Tangotiger Blog

Monday, May 10, 2021

Reverse Trout

Mike Trout has reverse platoon splits. This is highly unusual for someone with as much playing time as he’s had. Ichiro is one of the few batters with reverse platoon splits. Mike Trout, against RHP, is +2.2 runs per 100 pitches. This is the highest value in the pitch tracking era. I’ll remind you that Trout is a RHH. Against LHP he is +1.4 runs per 100 pitches. While high, it is not close to being a league-leading figure. J.D. Martinez leads at +2.4 runs per 100 pitches against LHP. He too is a RHH. Trout’s difference, a -0.8 runs per 100 pitches in the reverse direction is by far the league leading figure.

We can look at how Trout does against each pitch type in his career. Against LHP, when they throw a changeup, he is +3.6 runs per 100 pitches, an extremely high number. Against RHP, he is +2.9 runs per 100 pitches, also very high, but naturally not as high. As you would expect of a RHH, he does better against LHP than RHP. In this case, against the changeup, he is +0.7 runs per 100 pitches better.

He also has the platoon advantage with curveballs: +1.9 against LHP and +1.75 against RHP. So, a +0.15 with the platoon advantage.

But that’s where it ends. And pretty dramatically too. With the 4-seam fastball, he’s +1.0 against LHP but +1.9 against RHP. So, he ends up with a -0.9 runs per 100 pitches (the minus signifies that it’s a reverse platoon split).

Slider? +0.2 against LHP, +1.5 against RHP, or a -1.3 reverse platoon split on the slider.

Sinker? +1.0 against LHP, +2.7 against RHP, or a -1.7 reverse split.

Cutter: +1.5 against LHP, +3.4 against RHP, or a -1.9 reverse split.

Now, as I said, Trout is an exception, like Ichiro, when it comes to the reverse splits. How do all batters look? Here it is, since 2018, for LHH and RHH, against each pitch type in terms of their platoon advantage.

To read the first line:

RHH have a +0.74 runs per 100 pitches platoon advantage (they do better against LHP than RHP) against the Slider.

The big winner here is the Sinker for LHH: they demolish RHP. Matt Olson has faced the most sinkers among LHH: he’s -0.25 against LHP and +1.45 against RHP, for a +1.7 platoon advantage. Christian Yelich, +1.8. Michael Brantley, +1.0. Freddie Freeman, +2.7. Joey Votto has reverse splits: -0.5 runs. Cody Bellinger, +2.3. These are the most frequent LHH facing sinkers and they are, as a group, demolishing RHP.

Overall, you see that Sinkers and Sliders have the largest platoon advantage, followed by Cutters and Curves. The pitches that shows the least amount of platoon advantage are 4-seamers and changeups.

Next step? Control for the quality of pitcher. Is it possible that the LHH are feasting on RHP sinkers because those happen to be poor pitchers overall, who happen to throw disproportionately sinkers? Hard to believe, but, we’ll leave no stone unturned. Or if I’m lucky, an Aspiring Saberist will take this to the next level.

Monday, October 29, 2018

How likely is it that we can identify a RHH to have reverse platoon splits?

?First, I will stipulate that there IS, at least one, RHH with a reverse platoon split.  But the question is: can we IDENTIFY such a player.  And if we can identify such a player, how LIKELY is it that it is true.

In The Book, Mike Sweeney had an OBSERVED reverse platoon split in 2000-2004.  And based on our method, which is Bayesian in nature, we estimated that he had a true talent platoon split that was slightly positive.  So, not reverse, but, close enough that there was a chance, say about 20%, that he had a true reverse platoon split, which probably meant we'd OBSERVE a reverse platoon split at say a 30-40% chance.  And from 2005-2010, we in fact observed a reverse platoon split.

Puig is that player today.  He has a similar reverse split as Sweeney did, in a similar number of opportunities.  And so, he has a similar estimated true talent split, with a likelihood of having a true reverse split of under 50%.  Meaning there's close to a 50% chance that we'll OBSERVE a reverse platoon split over a similar number of PA.

In a tweet thread, that you can follow here and work backwards, I provided some background as to how you can do the research.  With notes in other tweet threads.

Sean Forman generously provided splits data in a one-stop shop Dropbox for aspiring saberists.

