An Instant Analysis of the 2010 NCAA Tournament

Today’s guest post – and instant analysis of the 2010 NCAA Tournament (posted within hours of the brackets being announced) — is yet another excellent offering from Erich Doerr .  Erich first contacted me prior to the 2006 NBA Draft with a statistical preview in hand.  Each subsequent year has seen improvement in the depth and breadth of his analysis. Outside of his basketball writing, Erich does consulting work for major software products by day and has started a fledgling sports-themed Open Source software initiative by night.  

Sticking to what works, I’m here to share a statistical preview of the NCAA tournament.  The text may appear familiar, but the numbers are fully updated and relevant to the 2010 NCAA tournament.

Figure One: Bracket based on Pomeroy Numbers

Figure Two: Bracket based on Sagarin Numbers

Again in 2010, I am relying on the two strongest public NCAA metrics in the Sagarin Ratings and Ken Pomeroy’s Pythagorean Ratings.  Statistics used by the Wages of Wins parallel Pomeroy’s approach, as both build off of offensive and defensive efficiency. 

Figure Three: Teams Probability Tables

 Since top seeds represent the best teams in the land, this approach will appear to heavily favor those teams due to their quality and favorable draw.  The final results here attempt to predict the statistically most probable brackets, which are not necessarily the picks most likely to win an office pool.

By the numbers, who has the right to gripe?  Well, both metrics agree that Kentucky has a difficult road ahead.   Besides being the weakest of the #1’s, they have a brutal bracket that reduces their Final Four chances by 5-10%.  The sweetest draw belongs to the Syracuse Orange. 

For those looking to call that historic upset, both Syracuse and Kentucky are about twice as likely to lose their first round matches as Kansas & Duke.

Stepping back, both the Sagarin & Pomeroy brackets come out with similar predictions, differing in 6 out of 64 matchups.  Nevertheless, both measures see Kansas & Duke in the championship game, with Sagarin’s Predictor preferring Kansas & Pomeroy’s Pythag backing Duke. 

The tables linked above also provide odds by conference, seed, and region.  Either the ACC and Big 12 win the title in 60% of all scenarios.  Adding in the Big Ten and Big East accounts for 90% of all championships, leaving all other conferences a 10% NCAA title chance, combined.  See the tables above and comment on your own favorite observation.

Finally, please note the Wages of Wins Journal does not condone gambling.  These picks should perform better than average overall, but typically variance rules these types of pools.  In general, an entry in a bracket pool has a 1/N chance of winning, where N = number of entries.  Due to the layout of the NCAA tournament, it is highly improbable that a good set of picks could raise the pre-ante odds to even 2/N.  Generally, there may be more gains to be had in shopping for the right office pool (i.e. the one containing the least informed participants) or game theory analysis if one was so interested in improving their office pool odds.  Always note that past returns do not guarantee future performance.

For readers that would like to run their own simulations, I have made the tool I use to generate these odds available at www.xlssports.com.  Feel free to download the Excel file and adjust any numbers as you see fit.  Furthermore, advanced users should be able to modify the tool for other statistical measures.   Enjoy the tournament, and best of luck.

- Erich Doerr 

Notes:
Sagarin & Pomeroy stats are as of March 14th
For simplicity I assumed Arkansas Pine Bluff would lose in the play-in game

No injury information is taken into account in this analysis. 

The WoW Journal Comments Policy

Kobe Makes Pau Gasol Unhappy

According to Adrian Wojnarowski (of Yahoo! Sports), Pau Gasol isn’t happy.  Gasol seems to think the Kobe Bryant is taking too many shots.  Or more specifically, Gasol is not taking enough.

The Focus on Scoring Leads to Frustration

When we look at the data, we see that Gasol has a point.  His per-game scoring average is at an all-time low. Kobe Bryant is also taking nearly 10 more field goal attempts per game than Gasol.  And since scoring is the primary focus in the NBA, Gasol is now bemoaning his lack of touches.

Of course, one wonders if NBA players shouldn’t be able to focus on more than scoring.  After all, a player’s impact on wins is about more than his points scored per game.  As Table One reveals, when we look at everything in the box score, Gasol should be very happy.  His WP48 [Wins Produced per 48 minutes] currently stands at 0.287.  In contrast, Kobe’s mark is only 0.185. So when we look beyond scoring, Gasol is doing more.  Yet, Gasol is unhappy.

Table One: The LA Lakers after 66 games in 2009-10

This story highlights a problem with player evaluation in the NBA.  Because so much attention is paid to scoring (and we see this when we look at the pay of free agents, voting for the All-Rookie team, allocation of minutes, and the NBA draft) players tend to obsess on their own shot attempts.  And when those shots don’t happen with the frequency the players prefer, unhappiness and resentment is the result.

The obvious solution to this problem is to teach players like Gasol that their impact goes beyond scoring.  Coaches often try and teach this lesson.  Much of what the players hear — and how players are rewarded — contradicts this story.  Consequently, Kobe is considered one of the greatest players to ever play the game. And Gasol keeps expressing his frustration.

The Greatness of Kobe

Recently a few of the comments in this forum have once again focused on the issue of Kobe’s greatness.  Many people who comment here (and this is not a surprise), question the notion that Kobe is equal – or even close – to Michael Jordan.  Some have also wondered where Kobe ranks in NBA history.

In an effort to address that issue, let’s consider Table Two.

