Major League Baseball is preparing to crack down on pitchers’ use of foreign substances, which could have important ramifications for how the game is played not just the rest of this season, but for a long time to come. Such a remarkable midseason change in enforcement — one report from ESPN’s Buster Olney suggested that umpires might randomly check baseballs 8–10 times per game — could alter league-wide offense, perhaps to a rather large degree depending on the number of pitchers who doctor the baseball.

Two things seems fairly certain, though. First, foreign substances increase spin rates; second, spin rates significantly impact pitcher performance. An experiment run by Travis Sawchik at theScore demonstrated that certain substances, like Spider Tack, could add as much as 500 rpms to a fastball. One college pitcher, Spencer Curran from Seton Hall University, saw the baseline rpm on his fastball go from 2,096 without any substances to 2,516 with Spider Tack and without any velocity increase — a jump that likely cannot happen naturally.

“It’s probably pretty hard to change that [fastball spin] ratio for an individual,” University of Illinois physics professor Alan Nathan told Sawchik at FiveThirtyEight. “I can see that you could do it for a curveball because a curveball involves some technique, whereas a fastball is pure power. There is no finesse.”

In a comprehensive story published by Stephanie Apstein and Alex Prewitt at Sports Illustrated, one recently retired pitcher estimated that 80% to 90% of pitchers currently use some form of foreign substances. But even with pervasive use, not all sticky stuff has the same impact. As Sawchik showed in his experiment, some substances — like a sunscreen mix he used — may actually decrease spin rates. Some of it may depend on how much time each pitcher has had to experiment in front of a Rapsodo, trying different concoctions until something works to their liking.

In both articles, the authors highlighted some basic stats to show how spin rate impacts batter performance. Sawchik noted that batters are hitting .264 on four-seam fastballs that range from 2,250–2,350 rpms, but just .217 on those above 2,500. That’s a sizable gap, and numbers like that have definitely caught MLB’s attention. As one executive told SI, though MLB is considering many changes to increase offense, he believes that better enforcement of the foreign substances rule already on the books — Rule 6.02(c) — would go a long way.

“I think people would be absolutely shocked if they actually enforced this, how much you’ll start to normalize things without rule changes,” the executive said.

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With the adoption of the Hawk-Eye tracking system before the 2020 season, analysts and fans alike can directly measure the orientation of the baseball’s spin axis as it heads towards the pitcher. Previously the readings we would see on Baseball Savant were based on the movement of the pitch; the spin axis was inferred. Tom Tango, Senior Data Architect over at MLBAM and author of The Book, delves into the nuances between the spin axis readings here. The differences are derived from the nature of the tracking system before (TrackMan radar) and after (the aforementioned technology from Hawk-Eye, which consists of a series of high-speed cameras placed around the ballpark) 2020. During the offseason, the good people at Baseball Savant rolled out some leaderboards with the new measured spin axis data and compared that to inferred spin axis by pitcher and pitch type. The deviation between the two quantities is the result of the seam-shifted wake effect, a new idea permeating the baseball analyst community. Christian Hook from Driveline has a good piece introducing the phenomenon, as do our very own Ben Clemens and The Athletic’s Eno Sarris; I’d also point you to Barton Smith, Alan Nathan, and Harry Pavlidis’ excellent piece at Baseball Prospectus, as well as Barton’s other work on the subject.

At some point in the future, I hope to add to the discourse regarding seam-shifted wake. For now, though, I want to look into another idea we can analyze with this new access to measured spin axis. Until recently the ability to dive deep into the new spin axis data has been limited. We, the public, only had access to data summarized by pitcher and pitch type. Now, thanks to the wonderful work of Bill Petti and his baseballR package (and MLBAM for deciding to release the information), we can extract the measured spin axis on the pitch level in 2020. With this influx of new data, I re-scraped and stored the pitch-by-pitch data in my Statcast database (which I could not have done without Bill’s tutorial).

