The Return of the Dream

Posted on July 27, 2008 by


On August 10th the USA Basketball Men’s Senior National Team will begin the 2008 Olympics with a game against China.  The hope is that the Senior Team will fulfill the dreams of basketball fans in the USA and conclude the Beijing Olympics with a gold medal.  In other words, it’s hoped that the Senior Team can remind people of the Dream Teams from the 20th century.

Dream Team History

Last August I noted that the 2007 Senior National Team was the best USA team since 1996.  This was determined by looking at the average Wins Produced per 48 minutes [WP48] of each Senior National Team since 1992 (when NBA players first started competing in the Olympics).  Here are the results I reported last year:

1992 Dream Team: 0.317 average WP48

1996 Dream Team: 0.277 average WP48

2000 Dream Team: 0.189 average WP48

2004 Dream Team: 0.165 average WP48

2006 Dream Team: 0.200 average WP48

2007 Dream Team: 0.228 average WP48

As the above list indicates, the quality of the Dream Team decline from 1992 to 2004.  And as I argued last summer, it was this decline in quality – as opposed to improvements in the quality of the competition – that I believe primarily led to worsening of observed outcomes for Team USA. 

The 2006 Team -which took the bronze medal in the FIBA World Championship – stopped the decline.  This team, though, was only comparable to the 2000 team; a Dream Team that came within a failed Lithuanian three-point shot from missing out on the gold medal.

The 2007 edition continued the progress in the right direction.  And as Table One indicates, the 2008 Dream Team finally approaches the dominant teams of 1992 and 1996.

Table One: The 2008 Dream Team

To put the 2008 edition in perspective it is useful to look back on the past Olympic Dream Teams.

Table Two: The 1992 Dream Team

Table Three: The 1996 Dream Team

Table Four: The 2000 Dream Team

Table Five: The 2004 Dream Team

Looking through these tables we see that

the 1992 edition  

– employed two players with +0.400 WP48 marks

– employed five more players with +0.300 WP48 marks

– and employed three more players with +0.200 WP48 marks

the 1996 edition

– employed one player with a +0.400 WP48 mark

– employed four more players with +0.300 WP48 marks

– and employed four more players with +0.200 WP48 marks

the 2000 edition

– did not employ a player with a +0.400 WP48 mark

– employed two players with +0.300 WP48 marks

– and employed three more players with +0.200 WP48 marks

the 2004 edition

– did not employ a player with a +0.400 WP48 mark

– employed one player with a +0.300 WP48 mark

– and employed three players with a +0.200 mark.

And one should add, the 2004 team was led in scoring by Allen Iverson, a player who posted a 0.020 WP48 in 2003-04 (and who only shot 37.8% during the 2004 Olympic games).

When we look at the 2008 edition we see a level of production that clearly eclipses the 2000 and 2004 editions; and is quite reminiscent of what we saw from the first two Dream Teams.  Specifically, the current Senior Team

– employs one player with +0.400 WP48 marks

– employs three more players with +0.300 WP48 marks

– and employs three players with +0.200 WP48 marks

Even Better?

The average WP48 on the 2008 team is 0.250, which is the best mark since 1996. 

Two players on the 2008 edition, though, might be better than Table One reports. Dwyane Wade was hurt in 2007-08 and consequently only posted a 0.136 WP48.  When healthy he’s quite a bit better, as his 0.291 WP48 in 2006-07 indicates.

And then there is Carmelo Anthony.  A few weeks ago I noted that there are signs that Melo is finally the productive star people have always envisioned.  In the first half of the 2007-08 season Anthony posted numbers quite comparable to what he did in 2006-07.  This means that Melo was a bit above average.

In the second half of this past season, though, his shooting efficiency improved.  Plus his rebounding increased, his turnovers were down, and his steals were up.  As a result, Melo’s second half WP48 in 2007-08 was 0.245. 

If both Wade and Anthony are +0.200 players, then the 2008 edition has nine players who post per-minute productivity numbers that are twice the NBA average (average WP48 is 0.100).  And if we consider that Chris Bosh was a +0.200 player in 2006-07 (and quite close in 2007-08), the number of “perfect players” (a team of 0.200 WP48 players would be expected to win every game in an NBA season) rivals the numbers employed in 1992 and 1996. 

When we go back to average WP48 we again see the 2008 team is indeed quite dreamy.  If we consider Melo’s second half performance and what Wade and Bosh did in 2006-07, then the average WP48 in 2008 rises to 0.269.  This is just a shade below what we saw in1996.

In sum, when you look at the quality of the 2008 edition it is hard not to be optimistic about this particular USA Basketball Men’s Senior National Team.  Yes, the level of international competition has improved.  But this competition is going to be met by a team that rivals the great Dream Teams of the 1990s.  And one suspects, even if the teams the USA face have an abundance of international experience, the sheer talent level of the 2008 Dream Team will be enough to take the gold medal in Beijing. 

By the way, as we all know, the Internet is global.  This means people from around the world read The Wages of Wins Journal.  I would very much like to hear what basketball fans around the world think about the Olympics.  So if you have the time, please leave a thought (or two or three) in the comments section.

– DJ

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Our research on the NBA was summarized HERE.

The Technical Notes at 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.