (shooting 83%), while Minnesota attempted 33 free throws (68%).

A little bit about Ben, in his own words:

I graduated from Washington and Lee University two years ago with B.S. in Business Administration.  My areas of focus were finance and investments, with some statistics and economics as well.  My basketball career began at Lanier Middle School, then transitioned to Lamar High School, where I played on the varsity team for three years.  I played at Washington and Lee for four years, leading the team in rebounding and scoring and earning the ODAC Scholar Athlete of the Year award my senior year.

While I was still young during the days of Clutch City, I can remember waiting in line for hours to get a Rocket’s ’93-’94 championship T-shirt.  It is often fun to revel in the past, but I am generally forward-looking, using our league’s history only as a series of lessons, rewarding those with the patience and commitment to learn them.  Statistics in basketball have made much-needed progress in the last decade, and I am proud to know that our Houston Rockets are still on the cutting edge.

I look forward to exploring the depths of NBA statistics, challenging our collective intuition, and learning from you all as well.

Ben’s first post ensues after the jump. – Ed.

This is a two-part post.  First, I would like to introduce my general line of thinking with regards to basketball and the role of statistics, as well as introduce a general model.  That will be unique to this post only.  Next, and for all future articles, I will explore different biases and intuitive concepts around the NBA and compare them to measurable statistics, as well as take a look at interesting facts, ideas, and topics.

Part I

As the role of advanced statistical analysis in the NBA has expanded in both mainstream basketball chatter and internal management decisions, we are seeing a convergence between intuition and measurable data from the games played.  During the great Durant-Oden debate, statistical analysis within the Trail Blazers organization projected Durant as the superior player, yet the inescapable NBA notion that “you can’t pass up a chance at a legit 7-footer” was ultimately too powerful.   During this fiasco, which was just over three years ago, the difference between statistical analysis and the visceral conclusions of scouts and management (referred here perhaps in a derogatory nature as “intuition”, but certainly based on experience and basketball knowledge that go beyond the casual fan) was put on the greatest stage.    Both players are still young and hopefully their careers are far from over, but it is becoming increasingly obvious that Kevin Durant is the superior player by almost any definition.

The takeaway here is not that “statistics are smarter” or that all teams should fire their scouts (many of whom can still make successful decisions based on basketball intuition).  The point is that there existed market inefficiency during these player evaluation periods, and that teams with more information and better information were simply in an advantaged decision-making position.  More importantly, the appropriate use of this information is what will differentiate GMs, coaches, and scouts in the future.   The amount of data available now is, even to a more serious fan, completely overwhelming.   Just scanning through, for example, Basketball Reference’s glossary, it is hard to look at any given definition vs. another and say that one is definitively more important.   Do you want a player with a higher versatility index or effective field goal percentage?  Can you put up with a higher turnover percentage if it means an increase in offensive rebounding percentage?

For these and other questions, the relative importance of a basketball statistic must be correlated with some sort of definitively desirable (or undesirable) outcome, such as wins in a season or average points scored differential.   I originally wanted to equate the team points per game differential (average ppg / opponent average ppg) to a series of ratios that could be broken down further into more ratios.  The general concept is to have a model that, when broken down into enough moving parts, can show differences in various team performance measures over time, as well as compare them to the best and worst teams.  It would allow for us to measure the relative importance of the various sub-categories, which is a crucial tool when making important decisions within an organization.  Well, that all sounds nice, but I couldn’t quite make it work.  The good news is I came out alive and with a general formula nonetheless.   Skip to Part II if you want to take my word for it.

There are two basic equations:  offensive points per game and defensive points per game.  The ratio of one over the other can be viewed as one of the most predictive statistics related to wins other than wins itself, and may be a better predictor of future wins.  As the defensive equation is structurally the same as the offensive equation, I will focus on explaining just the offensive equation for now.  Here is the general equation before any decomposition:

PPG = pts/pos * pos / game

The norm right now is to define a possession as FGA + .44*FTA + TOs – OREB.  In this form, both teams generally will have the same number of possessions each game.  I think that causes the possession statistic to be a misnomer.  It misclassifies “hustle” (OREB) as “efficiency” by allowing multiple FGAs per possession, allowing for a greater points per possession number without increased scoring efficiency.   In that light, I prefer defining possessions as FGA + .44*FTA + TOs.