With that being said, my first inclination was to look at how pitches paired together in the context of spin mirroring. The idea behind spin mirroring is to deceive the hitter. Two pitches that rotate about the same axis but in opposite directions are hard to discern by the batter. For insight into spin axis and how it differs for different pitch types, I recommend checking out this comprehensive piece from Dan Aucoin at Driveline where he explains the importance of understanding a pitch’s spin axis, how it explains pitch movement, and deviations between axis and expected movement based on the axis via the magnus force. Mike Petriello at MLB.com has also given good insight into how spin axis allows certain pairs of pitches and repertoire’s to yield better results than just velocity and movement would indicate. He specifically dug into Shane Bieber’s diverse repertoire, which lacks elite velocity and correspondingly elite spin. Read the rest of this entry »

Last week at Baseball Prospectus, Rob Arthur looked at the rise of advanced defensive positioning since 2015. It turns out that every position has started playing deeper, but — perhaps unsurprisingly — first basemen have moved the least of all. As Arthur writes, “First basemen have barely budged, which makes sense since they are more anchored to the bag.” But this lack of movement feels like a concession that doesn’t necessarily need to be made. The base is fixed, and the defender has to reach it, but a quicker first baseman would be able to stray farther from the anchor. If the lack of an anchor is allowing these other positions to play in more optimal locations, then some of the range that has always been a prerequisite for playing those positions is potentially going to waste. Let’s get some of those more rangy players over to first base, which doesn’t allow for the defender to be so perfectly placed.

The Right-Handed Shift

One of the reasons I’m interested in the positioning of first basemen is how it relates to the current conundrum involving the right-handed shift, about which folks like Tom Tango, Russell Carlton and Ben Lindbergh have written countless words. The short recap is that the publicly available data suggests that the right-handed shift doesn’t really work. And yet, some of the most data-driven teams are the ones that employ the shift the most.

There are a few things that make the right-handed shift different than the more prevalent left-handed one, but what I’m focused on is first base and the existence of that “anchor” that was mentioned earlier. First basemen can only stray off the bag as far as allows them to return safely in time for the throw. Turns out, that isn’t nearly far enough to cover the tendencies of the hitter. Read the rest of this entry »

Jordan Montgomery put together a solid outing on Wednesday night. In 6.1 innings of work, he struck out six Rays and walked only two. He did get tagged for five hits, but avoided allowing any home runs, which made the whole package work admirably. He gave up three runs, but with a little defensive prowess, things could have gone even better; two of those three were unearned.

That kind of game happens all the time these days. On the other hand, that particular game has never happened before. That exact box score line — 6.1 innings pitched, six strikeouts, two walks, five hits, no homers, one earned run and three total runs — had never occurred in the more than 380,000 starts since 1913, the first year where earned runs were recorded, as James Smyth pointed out:

? Unique Box Score Line Alert ?

Jordan Montgomery 6.1 IP, 5 H, 3 R, 1 ER, 2 BB, 6 K

A first out of more than 1.1 million MLB pitching lines in the regular season or postseason since 1901 (thanks @baseball_ref @Stathead)

— James Smyth (@JamesSmyth621) June 3, 2021

I’ll level with you: I had a hard time believing Smyth at first. That line is so middle-of-the-road. Everything about it feels like a common enough occurrence. There are no truly strange parts in that score, nothing that stands out as an obviously rare feat. An easy example: Carlos Martínez also recorded a unique line on Wednesday. His was altogether stranger: 0.2 innings pitched, one strikeout, four walks, and 10 earned runs without a homer or an unearned run. That just sounds like an unprecedented start. Read the rest of this entry »

At the beginning of May, I wrote two articles about the slightly-deadened baseball’s effect on league-wide home run rates. The conclusion was pretty much exactly what you’d expect: A bouncier ball with more drag did reduce home runs, particularly among softer-hit balls at lower launch angles. In 2019, these events were the wall scrapers that barely went out of the yard. In 2021, these events are now doubles and outs, with the increase in fly outs likely contributing (at least somewhat) to baseball’s diminished run environment overall.