Table Two: The Best Performance by a Shooting Guard from 1977-78 to 2008-09

Table Two reports the 50 best performances – in terms of Wins Produced — by a shooting guard in the since the 1977-78 season.  As one can see, the top seven slots in the list are held by Michael Jordan.  And Kobe’s very best season doesn’t appear until the 27^th slot.   In all, Kobe appears three times on the list while MJ shows up 10 times.  So Jordan was much better than Kobe.  And really, the difference is very large (a point made back in 2007).

Kobe is also not number two on the list.  Clyde Drexler appears four times before Kobe shows up the first time.  Overall, eight of Drexler’s seasons rank in the top 50 overall. 

When we look at career Wins Produced, we see – as the following list indicates — that Kobe currently ranks 4^th among shooting guards who started their career after 1977. 

• Michael Jordan: 283.6
• Clyde Drexler: 222.8
• Reggie Miller: 162.9
• Kobe Bryant: 149.0

Again, the difference between Kobe and MJ is huge (and Kobe is never going to close the difference).  It does seem likely, though, that Kobe will surpass Reggie Miller.  But he needs to produce more than 60 additional wins to catch Drexler.  And Kobe is already 31 years old.  Yes, MJ did produce more than 70 wins after the age of 31.  But as has already been noted, Kobe is no MJ; and it doesn’t look like Kobe is Clyde the Glide either.

- DJ

The WoW Journal Comments Policy

Our research on the NBA was summarized HERE.

The Technical Notes at wagesofwins.com provides substantially more information on the published research behind Wins Produced and Win Score

Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:

Simple Models of Player Performance

Wins Produced vs. Win Score

What Wins Produced Says and What It Does Not Say

Introducing PAWSmin — and a Defense of Box Score Statistics

Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.

Assigning Blame in Atlanta and Boston

After 41 games the Atlanta Hawks and Boston Celtics posted the following marks:

Atlanta: 27 wins, 5.9 efficiency differential (offensive efficiency – defensive efficiency)

Boston: 28 wins, 6.4 differential

In the 22 games since this midpoint (as of Wednesday night), both teams have – as the following numbers indicate – performed somewhat worse. 

Atlanta: 13 wins, 2.7 differential

Boston: 12 wins, 0.7 differential

Clearly both teams have declined relative to what we saw after 41 games.  So what’s the problem?

A non-statistical approach might look at factors like teamwork, defensive intensity, and chemistry.  Player statistics, though, allow us be a bit more definitive.  Specifically, statistics allow us to separate a player from his teammates.  And that allows us to assign responsibility for the outcome we observe.

Let’s start with the Atlanta Hawks.  Tables One-Two provides two perspectives on this team.

Tables One-Two: The Atlanta Hawks after 63 games in 2009-10

Table One reports what the players for the Hawks have done across the entire season.  In addition, we see what the veteran players did in 2008-09.  As one can see, the story told since early in the season remains the same. Josh Smith – who has improved with respect to shooting efficiency, rebounds, assists, steals, and blocked shots – has increased his WP48 [Wins Produced per 48 minutes] from 0.116 to 0.284.

And as Table Two reports, Josh Smith is even better in the second half.  Since the midpoint, Josh Smith has posted a 0.305 WP48.  When we look at the remainder of the roster, for the most part we don’t see substantial changes in Wins Produced.  The lone exceptions are seen with respect to the production of Al Horford and Mike Bibby.  It appears those two players are the most responsible for the Hawks second-half decline.

Now let’s look at the Celtics. Unlike the Hawks – who have improved in 2009-10 (thanks primarily to Josh Smith) – the Celtics are winning less than their 2008-09 performance would suggest.  The veterans performance in 2008-09 suggests the Celtics should have already won 52 games this season.  Much of the twelve game drop-off – as Table Three indicates – can be linked to the production of Ray Allen, Eddie House, and Rasheed Wallace (three relatively old players).

Tables Three-Four: The Boston Celtics after 63 games in 2009-10

Since the midpoint, this decline has quickened.  Again, the team has only posted a 12-10 record across the past 22 games.  Virtually all of these wins can be linked to the play of Rajon Rondo, Kevin Garnett, and surprisingly, Ray Allen (who is not quite what he was last year, but is now closer).  Although this trio has played well, Paul Pierce, Kendrick Perkins, and Rasheed Wallace are offering much less. 

One could speculate as to why we see such declines.  Possible explanations include the problems of a small sample (although 22 games isn’t that small of a sample), injury, or age.  The best source for such explanations should be the coaches watching the players every day.  Answering the question “how productive are these players?”  serves to focus the attention of these coaches.

In talking to people in sports, though, one senses an unwillingness to be so focused.  In other words, coaches and teammates seem unwiling to single out individual players. It simply seems easier for people associated with the Celtics to say “well, we need to do better as a team.”  This approach, though, isn’t very helpful.   The data suggests the problems for this team are really linked to just three players (and for the Hawks it’s just two players).  For the Celtics to return to what we saw in the first half of the season it seems likely that the three players identified – and especially Paul Pierce — are going to have to produce more.  Yes, Paul Pierce really deserves the most blame for what we have seen in Boston since the midpoint.

- DJ

The WoW Journal Comments Policy

Our research on the NBA was summarized HERE.

The Technical Notes at wagesofwins.com provides substantially more information on the published research behind Wins Produced and Win Score

Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:

Simple Models of Player Performance

Wins Produced vs. Win Score

What Wins Produced Says and What It Does Not Say

Introducing PAWSmin — and a Defense of Box Score Statistics

Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.