So, what is next?  To see anything meaningful, the formula must be broken down further.  There are several routes to take from here, but I chose to break down major scoring categories:  2 point field goals, 3 point field goals, and free throws.  The next level of decomposition is as follows:

PPG = (2Pts/pos + 3Pts/pos + FTPts/pos) * pos/game

Where:

2Pts/pos – points from 2 point field goals per possession

3Pts/pos – points from 3 point field goals per possession

FTPts/pos – points from free throws per possession

The calculation of 2Pts, 3Pts, and FTPts is made by a weighted average of attempts multiplied by the percentage:

(2ptFG%*W_2ptFGA*2  +  3ptFG%*W_3ptFGA*3  +  FT%*W_FTA*2.2727  +  0*W_TO) * pos/game  =PPG

The weights are the percent of times that a play ends on a given category, and are calculated by attempts/pos (for example, W_2ptFGA = (fga-3fga)/(fga+.44*fta+to).

I chose to stop here, but the different percentages could be further broken down by locations on the court or situations (such as time left in the shot clock).  Of course, a different direction could be taken that tries to incorporate other important factors, such as rebounding, but I will leave that for another day.

Part II

Let’s take a look at the power of this equation in the pro basketball context.   I broke down the different regular season offensive values for the past 10 NBA championship teams (’00-’09) and the teams with the worst record in each of those years.  The idea is to see the sharpest contrast possible (at the expense of a large sample size…).

click to enlarge

Anything jump out?  There is an obvious difference in team PPG.  That tells us nothing other than good teams score more points.  Duh.  But how do they get their points?  If you look at possessions per game, the numbers are almost identical, meaning that pace does not influence wins very significantly.  Even subtracting TOs from pos/game (leaving just offensive attempts per game: FGA + FTA*.44), the Champs had 91.25, while the losers had 90.40.  All that means is that, through the course of a game, both winning teams and losing teams will, on average, have a very similar number of attempts at scoring.

What are the implications?  This next statement is going to drive my old W&L coach crazy: turnovers are not important.   Alright that was a little dramatic, but they are less important than we think.  It’s hard to believe, because conventional basketball wisdom will tell you that taking care of the ball is a key ingredient to an effective game plan.  However, statistically a turnover is only slightly worse than a missed shot (slightly worse because missed shots have a chance for offensive rebounds).  Because only about 13-14% of possessions result in turnovers, the real area for variability (the other 86-87% of possessions) is scoring efficiency.   The Champs averaged .943 points per possession, while the Losers averaged .876.   To expand on that, let’s take a look at defensive statistics:

click to enlarge

*Note, this second table shows the ratio of each group’s offensive value divided by defensive value, with the difference between the groups, showing the most drastic differences between the Champs and Losers.

The Champs are slightly better than the Losers with turnovers, but not by much.  The Champs turned the ball over .4% less than their opponents (13.2% – 13.6%), while the Losers turned it over .8% more (14.3% – 13.5%).  However, it’s time we start think about turnovers in terms of opportunity cost.  The Champs averaged 13.13 points per game lost due to turnovers (13.2% o_W_TO *.943 o_PPP *105.16 o_pos).  However, their opponents averaged a smaller loss despite having a higher turnover rate: 12.56 ppg. (13.6% d_W_TO *.881 d_PPP *105.22 d_pos).   Considering opportunity cost, the Champs were actually slightly worse off as a result of turnovers than their opponents because they score so much more efficiently.  Scoring efficiency, in a nutshell, is the single most important factor in basketball – by a long shot.

Looking back at the Champs vs. Losers one more time, some of the biggest differences were related to three-point shooting.  Better teams take, and make, significantly more three pointers than bad teams.  After three games, the Rockets are shooting 27.3% on 55 attempts, while our opponents are shooting almost 40% on 68 attempts.   Coming in to the season we were worried about our defense on the perimeter.  After all, Aaron Brooks and Kevin Martin are not exactly a menacing defensive duo.  So far, all of our fears are coming true, and we essentially have to be perfect offensively to stay in games.  It is still too early to hit the panic button, but something needs to change for our team to be competitive.

