There are a myriad of things pitchers can do to get a leg up on a hitter. Changing speeds, eye level, sequencing, and even spin rate are a few of the more popular methods. Changing speeds can interfere with a hitter’s timing, while changing eye levels can force a hitter to adjust to a larger focal point. Varying pitch selection based on the situation can help a pitcher become less predictable, and changing the spin rate can have an effect on the expected movement of a pitch.

But there are other subtle tricks pitchers have up their sleeves. One of these tricks, explored in an article for The Athletic by Joe Schwarz and elaborated on in another by Eno Sarris, is known as pitch or spin mirroring, and with the right pitch attributes, it can be a powerful weapon.

Being able to spot a pitch’s spin can tip a hitter off to what is coming, a skill some hitters claim to have. That’s a big advantage to have in a decision that transpires over the course of milliseconds. When pitches are spinning over 2000 times a minute, is the human eye really that good? Perhaps. As Preston Wilson points out in the piece linked to above, a hitter might see “more white or more red,” which gives an indication as to what pitch is coming. More white would indicate a faster or more abundantly spinning pitch, like a curveball or fastball, while more red could be a changeup.

In theory, a pitcher could use spin mirroring effect to parry those abilities, especially with a fastball and curveball combo because of the high spin on both pitches. Furthermore, if the rotation blur of the ball is mostly white, you’ll have a much harder time deciphering the direction in which the spin is oriented. Read the rest of this entry »

For the most part, a pitcher’s FIP is going to line up pretty well with his ERA over the course of a season or a career. There are 240 starting pitchers with at least 1,000 innings over the last 25 years and all but seven of them have a FIP within half a run of their ERA. Even over the course of an individual season, we typically see most pitchers with an ERA and a FIP around the same mark. Over the last 25 seasons covering more than 3,500 individual pitching seasons of at least 100 innings, the r-squared is .61. This season, there are over 100 pitchers with at least 100 innings; the graph below shows their FIPs and ERAs (all stats are through September 5):

With the exception of Antonio Senzatela way up top, we see a pretty distinct pattern moving up and to the right. Within this cluster of players, there isn’t a perfect relationship. A perfect relationship would make one of the stats duplicative and useless. ERA and FIP both measure results on the field, with ERA accounting for the players who cross home plate after getting on base when the pitcher was on the mound (and the trip home wasn’t made possible by an error), while FIP measures strikeouts, walks, and homers. Every year, a good number of pitchers have an ERA higher than their FIP and vice versa. As far as explaining the difference between the two numbers using readily available statistics goes, BABIP and left-on-base percentage explain much of the gap between the two numbers.

That LOB% would explain some of the gap makes a lot of sense given that stranding more runners than expected is going to keep a pitcher’s runs allowed (ERA) lower than his general performance (FIP). We can see the relationship between ERA-FIP and LOB% for pitchers this season below:

Without delving into whether there’s a skill involved in stranding runners (though better pitchers tend to have higher LOB% due to just being better at getting outs generally), we can see that the more runners stranded and the higher the LOB%, the more likely it is that a pitcher’s ERA is going to be lower than his FIP. The relationship over the past 25 years for individual seasons is stronger than the one above, with an r-squared of .56, but even over just one season, the pattern is apparent. What we are dealing with above is sequencing and what happens when runners are on base compared to overall performance. Generally speaking, pitcher’s perform similarly with runners on base and with the bases empty, with a slight increase in FIP for everyone with runners on base:

This isn’t to say that some pitchers aren’t worse pitching from the stretch, or that some pitchers don’t change their strategy to more effectively get batters out with runners on base. But generally speaking, pitchers perform a little bit worse with runners on base, though in a fairly uniform pattern as seen in the graph above. Unless you are Doug Davis, Scott Kazmir, Jeff Suppan, or Iván Nova, then with runners on base, you were within half a run with runners on base or worse.

We’ll get back to LOB% in a minute, but first, we should address BABIP. Here’s the relationship between BABIP and the difference between ERA and FIP:

The relationship isn’t as strong as LOB%, but with an r-squared of .41 this season, we can still see a pattern. Over 25 years of individual seasons, the r-squared is .52, nearly the same as LOB% over the same time. While we know that pitchers exert some control over the quality of their contact, over 90% of pitchers with at least 1,000 innings since 1995 are between .270 and .310, and 65% of pitchers are between .285 and .305 (around 10 hits per year at the edges), so even at the extremes we are talking about maybe three or four extra hits per month. That’s not nothing, but over long stretches of time, we generally see the seasonal outliers get closer to their peers.