The Wall Street Journal Looks for Teams that Make the Nets Look Good

David Biderman – of the Wall Street Journal – reports today on The Teams that Make the Nets Look Good.

Biderman’s story is based on the following question he asked me to consider:  Across the past 10 years, have their been other teams in professional sports as bad as the Nets?

To answer this question – as Biderman reports — I looked at how many standard deviations each team is below the average performance in the league.  For example, New Jersey’s current winning percentage of 0.111 is 2.4 standard deviations below the average mark of 0.500.  As Biderman notes, this marks actually trails the performance of the Detroit Tigers in 2003, the Atlanta Thrashers of 1999-00, the Kansas City Royals of 2005, and the Detroit Lions of 2008 (yes, two Detroit teams make this list).  So the Nets – by this measure – are indeed the worst team in the NBA across the past 10 years.  But they are not the worst team in professional sports.

The focus on standard deviations is necessary if one wishes to make comparisons across sports.  If one wishes to focus solely on the NBA, though, one can use a different measure that makes the Nets look slightly better.  New Jersey currently has an efficiency differential (offensive efficiency minus defensive efficiency) of -10.88.  Since 1973-74 (the first year one can calculate efficiency differential) the following teams have posted a differential below what we see from the current Nets.

• Dallas Mavericks [1992-93]: -14.70
• Denver Nuggets [1997-98]: -12.63
• LA Clippers [1999-2000]: -11.89
• Vancouver Grizzlies [1996-97]: -11.17
• Houston Rockets [1982-83]: -10.95

If we maintain our focus on the just the last 10 years, we see that only the Clippers of 1999-00 are doing worse.  So across the last decade – if we focus on efficiency differential – the Nets are only the second worse team in the NBA.

One might wonder how the Nets fell so far.  To address this issue we need to consider the performance of the individual players.  As Table One reports, the Nets do have four above average players this season (Brook Lopez, Kris Humphries, Josh Boone, Courtney Lee). None of these players, though, are far above the average WP48 [Wins Produced per 48 minutes] mark of 0.100.  And nine players employed by the Nets this season have posted WP48 marks in the negative range.

Table One: The New Jersey Nets after 63 games in 2009-10

If we look at how the veterans this team has employed performed last year, we see this team shouldn’t be this bad. The team’s current efficiency differential (and Wins Produced) is consistent with a team that should have already won 10 games (so the Nets won-loss record is a bit misleading).  The 2008-09 performance of these veterans, though, suggests this team should have already won 20 games. 

When we look at the individual players we see that much of the team’s decline is tied to the play of Rafer Alston, Devin Harris, and Keyon Dooling.  What do these players have in common?  All three log time at the point guard position.  So that one position has been the reason why the Nets have moved from “bad” to “all-time horrible.”

Let me close by noting – as the following list indicates — that this is the fifth time Biderman and the Wall Street Journal have referenced my work in recent months. 

Shooting Guards are Getting ‘Short’-Changed

After Age 25, It’s All Downhill for NBA Players

In the NHL, More Dollars Equals More Wins

Few Starting Lineups Could Top These Celtics

The sports section at the Wall Street Journal has focused tremendously on how numbers inform our understanding of sports.  So if you are interested in this aspect of sports (and since you are here I suspect you are), you might want to think about reading the WSJ sports section on a regular basis.

And one last note (this just came in as I wrote this post)… Martin Schmidt says a copy of Stumbling on Wins arrived in the mail today (I haven’t checked my mail yet).  The book is officially released on March 26, so it won’t be long until everyone is able to read our latest. 

- DJ

The WoW Journal Comments Policy

Our research on the NBA was summarized HERE.

The Technical Notes at wagesofwins.com provides substantially more information on the published research behind Wins Produced and Win Score

Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:

Simple Models of Player Performance

Wins Produced vs. Win Score

What Wins Produced Says and What It Does Not Say

Introducing PAWSmin — and a Defense of Box Score Statistics

Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.

Imagine Hasheem Thabeet using WP48 to defend his NBA career

Here is a fictional story – by Jonathan Bertag – about the early professional career of Hasheem Thabeet.  Bertag tells the tale of Thabeet coming to the Grizzlies, meeting and becoming friends with Allen Iverson, and ultimately being sent to the Dakota Wizards.  Along the way, we see the hero of the story (Thabeet) attempt to defend his production via Wins Produced.  He even defends WP48 when his coach claims it’s not a sound method.

Again, the story is fictional.  But I found it interesting to see someone note that WP48 [Wins Produced per 48 minutes] does indicate that Thabeet is not quite as bad as people might think.  As the following table indicates, Thabeet is an average NBA center (WP48 of 0.100).  In fact, he is one of only three players on the Grizzlies who have been above average this year (of those who have played more than five minutes).

Table One: The Memphis Grizzlies after 64 games in 2009-10

Unfortunately, Thabeet is stuck behind Marc Gasol on the Memphis depth chart.  And Gasol has joined with Zach Randolph to produce around 70% of this team’s Wins Produced.  These two players are also entirely responsible for this team’s improvement over what we would expect given the performance of this team’s veterans in 2008-09.  In fact, once we get past Gasol, Randolph, and Thabeet, the remaining players on this team are on pace to produce fewer than 10 wins this season. 