Brooks may not be the problem and Carmelo is definitely not the answer, but how much success can we really have long-term by trying to outscore everyone?  Judging by the playing styles of the Celtics, Lakers, Spurs, Pistons, and Heat in the past decade, our current team structure is simply not built for contention.   Against the Hornets on Wednesday, pay attention to our perimeter defense.   If Paul can consistently penetrate past our guards, and either get to the free throw line or open it up for Belinelli , Ariza, and Thornton to shoot open threes all game, it’s officially time to hit the panic button.

Not a whole lot going on regarding the Rockets other than Luis Scola dominating all but one game in the FIBA tournament. In nine games, Scola scored below 20 once and above 30 five times. He lead the tournament in scoring (ahead of the NBA’s new alpha dog) while shooting at the 5th highest percentage from the field. Who is going to be the more impactful Rocket – Luis Scola or Kevin Martin? Does this liberate Yao from a heavy burden on the offensive end so he can concentrate primarily on defense? This is not what I wanted to discuss.

I did, rather, receive an email defending Aaron Brooks from a high school buddy whom I had not seen since freshman year of college. You should know a thing or two about this guy. First, we can call him Ben. Second, in high school Ben was much smarter than me in terms of sheer I.Q. Subsequently, I placed a good amount of value on his side of things during lunch-time arguments. Because I know of no variable that has changed since then, I am going to continue to give merit to Ben’s opinions. I am also going to hope to high heaven Ben has not had any traumatic head injuries the past seven years that would influence his recent email.

So Aaron Brooks has Ben in his corner? Perhaps, my stance on Brooks requires a certain amount of reevaluation. I suppose it might be relevant to mention that my brother really likes Aaron Brooks. Relevant, I conclude, because this means that people who actually watch Rockets’ games enjoy having Brooks on the team.

Let me say first, I did not have the opportunity to watch many Rockets’ games last season. I lived well outside the range of Fox Sports SW, and the club was never shown on national television. I was relegated to the “box score,” and I use that term to encapsulate any modern statistic of my choosing.

I now know I have been prejudiced in my view of Brooks. Living by the box score, I have fit Brooks into a mold and have not allowed him to show me otherwise. For this I look forward to the season upcoming. It will provide an opportunity to evaluate my blind evaluation of a player.

Last season this is what I saw: Brooks was only 43.2% from the field, 36th amongst guards (qualified). Of the entire league, which averaged 46.1%, Brooks was 101st. His True Shooting Percentage (which I value) was 54.9%, according to hoopsdata.com. The 20th ranked TSP was Al Horford’s 59.4%.

For more perspective, let us compare a notoriously horrid shooter with Brooks. Step on down, Rajon Rondo!!!

If you do not follow the NBA (insert joke here…i.e. “you’re leading a more fulfilling life because you follow politics”), Rondo’s aim is bad. Last season he made 31.7% of his shots outside 10 feet. He was able to compile an overall FG% of 50.8% (excellent for a guard) because over half of his 11.4 field goal attempts per game came within 10 feet (with 5.4 at the rim).

Stir in Rondo’s 62.1 free throw percentage, and you get a TSP of 54.0. We can see that Aaron Brooks has scoring efficiency percentages that are comparable to someone who is known as a bad shooter. Am I comparing cat poop to dog poop? Maybe, but either way, something stinks.

Brooks shot 45.4% on 2-point field goals last season. That is good for 108th out of 119 qualified players. He did shoot 39.8% from beyond the arc, ranking 19th last season. Combined with his 82.2% clip at the stripe (40th in the league), we can account for the buoyant force in his true shooting percentage, which is hardly seaworthy.

I am not disparaging Aaron Brooks. He appears to be a great guy, and perhaps, the Rockets value him as such. What he does on the court, he does well. Knowingly operating as an outside threat, Brooks took 6.5 three pointers last year and made 2.4 of them. Brooks’ scoring efficiency is, thusly, predicated on the 3-ball. With the arrival and emergence of shooters Kevin Martin and Chase Buddinger, respectively, do the Rockets need place such high premium on outside shooting from the point guard spot? If that skill is found elsewhere in the rotation, is there a skill the Rockets lack that can be filled at the 1-spot? Does this make Brooks expendable?

Maybe not.

Ben was eager to point out: “by the second half of the season, his assists were climbing and his scoring was fantastic, especially the 3. If he hadn’t been passing to Trevor (Ariza) and Chuck (Hayes) all season, his assists may have been around 7.5 or so, no?”

