The Baseball Economist: The Real Game Exposed by J.C. Bradbury Page A
Authors: J.C. Bradbury
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factors.
Measuring Big-Market Advantage
In order to determine whether or not there exists a problem that needs correcting we must measure how market size translates into wins and losses. Do big-market teams have an insurmountable advantage over clubs in small markets? To find the answer, I use regression analysis to measure the effect of city size on wins. Using the metropolitan population of MLB cities as a proxy for the size of the fan base, the regression estimates the magnitude of the impact of population size on on-field success. The regression uses the data to identify how much differences in population are associated with differences in wins. With this information the regression procedure generates a predicted number of wins based on population size. 45
The results from the analysis both confirm and reject some widely held beliefs regarding market size and winning. It is true that larger populations are associated with more wins than smaller markets; however, the magnitude of the impact explains only a minority of the difference in wins between the best and worst teams in recent history. Figure 2 maps population and average wins per season.
Each point in the figure plots the average wins by team from 1995 to 2004 and the population of the metropolitan area of the city as measured by the 2000 U.S. Census. The upward-sloping line shows the estimated relationship between wins and city size. The upward slope of the line matches the casual observation that for the past decade teams in big cities have won more games than teams in smaller cities. This is consistent with the theoretical prediction that big cities have more revenue than small cities to use on free agents, coaches, management, minor leagues, etc.
However, the story does not end here; the real question is how large is the big-city advantage? The regression estimates that every 1.58 million residents generates one extra win per season. For illustration, the largest market (New York) is expected to win 10.61 more games than the smallest market (Milwaukee) in terms of wins predicted solely by population. In this sample, the most successful of the New York teams
(the Yankees) won an average of 26.3 games more than the Milwaukee Brewers. This means the difference in market size explains about 40 percent of the difference in wins between the top and bottom markets. Forty percent isn’t chump change, but what about the other 60 percent? These factors include the ineptitude and skill displayed by the front offices of these organizations.
On the ineptitude side, Joe Posnanski of the Kansas City Star recounts a story that points to an example of mismanagement of the Kansas City Royals—the second smallest MLB market and a perennial looser—that ran sabermetrics guru Bill James out of town and into the open arms of the Boston Red Sox:
It was Brent Mayne who finally broke Bill James. It’s nothing personal. Mayne is a fine fellow. But he’s also a 34-year-old catcher who hit .236 with no power, ran like he was a mime fighting the wind, guided the Royals pitchers to the second-worst ERA in baseball and got paid $2.5 million. This year, the financially strapped Royals will pay him $2.75 million. Meanwhile, catcher A. J. Hinch, who hit .298 the last two months of the season, banged the ball with significantly more power than Mayne and had a much better record behind the plate—plus, he’s a bright, loyal team player who got paid $250,000—was cut during the offseason. And that’s when James threw his hands up in the air. It’s not that he thinks Hinch is Johnny Bench or that he blames Mayne for the Royals’ downfall. It’s not that this was the dumbest thing the Royals have done, or even in the top 100. No, it’s just another spectacularly illogical move by a team that has become the new sports leader in spectacularly illogical moves. This is just the move that finally pushed Bill over the cliff. 46
If you are looking to blame something for the woes of your
Measuring Big-Market Advantage
In order to determine whether or not there exists a problem that needs correcting we must measure how market size translates into wins and losses. Do big-market teams have an insurmountable advantage over clubs in small markets? To find the answer, I use regression analysis to measure the effect of city size on wins. Using the metropolitan population of MLB cities as a proxy for the size of the fan base, the regression estimates the magnitude of the impact of population size on on-field success. The regression uses the data to identify how much differences in population are associated with differences in wins. With this information the regression procedure generates a predicted number of wins based on population size. 45
The results from the analysis both confirm and reject some widely held beliefs regarding market size and winning. It is true that larger populations are associated with more wins than smaller markets; however, the magnitude of the impact explains only a minority of the difference in wins between the best and worst teams in recent history. Figure 2 maps population and average wins per season.
Each point in the figure plots the average wins by team from 1995 to 2004 and the population of the metropolitan area of the city as measured by the 2000 U.S. Census. The upward-sloping line shows the estimated relationship between wins and city size. The upward slope of the line matches the casual observation that for the past decade teams in big cities have won more games than teams in smaller cities. This is consistent with the theoretical prediction that big cities have more revenue than small cities to use on free agents, coaches, management, minor leagues, etc.
However, the story does not end here; the real question is how large is the big-city advantage? The regression estimates that every 1.58 million residents generates one extra win per season. For illustration, the largest market (New York) is expected to win 10.61 more games than the smallest market (Milwaukee) in terms of wins predicted solely by population. In this sample, the most successful of the New York teams
(the Yankees) won an average of 26.3 games more than the Milwaukee Brewers. This means the difference in market size explains about 40 percent of the difference in wins between the top and bottom markets. Forty percent isn’t chump change, but what about the other 60 percent? These factors include the ineptitude and skill displayed by the front offices of these organizations.
On the ineptitude side, Joe Posnanski of the Kansas City Star recounts a story that points to an example of mismanagement of the Kansas City Royals—the second smallest MLB market and a perennial looser—that ran sabermetrics guru Bill James out of town and into the open arms of the Boston Red Sox:
It was Brent Mayne who finally broke Bill James. It’s nothing personal. Mayne is a fine fellow. But he’s also a 34-year-old catcher who hit .236 with no power, ran like he was a mime fighting the wind, guided the Royals pitchers to the second-worst ERA in baseball and got paid $2.5 million. This year, the financially strapped Royals will pay him $2.75 million. Meanwhile, catcher A. J. Hinch, who hit .298 the last two months of the season, banged the ball with significantly more power than Mayne and had a much better record behind the plate—plus, he’s a bright, loyal team player who got paid $250,000—was cut during the offseason. And that’s when James threw his hands up in the air. It’s not that he thinks Hinch is Johnny Bench or that he blames Mayne for the Royals’ downfall. It’s not that this was the dumbest thing the Royals have done, or even in the top 100. No, it’s just another spectacularly illogical move by a team that has become the new sports leader in spectacularly illogical moves. This is just the move that finally pushed Bill over the cliff. 46
If you are looking to blame something for the woes of your
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