Maybe You Just Need a Change of Scenery: A (Hopefully) Unbiased Look at the Impact Changing Teams Mid-Season Has on Wins Produced

So, the trade deadline has passed.  Everyone knows that Wins Produced’s consistency is worse when players change teams.  In fact, I believe that this is one of the main criticisms of Wins Produced and advanced stats in general.  So, I have decided to do some investigation.

Now, I’m honestly not sure if this has been done before.  In fact, I’m sure that Arturo Galletti has done something about this at some point, but I can’t remember the exact details.  What I have done is looked at every player since the 2000-01 season who has played at least 250 minutes each with multiple teams (although the word “multiple” is somewhat misleading as no player over this time span has played more than 250 minutes with three or more teams in a single season) and compared their Wins Produced numbers between the two teams.  Here goes nothing:

In all, 287 seasons met the criteria.  First, I found the correlations between the two data sets.  Here is the data:

C0rrelations

• 2011-12: 14 Data Points, 60.4% (I’m not sure if listing these as percentages is necessarily proper, but it is much easier to visualize this way.)
• 2010-11: 34 Data Points, 56.2%
• 2009-10: 26 Data Points, 72.3%
• 2008-09: 27 Data Points, 64.7%
• 2007-08: 29 Data Points, 59.6%
• 2006-07: 15 Data Points. 20.0%
• 2005-06: 22 Data Points, 63.5%
• 2004-05: 28 Data Points, 37.4%
• 2003-04: 40 Data Points, 18.1%
• 2002-03: 16 Data Points, -8.7%
• 2001-02: 14 Data Points, 45.8%
• 2000-01: 20 Data Points, 60.0%
• Overall Data: 286 Data Points, 46.6%

There seems to be a definite leap in consistency in 2005-06, with a drop perhaps caused by a smaller sample size in 2006-07, followed by continued years of consistency since then.  At this point in the post, I have no intention of making conclusions; I’ll save those for the end.  Well, while correlations are definitely useful, they only measure consistency; if the two data sets are parallel, the correlation would still be high even if there wasn’t much or any overlap.  (For example, the data sets {3 6 8} and {7 10 12} have perfect correlation.)  So, I decided to find the average distance both as the straight-up difference and absolute value, as well as the standard deviations for each season.  Note that for the average difference in absolute terms, a negative value actually means an increase when playing for the second team rather than the decrease that would be seemingly implied:

Other Data Points

• 2011-12: 14 Data Points.  Average Absolute Difference: .082.  Average Unabsolute Difference: -.060.  Standard Deviation: .108.
• 2010-11: 34 Data Points.  Absolute: .052.  Unabsolute: -.017.  Standard Deviation: .072.
• 2009-10: 26 Data Points.  Absolute: .050.  Unabsolute: -.002.  Standard Deviation: .065.
•  2008-09: 27 Data Points.  Absolute: .055.  Unabsolute: -.017.  Standard Deviation: .070.
• 2007-08: 29 Data Points.  Absolute: .079.  Unabsolute: -.006.  Standard Deviation: .094.
• 2006-07: 15 Data Points.  Absolute: .057.  Unabsolute: .042.  Standard Deviation: .081.
• 2005-06: 22 Data Points.  Absolute: .073.  Unabsolute: -.041.  Standard Deviation: .086.
• 2004-05: 28 Data Points.  Absolute: .073.  Unabsolute: -.024.  Standard Deviation: .091.
• 2003-04: 40 Data Points.  Absolute: .088.  Unabsolute: .060.  Standard Deviation: .122.
• 2002-03: 16 Data Points.  Absolute: .107.  Unabsolute: -.063.  Standard Deviation: .128.
• 2001-02: 14 Data Points.  Absolute: .045.  Unabsolute: .013.  Standard Deviation: .065.
• 2000-01: 20 Data Points.  Absolute: .076.  Unabsolute: -.036.  Standard Deviation: .097.
• Overall Data: 286 Data Points.  Absolute: .070.  Unabsolute: .022.  Standard Deviation: .090

I also have some other miscellaneous statistics that help paint a picture:

Miscellaneous Statistics

• Five-Number Summary: Minimum -.349, Q1 -.071, Median -.023, Q3 .032, Maximum .278
• Interquartile Range: .103
• % of Players Whose Wins Produced Rate Increased After Switching Teams: 59.8%
• % of Players Whose Wins Produced Rate Decreased After Switching Teams: 39.2%
• % of Players Whose Wins Produced Rate Remained Exactly the Same After Switching Teams: 1.0%
• % of Players Whose Wins Produced Rate Increased More Than One Standard Deviation (roughly .090 Wins Per 48 Minutes): 20.3%
• % of Players Whose Wins Produced Rate Decreased More Than One Standard Deviation: 9.4%
• % of Players Whose Wins Produced Rate Did Not Change More Than One Standard Deviation: 70.3%
• % of Players Whose Wins Produced Rate Would Be Considered Outliers Using the Formula 1.5*Interquartile Range From Mean: 7.7%
• Outliers Because of Decreasing Productivity: 9
• Outliers Because of Increasing Productivity: 13 (Note that if the mean had been 0, the numbers would have been 5 and 18 respectively using the same interquartile range.)
• Data with the Removal of Outliers (Simplified to be a change of .15 Wins Per 48 Minutes): Correlation: 64.1%.  Mean Absolute Value: .057.  Mean Absolute Difference: -.013.  Standard Deviation: .070.

Now, it’s time for some conclusions.  First of all, it seems very clear that, at least from a correlations standpoint, players’ play has been more consistent when switching teams over the last five seasons.  While I have some theories as to why the numbers have become more consistent in recent years, I have nothing to corroborate them with.  One of my guesses is that the game has become more homogeneous over the past few years, but I have no idea where that would come from besides the recent advances in technology, nor do I have any idea about how that would be measured.  Furthermore, I have no glimmer of an idea as to how the game has become more homogeneous.

It also seems very clear to me that there seems to be some benefit to switching teams as the unabsolute difference is usually at least slightly negative, although it positive for the data set as a whole because of the strange 2003-04 season where there were lots of trades but little consistency among the halves of the season for traded players.  Also fueling the fire for the benefits of switching teams is the fact that the median value for the sample is negative, that there was a signficantly greater number of players whose productivity increased rather than decreased after the trade, and the fact that there were more players with drastic increases in productivity rather than drastic decreases, with “drastic” being defined as a change of +.090 Wins Produced Per 48 Minutes, which is approximately one standard deviation as measured by Josh Weil, and by outliers in my study found by taking the interquartile range.  However, there appears a great degree of randomness in the sample, which makes sense as all NBA players are individuals.

Part of the reason why I wrote this article was to investigate the claim that Wins Produced is inconsistent as players switch teams.  While this appears to be true in the first half of the sample, this becomes less apparent in recent years, and I think that it is fair to say that Wins Produced does “keep” well even when players are traded mid-season, which is arguably when one would expect it to be the least consistent.

The spreadsheet I used for the data can be found at TeamSwitchCorrleation.  Thank you for reading, please comment, and please come back.

Time for Saer Sene to Serenade Some Scouts

In 2006, the Seattle Supersonics made a suspect move to select Mouhamed Saer Sene with the tenth overall pick.  Saer Sene, then a 20-year old Senegalese center, had only one year of professional experience-in Belgium no less-while Arutro’s hits of Renaldo Balkman, Rajon Rondo, and Paul Millsap.  All three would become great NBA players, although Balkman never received much of a chance.  Other solid players available included J.J. Redick, Thabo Sefolosha, Ronnie Brewer, Kyle Lowry, Steve Novak, and Leon Powe.  What Sene did have going for him was a 7’8.5″ wingspan, but height alone does not make an NBA player.

