Full Court Press: Chasing the Gold Medal Since 1992

(An important note: standard deviations and effect size were purposely left out of the reporting of the statistics for ease of reading)

An Overview

When the conversation began to build and people started to take sides, I decided that I wanted to investigate the 1992 Olympic Men’s Basketball team, affectionately known as the Dream Team, and see if I could come to some kind of statistical conclusion about whether or not the 2012 team stacked-up in a head-to-head contest.

The conclusions that I will draw will say nothing of what would happen, since we do not have a time machine (at least I do not) and as such we cannot see what would actually happen if the squad in 2012 met the 1992 version of the Hall of Fame members of the Dream Team.

Instead, my goal is to look at the statistics of the game, the numbers that are used to generate fantasy leagues and talk about the hierarchy of all-time greats with a standardized precision. I will utilize the statistics from Basketball-Reference.com, which is a database of extensive statistics that are wonderfully robust for the time periods I am investigating.

I will use statistical tests to compare the two teams as a whole, and as well a position-by-position analysis. For the sake of an objective analysis, I will not be taking into account the career statistics or career accolades of the much-lauded 1992 Olympic Team. Also, I will be omitting the statistics of rookie Anthony Davis from the 2012 team (because he does not have professional basketball numbers) in addition to Christian Laettner of the 1992 team, for the same reason. I will be using statistics from the year immediately preceding the Olympics games for all of the players included in the analysis, with the notable exception of Magic Johnson. I will use his career numbers, as 1991-1992 averages were not available. I had, briefly, considered using his 1990-1991 numbers, but they would be many months removed and felt that the career numbers were a more conservative estimate of his numbers (as his career numbers reflect comparatively) and a better representation of his contribution at the time.

The teams will be evaluated on a variety of variables. I will not take into account point differentials during Olympics wins because of the difficulty presented in comparing the other national teams. The PER rating (the per minute efficiency rating of a player) as well as true shooting percentage will be used in conjunction with a variety of statistics (averages over the year leading up to the Olympic games) taken from Basketball-Reference.com: points per game, assists per game, rebounds per game, steals per game, blocks per game, free throw percentage, field goal percentage, offensive rebounds per game, defensive rebounds per game, three-point field goal percentage, and defensive rating. I will break down the comparisons in the following three ways: team, front court, and back court.

What will follow will be a statistical analysis of the Olympic squads from the Dream Team forward. While statistics do not reveal everything, there is something to be said about an objective, comparative analysis in the only way that is possible: statistics. The intangibles of the game are what make watching it so great. This is an exercise in combining two great loves: statistics and basketball.

What Does It All Mean

For many of the readers, I imagine statistics are simply that awful word-based math you had to sit through during school. Perhaps mentioning statistics conjures the famous Mark Twain quote: “There are three kinds of lies: lies, damned lies, and statistics.” For the sake of this analysis (and subsequent analysis), I will be using group comparisons. I had contemplated the variety of ways with which I could talk about the data and the most pertinent was using each Olympic year as its own group and drawing conclusions from there.

What we are interested in when considering group comparisons is whether or not a group is distinguishable from another group in a statistically significant way. This is to say that if there were no labels that we would be able to say one group was larger or smaller than another group in a meaningful way and not just due to chance. So when I talk about the mean values (the averages) of any particular statistic, I will be sure to differentiate purely numerical differences and statistical differences. Since I know that statistical analysis is about as interesting as pulling teeth for most fans, I will try and rephrase everything in more palatable terms. Much of what we love about fantasy sports is due to statistics. I love using statistics to examine basketball because it allows me a unique perspective into the game that is often only afforded to basketball-operations types.

The first step when considering so many variables and groups is to perform a multivariate analysis of the data. There are multiple dependent variables and we want to guard against statistical errors, so performing due diligence toward that end did indeed yield a significant multivariate effect at the p < .001 level. This means something and nothing all at once.

Take a breath.

