In my last article on swing-mirroring, I detailed how, on average, one hitter’s result impacts the first-pitch swing decisions of the next hitter. I was inspired by Asch Conformity, or social influence, something I’ve experienced in my own life whenever I’ve been the last among family and friends to tune into a TV show or movie series. Usually, I cave and watch because I either want to be able to join cultural conversations and/or I convince myself that if everyone else likes a piece of media, so will I. These reasons typify the two general types of social influence: Normative, or when you are enticed to conform for the sake of fitting in; and informational, or when you conform because you think doing so is the right course of action (i.e., maybe I’ll actually enjoy the TV show).

Going back to baseball, each offensive result didn’t fall neatly into either category of social influence (nothing in life truly does). Additionally, for some results like double plays, other psychological factors such as reference dependence played a part. So I instead went very general and ended up categorizing outcomes based on whether they tended to increase, decrease, or have no consistent impact on the subsequent hitters’ first-pitch swing rate (FPS%).

This process served as a lesson in how difficult it can be to disentangle individual psychological drivers of behavior from the broader workings of the environment and the mind, especially when using observational data. But at the same time, I also noticed that the general trends varied based on the first-pitch swinger in question. This opened up another avenue to explore: Examining the patterns of individual differences in swing-mirroring could get me closer to isolating the effect of social influence. Read the rest of this entry »

Every few years, the same old question sets the internet aflame: Why do Americans care so much about the British royal family Does batting average matter? If you haven’t seen my favorite formulation of the problem, here’s Tom Tango’s version of it:

Which is the more productive batter (all other things equal)

A.
.315 batting average
.365 OBP
.510 SLG

B.
.260 batting average
.365 OBP
.510 SLG

— Tangotiger ? (@tangotiger) January 9, 2023

I’ve taken a crack at this exact question before. The answer simply isn’t very surprising. If two hitters have the same on-base percentage and the same slugging percentage, they’re similarly valuable to their team’s offense. That’s why OPS is a popular offensive statistic despite its relative lack of precision; it does a lot of the same work as wOBA and wRC+ because its two component stats are mostly found in similar ratios and correlate well to offensive production. Linear weights are still better, because they do a better job of accounting for how important each plate appearance outcome is when it comes to run scoring, but you can get most of the way there with OBP and SLG.

There’s not much reason to go through the exact math of how wOBA works again, because the people who would be swayed by that math have already been swayed. But sabermetric forefather Voros McCracken mentioned a novel way of looking at the problem, and I thought I’d take a crack at it now that there are no more Carlos Correa free agency articles left to write. Read the rest of this entry »

Free agency has come and gone this offseason. Earlier than we’re used to in recent years, we can look back on this year’s class and make some conclusions. To some extent, that’s a lot of our offseason coverage; what are ZiPS projections and positional power rankings, after all, if not catalogs of how teams have changed their fortunes in the offseason? Today and tomorrow, I thought I’d do something slightly more navel-gazey, and perhaps slightly more useful in the long run, by looking back at my contract predictions to see what went right and what went wrong.

First things first: let’s take an accounting of both the crowd’s and my predictions. I took the contracts signed by each of the top 50 players in free agency. A few clarifying remarks: I removed players who accepted a qualifying offer, as both the crowd and I made our predictions before qualifying offers were extended. I considered only guaranteed years, ignoring options of any type, be they vesting, team, or player. I also ignored incentives and trade kickers. Finally. I’m using Carlos Correa’s rumored deal with the Mets — 12 years, $315 million — even while it’s not yet official and may be amended.

With that out of the way, I grouped the players into positional groups and compared our predictions to real life. How’d it go? Pretty well, actually, for both sides. Positive numbers here mean we under-estimated, and negative numbers represent an over-estimate:

Read the rest of this entry »

If you’re anything like me, you care what your friends think of you. I’m not talking about the middle school I’ll-do-whatever-they-do kind of caring; I like to think I’ve outgrown that. But I certainly still want to fit in. I think these feelings are fairly universal among adults, whether the in-group in question is composed of friends, co-workers, or just a collection of peers with whom you happen to share something.