Since Rudy Gay is part of this list of remaining players, it suggests Memphis should think again before re-signing Gay to a new contract.  It seems unlikely that Gay will produce wins at a level that would justify the kind of money people think a scorer like Gay is worth.  Unfortunately for Memphis fans, Michael Heisley – the team’s owner – seems committed to bringing Gay back.

Heisley – in his defense — is also committed to bringing back Thabeet from the development league, and seeing that Thabeet actually plays in the NBA.  After six games in the D-League, Thabeet has posted a 16.3 Win Score per 48 minutes.  This is an above average mark for an NBA center, although it’s not clear how D-League numbers translate into the NBA.  Plus a sample of six is still too small. 

His NBA sample isn’t much better.  But this sample does suggest that Thabeet can contribute (although taking minutes from Gasol seems like a bad idea).    So the fictional Thabeet was correct in the reported conversation with his coach.  And if Thabeet does ultimately contribute, that will break the trend we see with draft picks in Memphis.

- DJ

The WoW Journal Comments Policy

Our research on the NBA was summarized HERE.

The Technical Notes at wagesofwins.com provides substantially more information on the published research behind Wins Produced and Win Score

Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:

Simple Models of Player Performance

Wins Produced vs. Win Score

What Wins Produced Says and What It Does Not Say

Introducing PAWSmin — and a Defense of Box Score Statistics

Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.

A Short Post on the Hornets

The post on adjusted plus/minus involved an examination of Darious Songaila.  And that examination required that I look at the New Orleans Hornets this year.  It would be a shame to let this analysis go to waste.  So here is a brief post on the Hornets.

Let’s begin with the play of Darren Collison.  That story begins with Chris Paul.  CP3 was the MPP (Most Productive Player) in the NBA in 2008-09.  This year, though, he has been limited to 38 games.  So someone else (probably LeBron) will be the MPP this year.

Without Paul, the Hornets have turned to Collison.  As Mark Spears of Yahoo.com notes, Collison has been a pleasant surprise; averaging 18.5 points, 8.6 assists and 3.7 rebounds in 24 games as a starter. 

Collison – and this should not be surprising – is no CP3.  To see this, let’s look at the WP48 [Wins Produced per 48 minutes] numbers for the Hornets this season. 

Table One: The New Orleans Hornets after 63 games in 2009-10

As Table One reports, Paul has posted a 0.375 WP48 mark.  Collison – across the entire season – has only posted a 0.080 WP48.  So Collison has been below average in 2009-10.

Since the mid-season point (or the point where the Hornets had played 41 games), Collison has been better.  Across 819 minutes he has posted a 0.126 WP48 mark.  When we consider the fact that rookies tend to be below average, Collison has been very good.  Of course, “very good” is not as good as CP3.  So the Hornets are suffering some with Paul out of the line-up. 

Beyond the story of Collison, let me comment briefly on a couple of stories posted at Hornets247. 

Joe Gerrity has noted that Tyson Chandler played better than Emeka Okafor during each player’s first season with the Hornets.  Chandler posted a 0.301 WP48 for the Hornets  in 2006-07 (at the age of 24).  The next season his production declined to 0.244.  And in 2008-09, his WP48 mark fell to 0.078 (primarily due to injury).  Okafor is posting a 0.159 WP48 this season.  So relative to Chandler in 2006-07, Okafor is doing less. But relative to what the Hornets got last year, Okafor is an improvement.

Marcus Thornton is also a step-up for New Orleans.  Last year, Rasual Butler posted a 0.077 WP48.  This season, Thornton is posting a 0.078 WP48.  Okay, not much of a step-up.  But that analysis is for the entire season.  If we focus on performance since the midpoint, Thornton’s WP48 mark is 0.144. 

So it looks like the Hornets – despite the acquisition of Songaila – did make a few good moves last summer.  And when Chris Paul comes back, New Orleans might have enough to be a very average playoff team in the Western Conference. Yes, it will take something more for the Hornets to rise to be a serious contender for an NBA title.

- DJ
The WoW Journal Comments Policy

Our research on the NBA was summarized HERE.

The Technical Notes at wagesofwins.com provides substantially more information on the published research behind Wins Produced and Win Score

Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:

Simple Models of Player Performance

Wins Produced vs. Win Score

What Wins Produced Says and What It Does Not Say

Introducing PAWSmin — and a Defense of Box Score Statistics

Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.

John Hollinger, Dean Oliver, and Some Other People Comment on Plus/Minus

In the March 8^th issue of the ESPN the Magazine is an article by John Hollinger on the subject of plus/minus.  In “Fuzzy Math: Plus/Minus Tell a Story, Though Not the Whole One”, Hollinger details the problems with the latest addition to the standard box score.  Unfortunately I was unable to find an on-line version of the article.  So let me try and summarize the issues Hollinger raises.

• The first critique comes from Dean Oliver (author of Basketball on Paper and currently the stats person with the Denver Nuggets).  Oliver is quoted saying, “It’s (the plus/minus measure) noisy, uncertain and kind of a black box – you have a hard time understanding why its coming out the way it is.”
• Hollinger also notes the plus/minus stat doesn’thelp us compare players across teams.
• On a related point, the stat also doesn’t take into account substitution patterns.

Much of what Hollinger says in this article was originally stated in an article he first posted at ESPN.com in 2005 (insider access required). That article also noted that a player’s teammates impacted his plus/minus.