What this means is very simple: there are differences between the Olympic teams and that difference is statistically significant. The reason why it doesn’t tell us much is that teams are different and how they are different is not revealed in a multivariate analysis; instead, we know to look further and we shall. In addition to a multivariate analysis, I was curious whether or not there were factors that hung together when considering teams that won a gold medal since the 1992 Dream Team.

A factor analysis, in the simplest terms possible, is an attempt to understand how variables hang together. So when I say that the top three factors account for 67.37 of the variance, I am certain that means very little. The number goes over 80 percent when including two more, much smaller, factors.

So the first factor is comprised of the following: RPG, BPG, ORPG, DRPG, HGT, WGT (38.940%). This looks a lot like a defensive statistic, which would work well to describe a power forward or center. The second factor is comprised of these variables: PER, OFFRAT, PPG (16.384%). I would venture to say this factor is very much a scoring statistic, which might fit very well with a shooting guard or small forward. The final factor was comprised of these: TSPER, FGPER, USAGEPER (12.046%). This looks suspiciously like a leadership statistic that would fit well with a point guard (or the newly minted point forward).

What the deuce does that mean? Nothing, really. For a statistician such as myself, I might be inclined to take my analysis further utilizing these new categories, but I won’t. For the casual basketball fan, it means that there are definitely three factors to a great team: defense, offense, and leadership.

Alright, enough with the explanations. Let’s get to the statistics.

Age

Stat1The mean ages (in years) for the Olympic teams were as follows: 1992 (29.64), 1996 (29.58), 2000 (30.83), 2004 (23.82), 2008 (25.92), and 2012 (26.36). There was a significant difference in mean age, F(5,63) = 7.48, p < .001. There were also linear (p < .001) and cubic (p = .07) trends, which would suggest a gradual decrease in age over time punctuated by a stark decrease in 2004 (the only year since 1992 that the US did not win a gold medal).

How were they different?

The 1992, 2004, 2008, and 2012 teams all had significantly lower ages than the 2000 team. What about the question of whether or not the 2012 team was younger than the 1992 team. Numerically, this is true. However, there was no statistical difference in age between these two teams. This, in plain terms, means that we cannot be certain that the difference in the ages is not due to chance.

Steals

Steals

Steals might seem like an insignificant statistic when considering the whole of a team, but there is something to be said about how defensive pressure impacts the game. The means for each Olympic year were as follows: 1992 (1.86), 1996 (1.58), 2000 (1.14), 2004 (1.42), 2008 (1.38), and 2012 (1.36). The only significant difference was between the 1992 team (which had the highest average steals) and the 2000 team (which had the lowest average steals). There was also a significant linear trend (p = .040), which would seem to imply a steady decrease in average steals since 1992.

Defensive Rebounds

DEfReb

Unfortunately, there was no real difference in defensive rebounds between the different Olympic teams. Based on the scale of the graph you might be inclined to think otherwise, but there was nothing significant about the different years. For numerical sake (because we know I love numbers), here are the average means for each year: 1992 (6.23), 1996 (5.47), 2000 (4.96), 2004 (5.33), 2008 (5.27), and 2012 (5.11).

Blocks

BLKAnother defensive statistic that certainly looks like it decreased over time. While there were no significant differences between groups, there was indeed a significant linear trend (p = .037) that would certainly suggest a decrease in average shots blocked from 1992 through 2012. To better visualize the data, the means were as follows: 1992 (1.26), 1996 (1.03), 2000 (.96), 2004 (.86), 2008 (.64), and 2012 (.54).

Free Throw Percentage

Free Throw

The graph would seem to suggest that there is a polynomial trend at work here, but that is not the case. There were not significant differences between Olympic teams. However, because I know how much you love numbers, here are the averages by year: 1992 (79.5%), 1996 (74.8%), 2000 (77.9%), 2004 (75.2%), 2008 (77.7%), and 2012 (79.8%)

Defensive Rating

DefRating

The Defensive Rating statistic is based on how many points were allowed per 100 possessions (The lower the number, the better the defense.) There were not significant differences between any particular teams, but there was a 4th order trend (p = .023) that would seem to suggest that at least teams were different enough from the predicted trend that they stood out (1992, 2004, and 2012). However, this does not tell us anything about how the teams are different from each other, only how they are different from an overall trend moving from 1992 until 2012. Moving from oldest to most recent, the average defensive rating by year was: 1992 (102.55), 1996 (103.92), 2000 (103.50), 2004 (101.18), 2008 (106.00), and 2012 (102.91).

