1992 Dream Team vs. 2012 Olympic Team

Before the 2012 Summer Olympics began, there was a good deal of talk comparing the 2012 USA basketball team to the 1992 team. While most sensible people agreed that the 1992 team couldn’t be topped, some fans thought that the 2012 team was the most talented group of players to ever assemble. I decided to compare the two teams by using two sets of advanced statistics: win shares (WS) and player efficiency rating (PER).* Both stats attempt to account for a player’s overall value ( including offense and defense). I used both to get a more complete view of the talent that each team contained.

I looked at each player’s WS and PER for the two years prior to the Olympics. I figured that looking at two years of data would give a more accurate picture of each player’s value because one bad year may be caused by injuries,etc. Note that I adjusted the WS of the 2012 team’s 2011-2012 stats to compensate for that season being shortened to 66 games because of a labor dispute. Also, because Christian Laettner and Anthony Davis hadn’t played in the NBA prior to the Olympics, neither of them had stats to include, so I did not include them in my calculation. Davis and Laettner seem fairly similar in value, so excluding both shouldn’t affect my results.

Before diving into the results of my research, let’s review the rosters.

1992 Team

Guards: Magic Johnson, John Stockton, Michael Jordan, Clyde Drexler

Forwards: Chris Mullin, Scottie Pippen, Larry Bird, Karl Malone, Charles Barkley, Christian Laettner

Centers: David Robinson, Patrick Ewing

2012 Team

Guards: Deron Williams, Chris Paul, Kobe Bryant, Russell Westbrook, James Harden

Forwards: Andre Iguodala, Kevin Durant, Lebron James, Carmelo Anthony, Kevin Love, Anthony Davis

Center: Tyson Chandler

So how did the teams stack up? Well, it turns out that the Dream Team was ridiculously good at basketball. Their roster averaged 13.2 WS per year with an average PER of 24.1. In comparison, the 2012 team averaged 9.7 WS per year with an average PER of 22.5. The Dream Team averaged 3.5 more WS than the 2012 team, which represents a fairly sizable gap. Sorry, 2012 Olympians; this is a blowout.

But what if the 2012 team hadn’t been limited by injuries? What if all of America’s best players had participated in the 2012 Olympics? What if Williams, Harden, Igoudala, Anthony, and Davis had been replaced by Derrick Rose, Dwyane Wade, Blake Griffin, Lamarcus Aldridge, and Dwight Howard? Let’s look at the numbers again. But because Anthony Davis isn’t on my hypothetical team to cancel out Christian Laettner, it only seems fair to include Laettner this time. (I used Laettner’s rookie statistics for this calculation.) Because I included Latttner this time, the Dream Team drops to an average of 12.5 WS and a PER of 23.5. My hypothetical 2012 team averaged an 11.7 WS with a PER of 24.0. This one is so close that it comes down to which stat you think is a better measure of a player’s value. (Of course, if we’re going to make hypothetical teams, you could argue that we should include someone like Isiah Thomas or Horace Grant on the 1992 team instead of Laettner).

It looks like the 1992 team is still the best team ever put together by a sizable margin. However, the 2012 team could have been considered equals to the 1992 team if injuries hadn’t prevented many of the best players from playing.

*Win shares attempts to approximate the number of wins that a player is worth to his team. For example, if you put a player with a 15 WS on a 41 win team, that team should win 56 games. An average NBA player has a PER of 15. A PER of 20 makes a player approximately all-star level and a PER of 25 or above usually means that a player is a superstar.

Here’s a link to a PDF showing the player-by-player stat breakdown: Dream Team Comparison

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A statistical model to more accurately predict March Madness

As an economics undergraduate, I learned a decent amount about statistics. In my econometrics class, my most rigorous course in statistics, I had to do a research project. Unsurprisingly, I chose a sports topic. I looked at team data for every team that made the  NCAA men’s basketball tournament for a period of 4 years to see if there were any factors that helped predict a team’ success in the tournament. Most of my findings weren’t earthshaking, but I did find a few interesting nuggets.

-The strongest finding was that the number of a team’s regular season wins matters, so don’t go picking that 22-10 team to run the table. There’s a reason they lost 10 times in the regular season.

-A team’s conference matters; all else being equal, teams from the “power conferences” do better. The Butlers, George Masons, and VCUs are going to make a run to the final four once in a while, but don’t you dare try to predict it. This research was based off of the years 2006-2009, so I’m curious how the shakeup of conferences has affected this result. Additionally, the general consensus is that the “mid-major” conferences have gotten a lot stronger in the past couple of years. But if you want a winning bracket, you should still lean towards taking the team from the bigger conference.

-A team’s points, rebounds, and assists per game are mostly irrelevant. But a team’s turnover’s per game can be a decent predictor of success. Teams that limit turnovers and make the most of each possession are more likely to do well in the tournament. Turnovers per game being a better predictor than points, rebounds, or assists was the most interesting thing I learned from the study.

-The correlation is weak, but a team’s point distribution matters. A team with many decent scorers tends to fair better than a team that relies on one scorer. Teams with forwards as their leading scorers tend to do better than teams with guards as their leading scorers.

For those of you who are curious, I used this model to make a bracket for the 2010 tournament and the bracket scored in the 70th percentile. In other words, using these principles should give you a bracket that’s a little better than average.

An amateur's attempt to explain sports through statistics