What Sene never got was a shot at a real career.  To date, he has only played 260 NBA minutes in 47 NBA games, with no more than 169 minutes in a single season, his rookie season.  Those 162 minutes were absolutely awful, but his 62 in the following year were not, and the 29 the year after were ridiculously amazing.  All of these evaluations, though, suffer from extremely small sample size.  In 2006-07 and 2007-08, Sene also spent a somewhat substantial amount of time with the Idaho Stampede of the D-League, where he played fairly well if not exceptionally.  He was cut by Seattle in the middle of the 2008-09 season and was signed by the Knicks on April 9 after playing amazingly for ten games with the D-League’s Albuqerque Thunderbirds-he averaged nearly 17 rebounds, pace-adjusted, per 48 minutes!  Cut by the Knicks at the end of the season having only suited up for them once, he went off to Europe, and that may be the last most people on this side of the Atlantic will ever hear of him.

However, the draft bust did not stop playing basketball.  His first destination was Hyeres-Toulon of the French Pro A.  He shot a good 56% from the field, and his rebound rate was even higher than it was in Albuquerque.  Among centers with over 200 minutes played, only the immortal Nick Fazekas had a higher Win Score by a slim margin, and his sample size was only a third as large.  (Note that I am not being sarcastic about Fazekas; in 269 NBA minutes, he produced wins at a .320 clip per 48 according to Wins Produced, and was an absolute beast in college as well.)   Sene produced similar numbers the following season for BCM Gravelines Dunkerque, leading the league now that Fazekas was plying his trade with the D-League’s Reno Bighorns.  In fact, his rebound rate rose to a whopping 18.6 per 48.  In last season’s NBA, only Marcus Camby could exceed that for 1,000 minutes or more.

Now, the French league is not exactly made of world-beaters, so Sene traveled to Spain to play for Baloncesto Fuenlabrada, which in my opinion was a really awesome name, in the Europe’s best domestic league, the ACB.  His dominance didn’t stop in France, though, as the man was third in the league for center Win Score Per 40 Minutes among those with more than 200 minutes, behind only James Augustine and Boniface Ndong.  He has at it again this year as he is tied for fourth among centers behind Ognjen Kuzmic, Ante Tomic, and Lucas Nogueira.  Draft buffs will recognize Kuzmic and Nogueira’s names from recent draft discussions, as Kuzmic was the No. 52 pick last year courtesy of the Warriors and the Brazilian Nogueira is No. 37 on the www.draftexpress.com Top 100 Prospects list.  Tomic is a regular in Eurobasket competition and was selected 44th overall by Utah in 2008.

I honestly believe that Mouhamed Saer Sene should get another chance in the NBA.  He has paid his dues overseas, and he feel like he has the right to examine what he can do, as the only players better at him at his position are either well-recognized Eurostars or players who have received legitimate NBA attention.  NBA front offices love sinking further money and attention into sunk costs, but it seems to be working the other way here as they appear to have given up on him for good.  In fact, he essentially got a raw deal to start with because of the aforementioned grand total of 269 minutes.  It is definitely time for Saer Sene to serenade some scouts.  Thank you for reading, please comment, and please come back.

Defending the Indefensible: Marvin Williams over Chris Paul

In recent memory, a bad transaction move as perceived by many that NBA GM’s have made is the selection of Andrew Bogut, Marvin Williams, and Deron Williams over Chris Paul in the 2005 NBA Draft, particularly Marvin Williams.  Even Deron’s greatest defenders probably would not state that he is better than Paul after his self-destruction as a Net, Bogut is oft-injured, and Marvin Williams is often derided as a bust.  Well, as you can see from the title, I am not calling the selections of Bogut and Marvin bad moves.  While not “must-takes”, they were certainly not unfathomable.

On his old website arturogalletti.wordpress.com, the URL’s namesake has the initial calculations of his draft predictors, Yogi and Booboo.  Yogi and Booboo are two separate systems designed to predict an NBA prospect’s rookie year Wins Produced based on his college statistics, height, position, and age.  If they rich certain thresholds-I think it is .070 WP48 for one and .080 for the other-the system “picks” the player.  The formulas have a correlation with actual Wins Produced levels in the 40% range.  In a post from October 8, 2010, Arturo lists all the “picks” dating from 1997-2009 using Old-Style Wins Produced.  Considering that that was the formula used at the time, that is the formula that I am going to use.

Do you know who the picks were from that season?  They were, in order from greatest to least average Wins Produced Per 48 Minutes: Marvin Williams (.139), Paul (.122), Bogut (.121), Danny Granger (.115), and Nate Robinson (.106).  Robinson was a bust, but the others have all been dependable players.  Considering that Granger and Robinson were mid-round prospects, let’s leave them aside.

When you really look at it, the differences in projections between Williams, Paul, and Bogut are almost inconsequential, so that’s certainly not a factor.  Now, let’s move on.  Williams was a 6’9″ forward who had just been the sixth man on the national champion North Carolina Tar Heels and was 19, Paul, aged 20, was the star 6′ point guard on a Wake Forest Demon Deacons that was a 2 seed but upset by 11 seed West Virginia in the second round, and Bogut would turn 22 in November and was a 7′ center on a Utah team that rode a 6 seed to the Elite Eight.  As far as I can tell from his Wikipedia page-yes, I know-Bogut’s injury issues had not cropped up yet.  So far, I still cannot make much difference between the three, aside from the fact that Bogut is older.

Milwaukee had the first pick.  They really looked a team that would have a first round pick, as the often under-appreciated Dan Gadzuric was their only real player of note.  Really, I can’t make any difference between the three candidates.  Hindsight is weighing heavily on me taking Paul, but I probably still would have gone with Bogut because a) you need a decent center in order to win the championship and b) as good as his Wins Produced numbers are, I just can’t picture a team winning a title with Dan Gadzuric as its starting center.

Atlanta.  They have Josh Childress and Josh Smith, who look like their starting wings forever and all-time.  Josh Smith was really good when evaluated as a small forward for that rookie season, and he could have been like LeBron light, and I have written about Childress before.  Marvin Williams looks a little excessive, but this is why the move is defensible from a Wages of Wins standpoint:

1. Josh Smith is really a power forward.  Adjust his numbers to the 4 for that season, and his numbers become average, but his ultimate future was as a 4 because of his shotblocking prowess.  If he had acted like a 4, the man would have been a superstar.
2. Williams may have been a sixth man and Paul a star, but their Arturo numbers are virtually identical.
3. Williams is a year younger than Paul.  That gives him an extra year at a growth at an ever-so-slightly-accelerated rate.
4. Williams has/had a reputation as a good defender.

I still want to say that I would take Paul, as Childress and Smith can both play the 3, Marvin’s natural position, and because of our good friend hindsight.  But I just can’t do it.  It would have been so much waffling back and forth that I honestly have no idea which player I would have picked!  Regardless, if I’m Utah, I’m picking the other guy third.  It would not have been Deron Williams, as his stardom was unexpected if not particularly long-lasting.   Thank you for reading, please comment, and please come back.

P.S. I may do a mini-series of posts like this.  I don’t know; if I do though, you can count on Greg Oden vs. Kevin Durant being featured.

Funniest Joke I Ever Heard: Another Epic Mistake on the Part of ESPN and Sheridan Hoops

I’m back!  Sorry, it’s been so long; I’ve been having some serious motivation issues because of my somewhat depressingly low readership count.  As you may know, I like reading Patrick Minton at www.thenbageek.com, which is a part of the Wages of Wins Network.  Well, Patrick wrote a post about Pau Gasol today, decrying the media backlash behind Pau Gasol’s benching in favor of Earl Clark.  Clark’s on the hot streak of his career, he’s playing great, and Pau’s not; thus, I agree with Patrick.  No big deal, I do this all the time.