Fans of this approach, though, might argue that all that’s needed is adjusted plus/minus.  This approach – originally developed by Wayne Winston and Jeff Sagarin – employs regression analysis to control for a player’s teammates.  Theoretically, adjusted plus/minus should answer Hollinger’s critiques (but doesn’t – as I will note in moment – Oliver’s criticisms).

When we look at the adjusted plus/minus numbers, though, it doesn’t look like Hollinger’s issues have gone away.  Consider the case of Darius Songaila.

Last season Songaila posted a -0.076 WP48 [Wins Produced per 48 minutes] with the Washington Wizards.  This result is not unusual.  Songaila posted WP48 marks in the negative range in 2005-06, 2006-07, and 2007-08 (he posted a 0.056 mark – his career best – as a rookie at the age of 25 in 2003-04). 

Adjusted plus/minus, though, told a very different story.  According to basketballvalue.com, Songaila was the third best player on the Washington Wizards in 2008-09.  Again, adjusted plus/minus is supposed to control for a player’s teammates.  So when the New Orleans Hornets acquired Songaila, they probably expected to see a positive adjusted plus/minus as well.  But currently Songaila is posting the lowest adjusted plus/minus in New Orleans.   So with completely different teammates, Songaila – according to adjusted plus/minus – is a very different player.

This should be thought of as odd, though, since Songaila’s WP48 with the Hornets is still in the negative range.  In other words, his box score numbers are not very different despite the fact his teammates have all changed.

This result is not just confined to Songaila.  JC Bradbury and I – in a forthcoming article in the Journal of Sports Economics — report that only 7% of a player’s adjusted plus/minus is explained by what a player did the previous season (oddly enough, unadjusted plus/minus has a stronger – albeit still relatively weak – correlation).  In other words, the correlation coefficient for adjusted plus/minus from season-to-season is below 0.30.   And when we look at players who switch teams – as Songaila did – we fail to find a statistically significant relationship. In contrast, any measure (PERs, Wages of Wins measures, NBA Efficiency, Win Shares, etc…) based on the box score will have a correlation coefficient of at least 0.65, and often these marks are above 0.80.   And that correlation remains strong even when a player changes teams.

What does this mean for decision-makers?  Decisions are about the future.  Unfortunately – because plus/minus is so inconsistent across time — it doesn’t appear this measure can be relied upon to make decisions about the future.

It’s important to note that inconsistency is not the only problem with this measure.  The standard errors associated with this measure – even when multiple years are added – tend to be so large that for many players the results are statistically insignificant (Bradbury and I make this point in our article as well). 

Even if the problems of inconsistency and the standard errors could be solved, the critique from Dean remains.  As Dean notes, this measure is essentially a “black box.”  A decision-maker has no idea why a specific result is obtained.  So it’s hard to know what the results mean.

One can state this last critique as follows:  What plus/minus can show is a correlation.  When a specific player is on the court, a team tends to do good or bad.  But it doesn’t show causation.  And therefore, it’s hard for a decision-maker to know really what this means.

Of course, all of this doesn’t stop decision-makers from using this information. And as Avery Johnson details, the Golden State Warriors upset of the Dallas Mavericks in 2008 can be partially attributed to Johnson following the dictates of plus/minus analysis.  

Let me close with three more observations. 

• One senses that people might be able to tell a story about why Songaila’s plus/minus numbers have changed.  Such stories, though, are also a problem.  Analysis should begin with a story, and then this story should be tested.  We should try and avoid looking at a test and then making up a story.
• We should note that adjusted plus/minus analysts have fully acknowledged Dean’s observation that this measure has “noise.”  Unfortunately, when specific players are analyzed this observation seems to vanish.  In other words, we never seem to see someone argue that a player’s current adjusted plus/minus is just “noise.”  But if there is “noise” in the model, some of these results have to also be “noise.”
• The fact that some teams have turned to such measures confirms what has been argued about a traditional approaches to player evaluation.  Teams are turning to these measures because the traditional approaches do not appear to work. 

So although adjusted plus/minus has problems, it is understandable that teams are turning to this measure.  One suspect, though, that the problems – detailed by Hollinger, Oliver, Bradbury, and I — are simply not well understood by everyone.

- DJ

The WoW Journal Comments Policy

Our research on the NBA was summarized HERE.

The Technical Notes at wagesofwins.com provides substantially more information on the published research behind Wins Produced and Win Score

Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:

Simple Models of Player Performance

Wins Produced vs. Win Score

What Wins Produced Says and What It Does Not Say

Introducing PAWSmin — and a Defense of Box Score Statistics

Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.

Kevin Durant vs. Carmelo Anthony

David Thorpe – of ESPN.com – recently wrote a column comparing Kevin Durant to Carmelo Anthony (insider access required).  Thorpe’s analysis considered a host of factors including shooting, scoring, making teammates better, on-the-ball defense, secondary defender, rebounding, and intangibles.  For each category the players were graded on a 10 point scale, and the player with the most points was…

Wait, before I get to Thorpe’s answer, let me comment on the word “intangible.”  This word means “not tangible” or something that we cannot discern or measure.  And yet, Thorpe is able to tell us that Durant offers more “intangibles” than Melo (by a score of 7 to 5).  So we can’t measure “intangibles” but we know Durant offers more? 

Thorpe argues that Durant’s value – according to Thorpe’s scoring system – is 48 while Melo scores a 44.  So ½ of the difference between Durant and Anthony can be linked to something that – by definition – cannot be measured.  Thorpe is not the only person to abuse the word “intangible”.  But it’s odd to see someone assign a number to something that by definition, isn’t tangible.