Ted Turocy also has other kinds of splits over at his Git site for  those interested in doing other things.

Saturday, May 06, 2017

Switch Hitters since 2008

?This is anyone we have marked as a switch hitter, or has batted from both sides of the plate.

I have included notes, in the atypical cases.

http://tangotiger.com/images/uploads/switch_hitters.html

Thursday, March 17, 2016

How to value a platoon player with extreme splits?

?Someone asked me an interesting question, one I never thought about it quite the terms it implied.  It goes something like this:

The average player has a 2 win / 162G platoon split.  Your typical star would be a 5 WAR player against opposite-handed pitching and 3 WAR against same-handed.  You can see therefore that such a player never gets platooned.

Your average starting player would be 3 WAR against opposite-handed and 1 WAR against same-handed.  You can see therefore that such a player will sometimes sit against same-handed pitching.

Your typical bench player would be 1.5 WAR against opposite-handed and -0.5 against same-handed.  You can see therefore that such a player is a good option to occasionally platoon and will almost always sit otherwise.

But, what about someone with wider-than-normal splits?  Say you have Lefty Louie who is 2 WAR / 162G against opposite-handed pitching and -1 WAR against same-handed.  If he were to play all 162 games, he'd be a 1 WAR player.  You also have Righty Robbie, also 2 WAR/162G against opposite-handed pitcher and -1 against same-handed.  If he were to play all 162 games, he'd be a 0 WAR player.  These two guys on 2 separate teams are a combined 1 WAR (in 324 games).

Put them on the same team and you have the perfect platoon monster, Louie Robbie, who together are 2 WAR (in 162 games).

So, before I go on, I'd like to hear from the Straight Arrow readers. How do you value these two players.  

1. How do you value them if they were on the same team?
2. How do you value them if they played on separate teams, as full-time players?
3. How do you value them if they played how much such players would typically play on separate teams?

Wednesday, October 21, 2015

Letting your starter bat and keep pitching, or bring in the replacements?

?MGL takes a look to see how much of an impact each decision has, and how much you have to assume in order to make the decision break-even.

Let's see if I can try to figure it out in one paragraph: a pitcher-as-hitter is some .13 runs per PA worse than league average, or .013 wins per PA, when LI = 1.  If LI = 2, then it's .026 wins.  If LI = 3, then it's .039 wins.  So, that's your cost of letting him hit.  What's the gain for letting him pitch?  If you assume that an almost-great pitcher gives up runs at 80% of league average, and his replacement is league-average, and an average pitcher gives up about 0.45 runs per inning, then you gain 20% of that, or .09 runs per inning, or .009 wins per inning.  If he pitches 2 innings, that's .018 wins at LI = 1.  At LI = 2, it's .036 wins.

All in all, prima facie, you have to work it out, but it may be break-even.  But MGL went through the machinations more closely, and he shows that the decision is pretty straight forward.  All to say: everyone should roll up their sleeves and work it out.  And don't forget the huge PH penalty too!

Tuesday, October 13, 2015

Platoon splits and regression

?We had several pages in The Book on the topic.  But for those who want a refresher, MGL does a great job by giving it to you with more recent data and more focused attention.

Friday, August 14, 2015

Forecasting pitcher platoon splits

?Terrific stuff from Jared.

Monday, May 18, 2015

PH penalty for career PHers

?Shane looks at the PH penalty for guys who have spent ALOT of time as pinch hitters, and says:

Their tOPS+, comparing pinch-hitting performance to overall career production, was around 91, against an average of 100. Since OPS+ drops about two points for each percentage point of wOBA lost, this shows a penalty closer to five percent than 10.

To understand OPS+, think of a hitter who has a .315 OBP and .382 SLG in a league of .330/.400.  Each is 95.5% of league average, and so OPS+ does 95.5% + 95.5% - 100% = 91%.  Yes, the math is silly, and I'm not arguing in favor of it.  Just explaining it, since 95% of readers out there have no idea this math exists.

The Book said the normal PH penalty is to expect a .300 wOBA for a league of .330 wOBA (and wOBA is on the same scale as OBP).  Shane notes that for "career PHers", the PH penalty is actually closer to the DH penalty!