Patrick also likes to make fun of the people at www.sheridanhoops.com for their conventional views.  A certain James Park wrote an article entitled “Tweet of the Night: Jalen Rose” where he..referenced tweets from Jalen Rose and, as a bonus, Rose’s buddy Bill Simmons.  They are:

From Jalen: So let me get this right. Gasol won 2 chips playing alongside Bynum yet can’t play w/D12 who is faster than both of them?! #Confused#NBA.

And from Bill: If I were a Laker fan I’d be so pissed off right now. They won’t even get 50 cents on the dollar for Pau at this point. Well done Mike D.

Aside from Patrick’s point about the massive increase in Earl Clark’s trade value-an increase substantially larger than any hit the Spaniard’s market value took-I have anything to add.

I honestly don’t understand why Jalen Rose is a trusted analyst considering that THE MAN GREATLY BENEFITED FROM PLAYING BAD BASKETBALL, NAMELY THE ART OF VOLUME SCORING!!!

Of late, my favorite statistics have been a) Wins Produced, b) EzPM, if I manually eliminate the Usage adjustment, c) Estimated Impact, and d) Win Shares.  EzPM and Estimated Impact are not publically available for any portion of Jalen Rose’s career as far as I can tell, but Wins Produced is available since 2000 and Win Shares is available for basically the entire history of the NBA.

Well, www.basketball-reference.com has player salaries listed on its website for each season of their career.  Over the course of his NBA life, Jalen Rose made $102,438,250, an average of roughly $7,850,000 a year for thirteen seasons.  That is a boatload of money, even when you consider that a lot of that is lost as taxes.  According to Win Shares, if you value a win at $1.7 million as Arturo Galletti graciously calculated as the average value a couple years ago, do you know how much of that Jalen Rose actually earned?

He earned exactly $80,240,000 of it, also known as only 78.33% of what he actually made.

Remember that Win Shares is a bit of a High Usage apologist-look at Allen Iverson’s statistics for proof.  Over the course of Jalen Rose’s career, he averaged 1.21 Points Per Shot, which is my favorite “mini-metric” for evaluating scoring prowess, which is not a considerable amount above average.  According to Wins Produced, Jalen Rose produced a measly 14 wins or so in the final seven seasons of his career (which is all that can be found at The NBA Geek), which equals $23,800,000 in worth.  Over that span, the man made more than that in any combination of three seasons, or any two of his final four.  His valuation looks even worse when you consider something pretty major.

I DIDN’T ADJUST FOR INFLATION.  HOW ABOUT THEM APPLES?

Yeah, Jalen Rose is a bit of a microcosm of basketball’s major problem of his generation, a greater focus on points per game than points per shot.  Even PER thought he was an average player; if you put it on an exponential scale look I have done with PERrate, Jalen’s best season would have produced only .141 PERwins per 48 minutes, and his career average would have been .104 on that scale.  Furthermore, a truly mathematical glance would scale this done further, as the nature of exponentiality dictates that the distances between values increase the farther to the right of the scale you go, meaning that players at the high end have more positive value than those on the low end have detrimental value.  In other words, this examination is skewed in favor of above-average players by nature, so Jalen’s numbers would be even less!

ESPN claims to give a darn about statistics, but it only cares about the “E” that stands for entertainment.  I still don’t understand how Jalen Rose is such a vaunted analyst there, but I can be a stubborn mule myself.  Thank you for readng, please comment, and please come back.

People I Hope Are Lakers Free Agent Targets

Even though the peak period for free agency is long gone, there are plenty of solid players who are still available.  One of the pressing concerns for my favorite team, the Los Angeles Lakers, is that their bench is very weak.  While this is not much of an issue come play-off time, to succeed in the regular season, a strong bench is a necessity.  Using Wikipedia and www.thenbageek.com as my guides, I will list players who I hope the Lakers will sign, and who will then produce well in their time on the floor.  Most of these players have some reason that has affected their market value, but they all have or can reasonably projected to (in the case of rookies) produce.  Players are categorized by the need that they would potentially fill, and then listed in alphabetically order by team, then last name.  Enjoy.

Possible Point Guard Plug-Ins

• PG Scott Machado (Iona College): In Arturo Galletti’s Wins Produced rookie rankings, Machado is ranked fifteenth, sandwiched in between Thomas Robinson and John Henson.  That is good company.  Machado was the leading player on Iona’s NCAA tournament team last year, and Arturo projects that he will produce .076 Wins Per 48 Minutes next year.  When you consider that he’s only twenty-two and that rookies generally don’t perform all that well, it makes him a legitimate free agent target.  He’ll probably be available for the minimum, and I’d rather him on my roster than Chris Duhon and Steve Blake, who would give you similar, more expensive, older production.  If nothing else, he’s easy to jettison if he doesn’t pan out.
• PG Jesse Sanders (Liberty University): Jesse Sanders was the first edition of James Brocato’s underrated draft prospects series, back when it was still called “A Search for the Next Jeremy Lin.”  Sanders is ranked fifth in Arturo Galletti’s rookie rankings, behind only Anthony Davis, Jae Crowder, Bradley Beal, and William Mosley.  His only below-average areas last year compared to Draft Express’s Top 100 prospects at the position were steals, blocks, and points per minute, and his assist totals were amazing.  Liberty is a small school that did terribly last year, but Sanders was a shining light.  Arturo’s systems grade him to be an average player next year, which is better than both Duhon’s and Blake’s level last year.  He’s just waiting to get signed; I don’t think he’s on anybody’s radar. 

Guys to Fill In on the Wing

• SG Tracy McGrady (ATL): Five or six years ago, this guy would have been gone in the first days of free agency.  It’s not five or six years ago anymore.  However, in 837 minutes of action last year, T-Mac had the best season per-minute that he has had in nine years.  While his scoring efficiency is not stellar, and hasn’t been in a long time, he only takes half the number of shots that he used.  His rebounding numbers have also been bolstered lately, and his assist totals were always phenomenal for a non-point guard.  His only weaknesses lie in turnovers and steals, but those flaws have been with him his entire career.  If there’s any concern, it’s that he’s thirty-three, but I cannot see any possibility that a contract for him lasts longer than two years.  Considering his age, the time of year, his injury history, and his supposed decline, he should be available for the mini mid-level exception.
• SF Derrick Brown (CHA): I cannot believe that this guy ever hit free agency for one simple reason: last year, he was the only Bobcat who played at an above-average rate.  This combo forward is a solid rebounder, a low-usage, high efficiency scorer, and only twenty-five.  He also record steals at a good clip.  In all three of his NBA seasons since graduating from Xavier, he has played at an above-average rate.  However, his assists are low and his other stats are inconsistent, but these flaws are balanced out by his positives.  Sample size is not an issue; he played in 1,443 minutes last year.  If it requires more than the minimum to get him I don’t imagine that it would be much more.
• SG Alonzo Gee (CLE): He rebounds well, he scores decently well, he gets steals, and he’s only twenty-five.  His other stats are mostly mediocre, but only his assits are truly bad, and playing with Steve Nash will render that point moot.  Because of Anderson Varejao’s injury, he produced more wins than any other Cavalier.  He played 1,827 minutes last year and 1,103 the year before, so we know he’s for real, and he won’t demand touches that otherwise would have gone to Nash, Kobe, Pau, or Dwight.  He would be a good swingman, backing-up Kobe and platooning with Metta World Peace, but signing him and any other candidate who only plays wing might me dangerous-there wouldn’t be enough time to go around.  I would be shocked if he signs anywhere for more than the mini mid-level.
• SF Dominic McGuire (GSW): This guy is a small forward who rebounds like an average center and blocks shots twice as frequently as the average man at his position.  Enough said.  I can keep talking and say that he’s in the middle of his prime, he doesn’t turn the ball over that much, and his extremely low shot attempt rate counteracts his abysmal shooting efficiency.  He’s also from Southern California, and he has played at an above-average rate every time that he has gotten significant minutes because of that outstanding rebounding.  Furthermore, he’s another guy you could probably get for the minimum.     
• SF Matt Barnes (LAL): Oh, wait, I’ve heard that this guy probably will not be a Laker again next year.  Since there is no shot at all of him coming back, I won’t waste my breath.
• SG Josh Childress (released by PHX): Remember Scott Hatteberg from Moneyball, the catcher with a dead arm who converts to first base and becomes one of the team’s better players?  In the five seconds I spent thinking about it, Hatteberg was the baseball equivalent of Childress: highly underrated but incredibly valuable.  Hatteberg was cast aside because he was a catcher who couldn’t catch; Childress is being cast aside because he’s a shooting guard who doesn’t shoot.  However, until last year, Childress shot the ball well when he did shoot.  He is also an outstanding rebounder for a guard, especially on offense, and he almost never turned the ball over last year.  Part of the reason people are down on him is because he skipped town and played in Greece for two years, but he has produced at least .200 Wins Per 48 Minutes in every season that he has played in the NBA.  He is twenty-nine, so he might decline, but he’d back-up Kobe and Metta anyway so he wouldn’t be too much of a minutes drain.  While I can see him asking for anywhere up to the mini med-level, it is conceivable that he could go for the minimum.  What a steal that would be; just the possibility has me salivating.
• SF Ken Horton (Central Connecticut State University): Horton was only three rungs below Jesse Sanders, and his projection ranks higher than Metta World Peace’s stat level from last year.  He averaged 1.36 Points Per Shot last year; if he were able to have something in that neighborhood in the pros, it would be phenomenal.  Again, if he doesn’t pan out, you just cut him because he is not going to demand a guaranteed contract, I’m sure.  Just pick this guy up, please.