As for just how much BABIP and LOB% capture the difference between ERA and FIP, the answer is they account for the great majority of it. I took all individual seasons from 1995 through last season and ran them through a multiple regression calculator to come up with a formula for predicting the difference between ERA and FIP. The r-squared for the formula for the 3,400 seasons was .75, so BABIP and LOB% are doing a huge amount of the heavy lifting when it comes to explaining the difference between ERA and FIP. I put the same formula into this year’s numbers and this is how they came out:

We still see some outliers, but overall, the formula did a very good job predicting the difference between ERA and FIP using LOB% and BABIP. There are a few outliers. Dakota Hudson jumps out, but his larger ERA-FIP discrepancy is pretty easily explained by 15 unearned runs. If he had a more normal five earned runs, the difference would be under a run and he’d be in the big group with everybody else. Justin Verlander, on the other hand, appears to be breaking the formula entirely. To see how, here are 3,500-plus individual pitcher seasons with over 100 innings since 1995, and their LOB% and BABIP:

Quite simply, Verlander is having one of the most unusual seasons we’ve ever seen, with the highest LOB% and lowest BABIP in the last 100 years in the same season. As we can see above, there is some correlation between LOB% and BABIP, with an r-squared of .2, but that’s not as strong as either statistic’s relationship with FIP-ERA, and a pitcher’s BABIP’s relationship with his team’s BABIP is around the same strength, with team BABIP and team UZR having a slightly stronger relationship.

While there is certainly a case to be made that pitchers have control over the quality of contact they yield to some extent — no one would deny the existence of groundball pitchers or fly ball pitchers — BABIP doesn’t even necessarily measure contact quality. It counts every batted ball in the park as either a hit or an out, doesn’t include homers at all, and it varies greatly from year to year. Even xwOBA, which includes homers and dials in on the quality of contact, has difficulty finding a relationship year over year on contact. Looking just at in-season results, wOBA on contact has a difficult time becoming reliable.

It’s only natural to want to find a reason why a pitcher’s ERA and FIP are so different, and for that reason to be related to something the pitcher is or isn’t doing. Unfortunately, that isn’t always likely to be the case. In any single season, there are going to be outliers due to the relatively small sample of plate appearances we are dealing with, and almost all of the difference between ERA and FIP can be explained by BABIP and LOB%. While not all of a pitcher’s BABIP and LOB% are due to a pitcher’s defense, sequencing luck, and just general good fortune, a decent amount is just that. Baseball is a team sport and defenses play a large role in run prevention. While it isn’t always easy to admit, luck plays a role as well.

Back in March, we introduced catcher framing numbers on FanGraphs. Not long after, Tom Tango noted in a blog post that pitch framing numbers should be park-adjusted since pitchers and catchers in some parks are getting more strike calls (relative to Trackman’s recorded locations) than others.

We can see this in the graph above, which is based on called pitches within a 3.5 x 3.5 inch area in and around the strike zone. There are, on average, 64 pitches per game that meet this criteria so this graph essentially shows how many extra “framing” strikes pitches and catchers were assigned in each park per game. Put another way, this tells us how many more strike calls they received than we’d expect based on the recorded locations of the pitches. We’d certainly expect some spread in the results for home team pitchers and catchers, since some teams have better framers than others, but we shouldn’t see such a large spread for road pitchers and catchers, whom we’d expect to have essentially average framing talent. We also see that there’s a strong positive correlation between extra strikes for the home team and extra strikes for road team, suggesting that the park itself plays a role. There are two big outliers here — Sun Trust Park and Coors Field, both in 2017. Something must be amiss at those parks and we should control for it when calculating our framing numbers.

Adjusting Pitch Framing Numbers for Park Effects

Just as when constructing other park factors, we need to be careful to account for the quality of the players playing in each park. We’ll need to account not only for the pitchers and catchers who played in each park but also for the batters, some of whom have fewer strikes called against them. What we need is essentially a WOWY (with or without you) calculation where we find each park’s tendency to yield strikes, controlling for the pitcher, catcher, and batting team. In practice, it’s easiest to do this with the help of a mixed effects model. We can take the mixed-effects model we used to estimate pitcher and catcher framing and simply add random effects for the ballpark and batting team.

After adjusting for the park and batter effects that we find, we can take another look at the graph that led us here and compare home and road framing at each park, but this time with park-adjusted numbers.

This looks much better! With park effects removed, we still have a significant spread in home-team framing but a relatively small spread in road-team framing.

New Pitch Framing Numbers

For most catchers, our park adjustments make little difference. The graph below plots the new framing runs for catcher-seasons against the old framing runs with 2017 performances shown in red.

The tables below show the team-seasons, catcher-seasons, and catcher careers most affected by the park adjustments.

After finishing up some research noting the wide gap between the quality of relief innings depending on the importance of the situation this season, it felt necessary to take a similar look at team performance. If teams were deploying less-good relievers in low leverage situations and good ones in high leverage situations, it could distort our sense of the quality of a bullpen when looking at overall numbers.

