My attempt at sabermetrics research: Analyzing the sac bunt.

[ArcticKnight]

Hi Kyleb

Is the aluminum bat still in play in the amateur leagues you are refering to? If so, this will impact the answer.

[kyleb]

Yes to some, no to others. We'll go with no.

[J.R.]

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No, it's from 1999-2002 MLB. That's another assumption used in the process. That kind of data is not available in amateur leagues, so I have to use major league data. I used 1999-2002 because it was readily available and even if it changed some for amateur leagues, I would imagine that most of the values would change in a similar fashion (i.e. all of the values are depressed, not just some of them).

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Kyle,

If you want to improve on this assumption you may want to consider trying to get a sense of the run environment in your league and then use Tango's "Run Expectancy by Environment" to get the run expectancies given your amateur league's run environment.


How to use the "Run Expectancy by Environment" document is explained: here


If you have access to league statistics you could really geek out and use this run modeler program from tango to more precisely estimate your league's run environment. This app is discussed here

There are still some issues in translation, but it gets you closer.

[kyleb]

That first document looks impossible to use, but the Markov model would be easier to use for the advanced levels of the amateur leagues I play in, since those stats are recorded.

Awesome! Maybe I'll do this someday.

[J.R.]

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That first document looks impossible to use, but the Markov model would be easier to use for the advanced levels of the amateur leagues I play in, since those stats are recorded.

Awesome! Maybe I'll do this someday.

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Although it looks intimidating, the first document ("Run Expectancy by Environment" ) contains the same information as the Run Expectancy Matrix, 1999-2002 document you used in your original study, (although in a different format), but simply provides all of this info for lots of different run environments (from teams who score 2 runs a game up to teams who score 7.5 runs a game).

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Here’s how to read the first line
In a 2.00 runs per game environment, with the bases empty and 0 outs, this state will occur 25.9% of the time. The run expectancy (RE) is 0.222 (this is the only “duh” part, as it’s 2.00/9). You will be held scoreless from this point onward to the end of the inning 84.9% of the time. You will score exactly 1 run 10.4% of the time.

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For example, the chart shows this same base state (bases empty, 0 outs) yields a run expectancy of .833 in a 7.5 run per game environment , as opposed to the .222 run expectancy in a 2 run per game run environment. So while these are two very extreme run environments, this example does help demonstrate how great of an impact the run environment can have on run expectancy.

The "Run Expectancy Matrix, 1999-2002" run expectancies just about matches the 4.95 run per game environment from the "Run Expectancy by Environment" document. So the 1999-2002 run environment was about 4.95 per team per game.

If you could calculate how many runs your team averages per game (not as accurate as using a run modeler to calculate the run environment because of sample size issues), you could easily adjust the two below assumptions you made in your original study based on your league's run environment to improve your study's conclusions:

Runners on 1st/2nd with 0 outs is worth: 1.573
Runners on 2nd/3rd with 1 out is worth: 1.467

[J.R.]

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I think I'm seeing your point, though - that ExpRuns with a runner on 1st/2nd with 0 outs embodies the sacrifice bunt already based on the chart, so comparing the two situations is not analogous. That being said, I disagree with what you're saying - we're just comparing two gamestates and I really don't see how that affects my analysis at all (or at least in any meaningful sense).

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FWIW, Tango tries to control for this problem in both the Run Expectancy chart you used and the "Run Expectancy by Environment" chart I linked:

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You will of course notice that I removed all 9th and extra innings, which means alot of smallball-9thInning-style data is not represented in the RE charts. This may actually be a good thing, since one-run strategies are best evaluated in terms of “what if I don’t play for 1-run?”. Therefore, these charts are not polluted with such events.

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link

[J.R.]

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It seems as though the breakeven error rate is about 6.69%.

Do I think that amateur players in the leagues I play in commit overthrowing errors on sacrifice bunts at least 7% of the time? Yes, I do.


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FWIW MLB batters reach base on an error about 4% of the time in bunt attempts (fielding and throwing).

Although I can only speculate on the quality of your league, I suspect sacrifice bunt attempts end up as "hits" quite a fair bit, and I suspect this would be a significant enough factor such that it will pull the "actual" break-even down quite a bit lower. This is just my sense based on how significantly the "The Book" demonstrated the defense's positioning (which is a decent proxy for the ability to reach on a bunt hit in a sacrifice situation) impacted the decision to sacrifice.

[kyleb]

Cool stuff. I'd love to look into it more if I have time. I'm going to start a new baseball site that focuses on youth pitching/hitting mechanics, so that's going to take up most of my baseball research time, but I usually find a few hours a week to work on baseball numbers (usually betting).