Back-up Bigs to Join Jordan Hill

• C Erick Dampier (ATL): He only played 83 minutes for the Hawks last year, but he had been an above-average player in 750+ minutes of action in each of the previous nine seasons.  His rebounding is usually very good, his shots, when he takes them, go in at a very high rate, and his shot blocking is very proficient.  As a thirty-seven year-old with almost 1,000 career games under his belt, he has a lot of mileage on him, but that hasn’t stopped the Lakers before.  At his best, he has been amazing, and we haven’t seen his worst in ten years.  He’ll probably be available for the minimum salary as well.
• PF Ivan Johnson (ATL): Johnson is one of those guys who truly has been everywhere: the D-League, South Korea, Puerto Rico, China, and finally the NBA.  When he got his shot, he made the most of it; he produced .100 Wins Per 48 Minutes, led by his efficient scoring and a solid number of steals.  However, Johnson would be something of a risky acquisition.  He was “banned forever” from the Korean league and fined by the NBA for liberally using the middle finger, he has less than 1,000 minutes of experience, and he will be twenty-eight and thus unlikely to improve.  On the other hand, I doubt that he would command much more than the minimum salary, and Kobe should keep him in line for the most part were he to become a Laker.  All bets are off, though, if he spends too much time with Metta World Peace.
• PF Shelden Williams (BRO): Williams is an often-underappreciated player who has has played at an above-average rate in each of the past three seasons.  While he does not any one thing particularly well, he also does not do anything particularly poorly except score, and he does not take that many shots anyway.  While that is not really a bad thing, it may mean that coaches will not play him because they’re unsure of what they’d use him for.  Furthermore, he is twenty-nine, meaning that he is not going to blossom into something greater.  A plus is that his wife, Candace Parker, plays for the Sparks in the WNBA, meaning that he is familiar with the area.  I would expect him to be available for the veteran’s minimum.
• C Josh Harrellson (released by HOU): In 540 minutes with the Knicks last year, Josh produced 1.6 wins.  His rebounding and scoring efficiency are below-average, but his steal, foul, and turnover rates are all good, and his rebounding is not much of a problem.  Besides, we have Dwight Howard and Jordan Hill, who are both outstanding rebounders, and Pau Gasol, who’s solid, and I would have to assume that at least one of those three would share the court with Harrellson at all times.  He would definitely be available for the minimum, and I wouldn’t be surprised at all if he went to Europe,
• C Jerome Jordan (released by HOU): He only played 108 minutes for the Knicks before being packaged in the Marcus Camby trade with Harrellson and Toney Douglas, but he played well, blocking lots of shots, scoring well, and not turning the ball over.  He’s unproven and he turns twenty-six in September, but I think he’s worth a look.  Like Harrellson, he’s a minimum/D-League/Europe guy.

There are some players who I would have included on this list but couldn’t because they had already signed elsewhere.  They were Vernon Macklin (Gaziantep), Drew Gordon (Partizan Belgrade), Bo McCalebb (Fenerbahce), Marcus Slaughter (Real Madrid), Kevin Jones (Cleveland), Taylor Rochestie (Caja Laboral), and William Mosley (Biancoblu Basket Bologna, a second-division Italian team).  I’m sure that Mosley will be available again next year, but I know that McCalebb and Jones are on three-year contracts.  If only I had written this article sooner…

If I were the Lakers, I would hunt Sanders and Childress most intensely, with McGrady, Harrellson, Machado, Gee, and Brown all being targeted intensely.  I would also have the other players as back-up options while assuming that I would get at least one of the point guards and one of the wings.  I would look at McGuire and Horton if everything else failed, then proceed through the list of big men.

I apologize that I haven’t been posting recently.  I’ve had this post on my mind for a long time, but I just never could force myself to do it until today.  Once the season starts, I think that the posts will start becoming more frequent once again; I just don’t have a whole ton to write about right now.  Thank you for reading, and please comment.

Is Mark Cuban Implementing the Bullet Scenario?

I am a big fan of the Wages of Wins, Arturo Galletti in particular.  I have often visited Arturo’s old site at http://arturogalletti.wordpress.com to read articles he wrote before moving to the Wages of Wins full-time.  One of those that he wrote, in September of 2010, was called “Build Me a Winner Rev. 2.”  In it, he mentions the Bullet Scenario which he describes as:

 If you inherit a [bad team], do whatever possible to clear all the [junk] from your roster and cap prior to initiating a rebuild. While locked into the bad players from a previous administration hire the worst possible players to short term contracts and pair them with rookies to guarantee the most ping pong balls (referring to the NBA Draft Lottery).  We’ll call this one the Bullet Scenario.

Brackets are used to replace language that could be seen as offensive.  When I was looking at Wikipedia’s “List of 2012-13 NBA Season Transactions” page, my mind zeroed in on Dallas’s signing of O.J. Mayo, which was apparently finalized yesterday.  Immediately, a thought entered my brain-is Mark Cuban implementing the bullet scenario?

Think about.  In the past year, he lost out on both Dwight Howard and Deron Williams, the (should-have-been) marquee players in this year’s free agent class, after getting rid of Tyson Chandler for next-to-nothing to get them, Lamar Odom absolutely imploded, Dirk, who their team is believed to be built around, had a below-average season-again (check out www.thenbageek.com), and now Shawn Marion is the only remaining player with above-average Wins Produced who is still on the team, although the newly-acquired Elton Brand and Darren Collison are as well.

Checking out the NBA Trade Machine at http://espn.go.com, I see not a single Maverick with a contract lasting longer than two years.  They had three draft picks, the steal of the draft (Jae Crowder) and a couple of mediocre selections (Jared Cunningham and 27-year old Bernard James).  They traded Ian Mahinmi to Indiana, they amnestied Brendan Haywood.  Both of those players are centers who have/had four years left on their contract.  (Note that the Mahinmi deal was a sign-and-trade.)  They signed O.J. Mayo and Chris Kaman in free agency, both of whom are highly overrated.  This would not be so much of an issue if Kaman produced positive value and if Mayo wasn’t an inefficient volume scorer who doubles as a turnover fiend.  Although they did make an effort to reacquire borderline star Jason Kidd, their push to bring back Jason Terry (who played exactly 2,000 minutes at a WP48 rate of .099) was not exactly hard.