There were a handful of outstanding questions that I still had, one of which was the impact of the new baseball on a ballpark-by-ballpark basis. Though league-wide trends are certainly an interesting and informative way to see the effects of a new baseball on run scoring, it is also important to examine in which parks hitters are having a more difficult time getting the ball into the seats. That allows us to understand better how park effects may have been altered to different degrees as a result of MLB’s switch to the new baseball.

But it’s not just the baseball that is contributing here. MLB reportedly added humidors to five stadiums for the 2021 season, bringing the total league-wide to 10. The Rockies, Diamondbacks, Mariners, Mets, and Red Sox already had humidors in their stadiums pre-2021, but which five teams are new to that list has yet to be disclosed. We can only guess which parks now have them, but it is important to keep in mind that the ball is not the only difference.

Also important to remember when looking at ballpark-level data: The players on the home team make a huge difference in determining home run rates. It’s entirely possible that, between 2019 and ’21, a team added home run hitters to its lineup or acquired home run-adverse pitchers for its staff, or the opposite could also be true. To mitigate these effects, I only analyzed a specific slice of fly balls: those hit at an exit velocity at or above 95.0 mph, at an exit velocity below 110.0 mph, and at a launch angle below 30 degrees — the very fly balls most impacted by the new baseball in my prior analysis. I also only included fly balls hit in games on or before May 31 to control for weather effects. (That is why I am comparing 2019 to ’21.)

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Mike Shildt had a decision to make. It was only the first inning, but the Brewers were all over Daniel Ponce de Leon. They’d already scored three times, and had runners on second and third for number eight hitter Luis Urías. A hit here could break the game open, so Shildt took a risk and intentionally walked Urías. With a pitcher batting next, maybe he could salvage the inning.

There was just one problem: Daniel Ponce de Leon was pitching. His 11.6% walk rate this year has actually lowered his career mark. Not only that, but he’d already walked a batter unintentionally this inning, though it’s unclear whether that’s predictive. Either way, though, whoops:

Maybe that was a strike, and maybe it wasn’t. In any case, it turned into a run, and the game eventually turned into a Brewers rout. The Cardinals scored only three runs; as it turns out, the first inning was all Milwaukee needed. Urías didn’t have a hit on the day, not that that’s a particularly telling statistic.

Normally, I’d break down the pros and cons of Shildt’s decision in minute detail. Avoiding Urías and his career .318 OBP to face a pitcher seems good. Turning a walk into a run with Ponce de Leon on the mound seems bad. It’s certainly not a slam dunk in either direction. Read the rest of this entry »

There is a lot to dissect when it comes to understanding the increase in strikeouts in baseball. Pitchers at the plate are striking out at a higher clip than ever, but even when filtering out their plate appearances, we’re still seeing yearly increases in strikeout rate. A continued increase in velocity and an improved ability to spot fastballs up in the zone was always going to boost strikeouts, but we are also coming to shifts in pitching approach that are directly attacking long-standing hitter’s comforts, making even hitter’s counts unpredictable.

Since I’ll be going through league-wide pitching trends, it’s useful to take a quick glance at pitch usage for the year.

Fastball usage is holding steady from last year at just over 50%. In addition, the increase in slider usage continues, taking a chunk out of curveball usage. Still, the takeaway is that we’re approaching true 50/50 fastball/non-fastball usage splits over all counts, and it’s probably here to stay. Read the rest of this entry »

I’ll spare you the description of how I came up with the idea for this article. There was a lot of Alex Rodriguez’s announcing involved, and this is a family website, so my opinions on that will remain undiscussed. The point is, though, that it made me wonder about something I used to take for granted but have increasingly questioned: how do pitchers change their game plan with a runner on third base?

