At the end of October, I wrote about the alterations José Urquidy made to his arsenal during the postseason, trying to estimate how much better or worse his pitches might have been given their velocity and movement changes. In that piece, I briefly touched on raw movement changes but mostly relied upon the vertical and horizontal approach angles (along with location and release point changes) of Urquidy’s pitches to try to fully encapsulate their movement. While the concept and value of vertical approach angle is rather intuitive, I figured it would be useful to go into horizontal approach angle here today and have fun with a little exercise.

This post is inspired in part by all that we have been able to grasp in the public sphere when it comes to vertical approach angle. Measuring the up-down angle of a pitch as it enters the zone is largely derived from the vertical movement measures we’re so accustomed to. Steep vertical movement on breaking pitches and “rising” four-seam fastballs in the upper parts of the zone have been coveted for a while now because we know those pitches are associated with whiffs and therefore success. Much less can be intuited by simply looking at the pitches with above average horizontal movement. Read the rest of this entry »

With last week’s lockout of the players by ownership, the league has frozen all transactions for the foreseeable future. That’s bad news — but it also makes this a good time to take a snapshot of team payrolls, because there are no new deals coming down the pipe to mess up the analysis midway through. As such, the following is an update on each team’s payroll as it stood at the start of the lockout. Here are our team-by-team RosterResource projections:

A few notes, some of which will likely be familiar to you if you followed past versions of this exercise written by former FanGraphs writer Craig Edwards. The above data counts salaries for 2022, not average annual values. It includes estimates for arbitration, as well as estimated minimum salaries paid throughout the season; our payroll pages currently use the 2021 league minimum, but that number could change in the upcoming CBA. The numbers don’t include incentive bonuses, or a few specific CBA wrinkles, such as the approximately $2 million that teams pay to players who aren’t in the majors but are on the 40-man roster or the roughly $16 million per team spent on player benefits. They also don’t include as-yet-unsigned free agents, naturally.

The Mets’ recent signing spree, combined with the Dodgers’ losses in free agency, has seen the two teams change places at the top of the payroll standings (somewhat surprisingly, the Mets finished as the second-highest spenders in 2021). All told, there are three teams (the Mets, Dodgers and Yankees) that are currently projected for more than $200 million in player salaries in 2022, the same number (and the same teams) as last year. Meanwhile, there are two teams (the Pirates and the Guardians) with projected payrolls below $50 million, one more than last year, and neither of those teams is likely to make meaningful free agent signings when transactions resume. Read the rest of this entry »

MLB’s plan to de-juice the baseball this year seems to have worked. Home runs were down in 2021 compared to the last couple of seasons, though not by so much as to warrant a complete shift in the game’s current offensive paradigm. Instead, run scoring — which remains heavily concentrated around home runs — decreased overall. Non-pitchers hit just .247/.321/.418 this year, representing the second-lowest batting average in the live ball era (1920-present and excluding 2020) but the fourth-highest isolated power.

In slightly de-juicing the baseball, MLB erased some home runs. But as we can see in the sudden drop in batting average, which is down nine points for non-pitchers compared to 2019, shaving off homers didn’t result in other types of hits. As I wrote in my second of two pieces analyzing early 2021 home run trends, these lost homers mostly just became outs. That explains the significant reduction in overall offense, even as the league-wide home run total remained quite high in the context of baseball history.

With the season now officially complete, it’s time to revisit some of those early trends to get a final estimation of the effect of MLB’s de-juicing. Of course, there is a significant, potentially confounding, variable that makes 2021 different from other full seasons and complicates our analysis: the midseason enforcement against the use of sticky stuff. I will try to account for that here, though it probably deserves its own standalone examination. Read the rest of this entry »

Every season, we brace ourselves for the inevitable decline of baseball’s stars of yesteryear. Unfortunately, we’re all getting older, and despite their rare talents and athleticism, major leaguers eventually lose production. But while each individual has their own rate of decline, generally speaking, we know a fair amount about how age affects hitters’ abilities: A typical player peaks around age 26 and gradually declines afterward in what we know as the age curve. For those fortunate enough to stick around into their late 30s. that decline becomes quite sharp.

The basis for the age curve is the difference between a player’s performance across two different seasons. The choice metric for hitters tends to be wRC+, because it’s adjusted for league average and should be less impacted by league-wide trends and fluctuations from one season to the next. The chart below shows an age curve for players using data from the last 10 seasons, preceding 2021:

The shape of the age curve makes intuitive sense, for two reasons. The obvious one is that the primary skills needed to succeed in baseball (eyesight, strength, agility, etc.) decline as the human body gets older. Another, however, is that players only remain at the big league level if they’re successful. Whether it be due to injury, a slump, or even a little bad luck, a decrease in a hitter’s performance is an inevitability before they decide to retire (or the decision is made for them). Read the rest of this entry »

The playoffs feature the best teams, with the best hitters squaring off against the pitchers with the best stuff. The stakes and the quality of the competition force teams to respond to fluctuations in leverage more quickly than they would in the regular season. This response makes sense: every change in win probability has an outsized effect on championship probability, so major league clubs act accordingly.