The way I see it, Dallas is trying to get younger and/or overpay for players on short-term contracts.  In exchange for Mahinmi, they received Darren Collison, a 25-year old above-average point guard, and Dahntay Jones, a recently dependable, now 31-year old role player at the 2.  Both players have expiring contracts in the $2-3 million range.  Kaman and Brand, who are thirty and thirty-three, respectively, have only one year left on their contracts.  (It should be noted that Elton Brand was still really good last year and the year before.)  As I was writing this article, I heard a rumor over on ESPN that Dallas may trade Dirk.  Good for them-Dirk is overrated, largely because of poor rebounding and defensive statistics.  It seems to help confirm my theory, too.

In the near future, I will be grading the amnesties that were made official this summer.  Thank you for reading, and please comment.

Adjusting Composite Score and Wins Produced

Yesterday, Arturo Galletti at wagesofwins.com adjusted Points over Par (PoP) so that it accounted for defense.  He included (almost) all players who had played at least 400 minutes for a single team.  This morning, I decided to convert the Points over Par stats into Wins Produced, of which it is a derivative.  (Please note that Points over Par has the league average set at 0.)  I have also revamped my Composite Score amalgamation so that it gives both Wins Produced and Win Shares equal weight.  The following chart has all players sorted by position, then Composite Score.  Before hand, I would like to note the averages for each statistic and position; even though Wins Produced’s average is always set at .099 for each position, it has been skewed with the adjustedments because I did not account for players who have played multiple positions.

PG: Adjusted Wins Proudced Per 48 .090, Win Shares Per 48 .087, Composite Score .088

SG: Adjusted Wins Produced Per 48 .104, Win Shares Per 48 .091, Composite Score .097

SF: Adjusted Wins Produced Per 48 .087, Win Shares Per 48 .082, Composite Score .085

PF: Adjusted Wins Produced Per 48 .091, Win Shares Per 48 .106, Composite Score .099

C: Adjusted Wins Produced Per 48 .095, Win Shares Per 48 .114, Composite Score .104

Here’s the giant table:

