Earlier this month one of the pioneers in sports economics – Gerald Scully – passed away.  Given Scully’s importance to the field of sports economics I wanted to offer a few thoughts. 

Scully and the Developing Field of Sports Economics

In about six weeks the Western Economic Association meetings will convene in Vancouver.  A perusal of the preliminary program reveals that at least 16 sessions will be devoted to the topic of sports economics.  Over all, nearly 60 papers will be presented at these sessions, written by economists from around the world. 

After these papers are presented and discussed, many will be submitted to academic journals (where they will be reviewed again).  Within the field of sports economics there are now two journals: The Journal of Sports Economics and the International Journal of Sport Finance.  In addition, Economic Inquiry – one of the top general interest journals in economics – has recently declared that it considers itself one of the primary outlets for work in sports economics. And beyond these three choices, sports economics research has been published by a number of other journals, including the American Economic Review, the Quarterly Journal of Economics, and the Journal of Political Economy (perhaps the top three journals in all of economics).  In sum, the study of sports economics is now considered a legitimate – and rapidly growing — field within economics.

If we go back 35 years, though, this was hardly the case.  In 1974 Gerald Scully published – Pay and Performance in Major League Baseball – in the American Economic Review (again, the top journal in economics).  Prior to Scully’s work, sports and economics had been touched upon in seminal works by Simon Rottenberg (1956), Walter Neale (1964), and Mohamed El-Hodiri and James Quirk (1971).  Each of these works, though, was a theoretical treatment of sports economics.  And beyond these papers, little else had ever been said.  In sum, the study of sports and economics was not considered something economists should spend much time upon.

Scully’s work caused this to change. What Scully did is demonstrate that the productivity data generated by sports (i.e. all those stats people love to discuss) could be used to answer fundamental questions in economics. 

When we look back on Scully’s paper we are struck at how an empirical paper written more than three decades ago has managed to stand the test of time.  Although computing power has increased dramatically – and along with this data availability and econometric technique – the essential story Scully told remains true.  When workers are faced with a monopsonistic employer (or employer with buying power), workers will tend to be exploited (wages will be less than marginal revenue product).  Tony Krautmann, Peter Von Allmen, and I have just had a paper accepted that examines recent data from baseball, football, and basketball that reaches the same conclusion. 

Beyond providing an empirical test that has endured, Scully also did something even more important.  He demonstrated that sports data could be used by economists to conduct research on topics of interest to the entire discipline (not just sports fans).  As a result, a host of economists have turned to the study of sports.  If we go back to the 1970s and 1980s, this interest was somewhat sporadic.  Today, though, a number of economists have essentially focused their entire research program on the study of sports (including myself).  Again, I don’t think this is possible without Scully’s paper in 1974.

So when the sports economists convene in six weeks to discuss the latest research in the field, we will all be thinking about the work of Scully’s that did much to get the whole field started.  Without him we might all be doing something else that is far less interesting.

More Comments on Scully

In addition to my thoughts, let me repost the words of Phil Miller, Skip Sauer, and JC Bradbury (three sports economists who were much quicker in discussing the importance of Gerald Scully). Continue reading →

After the games on Sunday I believe I have now taken over first place in the TrueHoop Stat Geek Smackdown.  And this means… well, let me make my Conference Finals picks and I will then discuss what this means.

Once again, my approach is quite simple.  All I am considering is a team’s efficiency differential (offensive efficiency minus defensive efficiency), home court advantage, and any relevant injuries.  With this approach I have correctly chosen the winner in every series except for Portland vs. Houston.  And now I am going to apply this approach to the Conference Finals.

Cleveland Cavaliers vs. Orlando Magic

The Magic finished the season with a 7.1 differential while the Cavs led the league with a 9.7 mark.  Given these numbers, the Cavs – who have the home-court advantage – should win in six.  But the Magic’s efficiency differential for the season was inflated by the play of Jameer Nelson.  The Magic’s differential in the first half was about twice what we observed in the second half (when Nelson didn’t play).  If we take the second half numbers as more indicative of the Magic’s true quality – and their play against the Celtics suggest this is true – then the forecast should change to…

Pick: Cleveland over Orlando (4-1)

LA Lakers vs. Denver Nuggets

Dean Oliver is a friend of mine and he works for the Nuggets.  And I would like to see Dean and his team advance to the Finals.  But the numbers suggests otherwise.  So although I will be rooting for Denver, my forecast will be…

Pick: LA Lakers over Denver (4-1)

Okay, those are my picks.  Now let me put this contest in perspective.  The biggest story from this contest is the similarity in everyone’s picks.  There have been 12 series to date and in eight of these everyone picked the same winner.  This consistency reflects the fact that everyone is essentially looking at the same thing. I think all of us agree that teams are best evaluated by looking at points scored and surrendered per possession.  And since we agree on this point, we tend to agree on the identity of the “better” team in each series. 

