Below I explain (part of the reason) why.
In order to understand the level of competitive balance across the four leagues, we need a way to measure this phenomenon. There are a few different tools for this job, and we will use a few of them. The first one we will use is, conveniently, the same tool we used to measure payroll disparities: the Gini Coefficient.
Competitive imbalances should reveal themselves in an unequal distribution of wins across teams in a league during a given period of time. A chart illustrating the variance of win inequality in the MLB, NFL, NBA and NHL (across time) is posted below:
The chart above paints a rather interesting and unexpected picture: the MLB has the lowest level of win inequality, followed by the NHL, NBA and NFL. In other words, the average maldistribution of wins is in fact the perfect opposite of what we see when we model the disparity of payrolls. As you can see, these figures are rather stable over time.
What might account for this reversal in disparities? Some of this variance is certainly due to granularity issues in calculating the Gini coefficient, considering the fact that there are only 16 games in an NFL season versus 162 games in an MLB season (the NBA and NHL falling in the middle at 82).
Why should this matter? Consider the following thought experiment: if we determined game outcomes by coin flip, no team should fare any better than another if they played an infinite number of games. Just the same, the Gini coefficient of the win distribution in this universe should approach 0.00 as the season length approaches infinity. But as we reduce the number of games per season (and coin flips), a disparity should arise as random chance takes over: 162 flips per team per season and we should see a small disparity; 16 flips per team and we should see a very large one, along with a larger Gini coefficient that increases exponentially. At one game, the Gini Coefficient would equal 1.00, or perfectly unequal.
Therefore, we should expect to see an unusually high disparity in the win distribution for the NFL simply due to the short schedule. However, there are ways of compensating for this measurement error, and this effect does not account for the entire variance between coefficients (more on that below). Moreover, this effect does not account for the variance in competitive balance in the NBA and NHL (each with 82 game seasons), or the fact that the MLB is more balanced than either of those leagues (the level of error in a 162 game season not being that much greater than in a season of 82 games).
Something interesting is going on here, and I can confidently say that I'm not sure what it is. On the other hand, I have a pretty good idea of what this means, especially after analyzing at the chart below:
The graph above plots the disparity in the distribution of regular season wins against the disparity of payroll distribution, separately labeling each league. Even though there is a small but positive relationship between payroll inequality and competitive imbalance in hockey and baseball, the overall relationship is just the opposite: league-seasons with the greatest level of payroll inequality have the best competitive balance when we don't control for the leagues individually. The relationship is confirmed by an Ordinary Least Squares analysis of the data, presented below:
|IV||Coefficient||Std. Error||P-value||*NHL Dropped|
|Y-Intercept||0.1035||0.9874||0.917||Adjusted R^2 =||0.9324|
The relationship we see in the previous chart is entirely explained by the inherent levels of competitive balance within (but not across) the leagues. In other words, the non-league-controlled trend is inverse simply because the MLB and NHL enjoy greater levels of competitive balance than the NFL and NBA. When we control for this effect, the source of which is unclear, there is a significantly positive relationship between payroll inequality and competitive imbalance.
In layperson's terms, a 0.10 increase in the level of payroll inequality should result in an approximately 0.01 increase in the level of competitive imbalance. At the same time, the regression fails to reject the hypothesis that the MLB is inherently more balanced* than the NFL and NBA: a typical baseball season is 4% more balanced than the average season during any given league-year, while NFL seasons are 11% less balanced, and NBA seasons are about 6% less balanced.
*Analysis with dummy variables requires the researcher to drop one of the leagues from the analysis. When the NHL is included and the MLB excluded, we see a negative but insignificant relationship between the NHL binary and competitive imbalance. Moreover, a more rigorous analysis would have included interaction terms rather than simple controls, but such an analysis would have required a larger sample.
As I wrote above, some of the variance among Gini Coefficients is the result of short-season bias. We can compensate for this by looking at ten full seasons for each NFL team rather than one. But as the graph above indicates, even when compensating for this bias, the NFL retains the largest competitive imbalance of all the leagues, with a 10-year Gini Coefficient higher than any single-year MLB value (although the NFL coefficient drops a full 0.10 when expanding the sample to 160 games).
So there you have it--based on win distributions, the MLB is clearly the most balanced American sports league, and the NFL the least balanced, contrary to popular opinion. This tells us that there is something inherent in baseball that is generating a great deal of fairness for the teams that play, regardless of payroll disparity. This also raises the possibility that Baseball's "competitive balance problem" may be nothing more than a public relations problem (which isn't insignificant, it's just not a problem that can be fixed by modifying the distribution of payrolls).
That said, we don't yet have enough evidence to make this claim just yet. A deeper investigation of the level of competitive balance in baseball and other sports requires more than a look at regular season win distributions. We also need to look at the distribution of playoff appearances, as well as the volatility of win totals from year to year (what sociologists and economists would refer to as "mobility" were we discussing household and personal incomes rather than success in sport).
Stop by next week as we investigate these phenomena in greater detail for Part II of RPBlog's Competitive Balance series.
Competitive Balance Series
Prologue: Payroll Inequality
Part I: Regular Season Competitive Balance