By Brian Burke
Imagine a world in which baseball teams had no idea how good or bad a player could be expected to perform from year to year. All players would be total blank-slate mysteries. In this world, it wouldn't matter at all how high or low a team’s payroll is. A team that spends $1M on payroll would be just as likely to win a championship as a team that spent $200M. The correlation between payroll and wins would be zero.
Now imagine the opposite scenario in which teams had perfect crystal-ball foresight about exactly how well a player could be expected to perform. Now the team with the highest payroll would always have the most wins because there would be a 1:1 direct linkage between performance and pay. This would make the correlation between payroll and wins a perfect 1. We wouldn't even need to play out the season. Just mail the trophy to the team with the highest payroll and save everyone the trouble.
In reality we’re about halfway between the first case and the second case. We neither have no idea about expected performance, nor do we have perfect predictive knowledge. MLB's correlation between team payroll and wins has been trending at about r = 0.4. It varies from season to season, but its recent central tendency appears to be right round 0.4.
The problem that analytics causes is this: The better the sabermetrics (or the more uniformly adopted it becomes), the better the crystal ball becomes and the closer we move toward a correlation of 1...which, by the way, is a bad thing. Of course, we'd never see a correlation of 1 because of injuries and simple randomness, but it could get much higher as it did in earlier eras. The correlation has been higher and lower in years past, varying for reasons that have nothing to do with analytics. But the point is that sabermetrics, to the degree it is effective, makes the correlation higher than it otherwise would be, and this gives an ever greater advantage to big-payroll teams.
I sense there is a widespread misunderstanding about sabermetrics and analytics, probably attributable to Moneyball. It doesn't even the playing field as many think. Sabermetrics allowed a small-market team like the A's to compete with the big boys only because it was the sole team using the right analytic tools and could exploit a market inefficiency. Once all the teams jumped on the sabermetric bandwagon, the advantage was not only lost, but turned on its head as the correlation between pay and wins improved.
Additionally, sabermetrics has helped make MLB depressingly predictable, despite some rare notable surprises. Last night, the team with the 4th most all-time championships beat the team with the 2nd most all-time championships. Sure, the Red Sox went from worst to first in their division, which to the naive observer probably seems amazing. But if you know your PECOTA, you knew last spring they would be strong contenders and among the favorites to win the World Series.
The really depressing thing is that MLB apparently likes things this way.
The short-term interest of both MLB and their broadcast partners is to see large-market teams dominate the post-season, because gives a temporary bump to broadcast ratings. But the lack of churn in the stratum of top teams comes at the expense of developing a long-term national following, where every fan has hope in April, at least every few years. The result of MLB's shortsightedness is that more sports fans will now watch a meaningless week 7 NFL game featuring teams with the worst combined winning percentage of any Monday night game in history than Game 1 of the World Series. I don't say that to gloat as a football guy; I am lamenting the national irrelevance of a sport I love.
MLB seems to be in a self-devouring spiral in which interest is dwindling nationally but strengthening locally, particularly in large markets. This is partly due to the lack of churn among top teams and a high level of predictability. Every season Brewers fans know deep down they have virtually zero chance to win a championship, but Packers fans always feel they have a decent shot. We know before the first pitch of the season how teams are probably going to finish. There is relatively little drama in the regular season, and if there is, the chances your team will be a part of the conversation are slim…except in July when your team is trading its best player to a big-market contender for cash and a player to be named later. Compare this to the NFL where guessing 8-8 for every team's record is imperceptibly less accurate than the best predictive analysis.
Defenders of MLB's status quo will quickly point to the fact that raw attendance numbers have been climbing to record levels. But as a proportion of the population, attendance has been flat since the the late 1990s when MLB added a 29th and 30th team. So I think MLB needs less predictability, and unfortunately sabermetrics makes things more predictable.
Don't get me wrong. Of course I'm a numbers guy and I understand the power and limitations of statistics. I'm a fan of analytics in general. Sabermetrics isn't a bad thing. It's that widely-adopted sabermetrics combined with a lack of a salary cap can't help but create a greater concentration of talent on large-market teams.
The NFL's correlation between salary and team wins is much lower, near 0.15 the last time I measured it. The difference is due to a lot of things, but primarily due to the short season and relatively narrow range of team salaries. The NFL has both a cap and a floor for team payroll, which keeps things less predictable. And I think a hard salary cap and floor are what MLB needs if it wants to regain the national attention it once had.
published on 10/31/2013 in opinion