It is true that for the 31 games after the All-Star break, Brooks did average one assist more than he had in the 51 games prior (4.9 v 5.9). And Ariza (39.4% shooter) and Hayes (its bad, just trust me, the dude can’t shoot) are not good at putting the ball through the hoop. Maybe Brooks could have averaged more assists.

But are assists really an important measure of an individual player’s value? Much like RBIs (not “errors”) in baseball, assists are completely dependent on another player’s performance in a given play. I would rather see a statistic that quantifies the amount of “Demonstrably Easy and Not Nasty Instances for Shooting ” (or D.E.N.N.I.S. System) Brooks was able to create.

How often was he able to pass the ball into the shooter’s pocket? By pocket I refer to the area where a shooter can catch the ball and shoot in stride/rhythm. If Brooks drives and kicks out to Shane Battier for a corner three, but the pass is at Battier’s feet: that cannot be considered a D.E.N.N.I.S.

In that scenario, Battier has had to do most of the work by gathering himself for a shot. Besides allowing time for a defensive rotation, the act of having to catch an errant pass and initiate a shot is antithetical to the meaning of assist. In fact, such passes should be viewed as passing the conch, or burden of responsibility. But if the pass hits Battier just above his hip on his shooting-hand side and a good shot results from the pass itself, I credit such a pass as a D.E.N.N.I.S. and as more valuable than an “assist”.

Alas, I do not have such detailed information for Aaron Brooks, nor any other player for that matter, and so you might consider the prior couple paragraphs tangential. My point is that a simple assist is too broad a concept to be weighed comparatively. I do not enjoy using it as means for differentiating value, at least not on its own merit.

Brooks’ value is as a scorer. He would be ideally suited coming off the bench as a more versatile Eddie House-type. When he catches on fire, he can score off iso’s, hand-offs, screens, etc. like he did on March 17th, 2009. His 19.6 ppg last year will probably command more than his type of role player is worth…I will get into that next time.

Thank you, all, for the warm reception. It is much appreciated. I especially enjoyed the comments. They provided some ideas I had failed to incorporate for one reason or another.

Being my own editor, editing comes to mind (good thing I’m not a Mohel, zing). I felt that my simple post could have ended up at 736 pages if it wasn’t reined in at certain points. There are, obviously, many scenarios in which the Rockets could dupe the Nuggets out of their star. I chose to discuss the one that made the most sense to me, and I should have explained that. Luckily for all of us, we have a little time until season’s beginning, and I’m good for another couple thousand words between now and then.

I feel like I am taking crazy pills! I invented the piano key necktie!

One reader comment, courtesy of Bob Schmidt, lamented the depletion of depth my proposed trade created. Mr. Schmidt suggested, instead, offering Kevin Martin, Jared Jeffries and a draft pick – for Carmelo Anthony. That is a viable trade from a financial perspective. Remember, player salaries are typically the largest impediments to deal making, as they must be within 125% + $100,000. (i.e. Team 34 wants to trade Player H, who makes $1,000,000 to Team 11 for Player V, who makes $1,500,000. Team 34 would have to include another player because the max salary it can take back by trading just Player H is ($1,000,000×1.25) + $100,000 = $1,350,000.)

Kevin Martin, however, is such a valuable asset, I would hate for the Rockets to part ways with him. Obviously, that sentiment reeks of fandumb (a stupid play on words–fandom, but fitting as I have no insider knowledge of the Rockets front office). Because I do not want to get tangled in a web of Labor Theory economics, I am sticking to good, and very arguable, common sense.

Keep these figures in mind: Kevin Martin makes $10,600,005 this upcoming season and $11,519,840 and $12,439,675 in subsequent seasons 2011-12 and 2012-13, respectively.

Martin has averaged over 20 points per game for four straight years and is just now entering his supposed prime at 27 years of age. I am usually not one to put faith in points per game, as Dave Berri has more than proven it is the overhyped statistic in basketball: “Scoring dominates the perceptions of performance but does not capture a player’s contribution to wins.” But, and we are talking Sir Mix A Lot proportions, Martin scores in a most peculiar way. Yes, he shoots the ball like he’s Roseanne Rossannadanna trying to flick a sweat ball off Dr. Joyce Brother’s nose, but that is not the peculiar to which I was referring.

