Tangotiger Blog

Thursday, December 28, 2023

Improving WAR - Re-solving DIPS (part 2)

From 2016 to 2023, the pitcher among the lowest BABIP in MLB is Justin Verlander. With 3196 balls in play, his hits allowed rate is 142 fewer hits than league average. That is a substantial number, by far the highest number in that time frame. In second place is Kershaw, at 95 hits better than average. In third place is Scherzer, at 83 better than average.

This seems like a perfect refutation of Voros and DIPS, which we discussed in Part 1. If I asked you to name the three best pitchers since 2016, Verlander, Kershaw, and Scherzer could very well make up that top 3. So that potentially the three best pitchers in MLB also happens to have the best hits on balls in play is not noteworthy in the least. The next names on the list however are Julio Teheran, Cristian Javier, John Means, Tony Gonsolin, Yusmeiro Petit, and on and on it goes. deGrom is 114th out of 654 pitchers. Gerrit Cole is 89th. Aaron Nola is 466th. Wheeler is 296th. The second WORST pitcher on hits on batted balls also happens to be the 8th best in FIP-based WAR: Kevin Gausman. These 8 pitchers by the way lead in WAR on Fangraphs, a metric that ignores balls in play. Looked at it holistically, this better describes the original issue Voros found: how much attribution can we possibly give the pitcher on balls in play?

Part of the problem we have is how I even introduced it in the first paragraph. I said Verlander is among league-lows in BABIP. But more accurately, we should say that Verlander AND HIS FIELDERS are among the league-leaders. We can't just bypass his fielders. And we still have the issue that so much of what happens on batted balls goes beyond the pitcher and his fielders and their park. Random Variation weighs heavily, in ways that you don't see in other stats.

Ok, let's get into it. We can use Statcast data to directly determine the contributions of the pitcher. We can look at their launch angle and speed to determine how well effective they are. When we do that, we can see that Verlander gives up alot of soft batted balls, to the point that he ALSO happens to be the best pitcher in baseball since 2016 on launch-based hits on balls in play. Well, take that Voros! Except, well, the magnitude is not there. When we look at Verlander and his fielders, their BABIP suggests 142 fewer hits than league average. But when we look at Verlander and his allowed launch angle and speed, that suggests 76 fewer hits than league average. The breakdown for Verlander looks like this:

• +22 fielders with Verlander
• +76 Verlander using launch angle+speed
• +44 everything else, including Random Variation
• ====
• +142 Verlander's team, when Verlander is on mound

Here is Kevin Gausman:

• -15 fielders with Gausman
• -68 Gausman using launch angle+speed
• -2 everything else, including Random Variation
• ====
• -85 Gausman's team, when Gausman is on mound

Gausman is interesting in that we can explain the ENTIRETY of the poor BABIP with himself and his fielders. He's had the bad luck of having poor fielding behind him, 15 hits worse than average. But the rest of the outcomes is because of Gausman himself. Relying on FIP-only for Gausman would not be a good idea.

We can do this for every pitcher. I will show you a chart (click to embiggen), that shows, on the x-axis, how well each pitcher, and their team, do, compared to league average. You can see Verlander on the far-right and Gausman on the far-left.

On the y-axis is the direct contribution of each pitcher. While in some cases, the two correspond, like with Verlander and Scherzer and Kershaw and Gausman. In other cases, there is little overlap. Take for example Adam Wainwright:

• +10 fielders with Wainwright
• -75 Wainwright using launch angle+speed
• +26 everything else, including Random Variation
• ====
• -39 Wainwright's team, when Wainwright is on mound

This is a mess to resolve. Wainwright has been hit very very hard. Indeed, he's been league-worst, using launch angle and speed, at 75 more hits allowed than league average that we can directly attribute to his launch characteristics. He's had the good fortune of playing with good fielders. When he was on the mound, they made 10 extra outs than the average fielder. There was another 26 extra outs that we can't attribute to the pitcher or his fielders. Whether this is Random Variation, or it's the fielding alignment mandated by his coaches, or Wainwright somehow managing to gets more balls hit closer to his fielders, we can't really tell. All in all, Wainwright's team, with Wainwright on the mound, only gave up 39 more hits than league average.

Sometimes you get into issues like Zach Eflin, who is better than league average on how hard he is hit, and yet is worse than league average when he is on the mound with hits on balls in play. Do we really want to attribute to Eflin things that he has no control over, simply because he happens to be on the mound when those bad things happen? Why not attribute some of that to his fielders, who are equally not-complicit, but are equally present? Or maybe, stop attributing things that we don't know who to attribute to, simply because we've identified they are present? Sins of the Father and all that.

This is what it looks like if you compare the direct contribution of the pitcher, using their launch angle and speed, to the "everything else" I've been talking about. As you can see, virtually no correlation. In other words, after having identified the contribution of the pitcher directly by how hard they are being hit, whatever is left over has no association to that. Whatever is left, which is going to be mostly Random Variation, has likely very little to do with that pitcher.

When you look closely at first chart, we can come up with the general point: about half of the results we can attribute to the pitcher. In some cases more, in some cases less. In some cases, there's a reverse effect (like Eflin). But, if we simply use as our starting point that we'll count half of the outcome and give it to the pitcher, then we've taken a big step forward in better attributing outcomes to the underlying contribution.

Should we completely ignore hits on balls in play? No. The pitcher is not a pitching machine. There is some influence there.

Should we completely accepts hits on balls in play? Also no. The pitcher is not in total control here. There's alot happening that has no bearing on the pitcher.

Should we split the difference, give them half, and move on? For the pre-Statcast years: yes. Without any additional information as to their direct impact, then we have to infer their impact. And it's about half of what you see. Basically, BABIP is somewhere between Pitching Machine 4587 and that pitcher. And that's how much attribution we should give the pitcher.  In Statcast years, we have more information, and so we can better attribute the impact of the pitcher to the outcome when they are present.

We can of course be a little fancier, and figure out fielder influence as well, but that's a story for another thread.