Of course, despite such agreement, I am currently in the lead.  This must mean I know a little more than everyone else. 

Although I like that story, it really is just a story.  In other words, my current lead is probably just luck.  A key component of this contest is the requirement that we pick the number of games in each series, and although I think the data helps somewhat with that question, I am not sure it helps that much.

It’s important to remember that despite what you hear on television, the playoffs are not really designed to identify the best team.  Currently I am reading The Drunkard’s Walk: How Randomness Rules Our Lives by Leonard Mlodinow.  This wonderful book contains the following passage relevant to any discussion of predicting the winner in a best-of-seven playoff series.

“…if one team is good enough to warrant beating another in 55% of its games, the weaker team will nevertheless win a 7-game series about 4 times out of 10.  And if the superior team could beat its opponent, on average, 2 out of 3 times they meet, the inferior team will still win a 7-game series about once every 5 match-ups.  There is really no way for a sports league to change this.  In the lopsided 2/3-probability case, for example, you’d have to play a series consisting of at minimum the best of 23 games to determine the winner with what is called statistical significance, meaning the weaker team would be crowned champion 5 percent or less of the time.  And in the case of one team’s having only a 55-45 edge, the shortest significant “world series” would be the best of 269 games, a tedious endeavor indeed! So sports playoff series can be fun and exciting, but being crowned “world champion” is not a reliable indication that a team is actually the best one.” (p. 70-71).

For the TrueHoop contest we are each offering our evaluation of who the better team is in each series.  And then the series is played to see if we are “right”.  But the words of Mlodinow remind us that a seven game series is simply not up to this task.  In sum, the playoffs are about fun, not science.

In this current contest I expect all of us to pick Cleveland and LA to reach the Finals.  So I should still rank among the leaders when we get to the last round.  Assuming the Conference Finals go as expected, when we get to the Finals we will be mostly guessing.  There simply is not that much of a difference between Cleveland and LA.  So the winner of this contest is really going to be the person who ranks among the leaders entering the Finals and manages to guess right on the outcome of a series between two close competitors. 

So this contest really is not a “test” of anyone’s ability to evaluate teams.  Again, this is because a) I think we essentially have the same evaluation and b) the playoffs are simply not designed to test that evaluation.   Of course if I get this test right, then we will forget everything I just said and conclude that I really do know something :)

- 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.

We are currently racing to finish our second book.  So although I don’t want to completely ignore this forum, I need to keep my posts as brief as possible.

With that in mind – and given the media’s selection of the All-NBA Teams this week – let me simply post the Top 15 players at each position from this past season. 

Table One: Top 15 at Each Position in 2008-09

As one can see, the players are ranked in terms of Wins Produced.  And I suspect there will be a few surprises for the casual fans. Unfortunately, as noted, I don’t have time to offer much explanation or thoughts for further discussion.

Let me do my best to kick off the discussion by re-posting something that jbrett offered in the comment section this week.  As jbrett notes, it appears that a number of comments offered in this forum are repetitive.  Consequently, we might see a gain in efficiency by assigning letters to the comments that most frequently appear.  Hopefully everyone will find jbrett’s observation as funny as I did.

It seems to me your blog could benefit from posting, at the beginning of each Comments section, a list of time-saving conventions for the new or unindustrious poster. I only found it a few months ago; I spent a long time reading the older articles, and eventually I bought the book. This seemed the sensible approach, though, judging from the tone of many of the comments left, not the favored one. For the benefit of the many posters who consider this site homework-optional, I submit the following list of generic positions that NEED NOT EVER BE ELABORATED UPON EVEN ONE MORE TIME:

A. I have little or no training in statistics (me, for one)

B. Obviously, any metric that says Player A (let’s say, oh, Jermaine O’Neal) is not as good as Player B (how about, um, David Lee) is clearly flawed

C. Anyone who’s ever watched a game can see that Superstar A (Allen Iverson, anyone?) is ten times the player that Serviceable Role Player B (Chauncey Billups, maybe–or how about Andre Miller?) will ever be

D. Superstar A and his ilk cannot be quantified in the same way as mortal players can; they only shoot 42 percent from the field and 28 percent from 3-point range because their teammates DEMAND they do so, by leaving them with the tough shots at the end of the 24-second clock

(See how much space that one will save, when all you have to type is ‘D’?)

E. My friend/ brother-in-law’s boss/ opinionated alter-ego hasn’t read or studied your work, but I told him the results say Mike Miller is way better the Richard Jefferson OR Rip Hamilton, and he says you’re clearly deluded

F. I haven’t read THE WAGES OF WINS, nor am I likely to, and as a result I will begin by gainsaying basic tenets of the book

G. I read your book, and I say “Nunh-unh.”

I’ll stop there–but, obviously, as other arguments become hackneyed, they can be assigned the next letter. Think how much easier it will be to find the genuinely interesting discussion when the endless repetitive jabber is distilled to a handful of letters one can note and skip past. It seems like an idea whose time has come. Any thoughts?