Okay, let’s take a more tangible approach.  We begin with Durant.  Table One reports what Durant – and his teammates with the Oklahoma City Thunder – have produced after 60 games in the 2009-10 season.

Table One: The Oklahoma City Thunder after 60 games in 2009-10

As one can see, Durant leads the Thunder in Wins Produced.  Of the team’s 36 wins, 13.3 can be linked back to Durant. 

Moving away from the subject of Durant for a moment… one can see that the Thunder are led by a collection of young players.  Durant, Russell Westbrook, James Harden, and Serge Ibaka are all younger than 22 years of age.  And this quartet are on pace to produce 36 wins this season.  As has been noted in the past, young players (younger than 24 or 25) tend to get better (so although Thabo Sefolosha is already quite good, he is not likely to get much better).  This means that prospects for Durant and the Thunder are extremely bright.

Now let’s turn to Carmelo Anthony and the Denver Nuggets.  This team already is quite good.  But as Table Two indicates, Denver’s success is not really about Melo.

Table Two: The Denver Nuggets after 61 games in 2009-10

Of Denver’s 40 wins, only 4.7 can be tied to the production of Anthony.  And four players – Chauncey Billups, Nene, Chris Andersen, and Kenyon Martin – have done more for the Nuggets this season. 

Now we “know” what Thorpe said (remember he favored Durant over Melo) and what we learn from this analysis must be wrong.  On Wednesday night, Anthony and the Nuggets crushed Durant and the Thunder.  And when we turn to the box score, we see that Melo posted a 16.5 Win Score.  Meanwhile, Durant only posted a 1.5 mark.  So there you have it.  Melo is clearly better than Durant.

Okay, obviously one game is not much of a sample.  Let’s look at Table Three, where what Durant and Anthony have done with respect to all the box score statistics across the entire season is noted.

Table Three: Comparing Kevin Durant and Carmelo Anthony in 2009-10

When we look at the entire season we see – as we saw when we looked at Wins Produced – that Durant has done more.  Durant is currently offering more with respect to shooting efficiency, rebounds, blocked shots, and personal fouls.  consequently, we shouldn’t be surprised that Durant is producing so many more wins than Melo.

When we focus strictly on Anthony we see that he definitely scores points in large quantities. But his shooting efficiency is only average.  So yes, he is above average (because he rebounds and gets to the free throw line).  But Anthony is not quite as valuable as his scoring average suggests.

Let me close by once again noting how far Durant has come.  Despite being named Rookie of the Year, Durant had a disastrous rookie season.  Last year, though, his production was above average.  And now – at the age of 21 – he is a star.  If he continues to improve – and the same happens with his young teammates – Oklahoma City is going to be a dominant team in the NBA for many years to come.  So although the Thunder were crushed on Wednesday, the Thunder will be more like to be the crushers – as opposed to the crushees (crushees???) in the future.  And that outcome should be quite tangible. 

- DJ
The WoW Journal Comments Policy

Our research on the NBA was summarized HERE.

The Technical Notes at wagesofwins.com provides substantially more information on the published research behind Wins Produced and Win Score

Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:

Simple Models of Player Performance

Wins Produced vs. Win Score

What Wins Produced Says and What It Does Not Say

Introducing PAWSmin — and a Defense of Box Score Statistics

Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.

Comments on Books and Other Short Stories

Not only do I write books, I also like to read once in awhile.  What follows is a brief discussion of several books that I recommend (and yes, I am pretending that other people should be interested in my thoughts on books).

Beyond Batting Average

Lee Panas – of Tiger Tales (one of my favorite baseball blogs)– has just released his first book. Panas describes Beyond Batting Average (available for immediate download and in paperback) as follows:

… this book is designed to help knowledgeable baseball fans gain a better understanding of the multitude of new statistics that have been introduced on the Internet and elsewhere in recent years. It puts everything in one place and ties all the metrics together into an organized 15 chapter story.

This comprehensive sabermetrics primer will introduce fans to these new measures with easy to understand explanations and examples. It will also illustrate the evolution of baseball statistics from simple traditional measures to the more complex metrics used today. You will learn how all the statistics are connected to winning and losing games, how to interpret them and how to apply them to performance on the field. By the end of this book, you should be able to evaluate players and teams through statistics more thoroughly and accurately than you could before

My copy of this book arrived yesterday.  Looking through the book it is clear that Panas – despite writing a relatively brief book – manages to cover much that’s known about the measurement of the performances of the hitters, pitchers, and fielders.  So if you are interested in learning more about the wonderful world of baseball statistics, I highly recommend this book.

Mathletics

Beyond Batting Average focuses entirely on baseball.  Mathletics – by Wayne Winston –covers baseball, basketball, and football.  Much of what Winston discusses on his blog – waynewinston.com – is adjusted plus-minus (APM).  Only about 30 pages of Mathletics, though, are about APM.  The vast majority of the book presents a host of interesting examples of how the study of statistics can help us understand sports. 

There are few observations to make about this plethora of stories.

• Mathletics is very much in the Moneyball tradition. In other words, one theme in Mathletics is that the traditional – non-statistical – approach to the study of sports will often lead people astray.
• Not only does Winston tell his stories, he often shows the readers how Excel can be used to study sports.  So this book is a marvelous tool for students.
• Of the box score methods used to analyze basketball, Winston has problems with the Player Efficiency Rating and says nice things about Wins Produced.  So I especially liked that section.