Player	Team	Position	AdjPop/48	AdjWP48	WS48	Composite
Tyson Chandler	NYK	C	7.9	0.353	0.224	0.289
Joakim Noah	CHI	C	4.6	0.247	0.214	0.230
Chris Andersen	DEN	C	5.5	0.276	0.181	0.228
Kevin Love	MIN	C	3.7	0.218	0.228	0.223
Dwight Howard	ORL	C	4.4	0.240	0.187	0.214
Andrew Bynum	LAL	C	4.3	0.237	0.189	0.213
Marcus Camby	HOU	C	5.2	0.266	0.131	0.199
Anderson Varejao	CLE	C	5.0	0.260	0.135	0.197
Kosta Koufos	DEN	C	4.1	0.231	0.151	0.191
Nikola Pekovic	MIN	C	3.3	0.205	0.172	0.189
DeAndre Jordan	LAC	C	3.4	0.208	0.155	0.182
Marcin Gortat	PHO	C	2.5	0.179	0.178	0.179
Greg Monroe	DET	C	2.9	0.192	0.161	0.177
Tiago Splitter	SAS	C	1.6	0.150	0.188	0.169
Kevin Garnett	BOS	C	1.5	0.147	0.181	0.164
Josh Harrellson	NYK	C	2.9	0.192	0.128	0.160
Elton Brand	PHI	C	1.2	0.138	0.172	0.155
Zaza Pachulia	ATL	C	1.7	0.154	0.144	0.149
Tim Duncan	SAS	C	1.3	0.141	0.156	0.148
Omer Asik	CHI	C	2.4	0.176	0.120	0.148
Amir Johnson	TOR	C	2.2	0.170	0.125	0.147
Marc Gasol	MEM	C	1.2	0.138	0.157	0.147
Greg Stiemsma	BOS	C	0.9	0.128	0.164	0.146
Carlos Boozer	CHI	C	0.0	0.099	0.185	0.142
Roy Hibbert	IND	C	1.3	0.141	0.143	0.142
Spencer Hawes	PHI	C	0.8	0.125	0.159	0.142
Ben Wallace	DET	C	3.3	0.205	0.068	0.137
Samuel Dalembert	HOU	C	1.1	0.134	0.136	0.135
Al Jefferson	UTA	C	-0.1	0.096	0.169	0.132
Brendan Haywood	DAL	C	1.2	0.138	0.126	0.132
Jared Jeffries	NYK	C	1.5	0.147	0.110	0.129
Chris Wilcox	FA	C	0.6	0.118	0.132	0.125
Ian Mahinmi	DAL	C	-0.2	0.093	0.153	0.123
JaVale McGee	DEN	C	0.9	0.128	0.112	0.120
Jordan Hill	LAL	C	0.6	0.118	0.121	0.120
Jason Thompson	SAC	C	0.9	0.128	0.107	0.117
Lavoy Allen	PHI	C	0.0	0.099	0.127	0.113
DeJuan Blair	SAS	C	-0.3	0.089	0.126	0.108
Shelden Williams	NJN	C	0.8	0.125	0.084	0.104
Enes Kanter	UTA	C	0.7	0.122	0.083	0.102
Aaron Gray	TOR	C	0.8	0.125	0.077	0.101
Derrick Favors	UTA	C	0.2	0.105	0.094	0.100
Chris Bosh	MIA	C	-1.8	0.041	0.153	0.097
Joel Anthony	MIA	C	-0.6	0.080	0.110	0.095
Andris Biedrins	GSW	C	0.0	0.099	0.088	0.094
David Lee	GSW	C	-0.9	0.070	0.116	0.093
Nikola Vucevic	PHI	C	-1.8	0.041	0.130	0.086
Jon Leuer	MIL	C	-1.6	0.048	0.123	0.085
Tyler Hansbrough	IND	C	-2.1	0.031	0.137	0.084
Emeka Okafor	NOH	C	-1.1	0.064	0.099	0.081
Nene Hilario	WSH	C	-1.3	0.057	0.104	0.081
Troy Murphy	LAL	C	-1.1	0.064	0.096	0.080
Kevin Seraphin	WSH	C	-1.4	0.054	0.095	0.074
DeMarcus Cousins	SAC	C	-1.1	0.064	0.076	0.070
Drew Gooden	MIL	C	-2.6	0.015	0.117	0.066
Ivan Johnson	ATL	C	-2.1	0.031	0.096	0.064
Jermaine O’Neal	BOS	C	-2.1	0.031	0.091	0.061
Nazr Mohammed	OKC	C	-2.1	0.031	0.088	0.060
Kurt Thomas	POR	C	-1.6	0.048	0.067	0.057
Robin Lopez	PHO	C	-2.9	0.006	0.106	0.056
Jason Smith	NOH	C	-3.2	-0.004	0.099	0.048
Ekpe Udoh	MIL	C	-2.4	0.022	0.072	0.047
Timofey Mozgov	DEN	C	-2.5	0.019	0.066	0.042
J.J. Hickson	POR	C	-2.3	0.025	0.055	0.040
Kendrick Perkins	OKC	C	-2.5	0.019	0.053	0.036
Chuck Hayes	SAC	C	-1.7	0.044	0.025	0.035
Tristan Thompson	CLE	C	-2.6	0.015	0.048	0.032
Andrea Bargnani	TOR	C	-4.5	-0.046	0.098	0.026
Bismack Biyombo	CHA	C	-2.2	0.028	0.019	0.024
Larry Sanders	MIL	C	-4.4	-0.042	0.070	0.014
Kenyon Martin	LAC	C	-4.1	-0.033	0.047	0.007
Darko Milicic	MIN	C	-4.2	-0.036	0.005	-0.016
Johan Petro	NJN	C	-5.3	-0.071	0.030	-0.021
Chris Kaman	NOH	C	-5.9	-0.091	0.020	-0.035
Byron Mullens	CHA	C	-6.4	-0.107	0.030	-0.038
Mehmet Okur	POR	C	-7.3	-0.136	-0.003	-0.069
Andray Blatche	WSH	C	-7.2	-0.133	-0.019	-0.076
Kenneth Faried	DEN	PF	6.7	0.314	0.219	0.267
Ryan Anderson	ORL	PF	5.1	0.263	0.228	0.245
Brandan Wright	DAL	PF	5.3	0.269	0.218	0.244
Serge Ibaka	OKC	PF	5.5	0.276	0.168	0.222
Paul Millsap	UTA	PF	3.8	0.221	0.183	0.202
Ersan Ilyasova	MIL	PF	3.4	0.208	0.184	0.196
Steve Novak	NYK	PF	2.7	0.186	0.177	0.181
Pau Gasol	LAL	PF	2.6	0.183	0.170	0.176
Blake Griffin	LAC	PF	1.5	0.147	0.181	0.164
Taj Gibson	CHI	PF	1.9	0.160	0.165	0.163
Matt Bonner	SAS	PF	2.3	0.173	0.144	0.158
Dante Cunningham	MEM	PF	2.2	0.170	0.143	0.156
Gustavo Ayon	NOH	PF	2.4	0.176	0.134	0.155
Kris Humphries	NJN	PF	2.7	0.186	0.115	0.150
Ed Davis	TOR	PF	2.2	0.170	0.119	0.144
Reggie Evans	LAC	PF	3.1	0.199	0.078	0.138
LaMarcus Aldridge	POR	PF	-0.1	0.096	0.178	0.137
Thaddeus Young	PHI	PF	0.0	0.099	0.171	0.135
Luc Richard Mbah a Moute	MIL	PF	1.5	0.147	0.113	0.130
Nick Collison	OKC	PF	0.9	0.128	0.127	0.127
Jonas Jerebko	DET	PF	1.3	0.141	0.107	0.124
David West	IND	PF	0.4	0.112	0.132	0.122
Josh Smith	ATL	PF	-0.3	0.089	0.135	0.112
Craig Smith	POR	PF	0.3	0.109	0.114	0.111
Trevor Booker	WSH	PF	0.5	0.115	0.106	0.111
Amare Stoudemire	NYK	PF	-0.3	0.089	0.131	0.110
Dirk Nowitzki	DAL	PF	-1.7	0.044	0.175	0.110
Udonis Haslem	MIA	PF	0.3	0.109	0.104	0.106
Josh McRoberts	LAL	PF	0.6	0.118	0.090	0.104
Carl Landry	NOH	PF	-0.3	0.089	0.115	0.102
Dominic McGuire	GSW	PF	0.7	0.122	0.070	0.096
Vladimir Radmanovic	ATL	PF	-0.6	0.080	0.100	0.090
James Johnson	TOR	PF	0.3	0.109	0.069	0.089
Brandon Bass	BOS	PF	-1.6	0.048	0.123	0.085
Jason Maxiell	DET	PF	0.2	0.105	0.060	0.083
Louis Amundson	IND	PF	-0.3	0.089	0.073	0.081
Marreese Speights	MEM	PF	-1.4	0.054	0.106	0.080
Lance Thomas	NOH	PF	-0.9	0.070	0.080	0.075
Anthony Tolliver	MIN	PF	-0.6	0.080	0.062	0.071
Channing Frye	PHO	PF	-1.9	0.038	0.100	0.069
Derrick Williams	MIN	PF	-1.5	0.051	0.066	0.058
Donte Greene	SAC	PF	-2.4	0.022	0.055	0.038
Jan Vesely	WSH	PF	-2.3	0.025	0.049	0.037
D.J. White	CHA	PF	-2.1	0.031	0.041	0.036
Patrick Patterson	HOU	PF	-3.2	-0.004	0.066	0.031
Al Harrington	DEN	PF	-3.8	-0.023	0.084	0.030
Hakim Warrick	PHO	PF	-3.4	-0.010	0.064	0.027
Markieff Morris	PHO	PF	-4.0	-0.030	0.073	0.022
Antwan Jamison	CLE	PF	-4.3	-0.039	0.081	0.021
Luis Scola	HOU	PF	-4.8	-0.055	0.081	0.013
Glen Davis	ORL	PF	-4.8	-0.055	0.053	-0.001
Lamar Odom	DAL	PF	-4.6	-0.049	0.019	-0.015
Boris Diaw	SAS	PF	-4.2	-0.036	-0.001	-0.019
Samardo Samuels	CLE	PF	-5.5	-0.078	0.029	-0.024
Tyrus Thomas	CHA	PF	-6.6	-0.113	-0.034	-0.074
Shawne Williams	POR	PF	-6.7	-0.116	-0.040	-0.078
Chris Paul	LAC	PG	8.2	0.363	0.270	0.316
Steve Nash	PHO	PG	6.0	0.292	0.159	0.225
Kyle Lowry	HOU	PG	4.8	0.253	0.165	0.209
Derrick Rose	CHI	PG	3.1	0.199	0.217	0.208
Rajon Rondo	BOS	PG	4.9	0.257	0.116	0.186
Stephen Curry	GSW	PG	2.9	0.192	0.144	0.168
Goran Dragic	HOU	PG	2.8	0.189	0.141	0.165
Jason Kidd	DAL	PG	3.6	0.215	0.100	0.157
Jose Calderon	TOR	PG	2.6	0.183	0.131	0.157
Mike Conley	MEM	PG	1.9	0.160	0.138	0.149
Russell Westbrook	OKC	PG	0.7	0.122	0.172	0.147
Jeremy Lin	NYK	PG	1.5	0.147	0.141	0.144
Mario Chalmers	MIA	PG	2.0	0.163	0.117	0.140
Tony Parker	SAS	PG	0.2	0.105	0.161	0.133
Ty Lawson	DEN	PG	0.9	0.128	0.137	0.132
Darren Collison	IND	PG	1.5	0.147	0.108	0.128
Brandon Jennings	MIL	PG	1.0	0.131	0.112	0.122
Jerryd Bayless	TOR	PG	0.3	0.109	0.133	0.121
Jeff Teague	ATL	PG	0.7	0.122	0.120	0.121
Kyrie Irving	CLE	PG	0.4	0.112	0.128	0.120
Isaiah Thomas	SAC	PG	0.7	0.122	0.115	0.118
Jarrett Jack	NOH	PG	0.4	0.112	0.115	0.113
Ricky Rubio	MIN	PG	1.5	0.147	0.073	0.110
Devin Harris	UTA	PG	0.2	0.105	0.114	0.110
Jameer Nelson	ORL	PG	0.5	0.115	0.100	0.108
Rodrigue Beaubois	DAL	PG	0.4	0.112	0.101	0.106
John Wall	WSH	PG	1.6	0.150	0.062	0.106
John Lucas	CHI	PG	-0.7	0.076	0.131	0.104
Chris Duhon	ORL	PG	1.1	0.134	0.062	0.098