Martin is peculiar in that when he shoots the ball, there is a better chance of him scoring than not. That little facet of his game, one would assume, makes for a great basketball player. Call me crazy.

You’re crazy, man. You’re crazy. I like you, but you’re crazy.

For his career, Martin averages a 59.8% True Shooting Percentage (TSP – “a measure of shooting accuracy that takes into account 2-point field goals, 3-point field goals, and free throws – basketballreference.com). Among active players, that puts him 4^th. This is a relevant statistic as it is one of the best measures of a basketball player’s scoring efficiency. eFG% is the other notable efficiency statistic, but it does not account for free throws (obscure trivia – Mike Dunleavy apparently developed the eFG% metric, and yet, he still signed Baron Davis to a now toxic contract after the 2008 season when Davis shot an eFG% of 48.3% when the league average that year was 49.8%).

If you happen to be a fan of logic, you could argue that Martin is actually second in true shooting percentage (behind the best shooter of all time, Steve Nash) because, of the three gentlemen ahead of him, one is named Dwight Howard (60.3% – 3^rd) and another goes by Amare’ Stoudemire (60.6% – 1^st). Both are good players but would we call them shooters?

No, we would not, and that answer barely necessitates qualifying. According to hoopdata.com, of Stoudemire’s 15.4 shots per game last season, he averaged 10 within 10 feet including 6.7 at the rim. His TSP for the 2009-10 season was 61.2%. Shocking. Howard had an even shorter tether to the paint as 9.4 of his 10.2 shots per game were within 10 feet. His TSP for the same season was 63.0%.Cough, Dunk Inflation, cough.

I’m an IRS agent. Everyone hates me.

Yes, dunking is cool. But cooler than a pull up 17 footer? I mean, I don’t see a bunch of 4^th graders dunking in pick-up games. So I think it is safe to assume they all want to be like Kevin Martin.

Last season, Martin played in only 46 games and had a relatively off year as he finished 13^th in TSP among shooting guards who played more than 25 minutes per game. And because that does not help my argument as much as the season before that, I am going to give you Martin’s 2008-09 numbers. In that season he played in a much more relevant 51 games, but was not traded half way through the season.

(You might be tempted to think, “But you just gave us Amare’ and Dwight’s numbers from last season. How about some consistency?” Fine, for those of you with the fine toothed comb: Stoudemire – 61.7% TSP, 14.1 shots per game total, 8.7 within 10 feet including 6.2 at the rim; Howard – 60.0% 12.4 total, 11.8 within 10 feet including 6.8 at the rim. Outside of Howard’s three percentage point dip, not that big a difference, and I wish now I had told you to look it up yourself.)

In 2008-09, Kevin Martin took 15.9 shots per game. That is almost two more than Stoudemire and three and a half more than Howard. Those differences are notable because Martin was not dunking 40-50% of his shots. His 15.9 shots per game breakdown to 3.1 at the rim, 1.2 within 10 feet, 1.4 between 10-15 feet, 4.7 between 16-23 feet, and 5.4 3-pointers.

Martin’s 2008-09 TSP was an absurd 60.1%. I use absurd, because almost two-thirds of his shots came from outside 16 feet. As I said before, TSP accounts for free throws which would help explain why Dwight Howard could ever have any type of high shooting percentage. In 2008-09 Howard made 6.5 of his 10.9 attempts from the stripe per game. Stoudemire made 6.1 of 7.3. Kevin Martin made 9 of his 10.3 attempts per game.

I find that interesting because if most of Martin’s shots are long range, how is he getting to the line more frequently than two very active in the post, big men?

Investors?

Now that we have proven Martin is a scorer, let us recall those 2-comma figures I noted above. Why? Because, not only do the Rockets have one of the most efficient shooters in a game heavily reliant on shooting, they have him at a cost below market value for such a trait. Stoudemire just signed a $100 million contract that will pay him an average of $20 million a year. Dwight Howard made $15.2 million last season after signing an $80 million dollar extension three summers ago. Steve Nash made $10.3 million last year but, much like Jennifer Aniston, he is willing to settle to get a baby…err, ring.

In sum, the Rockets have a player John Hollinger of ESPN labeled a “dark horse threat to lead the league in scoring” before the 2009-10 (granted that was predicated on Martin playing for Sacramento as their number one option) for a price that does not limit the team from acquiring more/larger contracts. Their flexibility remains intact by keeping him rather than trading him.

