The concept of one-out pitchers suggests a kind of dystopian future for baseball. Generic Matchup Righty Number One (let’s call him Adam Cimber for the sake of this sentence) comes in to get the first out of an inning. He’s replaced by Adam Kolarek to get a lefty, then Adam Ottavino to get another righty, and then, look, I’m out of Adams, but maybe Adam Wainwright was the starter?

In any case, it’s hard to imagine a more boring inning, a more surefire way to get Johnny and Jane Millennial to change the channel to Fortnite or American Gladiators or whatever it is the kids like these days. That, more or less, is the theory behind MLB’s newest rule change, a three batter minimum for relief pitchers that will go into effect for the 2020 season. The rule requires a pitcher to face three batters, or pitch to the end of the half-inning, with some exceptions for injuries.

There’s only one problem with that narrative: that all-Adam inning doesn’t exist much in the majors, even without a three batter minimum. In fact, the one-out relief specialist just isn’t much of a role in baseball anymore. I investigated the numbers to find out which teams would be most affected. To my surprise, essentially none of them were. Read the rest of this entry »

On Monday, we published a piece detailing how MLB’s proposal to reimagine the minor leagues would alter in-person access to professional baseball across the country. We were interested in how many people would lose their ability to watch affiliated baseball in person, or see that access shift from the minor leagues to more expensive major league parks. To arrive at those numbers:

[W]e took the geographical center of each ZCTA (a close relative of ZIP Codes used by the Census Bureau). We calculated the distance as the crow flies from each ZCTA to each ballpark in America, both in 2019 and in MLB’s proposed new landscape. From there, we took the minimum of all of those distances for each ZCTA. That gave us the shortest distance to baseball for each geographical center. We then matched the distance with the population of each area.

In the piece, we acknowledged the limitations of linear distance. It doesn’t account for natural barriers, like say, mountains or lakes, or things like the placement of roads. And, as several folks pointed out on twitter and in the comments, not all road conditions are created equally. How long it takes to drive 50 miles in the Washington D.C. metro area varies widely from how long that same distance takes in rural Montana. What’s more, residents of those respective areas likely view a 50 mile drive differently; if you have to travel a ways to go grocery shopping, your understanding of how burdensome a 100 mile drive to your “local” minor league ballpark is probably different than it is for someone who lives in a place with a meaningful rush hour and amenities that are closer at hand. So while linear distance is a good approximation of how the access landscape would change in the new minor leagues, we wanted to take a stab at being a bit more precise. Read the rest of this entry »

In October, Baseball America and The New York Times reported on a proposal from Major League Baseball that, if enacted, would dramatically reimagine the minor leagues. The proposal was the opening salvo of the League’s negotiations with Minor League Baseball over a new Professional Baseball Agreement (PBA), the agreement that governs the relationship between MLB and minor league teams, and includes plans to shift the timing of the amateur draft, realign parent-club affiliations, league geographies, and levels in some cases, and eliminate 42 teams. In justifying the shift, MLB pointed to a desire to improve minor league compensation and playing conditions, reduce burdensome travel, and elevate the facility standards of minor league parks. Those are worthy goals, though the fact that many of them could be accomplished within the existing minor league structure by simply spending more money suggests that this move may be one that is also motivated by cost-savings and efficiency, rather than just concern for minor leaguers on long bus rides.

Earlier this month, the Times revealed which teams are currently slated for closure under MLB’s proposal. Those teams, along with their 2019 total attendance figures are listed in the sortable table below:

With a more specific list of affiliates in hand, we wondered how these closures would affect access to professional baseball across the country. Read the rest of this entry »

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 »