My credence stems from a classical psychological study conducted by Solomon Asch, which spawned an entire category of literature under the “conformity” umbrella. The word has a negative connotation, but as long as it isn’t taken to extremes, conformity is a natural and adaptable human behavior. It’s likely even seen in baseball players (and not just in how they wear the same outfit to all of their games).

In Asch’s study, a subject was placed among a group of confederates, or research assistants posing as subjects themselves. The group was shown a series of “target” lines, each alongside another group of lines, and instructed to find the line in the group most similar to the target. There was always an obvious answer, and the confederates were instructed to never choose it. The majority of subjects agreed with the confederates at least once. Read the rest of this entry »

For years, the baseball offseason had a predictable rhythm. When the World Series ended, we’d hit a lull. Around the Winter Meetings, a few Scott Boras clients would sign while he spouted strange, vaguely nautical similes. A few more marquee names would get caught up in Boras’ wake (see what I did there?) and sign as well. Then we’d have a lull around year end, and contract activity would pick up again in the new year.

That pattern hasn’t held even a little bit this winter. As of this writing, 45 of our top 50 free agents have signed, including the entire top 30; many guys who just missed the cut have signed as well. Four years ago, plenty of teams were still looking for free agent help in the first week of January. This year, your options are Johnny Cueto, Jurickson Profar, and then tumbleweeds.

This feels different than previous years of free agency, but I wanted to put some quantitative rigor behind that. I set out to compare this offseason to each previous one. I’ll spend plenty of time going through my methodology below, but first, let’s give the people what they want. This year really is different. Here’s the percentage of all free agents, weighted by previous year WAR, that had signed new deals by December 31 of each offseason since the conclusion of the 2000 season, excluding last year’s lockout weirdness:

The 2022-23 offseason (which I’ll be calling 2023 for simplicity’s sake for the remainder of the article) is tied for the most front-loaded offseason of this millennium. Given that offseasons had been getting progressively slower, that’s a meaningful change. Now, let’s talk about how I got to this conclusion, and come up with a few takeaways about the new landscape of free agency. Read the rest of this entry »

Sorry, but this is going to be kind of a bummer. Our topic today is the crushing weight of statistical determinism. In researching this article, I learned something that increased my knowledge but also decreased my sense of the possible, and it made me a little bit sad. I would now like to share my sadness with you. We’re going to be studying the Victor Robles Problem.

You might not remember the days when Victor Robles was a star prospect. After short, impressive stints in 2017 and ’18, he had a breakout season in 2019, putting up a 92 wRC+ and 3.5 WAR, and finishing sixth in NL Rookie of the Year voting. ZiPS projected him for 3.3 WAR in 2020. If he could take the next step offensively, he’d be a star; if his offense remained just a bit below average, he’d still be a very productive center fielder. Instead, he turned in three straight seasons with a wRC+ under 70. Here are the Statcast gearboxes for his rookie season and 2022:

There’s a whole lot of blue in the top two rows. Plate discipline was the concern when Robles was first called up, and that was certainly an issue, but the lack of power stands out much more. Although his max exit velocity indicates that he has the capacity to hit the ball hard, Robles’ average exit velocity has been in the first percentile in each of his big league seasons, and his hard-hit rate has never been better than fifth percentile. The Victor Robles Problem is a question: Can a player who didn’t hit the ball hard as a rookie ever turn into a good hitter? Read the rest of this entry »

During the playoffs, Nick Castellanos made a few noteworthy catches, darting and diving to his left. I wondered whether the Phillies’ right fielder, with his mitt on his left hand, was alone in relishing plays to his gloveside. After all, Castellanos didn’t have to reach across his body to make the catch when moving in this direction. I also wondered what, if any, implications this would have on outfield positioning and alignment.

In terms of directional Outs Above Average (OAA), my findings demonstrated that right-handed outfielders actually performed marginally better moving to their armside than their gloveside. But they were considerably stronger to their left than left-handers, who in turn performed much better to their own gloveside. If that word salad confused you, not to worry. These numbers might be easier to digest:

Read the rest of this entry »

You might want to buckle up. This is an article about small sample sizes, so there’s a statistically significant chance that things are about to get rowdy. It was supposed to be an article about which outfielders are better or worse than you’d expect them to be based on their sprint speed.