That being said, I am not a fan of APM (and we very briefly discuss some of the problems with APM in our next book).  Again, much of the Mathletics is not about APM, so even if you share my concerns with this method you will still really like this Winston’s book. 

How We Decide

Jonah Lehrer’s How We Decide has just gone to paperback.   This book offers a wonderful discussion of how human beings process information and make decisions. 

One issue Lehrer emphasizes is the limitations of the human mind.  People often state that one should try and look at “everything” before making a decision.  With respect to basketball, that would imply a decision-maker should look at the box score statistics (via PER, Wins Produced, and other measures), APM, and scouting reports in evaluating a player.  However, the human mind – as Lehrer notes – is limited in how much information it can actually process.  So people who try and look at “everything” are not actually processing information as well as they would like.  A better approach is to systematically uncover which information is actually important and which information should be ignored. 

Again, this is just one story in Lehrer’s book.  There are of course many others.  So if you have not read this book I also recommend adding this paperback to your library.

By the way, for those interested in a discussion of how data analysis trumps the non-systematic approach taken by many decision-makers, I would recommend Super Crunchers by Ian Ayres.

More than a Game

Okay, this is a book I have not read (but hope to in the future).

Dennis Coates – the first president of the North American Association of Sports Economists – has read a recent book by Brian Billick and offered the following comment at the Sports Economist:

I am reading More than a Game, written by Brian Billick, former head coach of the Baltimore Ravens and current analyst for Fox and the NFL Network. It is an interesting book on several levels.

Two points I want to bring up here I found interesting. The first is that Billick is quite forceful in arguing that finding a quarterback is very difficult, saying that nobody knows anything. People who have been very successful at picking/finding quarterbacks have all indicated that they were high on some of the bigger quarterback busts in draft history (think Ryan Leaf). Billick mentions in passing that scouting and player evaluation uses regression models. I would love to see those equations. The implication is that evaluation of other players is more successful. I wonder if pro football evaluators feel they are doing a good job in picking wide receivers.

The second point I wanted to bring to people’s attention is that Billick mentions the research by David Romer on fourth down. He points out that after the appearance of that paper, the share of fourth downs on which the teams go for it rose each year until 2008. Economists may not get politicians to understand that subsidies are not the best use of public funds for job creation but at least one economist may have successfully convinced head football coaches to go for it a bit more often. Billick also points out that Romer’s model does not account for things like media criticism. That is an interesting perspective. Better to do the conventional, if wrong thing, to avoid media criticism, than to give your team a better chance to win the game.
Billick’s perspective is interesting and worth a read.

It appears that Billick’s book supports the argument that Rob Simmons and I have offered that drafting quarterbacks in the NFL is largely a guessing game.  This is an important point to remember as NFL decision-makers and observers evaluate the latest group of quarterbacks in the NFL draft.

A Few Non-Book Stories

Dennis Coates doesn’t just comment on books.  Here is a comment he offered on an article by Bill Simmons:

I found this interesting article about the state of the NBA from Bill Simmons at ESPN. The article is worth a read.
I especially like this bit:

They arrived at this specific point after salaries ballooned over the past 15 years — not for superstars, but for complementary players who don’t sell tickets, can’t carry a franchise, and, in a worst-case scenario, operate as a sunk cost. These players get overpaid for one reason: Most teams throw money around like drunken sailors at a strip joint. When David Stern says, “We’re losing $400 million this season,” he really means, “We stupidly kept overpaying guys who weren’t worth it, and then the economy turned, and now we’re screwed.”
This isn’t about improving the revenue split between players and owners. It’s about Andre Iguodala, Emeka Okafor, Elton Brand, Andrei Kirilenko, Tyson Chandler, Larry Hughes, Michael Redd, Corey Maggette and Luol Deng making eight figures a year but being unable to sell tickets, create local buzz or lead a team to anything better than 35 wins.

I wonder if it might be because the NBA over values scoring, as Dave, Marty Schmidt, and Stacey Brook contend in Wages of Wins and other places. And maybe some NBA executives are beginning to see that.

By the way, Dennis Coates titled this post: Dave Berri Must Love This 

I should add, though, that Okafor, Kirilenko, and Iguodala are probably worth what they are being paid.  Inefficient scorers like Allen Iverson (a Bill Simmons favorite) are generally overpaid.  That being said, I do agree that if NBA teams are losing money – and I think the word is “if” since I do not know that anyone outside the NBA has actually seen the books (and sports owners do have a history of being less than honest on this subject) – part of the reason is that teams overpay for skills that do not generate wins.  Perhaps this current labor dispute will highlight that specific point.

Let me close with two more short stories.  First, I wanted to note that Brian Burke – of Advanced NFL Statistics – is now posting his statistical analysis of every quarterback, running back, and receiver in the league.  Plus his data goes back to 2000.  So that should be enough numbers to keep any NFL fan happy during the off-season.

And finally, I wanted to point everyone’s attention to the recent work fo Darren Rovell.  As I am sure most people know, Rovell is perhaps the leading journalist on the subject of sports and business.  One can see his work on CNBC and also at his blog (Sports Biz with Darren Rovell).  When we were looking for people to review advanced copies of our next book, Darren was one of the first names to come to mind.