Field Goal Percentage

Field Goal

Field Goal percentage is how many shots were made relative to how many shots were taken. (This number excludes free throw percentage.) While there were no differences between the different Olympic teams, a quadratic trend was observed (p = .023) that shows a clear drop-off during the 2004 Olympics. It should be noted (again) that the 2004 team was the only team since 1992 not to win a gold medal. The averages by year were: 1992 (51.3%), 1996 (50.0%), 2000 (46.6%), 2004 (45.4%), 2008 (48.5%), and 2012 (48.2%).

Usage Percentage

Usage

Usage percentage is an estimate of the percentage of team plays used by a player while he was on the floor. The graph would suggest much more is in play than the statistics reveal: there was nether an overall trend nor any differences between the groups. It is interesting to consider the averages from each team: 1992 (25.7%), 1996 (26.8%), 2000 (25.0%), 2004 (26.4%), 2008 (26.7 %), and 2012 (27.0%).
Height

Once again (thanks to the scale) there appears to be an overall trend of a small line-up, but there was neither a trend nor group differences to support that hypothesis. The means would suggest incremental differences (hence the lack of significance): 1992 (79.73), 1996 (79.75), 2000 (78.83), 2004 (78.91), 2008 (78.50), and 2012 (78.46).

Offensive Rating

Offrat

Offensive rating is the points produced per 100 possessions. There were indeed significant differences between the Olympic teams, F(5,63) = 5.54, p < .001. The 2004 team (109.17) had a significantly lower offensive rating than the 1992 team (117.27), the 1996 team (115.92), and the 2008 team (113.50). The 2000 team (109.17) and the 2012 team (111.09) were not different from any other group. There was as well a linear (p = .014) and quadratic (p = .002) trend, which would suggest a gradual decrease in offensive rating as well a dramatic dip in 2004.

Weight

Weight

Yet another statistic that was not meaningful in terms of helping us differentiate between teams. The averages (in pounds) are pretty close: 1992 (217.91), 1996 (224.75), 2000 (209.75), 2004 (221.82), 2008 (219.42), and 2012 (216.27).

True Shooting Percentage

True Shoot

True shooting percentage is a measure of shooting efficiency that takes into account field goals, 3-point field goals, and free throws. There was indeed a difference in Olympic teams, F(5, 63) 4.895 , p = .001. The 2004 Olympic team (52.4%) was significantly lower than the 1992 team (58.2%), the 1996 team (58.2%), and the 2012 team (58.1%). The 2000 team (54.4%) and the 2008 (56.6%) were not significantly different from any other team. There was as well a quadratic trend (p < .001), which would seem to suggest a subtle drop-off in shooting efficiency in 2004.

Points Per Game

PPG

While the graph of PPG (Points Per Game) looks like there is something going on, I can assure you that there is nothing of statistical significance. The average points per game by Olympic year are as follows: 1992 (23.23), 1996 (22.21), 2000 (19.58), 2004 (19.79), 2008 (21.61), and 2012 (21.50).

PER (Player Efficiency Rating)

PER

John Hollinger created the PER statistic that he described as thus: “The PER sums up all a player’s positive accomplishments, subtracts the negative accomplishments, and returns a per-minute rating of a player’s performance.” There was a statistical difference between Olympic years, F(5,63) = 2.901, p = .020. However, follow-up tests yielded no difference between individual groups. The means are as follows: 1992 (23.71), 1996 (23.28), 2000 (19.96), 2004 (19.76), 2008 (22.06), and 2012 (22.99). There was a quadratic trend as well ( p = .003) that would suggest a lull during the 2000 and 2004 teams in terms of efficiency ratings of the roster.