Andre Miller	DEN	PG	0.2	0.105	0.088	0.097
Jrue Holliday	PHI	PG	0.3	0.109	0.083	0.096
Beno Udrih	MIL	PG	0.4	0.112	0.076	0.094
Nate Robinson	GSW	PG	-0.3	0.089	0.097	0.093
Jordan Farmar	NJN	PG	-0.9	0.070	0.105	0.088
Deron Williams	NJN	PG	-1.1	0.064	0.108	0.086
Greivis Vasquez	NOH	PG	-0.3	0.089	0.075	0.082
C.J. Watson	CHI	PG	-1.8	0.041	0.113	0.077
Shelvin Mack	WSH	PG	0.4	0.112	0.038	0.075
A.J. Price	IND	PG	-0.3	0.089	0.056	0.073
Earl Watson	UTA	PG	-0.1	0.096	0.017	0.056
Raymond Felton	POR	PG	-1.6	0.048	0.056	0.052
Charles Jenkins	GSW	PG	-1.7	0.044	0.056	0.050
Jannero Pargo	ATL	PG	-2.6	0.015	0.084	0.050
Jamal Crawford	POR	PG	-2.9	0.006	0.070	0.038
Steve Blake	LAL	PG	-2.4	0.022	0.042	0.032
Derek Fisher	OKC	PG	-2.3	0.025	0.032	0.029
Ronnie Price	PHO	PG	-2.0	0.035	0.018	0.026
Jose Juan Barea	MIN	PG	-2.7	0.012	0.038	0.025
D.J. Augustin	CHA	PG	-2.5	0.019	0.025	0.022
Kemba Walker	CHA	PG	-2.6	0.015	0.021	0.018
Keyon Dooling	BOS	PG	-3.1	-0.001	0.022	0.011
Brandon Knight	DET	PG	-4.2	-0.036	0.020	-0.008
Reggie Jackson	OKC	PG	-4.1	-0.033	0.010	-0.011
Sebastian Telfair	PHO	PG	-5.8	-0.087	0.038	-0.025
Norris Cole	MIA	PG	-5.2	-0.068	0.008	-0.030
Jimmer Fredette	SAC	PG	-5.5	-0.078	0.015	-0.031
Toney Douglas	NYK	PG	-8.7	-0.181	-0.057	-0.119
Jeremy Pargo	MEM	PG	-8.8	-0.184	-0.085	-0.134
LeBron James	MIA	SF	8.3	0.366	0.292	0.329
Kawhi Leonard	SAS	SF	6.4	0.305	0.165	0.235
James Harden	OKC	SF	4.3	0.237	0.226	0.232
Kevin Durant	OKC	SF	3.9	0.224	0.233	0.229
Andre Iguodala	PHI	SF	4.9	0.257	0.158	0.207
Marvin Williams	ATL	SF	3.5	0.212	0.153	0.182
Mike Dunleavy	MIL	SF	3.2	0.202	0.154	0.178
Matt Barnes	LAL	SF	3.2	0.202	0.130	0.166
Nicolas Batum	POR	SF	2.6	0.183	0.135	0.159
Danilo Gallinari	DEN	SF	1.9	0.160	0.156	0.158
Jared Dudley	PHO	SF	3.0	0.195	0.120	0.158
Luol Deng	CHI	SF	1.4	0.144	0.134	0.139
Gerald Wallace	NJN	SF	1.6	0.150	0.121	0.136
Chase Budinger	HOU	SF	1.8	0.157	0.112	0.134
Danny Granger	IND	SF	0.5	0.115	0.152	0.134
Derrick Brown	CHA	SF	2.9	0.192	0.074	0.133
Paul Pierce	BOS	SF	0.4	0.112	0.149	0.130
Joe Johnson	ATL	SF	0.6	0.118	0.131	0.125
Shane Battier	MIA	SF	1.7	0.154	0.093	0.123
Shawn Marion	DAL	SF	1.5	0.147	0.093	0.120
Chandler Parsons	HOU	SF	1.5	0.147	0.092	0.120
Gerald Green	NJN	SF	1.3	0.141	0.097	0.119
Gordon Hayward	UTA	SF	0.7	0.122	0.112	0.117
Alonzo Gee	CLE	SF	1.5	0.147	0.073	0.110
Trevor Ariza	NOH	SF	1.4	0.144	0.075	0.110
Dorell Wright	GSW	SF	0.7	0.122	0.093	0.107
Quincy Pondexter	MEM	SF	0.4	0.112	0.082	0.097
Rudy Fernandez	DEN	SF	0.4	0.112	0.080	0.096
Carlos Delfino	MIL	SF	0.8	0.125	0.067	0.096
Evan Turner	PHI	SF	0.6	0.118	0.072	0.095
Richard Jefferson	GSW	SF	0.1	0.102	0.088	0.095
Carmelo Anthony	NYK	SF	-1.8	0.041	0.149	0.095
Martell Webster	MIN	SF	0.2	0.105	0.071	0.088
Dahntay Jones	IND	SF	-0.7	0.076	0.097	0.087
Rudy Gay	MEM	SF	-0.9	0.070	0.103	0.087
Vince Carter	DAL	SF	-0.4	0.086	0.084	0.085
Quentin Richardson	ORL	SF	-0.6	0.080	0.080	0.080
Corey Brewer	DEN	SF	-0.6	0.080	0.075	0.077
Tyreke Evans	SAC	SF	-0.7	0.076	0.063	0.070
Al-Farouq Aminu	NOH	SF	0.1	0.102	0.036	0.069
Anthony Parker	CLE	SF	-0.6	0.080	0.052	0.066
Grant Hill	PHO	SF	-1.0	0.067	0.064	0.065
Mickael Pietrus	BOS	SF	-1.6	0.048	0.078	0.063
Omri Casspi	CLE	SF	-1.0	0.067	0.052	0.059
Anthony Morrow	NJN	SF	-1.6	0.048	0.067	0.057
Metta World Peace	LAL	SF	-1.8	0.041	0.070	0.056
Bill Walker	NYK	SF	-2.0	0.035	0.070	0.052
Chris Singleton	WSH	SF	-1.3	0.057	0.042	0.050
Hedo Turkoglu	ORL	SF	-2.0	0.035	0.064	0.049
Francisco Garcia	SAC	SF	-1.7	0.044	0.048	0.046
J.R. Smith	NYK	SF	-2.8	0.009	0.079	0.044
Tayshaun Prince	DET	SF	-1.9	0.038	0.046	0.042
Linas Kleiza	TOR	SF	-2.4	0.022	0.059	0.040
Caron Butler	LAC	SF	-3.1	-0.001	0.067	0.033
Richard Hamilton	CHI	SF	-3.5	-0.014	0.070	0.028
Damien Wilkens	DET	SF	-2.3	0.025	0.022	0.024
John Salmons	SAC	SF	-2.2	0.028	0.017	0.023
C.J. Miles	UTA	SF	-3.7	-0.020	0.062	0.021
James Anderson	SAS	SF	-3.2	-0.004	0.041	0.019
Wesley Johnson	MIN	SF	-2.5	0.019	0.014	0.016
Klay Thompson	GSW	SF	-4.0	-0.030	0.051	0.011
DeShawn Stevenson	NJN	SF	-2.1	0.031	-0.011	0.010
Corey Maggette	CHA	SF	-3.7	-0.020	0.035	0.008
Josh Howard	UTA	SF	-3.6	-0.017	0.024	0.004
Michael Beasley	MIN	SF	-4.1	-0.033	0.030	-0.001
Nick Young	LAC	SF	-4.6	-0.049	0.041	-0.004
Rashard Lewis	WSH	SF	-3.8	-0.023	0.011	-0.006
Stephen Jackson	SAS	SF	-4.4	-0.042	0.003	-0.020
Rasual Butler	FA	SF	-4.1	-0.033	-0.028	-0.030
Ryan Gomes	LAC	SF	-4.5	-0.046	-0.019	-0.032
Austin Daye	DET	SF	-6.8	-0.120	-0.056	-0.088
Manu Ginobli	SAS	SG	6.6	0.311	0.242	0.277
Dwayne Wade	MIA	SG	4.5	0.244	0.224	0.234
Kyle Korver	CHI	SG	2.4	0.176	0.169	0.173
Mike Miller	MIA	SG	3.7	0.218	0.117	0.167
Paul George	IND	SG	2.8	0.189	0.142	0.166
Tony Allen	MEM	SG	3.0	0.195	0.135	0.165
Lou Williams	PHI	SG	1.3	0.141	0.179	0.160
Ray Allen	BOS	SG	2.3	0.173	0.145	0.159
Jodie Meeks	PHI	SG	2.7	0.186	0.131	0.158
Thabo Sefolosha	OKC	SG	3.0	0.195	0.117	0.156
George Hill	IND	SG	2.1	0.167	0.143	0.155
Ronnie Brewer	CHI	SG	2.8	0.189	0.110	0.150
Brandon Rush	GSW	SG	3.4	0.208	0.087	0.148
Danny Green	SAS	SG	2.0	0.163	0.124	0.144
Delonte West	DAL	SG	2.6	0.183	0.095	0.139
Rodney Stuckey	DET	SG	0.7	0.122	0.145	0.133
Wesley Matthews	POR	SG	1.1	0.134	0.109	0.122
Ramon Sessions	LAL	SG	0.7	0.122	0.121	0.121
Kobe Bryant	LAL	SG	0.0	0.099	0.141	0.120
Landry Fields	NYK	SG	1.5	0.147	0.090	0.119
J.J. Redick	ORL	SG	-0.1	0.096	0.140	0.118
Tracy McGrady	ATL	SG	1.3	0.141	0.086	0.113
Jason Terry	DAL	SG	0.9	0.128	0.096	0.112
Kevin Martin	HOU	SG	-0.2	0.093	0.129	0.111
Arron Afflalo	DEN	SG	0.0	0.099	0.118	0.109
Chauncey Billups	LAC	SG	-0.6	0.080	0.136	0.108
Luke Ridnour	MIN	SG	0.5	0.115	0.094	0.105
Shaun Livingston	MIL	SG	1.3	0.141	0.067	0.104
Marcus Thornton	SAC	SG	-0.1	0.096	0.108	0.102
Courtney Lee	HOU	SG	0.2	0.105	0.096	0.101
Willie Green	ATL	SG	-0.4	0.086	0.111	0.099
Avery Bradley	BOS	SG	0.4	0.112	0.067	0.089
Reggie Williams	CHA	SG	1.9	0.160	0.017	0.089
Raja Bell	UTA	SG	0.0	0.099	0.073	0.086
Mo Williams	LAC	SG	-0.9	0.070	0.101	0.086
O.J. Mayo	MEM	SG	-0.9	0.070	0.095	0.083
Daequan Cook	OKC	SG	-0.7	0.076	0.072	0.074
Gary Forbes	TOR	SG	-0.3	0.089	0.059	0.074
Sundiata Gaines	NJN	SG	0.1	0.102	0.046	0.074
Jason Richardson	ORL	SG	-1.1	0.064	0.079	0.071
Roger Mason	WSH	SG	-0.8	0.073	0.060	0.067
Marco Belinelli	NOH	SG	-1.2	0.060	0.065	0.063
Alec Burks	UTA	SG	-0.9	0.070	0.055	0.063
Randy Foye	LAC	SG	-1.8	0.041	0.080	0.061
Gerald Henderson	CHA	SG	0.2	0.105	0.009	0.057
Iman Shumpert	NYK	SG	-1.9	0.038	0.069	0.053
Daniel Gibson	CLE	SG	-0.8	0.073	0.032	0.053
Monta Ellis	MIL	SG	-1.6	0.048	0.055	0.051
Kirk Hinrich	ATL	SG	-1.9	0.038	0.059	0.048
Gary Neal	SAS	SG	-2.6	0.015	0.080	0.048
Wayne Ellington	MIN	SG	-1.9	0.038	0.039	0.038
MarShon Brooks	NJN	SG	-1.7	0.044	0.024	0.034
Shannon Brown	PHO	SG	-2.7	0.012	0.045	0.029
Jordan Crawford	WSH	SG	-2.9	0.006	0.033	0.019
Leandro Barbosa	IND	SG	-3.5	-0.014	0.051	0.019
DeMar DeRozan	TOR	SG	-3.4	-0.010	0.043	0.016
Xavier Henry	NOH	SG	-3.0	0.003	0.027	0.015
Ben Gordon	DET	SG	-3.3	-0.007	0.035	0.014
Michael Redd	PHO	SG	-4.3	-0.039	0.049	0.005
Matt Carroll	CHA	SG	-2.0	0.035	-0.026	0.004

