A New Way to Measure Payroll Efficiency (And what it says about the Yankees and Parity)

By David Roher

Note: This article originally appeared on the Huffington Post.

Did the 2009 New York Yankees have the most efficient payroll in baseball?

According to some recent research we’ve done, they did indeed.

Just not in the conventional sense. If the above assertion struck you as ridiculous, it’s because you think of payroll efficiency in the same way as most sabermetricians. Current studies that focus upon how well teams spend their money do so on a large scale: they almost always examine the relationship between how much the team spends and how much the team wins.

This is an intuitive and highly intelligent way of looking at the issue, but it’s not the only way we can do it. Daniel Adler’s recent research, the subject of a Wall Street Journal piece, showed that the relationship between salary and wins isn’t as clear as we’d like it to be. The problem is that we’re not moving out of this “Dollars per Win” framework. There are many ways of looking at how intelligently a team spends its money. Some new methodology can’t hurt, right?

I decided to examine payroll efficiency by looking on a more microscopic level – individual players. The new statistic, calculated with an ingenious formula devised by Dan Yamins, measures payroll distribution on a given team. It holds total wins and total payroll constant from team to team, and instead simply asks, “are the best players on the team making the most money, and vice versa?” I had no idea before I conducted the study if distribution was connected to winning. After all, if a minimum-salary player performed very well, that certainly wouldn’t be a bad thing, but would it be reflected that way in the study.

The Yankees topped the 2009 rankings with an Inefficiency Rating (IR) of just .11, meaning that their salary distribution contained only 11% of the error of their
worst possible salary distribution. This makes a lot of sense: the three players who made the most money, Derek Jeter, Alex Rodriguez, and Mark Teixeira, were also their three most productive, and all 9 players making over $10 million had productive years. Meanwhile, they had few low-salary players with a lot of production, in part because they have so few low-salary players in roles where they could produce. Their archrival Boston Red Sox finished right behind them at .16.

But efficient distribution comes in all market sizes. The Florida Marlins and Minnesota Twins, owners of the lowest and 7th lowest payrolls in baseball, were next in the rankings.

So, does intelligent distribution lead to more wins? Here’s where it get’s interesting: among the 15 highest-spending teams, IR was not effective at predicting a given team’s winning percentage, even when the 103-win Yankees’ season was not excluded as an outlier. However, for the 15 lowest teams, IR was significant. For every percentage point decrease in the index, those teams won another 1.1 games over the course of the season.

A possible conclusion of our study was thus that while teams with large payroll can absorb the hit of bad contracts, small-market teams likely have to be more discerning in whom they sign.

There are two ways to interpret that deduction. Perhaps it shows yet another way in which teams that don’t spend as much are at a disadvantage. It’s also possible that the data shows that big spenders are crucial – if there were no teams with large payrolls, then all bad contracts would be a death sentence, since no team would have a buffer to absorb them through trades.

Which is right? With debate brewing over a salary cap, the commissioner’s office will soon have to decide.

The team-by-team results are below.

Team 2009 IR
Yankees 11%
Red Sox 16%
Marlins 16%
Twins 17%
Cubs 17%
Cardinals 18%
Braves 18%
Phillies 19%
White Sox 19%
Blue Jays 19%
Rangers 20%
Mets 20%
Angels 20%
Orioles 20%
Pirates 20%
Rockies 20%
Dodgers 21%
Astros 21%
Tigers 21%
Mariners 21%
Rays 22%
Indians 22%
Reds 22%
Diamondbacks 24%
Athletics 26%
Brewers 26%
Giants 26%
Nationals 29%
Padres 31%
Royals 31%

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  • Might be fun to look at this kind of data historically, to perhaps judge if a specific GM hiring has an effect of lowering or raising the percentage of a team on this list.

    • Definitely a lot of value in looking back past 09. Unfortunately the process of bringing wins and salary data together (accounting for split seasons, players who weren’t on the roster on Opening Day, players who were paid but never played) for even one year was really work-intensive, so I probably wouldn’t try it until I found some data that already accounted for all that stuff.

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