That said, I'd like to see the controls in place, especially for handedness.  The article is a good first step, enough to inspire other saberists to try their hand at the issue.

Wednesday, March 18, 2015

Trajectory platoon

?Good stuff from Shane, as he follows up on some findings in The Book and looks at things in a more granular manner.  He's basically showing that when same-type pitchers/batters face off, you get extreme results, at the expense of line drives.  But when opposite-type players face off, they cancel themselves out, resulting in a bit more line drives.  It's a nice way to show the effect.

Tuesday, December 16, 2014

What’s the cost of stacking three lefties?

?Pizza shows you all the math, and comes up with 0.25 wins.

However, when you do the #GoryMath, you see that the penalty is only a quarter of a win over a season, and that number is propped up with some major assumptions that just aren’t going to hold.

Can we do an estimate in our head? Basically, all we're talking about is making sure that you don't do R-L-L-L-R, and instead do L-R-L-R-L.  Or even R-L-L-R-L.  Anything so that if that LHP comes into the game to face your three lefties, he faces at least one righty.  The platoon split is about .020 runs per PA, or .002 wins per PA.  And it's that ONE PA that we're interested in.  And .002 x 162 = 0.3 wins.

So, Pizza's more elaborate process seems to check out.

Friday, June 27, 2014

Splits happen

Love the headline.  I agree with Mushnick's basic point, that the level of splits we see have almost no meaning.  Given the choice between the level of splits we see today, and seeing no splits at all, we'd be better of, decision-making-wise, having no splits.

The reason is because of "over fitting".  What that means is that things that are in fact random are instead given meaning.  The problem is that by giving it meaning, you are then taking action on that, when you would have otherwise taken the OPPOSITE decision. 

You might even do this in real-life.  Say that the best way to get from NYC to LA is to fly there.  But, the week before, there was a plane accident.  Not only that, but it was with the airline you are going to fly on.  You decide "I'm not going to risk it!  I'll take the train!"  But, are airplane accidents more likely to occur based on the proximity of prior accidents?  I have no idea, but until I see evidence to that effect, I'll assume that it's random.  And even if it was NOT random, you'd then have to figure out if that puts the odds of a fatal accident higher by plane than by train.

It happens with stocks as well.  Investors who chase runs and wait for a clear sign they are out of the bottom before jumping in end up making less money than those who buy-and-hold the index.  That's because they think they've figured out the pattern.  When there is no pattern.  Maybe if you are a day trader, and you are focused on the market every second, you might figure out when to get in and get out.  But then transaction fees might be your undoing.

When you give random events meaning, you end up doing the exact opposite of what you should really be doing.  Basically, don't overthink the situation.?

Wednesday, June 04, 2014

Sinkers, changeups, and platoon splits

There have been a few good articles on platoon splits based on the pitch movement+speed.  Eno adds to that list.?

Tuesday, June 03, 2014

Reverse Platoon Splits

Way back in the 1987, maybe 1988 Baseball Abstract (someone can confirm), Bill James had a VERY lengthy treatment on the idea of the reverse-platoon split.

In The Book (released 2006), Andy gave his treatment on the platoon spli?t (for both pitchers and batters).  Since then, there have been more articles written on the topic, including at Hardball Times, Baseball Prospectus, and Fangraphs, the Hat Trick of Saber sites.

So, I'm perplexed here.  This fellow is clearly very interested in sabermetrics, he has some hobby time available to him, he rolls up his sleeves.  Basically, the kind of guy we need dancing on our saber floor.  And yet, he seems completely oblivious to the existing research.  I was going to mostly let it go until I read his conclusion:

It's not something that we should count on to carry over from year to year, but a manager shouldn't hesitate to leave a player in the lineup against same-handed pitchers if he's showing a positive reverse platoon split for the given season.

That is a terrible sentence.  The first half of the sentence directly contradicts the second half, relying only on "year to year" to be different from (the implied) "month to month" or "week to week".

Anyway, if you treat his work as a PRIMER, then I'm fine with it.  Though he really should therefore reference other works on the subject.  But that sentence needs to be corrected.

Wednesday, April 23, 2014

Identifying players to platoon

Max gives it a go, with a player at the top of the list that would seem to be impossible to sell: Shin-Soo Choo.?