In his dissection of Kevin Cash’s decision to pull Blake Snell in the sixth inning of Game Six of the 2020 World Series, Ben Lindbergh pointed out that starting pitchers leave playoff games earlier than regular season contests, with relief pitchers now throwing the majority of playoff innings. Lindbergh also noted that more playoff starters threw 3 1/3 innings or fewer in 2020 than went at least six, a product of teams’ acceptance of the third time through the order penalty. The third time through the order penalty is real, especially for starters who lack deep repertoires. Removing starters after they turn the lineup over once or twice in favor of a high-octane bullpen arm throwing 97 mph with a slider gives the pitching team a better chance of recording an out in situations where the outcome of the game hangs in the balance.

With starters aware that they are on a short leash and likely won’t see a hitter more than once or twice, I figured it was worth looking at how pitch usage changes in the postseason. I pulled every pitcher who threw at least 50 pitches in both the regular season and the playoffs from 2015-20. I calculated each pitcher’s pitch usage in the regular season and playoffs separately and took the differences in pitch usage for each pitch. My hypothesis was that hurlers who feature a bevy of different pitches would lean more on their more trusted offerings knowing they likely won’t be asked to go deep into the game and will be pulled at the first sign of any trouble. Similarly, I thought that pitchers who employ a limited arsenal would trust their favorite pitch with the increased pressure of getting their clubs back to the dugout without allowing runs. Read the rest of this entry »

Yesterday, I tested how well our playoff odds have predicted eventual playoff teams. Today, I’m going to slice the data a few more ways to get a more robust look at what our odds do well, and where they have fewer advantages over other models. It will be number- and picture-heavy, word-light. Without further ado, let’s get started.

A discussion with Tom Tango got me wondering about why our Depth Charts-based odds do so well early in the season relative to other systems. Their advantage is particularly strong at the beginning of the season and fades as the year goes on. For all charts in the article that are based on days into a season, I’ve excluded the 2020 season for obvious reasons. Here are the mean average errors for each of the three systems over the first 60 days of the season:

What’s driving that early outperformance? In essence, it comes down to one thing: the projection-based model is willing to give teams high or low probabilities of making the postseason right away. Our season-to-date stats mode is hesitant to do that, and the coin flip mode obviously can’t do it. Take a look at the percentage of teams that each system moves to the extremes of the distribution — either less than 5% or more than 95% to make the playoffs — by day of season:

Why does this matter? If you’re judging based on mean absolute error, making extreme predictions that turn out to be right is a huge tailwind. If you predict something as 50% likely, you’ll have an error of 0.5 no matter what. The further you predict from the center of the distribution, the more chance you have to reduce your error.

Of course, that only works if you get it right. If you simply randomly predicted either 5% or 95% chances without any information about the teams involved, you’d do just as poorly as predicting 50% for everything. Making extreme picks when you have information that suggests they’re likely to be right is the name of the game. Read the rest of this entry »

It’s the time of year when folks doubt the playoff odds. With the St. Louis Cardinals going from 71-69 long-shots to postseason clinchers, and the rollercoaster that is the American League Wild Card race, you’ve probably heard the skeptics’ refrains. “You had the Jays at 5%, and now you have them at 50%. Why did you hate them so much?” Or, hey, this tongue-in-cheek interview response that mainly makes me happy Adam Wainwright reads our site:

Adam Wainwright: “FanGraphs had us at like a negative 400 percent chance to make the playoffs, and we just proved everyone wrong. We're going to try to and keep doing that.”

— Zach Silver (@zachsilver) September 29, 2021

In that generic statement’s defense, it really does feel that way. In your head, 5% rounds to impossible. When the odds say “impossible” and then the season progresses to a point where outcomes are far less certain, what other impression can you take away than “these odds were wrong”?

I feel the same way from time to time. Just this year, the Cardinals and Blue Jays have been written off and then exploded back into contention. St. Louis bottomed out at 1.3% odds to make the playoffs – in August! It’s not quite negative 400 percent, but it sure feels that way. Can it really be that those odds were accurate, and that we just witnessed a one-in-100 event?

To investigate this question, I did what I often do when I don’t know where to turn: I bothered Sean Dolinar. More specifically, I got a copy of our playoff odds on every day since 2014, the first year when we calculated them using our current method. I left out 2021, as we don’t have a full season of data to use yet, but that still left me with a robust (some would say too robust) amount of data. Read the rest of this entry »

I’m on record as being against intentional walks in most situations. That’s hardly some bold claim — over the last 15 years, they’re on a steady downward path as front offices and managers come to grips with the ills of extra baserunners. That’s not to say there’s never a good situation for a free pass, but those situations are few and far between.