Just for fun, here’s the chart I started with. I turned Outs Above Average into a rate stat I’ll call OAA/150. It’s a player’s OAA per 1,350 innings, or 150 games. (I tried several other metrics, dividing by chances instead of innings, and working with UZR and DRS range metrics. This worked best for my purposes.) The sample is 544 outfielder seasons in which the fielder had at least 50 chances on balls with a catch probability below 96% (hereafter known as starred chances). I’ve labeled the two players who stood out the most in either direction in 2022.

Daulton Varsho good, Andrew Vaughn catastrophically bad. No surprise there, but I love charts like this, thick at the bottom and thin toward the top. They show how many paths there are to each outcome. Speed is a big component of OAA; the correlation coefficient of the two is 0.54. But outfielders also need to get good jumps, make plays at the wall, and be able to run down balls in all directions. There are lots of different combinations of skills that can land you in the bottom or middle of the chart. To get to the top, you need to be good at all of them. You also need to be lucky. Read the rest of this entry »

“He made a mistake, and Trout made him pay.” No doubt you’ve heard some version of that sentence countless times. Maybe the announcers called it a hanging slider, or a meatball, or any number of other ways of describing a poor pitch. But what exactly does it mean, and how can you know one when you see one?

I’ve discussed that question with my colleagues frequently, but we’ve never come up with a satisfactory answer because the pitches that get classified as mistakes aren’t always intuitive. Sometimes a pitcher hits the inside edge of the zone, only for a hitter sitting on just such a pitch to unload on it. Sometimes a backup slider ties up the opposing hitter. There’s bias to these observations, too: You’re far more likely to remember a pitch that gets clobbered for a home run than one that merely results in a take or a loud foul.

I still don’t have a definitive answer. I did, however, make an attempt at answering one very specific form of the question. One pitch that really does feel like a mistake, regardless of intent and irrespective of circumstance, is a backup slider over the heart of the plate. Spin a slider wrong, and it morphs into a cement mixer, turning over sideways without movement. Leave one of those middle middle, and the result is a slow and centrally located bat magnet. Read the rest of this entry »

Falling behind in the count puts an enormous amount of pressure on the pitcher. It’s in his best interest to throw a strike and retake control, but knowing this, hitters are more likely to swing. Aiming outside the zone is dicey: It’s great if the hitter bites but disastrous if he doesn’t, and the risk generally outweighs the reward. A pitcher would ideally execute a borderline strike that hitters can’t help but pass up, but that’s easier said than done. Navigating this situation is tricky, and just from a numbers perspective, whoever’s on the mound is pretty much always in trouble. The question isn’t “can the pitcher emerge victorious,” but rather, “Can he escape with minimal harm?”

For decades, pitchers have relied on their fastballs to fight these uphill battles. Part of that is because long ago, some of them actually believed throwing a slider or another secondary pitch wasn’t very manly. You ain’t tough unless you blow a 2–0 heater by your opponent, I guess. But really, it’s because a fastball is the pitch a majority of pitchers are comfortable with, and it’s the one they can most reliably lob in for a strike. If your goal is to equalize the count, why risk using an erratically moving curveball to achieve it?

Unfortunately for those old-timey hurlers, they’re probably rolling in their graves at the apparent cowardice of modern pitchers. Rather than adhere to axioms, pitchers today are challenging notions of what’s “right” or “wrong” in pitching, aided by advancements in pitch- and body-tracking technology. One example of such sacrilege is the continuously increasing rate of breaking balls — sliders, curveballs, and the like — thrown in disadvantageous counts:

It’s true that breaking ball usage is up no matter the count or situation, but I find it particularly interesting that the trend remains strong even when pitchers fall behind. The name of the game is optimization. If teams didn’t think opting for breaking balls when behind in the count granted them an advantage, we wouldn’t see this happening. Granted, just because teams do something doesn’t necessarily means it’s effective, but a league-wide jump of eight percentage points in pitch usage is significant and worthy of investigation. Read the rest of this entry »