Assists

AST

The Olympic teams were not significantly different from each other in terms of average assists per game. The averages for each year were as follows: 1992 (6.07), 1996 (5.18), 2000 (4.43), 2004 (4.18), 2008 (5.72), and 2012 (4.84)

Total Rebounds

TOTReb

Sadly, total rebounds per game lacked a significant result as well. The averages were: 1992 (8.37), 1996 (7.42), 2000 (6.79), 2004 (7.27), 2008 (6.90), and 2012 (6.56).

Offensive Rebounds

OffReb

It should come as no surprise that offensive rebounds lacked significant findings as well. The averages were as follows: 1992 (2.16), 1996 (1.96), 2000 (1.83), 2004 (1.96), 2008 (1.62), and 2012 (1.48).

Conclusions

Making sweeping conclusions about statistical data is part of the fun for me. Basketball is a team sport where one individual can dramatically change the course of a game. An Olympic team is, in theory, comprised of the best players at a given time. The only team that was significantly different than any other year was the 2004 team, which was the only team since 1992 to not win a gold medal.

Does that mean we can say that the other teams are better?

Not necessarily.

All we can really say is that the other teams were more similar to one another (teams that won a gold medal) than they were different from the 2004 bronze-medal winning team. I imagine that is slightly anti-climatic. The reality is that looking at averages of all positions created a homogenized sample.

 

Pulling on Superman’s Cape: A Brief Perspective of Sports Heroes in a Modern World

Once upon a time, sports were a metaphor for the strength of the human spirit; the tenuous connection between human and myth brought to life when the cameras rolled – in the modern age. The hero’s journey begins in obscurity, and it is the rise to fame – to prestige – that comes to define how we see them; it is in actions tempered by adversity and the opposing forces in their lives, on and off the court.We have lived long enough to see the death of the sports hero.

No longer is the player who puts on the bold colors of a hometown team a hero, but rather a clever con to the average sports fan. Cynicism aside, where to do we go from here? Strikes have become less about the love of the game and more about shouting matches between players and owners for the shares in a billion-dollar enterprise. When we talk about the pillars of the game in this era, we talk about individual players, not the teams they played for. Every player who can manage to carry a team a few games is ready to look for greener pastures, to play a market flooded with politics and consumerism. The real question becomes: What does it take to be a sports hero? When I think back to the posters I had on my wall, I am reminded of the players who transcended the game.

Let’s keep this to one sport, as I could very easily become highly tangential and follow a course through every major professional sport – with varying degrees of deviation from the mysticism of a sports hero. Basketball has always had an appeal to the average sports consumer who, if they wished, could set up a court in their backyard or beneath the rolling door of their garage.

Legends have played the game. Some – like Jerry West and Michael Jordan – have been immortalized on the memorabilia passed down through the canon of sports discourse. What a player does on and off the court determines the level to which we should respect and talk about them. In the modern area, the notion of the greatest to play the game has become a purely statistical question, and not a qualitative assessment of what a player has given to the game. When we discuss where a player as physically gifted as Lebron James is in the halls of the mighty basketball gods, we are not all asking the same questions. Statistically, his average points, rebounds, and assists over the course of his career are astounding – easily placing him among the greatest if we go by the numbers. His PER (Player Efficiency Rating) this season alone borders on the incredible. Taking another superstar of this generation into consideration, Kobe Bryant, we see a similar pattern of incredible numbers and statistical outliers. There is something to be said about the skills of each player, their strengths and weaknesses. But when considering both of these men, we wonder about some of the qualitative aspects of the game in lieu of the numbers they put up. What is truly the difference between these two men in terms of their place among the greats?

Leadership: to me the glaring difference between the two men is leading teams through a season, navigating the ups-and-downs, and being the type of player that is needed. This is not to say that James has not done this, but he certainly cannot claim the same level of authority and je nais se qua on the field that Bryant has exuded his entire career – most notably the two titles runs during the post-Shaq years.

