Controlling Baseball Salaries

As you may have noticed, I’m not putting a lot on this site. I have to be inspired to figure something out, or be inspired to write at a time that I actually have time.

That being said, Jason Stark at ESPN wrote an interesting article the other week.  In his Rumblings and Grumblings column, Jason talked about Pushing for a minimum payroll threshold. In it, he discussed how teams get a LOT of money from the luxury tax on salaries.  He then suggested that their should be a penalty for teams that don’t spend a lot of money.

I had thought a lot about that previously and had come up with a system.  Essentially, I think that both the luxury tax, and the related reduction of that cap for “cheap” teams, should be based upon a more statistical measurement rather than some arbitrarily set maximum and minimum. By basing it on the number of standard deviations from the average (mean) salary, the amount would be market based.  This would make the levels change based upon the current spending levels.

The upshot is that if a team maintained a constant payroll, and the average increased, then they would pay less tax, or collect more tax.

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Still Left On Base

Shortly after my last post on this topic, Derek Jacques posted a follow-up post titled Still Stranded.  I just got to read it today as it is a locked-down article and I’m not a subscriber.  It is a free trial this week, so if you aren’t a Baseball Prospectus subscriber, hurry on down and read it.

Derek looks at several additional methods that were presented as feedback to his article, including my LOBR (R/LOB).  I was pretty pleased with that.  When he compared the results, the correlation of my method, 0.87, using data back to 1971 was highest of those using LOB (or BLOB).  The correlation of TOB to runs was much higher (called BRE in my last post), but that is to be expected (obvious statement #2 from the last post) and wasn’t the point of the original question.  All those numbers for 2007 are in my last post as well as in Derek’s 2nd article, just with different headers.

What is a Good LOB?

The original question in the article Stranded!, from BP reader JP, was:

Does the ‘Left on Base’ Statistic have any correlation to a team’s offensive success or failure?

My answer is simple.  YES!!!  However, just don’t look at the raw number.  Compare it to the Runs.  If the ratio, LOBR, is greater that 2:3 (R/LOB > .67) then your team did a decent job, win or lose.  If they perform higher than that over the course of the year, they’ll likely be in the top half in Runs Scored.

Looking at the All-Star game this year, the score was 4-3 for the American League.  However, the LOBR wasn’t pretty for either team.  The AL had a LOBR of 0.235 and the NL had a LOBR of 0.273.  The NL had the better LOBR, but it was horrible and only a slight advantage.

In a more day-to-day measure, the Braves just played a three game set to the Nationals.  Here are their LOBR stats (LOB).  Guess which games they won.

  • Braves 1.400 to Nats 0.857
  • 0.250 to 1.000
  • 0.545 to 1.250

If you said that the Braves won the first game and then lost the next two to the Nats, you are correct.  In the first game, the LOB is pretty close, as was the score, 7-6.  In the second game, the LOB was the same, but when you look at the LOBR, you can tell that the score wasn’t close.  It wasn’t as it was 8-2.  The third one finished with a nastier score of 15-6.

These games are a perfect example of looking at LOB in context.  By itself, it doesn’t mean much.  When you use Runs, you can then determine if your team is failing to get those runs around.  The Nationals had a higher LOB in two of the games, the big blowout and the one that they lost.  In the smaller blowout, both teams had the same number of runs.  The Nationals were more efficient (1.074 LOBR for the Nats vs 0.625 for the Braves) in the series and won it.

More runners is still key and will always give a better correlation, especially over time.  LOBR will give you instant feedback on how effective your offense was firing on any given day, or for the whole season.

Left On Base, the Hidden Enemy

Recently, Derek Jacques at Baseball Prospectus wrote a great article called Stranded! It’s focus was trying to determine if the Batters Left on Base (BLOB) statistic for a team was a measure of offensive success or failure. It was a fascinating article. It confirmed some common sense and completely missed the crux of the matter. I sent him a quick note and I will share and elaborate here.

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