Last week, we had a rousing discussion about RE24.  We went over what it means, how to read the RE24 matrices, and alluded this this week’s topic.  This week’s topic, of course, is productive outs.  More specifically, are they actually productive and how can we use the RE24 framework to aid our decision making process?

The idea behind productive outs is very simple.  If you have to make an out, it would be preferable if the out would affect a positive change on the field, such as advancing a base runner.  I do not think anyone will ever argue that point.

However, there seems to be a disconnect between folks in baseball, fans, pundits, and analysts regarding the importance of productive outs.  Some players, seemingly, are attempting to record a productive out rather than just attempting to get on base and avoid an out at all costs.  My contention is that productive outs should be something that happen at random and we’re happy.  They should not be something a player strives for, except in very fringe cases.

Let us start with sacrifice flies.  A sacrifice fly, in the simplest example, is the trading of an out for a run.  This assumes the runner is not thrown out; then it becomes a trade of a base runner on third for an extra out.  That is a bad trade.  Here’s a chart with every sac fly situation (unless I missed one), along with the beginning and ending base-out states (BOS) and how much run expectancy (RE) was added as a result of the play:

chart1

The first thing that jumps out is likely the amount of red and green in the “RE Added” column.  Turns out 11 of the 16 sac fly scenarios end up decreasing run expectancy, even if a run scored.  Looking at the green scenarios lets us garner an interesting piece of data; 4 of the 5 positive outcomes come with 1 out.  As a general rule of thumb, we can say a sac fly is a more valuable play when it comes with 1 out rather than 0 outs.   The 5 worst outcomes come with 0 outs.  This is likely due to the “rally killer” effect that happens when you record an out and also trade your farthest-advanced base runner for a single run.

If a sac fly happens randomly, it’s a good thing.  It’s good to get a run on the board instead of striking out, for example.  However, if a player changes his approach in order to try and achieve the result of a sac fly, he could actually be hurting the team.  The better thing to do is stay with your normal approach and just try to get on base.  If a sac fly happens, view it as a silver lining. Never strive to record a sac fly.

Another type of productive out comes from attempting to hit behind a runner.  Again, if you hit behind a runner in your normal course of action, that’s great.  You advanced a runner.  However if you hamper your natural ability to get on base by attempting to hit behind the runner at all costs, you’ve likely hurt your team.  Here’s the pertinent table:

chart2

Perhaps a more interesting case than hitting behind the runner and sac flies are sac bunts, since they don’t happen by accident.  Because of this, we can do some math and figure out the actual run expectancy change by simply making the decision to bunt.   We are going to use a situation that came up during Brandon Finnegan’s no-hit bid on Monday night in Chicago.  More specifically, we will analyze a sac bunt situation with a man on 2nd and no outs.  At the beginning of this play, RE stood at 1.1 runs.  If the sac bunt happens as planned, the runner will reach 3rd base and Finnegan would be thrown out at 1st. This is defined as “success.” At that point, the RE of the inning would have stood at 0.95 runs; a decrease of 0.15 runs from the bunt.

To pull the thread a bit further, we know that not all bunts are successful, right?  Sometimes a pitcher strikes out.  Sometimes he gets to 2 strikes and the manager takes the bunt off.  Sometimes he’ll get a hit in that situation.  How do we make sense of all those different outcomes, you might ask? A table, of course!

chart3

For this table, I’m assuming an 80% chance of getting the bunt down.  Maybe it’s higher, maybe it’s lower.  We’ll go with 80%.  Also, once the bunt is down, I’m saying there’s an 80% chance it’ll be successful, and a 20% chance that the lead runner will be thrown out in some manner.  You can see the other outcomes and the percentages I applied to each other transition event.   If you fail to bunt and record a “bad out,” that means you created an out without advancing the runner.  Sometimes, you’ll fail to bunt and then hit behind the runner, advancing him anyways.  Sometimes you’ll get a hit!

Now, look closely at the Delta RE and Weighted Delta RE column.  Each transition event has an associated ending RE.  Calculate the different between beginning and ending, then multiply by the percent chance to occur, and we have our Weighted Delta RE metric per event.  If you sum all of those up, you end up with the total expected change in RE that is affected by simply making the decision.  As you can see, in this situation (given my assumptions) simply making the decision to bunt destroys 0.264 runs.

You might be thinking, “Dangit, Jeter! Finnegan is a pitcher!  He’s probably going to get out anyways if you let him hit!”  Well, perhaps.  But, he also may record a productive out which would be equivalent to a bunt.  He might single and knock the run in.  He might homer.  To wit:

chart4

The percentages on this chart were calculated a bit more “scientifically.”  In 2015, the average NL pitcher hit .132/.159/.169 with a 2.6% walk rate and a 37.2% strikeout rate.  Finnegan, by virtue of hitting safely in his first two starts this year, seems to be an above average hitting pitcher.  I’m going to make the not-so-bold claim that Finnegan could be a true-talent .152/.183/.194 hitter with a 3.0% walk rate and a 31.6% strikeout rate.

Also, the average NL pitcher had the following batted ball characteristics:  a 61.1% ground ball rate, a 5.8% bunt hit rate, a 26.3% pull rate, and a 40.2% up-the-middle rate.  All of that data is used to determine Finnegan’s chances of recording a productive out, such as hitting behind the runner.  If this happens, he advanced the runner anyways, so it was equivalent to having a successful bunt.  On the chart above, you’ll see most of the possibilities of what can happen broken out, as accurately as I can estimate on my lunch breaks.

So, with everything added together, letting Finnegan hit would result in…a decrease in RE of 0.152 runs.  Hrmmm.  That’s not very fun.   It’s not as bad as making him bunt (-0.264, as a reminder), though.  If you have a pitcher than can handle the bat, it’s generally going to be a better to let him hit than make him bunt, at least in a no-out, runner on 2nd situation.  However, pitchers hitting is a negative for the team.  There’s really no way to gussy it up

THE TAKEAWAY

Really, the only thing I hope we learned today is that productive outs are fine.  They represented a silver lining that is nice to have if your at-bat didn’t end up getting on base.  However, going out of your way to induce a “productive out” is generally a bad thing to do in most situations.

Doing a little research on my lunch breaks this week got me to this point.  What could a front office with an analytics team do?  Much more, I’d hope.  Every manager should understand how to break down the pros and cons of his decisions.  Maybe he won’t have tables like these in the dugout to look at, but he should know already the principles described herein.

Next week will be the last in the RE24 mini-series and we’ll discuss weird situations and try to put it all together.  Thanks for reading!