Depending on who you talk to, it might matter a lot or a little. Maybe pitchers won’t be willing to bounce one. Maybe they’ll pitch to a strikeout (assuming fewer than two outs), trying to keep the run from scoring. Maybe pitchers will completely ignore the runner on third and pitch normally. I’m legitimately uncertain. Not I think it’s 50% likely to be one and 50% likely to be the other — I have absolutely no idea how to weigh the relevant probabilities.

First things first; what about those bounced pitches? This is a classic announcer trope, but it’s a trope for a reason; throwing a pitch in the dirt really is more dangerous with a runner on third. Through the magic of run expectancy tables, we can see how much a one-base advancement costs the fielding team, based on whether there’s a runner on first, second, or third (I ignored other base/out states for brevity’s sake):

With no one out, everything is more or less the same; that runner on third was pretty likely to score anyway, in fact. As the outs pile up, allowing the runner from third to score hurts more and more — quite logical. Read the rest of this entry »

Last week, I laid out some broad categorizations of what makes a slider effective, when viewed in the aggregate. As a quick recap: The most important single characteristic is hitting the corners of the strike zone. If you have a slider with plus horizontal movement, it’s also okay to miss over the middle of the plate. The middle of the plate is a great location early, but a poor location late in counts. There’s more, but those were the key findings.

That analysis left some additional factors out, because there are only so many tables you can fit into an article before it all starts to look the same. Additionally, some of those factors are beyond the scope of this analysis. Sequencing and tunneling, for example, are too complex to reduce to a two-dimensional grid. Deception might be even more confusing; I’d struggle to quantify it at all, let alone simplify it into a few buckets for analysis.

Today, I’d like to look at the rest of the factors I found easy to quantify and analyze. First, let’s talk about pitch movement. Last week, I looked at horizontal movement, because that’s the classic action we associate with a slider. It’s not the only type that pitchers throw, however. Sliders are such a broad category of pitch that they encompass pitches that mostly break sideways, mostly break down (at least, relative to a fastball), or have some mixture of the two.
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Not everyone is interested in projecting the future, but one common thread in much of modern analytics in this regard is the attempt to describe a volatile thing, such as a play in baseball, using something less volatile, such as an underlying ability. This era arguably began with Voros McCracken’s DIPS research that he released 20 years ago to a wider audience than just us usenet dorks. Voros’ thesis has been modified with new information, and people tend to say (mistakenly) that he was arguing that pitchers had no control over balls in play, but DIPS and BABIP changed how we looked at pitcher/defense interaction more than any peripheral-type of number preceding it.

One of the things I want to try to project is what types of performance lead to the so-called Three True Outcomes (home run, walk, strikeout) rather than just tallying those outcomes. For example, what type of performances lead to strikeouts? I’m not just talking about velocity and stuff, but the batter-pitcher interactions at the plate — things like a pitcher’s contact percentage, which for pitchers with 100 batters faced in consecutive years from 2002 has a similar or greater r^2 to itself (0.53) than either walk rate (0.26) or strikeout rate (0.51) does. Contact rate alone has an r^2 of 0.37 when comparing it to the future strikeout rate.

As it turns out, you can explain actual strikeout rate from this synthetic estimate quite accurately, with an r^2 in the low 0.8 range.

Statcast era data works slightly better; the version of zSO which has that data is at 0.84, and the one that predates Statcast data is at 0.80. Cross-validating using repeated random subsampling (our data is limited, as there’s no “other” MLB to compare it to) yields the same results.

Like the various x measures in Statcast, these numbers shouldn’t be taken as projections in themselves. While zSO projects future strikeout rate slightly more accurately than the actual rate itself does, a mixture of both gets a better r^2 (0.59 for the sample outlined above) than either does on its own. Looking at zSO alone as a useful leading indicator, however, gives us an idea of which players may be outperforming or underperforming their strikeout rates so far this season. All numbers are through Wednesday night.

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