A sports hero is more than a mechanized entity capable of putting up astronomical numbers. On any given night, a player can channel one of the greats and perform at a level that appears otherworldly. But putting any one of those nights in perspective is important in understanding how one becomes a sports legend, and to a greater extent, a sports hero. Being able to do it one night, or one year, is not the same as playing the game with heart. There was a shift somewhere – if you were to ask my father’s generation, it was televising games – that replaced heroes of the game, those men and women of the sport who put everything on the line, with iconic statues like the Colossus of Rhodes. A hero of the game rises up when others might stay down, stands and battles when others might linger behind.

As we watched the madness of the Dwight Howard saga, which has in many ways sullied the otherwise superhero-like veneer of arguably the best center in the league, we are struck by a simple question: What does the game mean to this generation’s players? Calling for a coach’s dismissal and then holding a team hostage as he negotiates with larger markets certainly makes Lebron James’ ill-fated Decision seem less of an anomaly among the modern superstar.

There is something to be said of the child who as posters of sports icons on their walls, and what those giants of the game mean to them. Is it the case that they want to exude the same strength of character, the pioneering spirit that makes them transcend the game and inspire us to be better people? Or is that we want the same money, access, and prestige awarded with being immortalized so brazenly on the white plaster of a Midwestern bedroom? How do we reconcile heroism and the iconoclastic nature of the modern sports figure? Are we to blame the game, and not the player, as we so glibly comment in self-defense?

Unfortunately, we now only hear the negative aspects of a player’s life: dogfighting, murder, attempted rape, gun possession. The 2011-2012 regular season will be remembered for two things: the truncated, lockout-induced season and the vicious elbow Metta World Peace, the player formerly known as Ron Artest, visited upon an unsuspecting James Harden, making us forget all of the positive things he had done to distance himself from his previous image. We wonder, as intellectually honest people, if the seven-game suspension will be enough to deter this kind of behavior, but a question of whether or not the impetus for such a short suspension is an appeal to higher ratings for another possible altercation on the court is certainly forefront in my mind.

When we compare Michael Jordan, the player, to Michael Jordan, the owner, we are struck by the reality of possibility the greatest player to every don a basketball jersey being associated with the worst record in the league. In the legacy of the greatest basketball player ever, there is now an asterisk beside his name: the worst regular season record of any team as owner of the Charlotte Bobcats, and being the type of owner to hide behind yes-men and being responsible for the destruction of a culture that values being a team. Juxtapose this with the heir apparent for Jordan’s crown – Kobe Bryant – sitting out the last game of regular season, and allowing Kevin Durant to assume the scoring title for the season. If the selfishness of this season was not egregious enough, we have Amar’e Stoudemire punching a pane of glass after a loss in Miami. Are either of these the actions of the competitive titans we ascribe them to be in the annals of NBA history, or is a sad statement on the power of sports culture at large? Are we to blame of culture of sports that supports a cookie cutter factory of producing spotlight stars that fill stats sheets and highlight reels? Can we really blame that on players who simply want to make the best possible living with the skill set they are most adept with? Do guys like Jeremy Lin – steeped in hard luck stories and meteoric rises – present the greatest opportunity to revive heroism in sports?

I think the answer is much simpler than that. We often look externally for answers to the problems of our lives, and this problem of heroism in the modern sports figure is no different. The athlete does not have an obligation to be a hero, but rather chooses to be perceived one way or another based on the manner with which fans reinforce their behavior. We want to see more offense and less defense; we want more dunks than lay-ups. Is it any real surprise that vaulting over a Kia represents a shining moment in sports history when its value is traded so highly among fans? If we want to see a game where teams don’t throw in the towel at the end of the season to get a higher draft pick, then we need to demand it. Don’t like players who use their status to influence the trajectory of the game, then don’t support those athletes – invest your money in someone else. Heroes carry with them the dreams and hopes of those who aspire to be great. They are often lost in the wilderness and through the context of their lives find what it takes to be great, to be a hero. We have to want to be better, and we have to demand more of the sports we love and the men and women who play the game. We must help them in their journey. We must start looking for heroes once more.