Why pinpoint the last 15 years as the timeframe for this drop-off? In 1955, the first year where we have intentional walk totals, teams issued roughly 7.5 intentional walks per 1,000 plate appearances. In 2002, they issued 7.8 intentional walks per 1,000 PAs. Sure, there were peaks and valleys in between, but the data hardly indicated a trend. Take a look at the number of intentional walks issued per 1,000 plate appearances each year since 1955:

One note: I’ve excluded 2020 because of the universal DH, which created a meaningfully different backdrop for intentional walks — walking a decent hitter to face a pitcher is one of the best uses of the tactic.

I could end this article right there. That’s a convincing chart — the year with the least frequent intentional walks is 2021, and the year with the second-least is 2019. They’re roughly equivalent — four walks per 1,000 in 2019, 3.8 in ’21 — but even so, the writing is on the wall. Give the game 20 years, and we’ll surely see even fewer. Read the rest of this entry »

Let’s get right down to the question that all baseball analysis is asking at its core: Which of these two players would you rather have on your team, all else being equal?

It’s a close one, right? That’s largely because I decreed it to be so; these aren’t real players, just stat lines I made up that have the same wOBA. Who would you rather have? They’re extremely different, of course; one gets a ton of value from walks and singles, with some doubles sprinkled in for good measure. You can surmise that the other gets a ton of value from home runs — look at that slugging percentage — but does worse elsewhere.

Oh yeah, a few other caveats. These are underlying talent levels; you might look at Player A and say that the BABIP can’t continue, or Player B and say the HR/FB rate can’t be real, but for our purposes, these are the lines they’ll put up over 1,000 PA, or 10,000, or 1,000,000. This is their real skill level. Given that, in most cases, it doesn’t matter much which one you choose, because they’re about the same. That’s the point of wOBA, after all.

That’s not a very interesting answer, so I decided to go deeper. I constructed a generic American League lineup. I removed intentional walks so that we’re comparing apples to apples. The result looks like this:

I threw that lineup into a lightly modified version of my lineup simulator, a short snippet of code that lets you put in a lineup (based on the probability of each outcome every time they bat) and get an estimate of how many runs they’d score per game. This one comes out to 4.53 runs per contest, which is close enough to the actual AL average for my purposes. Read the rest of this entry »

If you’ve read any of my articles of late, you would know that I am currently fixated on plate discipline. My piece on Jarred Kelenic sparked an article on take value and hitter approach. After that, a discovery that Darin Ruf is succeeding with one of the lowest swing rates in baseball despite not having phenomenal plate discipline on the surface inspired research into zone-swing differential and what it may tell us about a hitter.

Under the plate discipline section of player pages and on our leaderboards, we list both O-Swing% and Z-Swing%. On a handful of occasions, though, writers here have used zone-swing differential. Chet Gutwein defined this stat as D-Swing% in his piece about the NL West, and Justin Choi wrote about it in an article on the Blue Jays’ aggressiveness in early counts. In my most recent piece on Ruf, I cited zone-swing differential to conclude that while his overall swing rate is low, his discipline might not actually be that good, as he’s still swinging at a fair amount of pitches outside the zone, which you can see when you look at his below-average D-Swing rate.

The idea behind D-Swing% is simple: Hitters should be better when they swing at strikes and take balls. This isn’t the only way to succeed at the plate, but you would think that better hitters would have higher D-Swing rates on average. There were a couple comments about D-Swing rate on my Ruf piece, and that inspired me to look into it further. Is this a stat that tells us more about hitters than what we already have with the standalone O-Swing% and Z-Swing% stats?

This exact question was actually explored on the FanGraphs community blog back in 2017, where user Dominikk85 broke hitters into top- and bottom-30 groups by wRC+, ISO, OBP, and BABIP to see if O-Swing%, Z-Swing%, or what they referred to as Z-O-Swing% had the biggest impact in explaining the difference between the groups. They found that being more aggressive in the strike zone “helps the power but seems to slightly hurt the OBP,” but overall, they saw an advantage in using D-Swing% over the individual components.

I ran a similar study, but I wanted to control for more variables — zone rate and contact rate — to isolate the effect of D-Swing%, as many plate discipline metrics are interrelated. You may swing less at pitches in the strike zone if you are seeing more pitches outside, or you may choose to swing at pitches in the strike zone with which you can make contact (preferably hard), which may lower your Z-Swing% but raise your Z-Contact%. Without at least attempting to adjust for some of these variables, we may be missing out on conscious hitter tendencies that may be more the result of the pitches that they are seeing rather than their inherent swing choices. Read the rest of this entry »