Dale, you’re not licking dog $***, okay? They’re kids.

So, instead of trading a supremely efficient scorer for a grossly inefficient player, why not be patient and wait for an opportunity to build wisely. If the Nuggets trade their cornerstone then it does not make much sense for them to keep the…patio?…I couldn’t extend that metaphor quite how I wanted. Trading Melo likely signals a reset, leaving superfluous an alternate Denver Nugget.

A plausible trade scenario, I think, would be for the 34 year old point guard, Chauncey Billups.

With 2 years at roughly $13 million per, acquiring Billups is a fairly limited risk, as Hollinger has noted that historically big guards who shoot the 3-ball well hold up longer (see Jason Kidd). He shoots well and has extensive playoff experience. He has played with a slippery two guard before in Rip Hamilton and is a much more capable set up man than either of our potential starters at the point.

To acquire Billups and his $13,150,000, the Rockets could send Aaron Brooks ($2,016,692), Jared Jeffries ($6,883,800) and either Patrick Patterson ($1,823,280) or Jordan Hill ($2,669,520). (Why Aaron Brooks should be traded, regardless.)

In my fantasy leagues, I am always the jerk who sends out semi-ridiculous trades and gets offended when they are rejected. That being said, I am not completely sure that Billups is worth quite as much as I just laid out because the Rockets might be able to get more for those pieces in a different package.

Billups would unquestionably be an upgrade and provide leadership. While he has big game experience, however, I would be more than hesitant if the ball were to end up in his hands with the clock ticking down. I believe strongly that his big game persona is over dramatized, and I would much rather have Yao or Martin taking a game determining shot.

I am leaning toward thinking the Nuggets are not our best trade partner.

Aside from his points-per-game increase, (which is a very obvious result of his usage inflation), I just don’t see any case for Houston Rockets guard Aaron Brooks as Most Improved.

In the ten games since the trade of Carl Landry, Scola is averaging 17.5ppg and 11.3rpg.

Not including the night against Orlando when he only played 16 minutes, Scola is averaging 19.4ppg and 12.2rpg during that period.

The chart below compares Scola’s production over this time frame to his season and career averages.  I have also included his per 36-minute averages as a frame of reference.

Luis Scola’s main fault is having the rotten luck of being born in the wrong decade of history.  Were he 24, rather than 30, he could undoubtedly expect an offer this summer somewhere in the range of what Anderson Varejao took home.

But because his athletic prime was wasted toiling in an inferior league, held hostage by the presence of the greatest big man of our decade, I can’t see Scola being offered more than $6million annually.

The greater question concerns his status with the Houston Rockets.  If such production is the norm, and at a still relatively cheap price, with the emergence of Jordan Hill, would the Rockets not best be served by simply bringing back Scola to anchor the position?

I’m not sure it’s that simple.  It’s true that Scola is one of the best role players in the game; he will also remain one of the greatest bargains.  At the same time, the team is still one impact player away from true contention.  With the emergence of Aaron Brooks and acquisition of Kevin Martin, an upgrade at power forward makes the most sense.

Scola’s production is undeniable, but it comes through filling a role.  There is a considerable difference between scoring 17ppg off hustle/open looks/single coverage postups and scoring at a similar rate against double teams.  To take the quantum leap, this team needs a player who will produce through the latter means, opening up opportunities for other players, and helping them match up against elite competition when half-court sets stagnate in the playoffs.  Yao Ming alone will not suffice.

By mere odds, its probable that the team strikes out in free agency this summer.  In that event, retaining Scola would be a no-brainer decision.  Still, exploring upgrade possibilities should be the preferred course of action.

From above, we see that the relationship between height and team defensive efficiency is not so clear, indicating that there are many other variables (team philosophy, experience) to be considered. The Houston Rockets, as poorly as they’ve defended of late, are actually not too far below league average (though note that the data for the chart above was from before the trade). Still, they have clearly slipped, and the absence of an interior shot-blocking presence has to be the considered the primary reason. But there are others:  many new players added to the team is a factor, as well as the Rockets playing more younger players.

Here is another look at this relationship, showing individual front-court players (who’ve played at least 500 minutes ).