One of his most recent stories was on the Sports Illustrated Swimsuit issue.   Rovell tells this story in an hour-long special on CNBC.  Here is how this special is described:

CNBC’s Sports Business Reporter Darren Rovell takes an unprecedented look inside the most profitable single-issue magazine franchise in the world. Find out how business, beauty, fashion and sports come together to create this much-anticipated, multi-dimensional franchise that alone generated 7 percent of Sports Illustrated’s advertising revenue in 2009.

The Sports Illustrated Swimsuit Issue means big business not only for parent company Time Inc., but also for the models, advertisers, fashion designers and locations that grace its pages. Rovell gives viewers a behind-the-scenes look at the scouting, set-up and inner-workings of the photo shoots as he travels to one of the exquisite undisclosed locations and interviews the models vying for the ultimate prize—being featured on the cover of this year’s issue and becoming a household name

Rovell’s report is still airing, or you can just watch it on-line.

And if you are interested , here is what Rovell says about our next book:

“‘Moneyball’ should have been called ‘MoneyBaseball.‘ Stumbling on Wins covers everything else. Every general manager needs to buy this book to save his owner money. Every fan needs to buy this book to know when it makes sense to yell at the general manager.”

Stumbling on Wins is scheduled to be released in three weeks.  So it won’t be long until everyone has a chance to offer comments on our latest.

- DJ

The WoW Journal Comments Policy

Darko Milicic Now Benefits from Very Low Expectations

Here is a surprising headline from the Detroit News:

Ex-Pistons castoff Darko Milicic has Timberwolves howling (HT: MLive Full-Court Press)

The article goes on to state the following:

The crowd chanted his name and gave him a standing ovation.

A teammate said: “You can see all of his qualities and everything he brings to the table.”

His coach said: “He obviously has tremendous potential and capabilities.”

Which NBA superstar in the making were they talking about?

Darko Milicic?

Milicic, a bust in Detroit, Memphis and New York who said in frustration he would quit the NBA after this season, has found new life with Minnesota.

He was dealt to the Timberwolves by the Knicks at the trade deadline as a throwaway. The Timberwolves, however, said they saw potential in the 7-foot-1 center.

Milicic, who spent the entire season on the Knicks bench, responded with spirited practices with the Timberwolves. He played his first game last Sunday, and won over everyone.

“It felt good to be out there,” he told the St. Paul Pioneer Press after scoring eight and grabbing eight rebounds in 19 minutes. “I didn’t expect to play that long. I got tired.”

The Timberwolves lost to the Thunder that night.

Darko played 24 minutes Tuesday, getting four points and three blocks in Minnesota’s victory at Miami.

He seems destined to play a role in the team’s final 24 games.

And don’t forget, he’s only 24.

When I saw this article I knew I had to go look at what Milicic was doing for the T-Wolves.  The results – reported in Table One – might be disappointing for those howling Minnesota fans.

Table One: The Minnesota Timberwolves after 61 games in 2009-10

Milicic has only played 102 minutes for the T-wolves.  So this is a small sample.  And across this small sample we see a WP48 of -0.104. 

Average is 0.100, so Milicic should not be generating howls.  At least, not in a positive sense.

It is true that what we are seeing is somewhat surprising.  Here is what Milicic has done across his career:

2008-09: 0.052 WP48, 1,034 minutes

2007-08: -0.045 WP48, 1,664 minutes

2006-07: 0.061 WP48, 1,913 minutes

2005-06: 0.049 WP48, 767 minutes

2004-05: -0.211 WP48, 254 minutes

2003-04: -0.171 WP48, 159 minutes

After Milicic departed Detroit, he was generally a below average player who tended to produce positive quantities of wins.  In his brief time in Minnesota, though, he is posting numbers similar to what we saw when he was with the Pistons.

So why is Milicic being cheered?  Early in his career Milicic was penalized by the high expectations that go with a player selected second in the NBA draft.  Today, though, one suspects Milicic is the beneficiary of very low expectations.  So although Milicic is not playing well, relative to what is expected, he looks like a star.

His lack of production does fit in with many of his teammates in Minnesota.  Six of his teammates are producing in the negative range.  And all but three are below average.  If we look at what these veterans did the previous season we see that most veterans were below average in 2008-09.  So what we see in Minnesota is not surprising. 

Essentially this team is Kevin Love, Al Jefferson, Damien Wilkins, and Ramon Sessions.  These players have combined to produce 20.2 wins.  Had the rest of this team produced nothing this season, the T-Wolves would be on pace to win 27 games.  Because the teams employ so many players in the negative range, though, Minnesota is only on pace to win 17 contests.

So obviously, Minnesota needs to replace the many negative players with some positive players.  And although Milicic could be one of these positive players, at 24 years of age he probably won’t be that much help.  No, the T-Wolves need quite a bit more.  And until that “quite a bit more” arrives, Minnesota is going to continue to struggle.

- DJ
The WoW Journal Comments Policy

Our research on the NBA was summarized HERE.

The Technical Notes at wagesofwins.com provides substantially more information on the published research behind Wins Produced and Win Score

Wins Produced, Win Score, and PAWSmin are also discussed in the following posts:

Simple Models of Player Performance

Wins Produced vs. Win Score

What Wins Produced Says and What It Does Not Say

Introducing PAWSmin — and a Defense of Box Score Statistics

Finally, A Guide to Evaluating Models contains useful hints on how to interpret and evaluate statistical models.