- 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.

Danny Granger was the subject of the following two posts in the month of March.

There is Not Much Difference Between Danny Granger and Kobe Bryant?

Bob Newhart, Danny Granger, and Group Therapy in Indiana

Of these two, I really like the one connecting the Bob Newhart show to Danny Granger.  In fact, it’s such a good post (well, I liked it) that I should move on to another subject.  But the sports media has decided that Granger is the Most Improved Player in the NBA in 2008-09.  And after looking at the data, I think I have to comment on Granger again.

Table One: The Last Two Years of Danny Granger Basketball

Table One reports what Granger has done the past two seasons.  Let’s start with the non-scoring aspects of his game. Granger did manage to improve with respect to blocked shots, assists, and personal fouls.  But he got worse with respect to rebounds, steals, and turnovers.  And the decline with respect to the net possession factors trumps the improvement in blocked shots, assists, and personal fouls.

What about scoring? Granger also increased his scoring totals.  But his level of shooting efficiency really didn’t change.  So Granger’s scoring totals only went up because Granger took more shots.

Now Granger is an above average scorer, so more shots from Granger will increase his Wins Produced and Win Score.   The overall improvement, though, is rather small.  As a small forward Granger posted a 0.148 WP48 [Wins Produced per 48 minutes] this year.  Last year his WP48 at small forward was 0.124.  Had Granger spent all his minutes at small forward (he also played power forward, which is not a good idea), this improvement would result in 1.2 additional wins for the Pacers.  So yes, technically Granger’s additional shots did mean he was more productive.  But the difference seems rather small.

Of course it’s possible that the sports media looked at every aspect of every NBA veteran and this is the biggest improvement they could find.  I suspect, though, that something else is going on.  I think many members of the sports media saw that Granger increased his scoring average from 19.6 to 25.8 points per game.  This scoring leap was enough to impress these members of the sports media who often only look at scoring totals in evaluating players.

Now if I was really working hard at this blog tonight I would go look at every player who played in 2007-08 and 2008-09 and discover the one player who was the Most Improved.  Unfortunately, I am less committed tonight and think I will just end the post here (if you need more, please go re-read the Bob Newhart post…. I really like that particular story).

- 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 sports media has declared LeBron James as the Most Valuable Player in the league.  If we define MVP as Most Productive (which seems reasonable), and we define Most Productive in terms of Wins Produced (which seems reasonable), then the sports media is correct.  That is, the media is correct if we are looking at just the Eastern Conference. If we expand our view to the entire league, though, then an argument can be made for Chris Paul.

The MVP of Each Team

At least, that was the subject of the last post.  For this post I want to look at the MVP of each team.  Again we are going to define MVP in terms of productivity.  And again productivity is defined in terms of Wins Produced.  The results of this analysis are reported in Table One.

Table One: The Most Productive Player on Each Team in 2008-09

There are a number of stories one can tell from Table One.  Here are a few (in no particular order).

• The average top player on each team produced 14.3 wins, or 36% of each team’s total.
• The one factor that dominates perceptions of performance is scoring.  Of the 30 top players listed in Table One, twelve were the leading scorer on their team.
• Of the players who received some consideration for the MVP award, only Tim Duncan and Chauncey Billups was not the leading scorer on their respective teams.  Both Duncan and Billups were the second leading scorers on their teams.
• There is a 0.60 correlation (the correlation coefficient is r) between a team’s Wins Produced and the Wins Produced of the team’s top players.  If we look at R² we see that 36% of a team’s Wins Produced can be explained by the productivity of their top player. So the top player isn’t everything, but it’s something.  
• Another way of looking at the same issue.  Of the fifteen below average teams, only three had a Wins Produced leader that produced more than 14.3 wins (Troy Murphy of the Pacers, Gerald Wallace of the Bobcats, and David Lee of the Knicks).  So if you don’t have an above average leader you are not likely to be an above average team.  This is an important lesson to learn about building a winner. It’s possible to build a dominant team without one dominant performer, but it’s not as easy.

Kobe and Flash

Kobe Bryant fans will note that Kobe is not the leader in Wins Produced on the Lakers.  Pau Gasol was actually a bit more productive.  Gasol had his best season of his career in 2008-09, although the actual difference between what Gasol did this past season and what he did in 2006-07 (his last full season in Memphis) is not very big.  Gasol posted a 0.240 WP48 [Wins Produced per 48 minutes] two years ago. Had he maintained this production this year his Wins Produced would have been about 15.0 (a mark that’s still good enough to lead the Lakers in 2008-09). Continue reading →