“DEF on” is simply the team’s defensive efficiency with each player on the floor, and “DEF On/Off” is the team’s defensive efficiency with the player on the floor minus the efficiency with the player off the floor. Note that the lower either of these metrics are, the “better” the team defense is with that player (presumably, of course this is a bit of a simplification).

Conclusions? There is a very minimal correlation between increased height in the front-court and better defensive +/- stats, but really the only conclusion one can draw is that  (again) height is not a strong indicator of defensive impact. We see that there is actually a somewhat stronger relationship between increased age and defensive impact, which isn’t a surprise.

The problem here is that height is not necessarily a useful indicator of how much “interior presence” a player provides due to his length. What we can do is combine height and shot-blocking prowess into a single metric and see if there is a stronger relationship between that and defensive impact. We will explore this issue further in coming weeks.

For those readers that are unfamiliar with what adjusted +/- is measuring, think of it this way. Suppose you have a particular 5 on 5 matchup, and you want to know what the expected point differential would be after some number of possessions. The idea is to come up with a number which describes each player’s +/- impact, such that if you add up all these numbers for one side and subtract all the numbers from the other side you’ll arrive at this expected point differential. Now in reality basketball isn’t so simple, and sometimes certain player combinations works better together or certain matchup are more favorable than others. And there are many other variables to consider. Still, if we knew for each player what those numbers were on average, that could be valuable input in deciding which players to pursue in an acquisition or which players to give more playing time to.

Alright, so what is offensive adjusted +/- and defensive adjusted +/-? Think of offensive adjusted +/- as the number of points a player is expected to add to his team’s score (per 100 possessions) by being on the floor, relative to a “league average player.” Similarly, defensive adjusted +/- would correspond to the number of points a player is expected to add to the opponent’s score (per 100 possessions) by being on the floor, relative to a “league average player”.  So, the higher the offensive APM the better, and the lower the defensive APM the better. Note that when I say “points a player is expected to add”, I’m not simply referring to the number of points he scores individually or the number of points the opponent he matches up against scores individually.

I’ll give the usual “stathead” disclaimer: these are interesting to look at, but are not by any means the end-all, be-all player rating. For most players, the ratings makes some sense and can serve as a sort of validation of our subjective opinions or what other methods (e.g. PER) might say. For some players, especially when only looking at data over just 1 year, there are some strange results, but that is to be expected. The model is not perfect, and there is noise in the data. It is interesting to look at nevertheless.

Below, we’ll take a look at some of the results. Again, refer to HoopNumbers for all the raw data.

League-Wide Trends

I want to start by looking at some league-wide trends. HoopNumbers provides RAPM, Offensive RAPM, and Defensive RAPM for each of the last 4 seasons, as well as combined numbers over all the 4 seasons (“equal weighting”), and combined numbers that are weighted more heavily to recent seasons (“time-weighted”). Consider the following scatter charts, relating RAPM results to PER for the last 4 seasons:

Average over last 4 seasons, minimum of 1000 total minutes played

PER is referring to Hollinger’s Player Efficiency Rating. For those that are unfamiliar with it,  it is essentially a per-possession summary metric that adds the good things a player does and subtracts the bad things a player does, according to the boxscore. We see that PER has almost no relationship whatsoever to Defensive RAPM, but there is a clear relationship to Offensive RAPM.  This makes sense. The boxscore, as you probably know, is heavily offense-centric. That isn’t to say you can’t do any better than PER at explaining defense with the boxscore, but there are obvious limitations.

Now, let’s look at the same charts, focusing on the current season:

Minimum 1000 played this season.

And here is the chart focusing again on current season, but the RAPM is stabilized by taking into account data from previous 3 seasons (these are the “time-weighted” RAPM stats):

Minimum of 1000 minutes played; RAPM stabilized using data from prior 3 seasons.

We see a similar pattern, as expected. Out of curiosity, I wanted to take a closer look at the “top 30 players” according to PER. What sort of RAPM do they have?

RAPM (4 year time-weighted) vs PER for top 30 PER players (yes, top 30, chart has typo).

I labeled the points with each player’s name. This type of presentation is interesting to me, because I see RAPM and PER as mutually validating eachother. RAPM is based purely on how a team’s performance changes with the player on the floor, while PER is based purely on the numbers the player puts up individually in the boxscore. If a player rates highly in both, and many of the above players do, that gives me more confidence in affirming their status as an elite performer. Here is another chart, again for the top 30 PER players, showing Offensive and Defensive RAPM (4 year, time-weighted):

Offense-Defense RAPM Map (4 year, time-weighted) for top 30 PER players; minumum 1000 minutes played (yes, top 30, chart has typo).