Harebrained Solutions to the Tanking Problem

Tanking is a huge problem in the NBA.  When teams realize that they have little to no shot at making the play-offs, they rest their starters and treat their fans to drudgery and an almost-guaranteed loss (unless the other team is also tanking).  However, as illustrated by Arturo Gallteti, tanking does not matter that much as talent is always available late in (or even after) the draft.  Of course, basketball front offices haven’t jumped on the “Moneyball” bandwagon to the degree that baseball teams have.  Until they do, this will continue to happen.  Luckily, there is hope; I have a handful of ways to end tanking.  Unfortunately, most of them are rather crazy.

• Change the lottery odds

If teams had less of a chance at obtaining the No.1 pick, then there would be less impetus to tank and more impetus to “gain momentum for next year”.  Maybe we should set the odds of the weakest team getting the pick at 20% instead of 25%, and making the odds the same for teams 10-14.

•   Have the lottery pick more teams

This means that teams cannot just be guaranteed a Top-4 or a Top-5 selection for simply being the weakest team in the league.  No longer will a team jockey for draft positioning because the odds will be so similar.  Although this indirectly increases the odds that the worst team will receive a higher selection, it also increases a chance that a team within reach of the play-offs will receive a higher selection.

• Televise the ping-pong balls rolling off the chute

This is more for peace-of-mind than anything else, but it does mean that the results will be more concrete.  We will not have to worry about any “Patrick Ewing” scenarios, so teams cannot rally for selection in a potentially-rigged election.

• Get Arturo Galletti a job as a sportswriter at ESPN

On the surface, this one makes no sense.  However, Arturo has been a very influential member of the Wages of Wins Journal, and he has used mathematics to prove or disprove many notions, including the importance of a high pick after the first overall selection.  Furthermore, just imagine the epic ensuing PER vs. Wins Produced war!

• Instiute the “Entertaining-As-HEdoublehockeysticks” tournament in some form

This is the brainchild of ESPN/Grantland’s Bill Simmons.  He proposes that seeds 7-15 play a double-elimination tournament for the last play-off spots, giving seeds 1-6 a two week break.  Although I am not falling all over this proposal, I do want something along those lines coming into existence.  My idea is a tiered tournament with best-of-7 elimination rounds not occuring until the Conference Semifinals.  More on that in a later post once I get all the details sorted out.

• Give a larger slice of the Revenue Sharing pie to play-off teams

This one won’t happen for several years since we just got a new CBA signed, but there obviously is not enough incentive for teams to fight for the final spots.  The NBA has ultimately become a Socialist organization where everyone is looking out for the little guy.  If this continues to happen and attendence doesn’t suffer, what incentive will there be for anyone to try to make the play-offs?  This would eventually lead to the NBA being knocked off its de facto pedestal atop the basketball world.  Possibly save the Olympics (I have no numbers for either NBA or Olympic viewership), what other basketball competition draws more fans?  This could end if playing well is not amply rewarded.

Tanking is a serious problem that leads to many NBA fans losing their hair.  I believe (but have no proof) that these proposals might help end this travesty.  (P.S. I hate that word, but I’ll use it anyway because I can’t think of a better one.)