We’ll start with a pretty generic view of bullpens this year, with FIP by team:

The Cardinals have the lowest FIP of any bullpen this season, as the group as a whole has pitched very well. The Rays coming in second and first in the American League is somewhat of a surprise given their use of an opener in half their games; they are losing about 60 good relief innings and replacing them with around 180 good-but-not-as-good starting pitching-type innings. The teams fall down in a nice cascade the rest of the way, with the Baltimore Orioles providing a a very heavy base at the bottom of baseball.

But not all innings are created equal, and some of the innings pitched by bullpens are more important than others. If we separate meaningful innings (medium leverage and high leverage) from less important innings (low leverage), we can get a sense of how good a team’s bullpen is when it matters. This also could provide a better sense of which teams might be better prepared for the playoffs, given the consolidation of relief innings in October:

Read the rest of this entry »

If you’re reading this article, you’re probably not dead, and if you’re not dead, you’ve heard all the fuss about soaring home run rates. I’m not here to judge your perspective on it — I think reasonable people can disagree on how they like their baseball, though I will say that I love a good strikeout and feel pretty neutral about home runs. But I think one thing everyone wonders about is who this all helps.

It’s not the pitchers, clearly. It doesn’t seem to be the big boppers — despite the stupendous home run totals, no one is threatening to hit 73 home runs any time soon. Heck, no one has approached 61 since Giancarlo Stanton’s chase in 2017, and that was a singular event rather than a wave of history-chasing sluggers. Is it the little guys? Freddy Galvis has 20 dingers on the year — that has to count for something.

There’s a lot of chicken-and-egg going on here and no real answers to the answer of who benefits the most from the livelier ball. That’s why I looked to the minor leagues to see which players were most affected by the new ball. That study was basically inconclusive, aside from showing that players with absolutely no power are barely affected.

I thought I’d take a different look at it today. It’s hard to say who has benefited the most from the new ball, but what if we could answer a different question: who would be most affected if the league surreptitiously replaced today’s baseballs with old ones overnight? Read the rest of this entry »

With so many variables, isolating specific trends in baseball can be tricky. Relievers have been pitching more and more innings. Strikeouts keep going up. The ball has been juiced, de-juiced, and re-juiced, making home run totals hard to fathom and difficult to place in context, both for this year and for years past. One noticeable aspect of this season’s play, influenced by some or all of the factors just listed, is that relievers are actually performing worse than starters. Our starter/reliever splits go back to 1974, and that has never happened before. Here is how starter and relievers have performed since 2002:

A healthy gap between the two roles has existed for some time, but seems to have taken an abrupt turn this year. Ben Clemens looked at the talent level between starters and relievers earlier this season in a pair of posts that discussed how starters are preparing more like relievers, as well as the potential dilution of talent among relievers. The evidence seemed to point toward the latter theory, though exactly how that dilution has affected performance comes in a rather interesting package. Providing some evidence for the dilution effect is the number of innings handled by relievers in recent seasons. While the idea of starters pitching better than relievers is a new one statistically, the trend of increasing reliever innings likely made this year’s change possible. Below, see the share of reliever innings and reliever WAR since 2002:

Read the rest of this entry »

As I’ve chosen topics to research and write about over the past few months, I’ve let an obsession creep into my writing catalog — I’m fascinated by pitcher and batter behavior in 3-0 counts. Whether it’s three-pitch strikeouts after falling behind 3-0, Ronald Acuña aggressively hacking on 3-0, or even just Brandon Belt sneaking in a bunt, I can’t get enough of the goofy ways baseball gets distorted in that most extreme of counts.

What’s so fun about 3-0 is that context matters. For a lot of baseball, looking at things in a context-neutral fashion is the best way to analyze it. A double is a double is a double, and it doesn’t make sense to treat one with the bases juiced in a tie game differently than you would a leadoff double in the first inning when you’re assessing a player. Hitters have little control over balls in play, and absolutely none over who’s on base when they come to the plate. Pitchers, likewise, can’t control sequencing — that’s why concepts like wOBA and FIP do a better job predicting future results than RBI and win/loss record (or, fine, ERA).

But one place context does matter is the count. The world of 3-0 counts is only barely related to 0-2 counts. A pitcher’s arsenal is limitless at 0-2, constrained mostly to fastballs on 3-0. Conversely, a batter has no choice other than to defend the strike zone on 0-2, whereas 3-0 opens up myriad possibilities. That context is what makes the realm of 3-0 counts so fascinating to me. Today, I thought I’d take a theoretical approach to the subject. Read the rest of this entry »

Until they arrive, all trade deadlines feel the same. The air is abuzz with whispers of marquee names finding new homes. The potential for splashy trades is endless. 2019 was no exception. Noah Syndergaard, the God of Thunder! Madison Bumgarner, indomitable postseason hero! Whit Merrifield… alright, I couldn’t think of an exclamation-point-worthy nickname for everyone. All were rumored to be in play in the lead-up to the deadline.