Again, note that the lower the Defensive APM the better. So players falling in the lower right quadrant have an “above-average” offensive and defensive RAPM. Players falling the upper-right quadrant are above average in offense, below average in defense according to RAPM analysis.

RAPM Stats for 09/10 Houston Rockets (the important ones)

Below are some charts for the Houston Rockets players that have played significant minutes this year (I include Kevin Martin):

The ratings for just this season are not so reliable, so I wouldn’t make so much out of Andersen’s good defensive rating, or Martin’s poor offensive rating. I’d consider these anomalies. By HoopNumber’s +/- analysis, Chuck Hayes rates as one of the top defensive player over the last 4 years in the league, and even for this season, in a starting role, he has the best defensive rating on the team, closely followed by Kyle Lowry. Some may doubt that a 6’6 center can rate so well defensively. I would ask those people to just take a look at Chuck’s performance against Toronto last night.

Interestingly, this analysis finds Battier to be a below average defender this season, and only slightly above average over the last 4 years. In fact, it gives more credit to Battier’s offense than defense, which is hard for me to rationalize so I don’t think I’ll bother.

It felt very odd seeing Howard dominate the team as I had become accustomed to the complete opposite in recent years.

click to enlarge

via basketball-reference.

Scorer-Assistant Table. Shown for 9 Rockets rotation players before All-star break.

Perhaps a more telling way of representing this information would be to show assist-rates when two players are on the floor together. For example, instead of showing that Brooks assisted 51 Ariza baskets so far this season, I may want to know how many assists to Ariza he gets for every 36 minutes they share on the floor.  That’s what the following table shows:

Scorer-Assistant Per 36 minutes Table. Shown for 9 Rockets rotation players before All-star break.

A few comments:

• Kyle Lowry does a nice job distributing to all his teammates when he shares the floor with them.
• Carl Landry is strictly a finisher.
• It is interesting that, per-minute, Chuck Hayes assists more Brooks baskets than any player other than Lowry. Brooks has the ball in his hands quite a bit and has to create most of his shots, but he does get some easy looks from Hayes backdoor passes or hand offs from the high post
• It is interesting that Brooks has a higher assist rate to Landry, while Lowry has a higher assist rate to Scola. Brooks does a pretty good of penetrating in half-court sets and finding Landry on the baseline. I think Lowry isn’t asked to create as much offense in half-court situations. Lowry does excel in transition, and Scola is a great running partner. Perhaps those pairs are better suited together?

Data in this post was tabulated from the play-by-play CSV files provided by Ryan Parker at BasketballGeek.com.

Let’s take a look at some of the numbers.

Nothing really jumps out here except that he struggled mightily in November and has been on a tear in his last 5 games.  I was surprised to see that he was scoring at nearly the same clip in wins and losses.  I assumed there would be greater disparity.

In terms of his shooting, except for November, you can see he’s been fairly consistent over the duration of the year.  However, despite scoring at nearly the same clip, you can see that there is a pretty substantial drop-off in his shooting from wins to losses.  Perhaps even more interestingly, despite the gaudy point totals, his shooting in his last 5 games has been on par with his overall season averages.

Taking a look at the shot attempts, we see that they have stayed roughly the same during wins and losses, but have gone up considerably in his last 5 games.  Unfortunately, it seems that rather than improved play, more chucking is the driving force behind Aaron’s recent ‘surge.’

I also thought it would behoove us to compare his performance thus far to last season’s playoffs.  The ’09 playoffs serve as an appropriate barometer for Brooks’ growth because, as I have explained in the past, the matchups were conducive to production inflation.  Blake and Fisher are the two worst starting point guards in the league so it doesn’t tell us much that Aaron exploded against them.  We wanted to see how he would fare this year against the rest of the league in relation to that production against the most favorable circumstances.

We see that his usage has gone slightly up while his shooting has gone slightly down.  However, he has assisted on a far greater percentage of the team’s plays.  With that said, I would not necessarily attribute this to improved playmaking abilities – he simply has the ball in his hands a far greater amount of the time with the absence of Yao Ming.