Well, the trade deadline has come and gone, and none of those players were traded. In their place, we got a few blockbusters: Trevor Bauer is taking his unique blend of trolling and analytics to Cincinnati, while Yasiel Puig orchestrated the first ever farewell fight before heading to Cleveland. Zack Greinke is joining Justin Verlander in Houston, the mythical land where former aces go to become legend. There were many lesser moves, of course. Teams upgraded bullpens or shored up weak platoon matchups ad nauseum. A whopping 64 players were traded yesterday alone.

As with most things in life, it’s hard to put this trade deadline into historical context while living in the moment. Yesterday certainly felt busy, with trades being announced seemingly every five minutes and a former Cy Young winner on the move to the World Series favorites. The previous few weeks, on the other hand, felt interminably slow. The Bauer/Puig swap was one of only two deals of consequence to take place before deadline day. How did this year stack up to past deadlines?

To answer this question, I updated methodology first used by Ben Lindbergh in 2015. Using data from Retrosheet and MLB, I compiled a list of every trade made in the month of July starting in 1986, the year baseball’s non-waiver trade deadline moved from July 15 to July 31. In terms of the raw number of players traded each July (excluding players to be named later), 2019 is in line with the latter half of this decade and its huge number of traded players:

Read the rest of this entry »

I was a Nationals season-plan holder for two years, and amid all the wins and losses, one thing in the game remained a constant delight: walk-up songs. Music is an integral part of a baseball game; it’s played between at-bats, after a run is scored, and also between innings. However, the best tunes are always chosen by the players themselves. A walk-up song is a crucial decision, one that could follow a player throughout the season. It should be a jam that both hypes them up and won’t be annoying when played three or more times a day.

Go to any ballgame and you will hear a dozen different walk-up songs, spanning musical genres from reggaeton to pop to metal. I remembered a wide variety of music from my days at Nats Park, and it got me wondering whether that variety was reflected throughout the rest of baseball. I decided to do an analysis of player walk-up songs, building off a similar “study” conducted by Meg Rowley in 2016, back when she was at Baseball Prospectus. MLB maintains a database of players’ chosen walk-up music. Using that, I was able to break players’ selections down by genre. Does the league as a whole demonstrate the same musical range the Nats do?

It does! The top genre is rap/hip-hop, while house music rounds out the bottom with 11 songs. Those listed under “other” include salsa, classical, and soundtrack music.

Now, let’s talk country. Only 8% of walk-up songs are country tunes. “Burning Man” by Dierks Bentley is the most popular, but that’s not the interesting thing about this list. When I think of hype-up music, there are several country artists who have appropriate jams. You could go with Carrie Underwood or Dolly Parton or Rascal Flatts. (Don’t laugh — I know you sing along to “Life is a Highway” any time you hear it.) I want to know why five players needed a hype song and ended up with Johnny Cash. Read the rest of this entry »

Recently, I’ve been pondering the strange way I think about HR/FB ratio. On one hand, it’s a way to explain away a hot or cold stretch from a hitter. When Joc Pederson got off to a blazing start this year, I looked at his HR/FB, a spicy 33.3% through the end of April, and told myself it was a small sample size phenomena. That’s the first way I use HR/FB for hitters — as a sanity check.

At the same time, HR/FB is something we’ve all used to explain someone’s power. Joey Gallo is powerful, obviously. How do we know that? Well, he hits the ball really hard, which gets expressed by more of his fly balls turning into home runs. Gallo had a 47.6% HR/FB at the end of April, and even though I didn’t expect that to continue, I was willing to accept high numbers for Gallo’s HR/FB much more easily than I was for Pederson.

This leaves HR/FB in a weird spot. It’s a number we use to see if players are getting lucky or unlucky relative to average, but it’s also a number we use to look for underlying skill. Problems arise when it’s unclear what is noise and what is signal. Is David Fletcher unlucky to have a 5.4% HR/FB? Surely not — he’s a contact hitter. Is Jose Ramirez unlucky to have a 6.5% HR/FB? I assume so, but I only assume so because he hit 39 home runs with a 16.9% HR/FB last year. What if last year was the outlier, not this one?

Another way to think about this conundrum is that HR/FB contains an inherent contradiction we have to work around mentally. Putting fly balls as the bottom of the ratio implies that all fly balls are created equal, and that’s clearly untrue. Gallo is unloading on the ball, crushing many of the fly balls he hits into orbit. Fletcher, meanwhile, sports one of the lowest average exit velocities in the game. Even though a home run counts the same for each, the population of fly balls is tremendously different. How do we handle this contradiction? Read the rest of this entry »