| 12:01 |
 Dan Szymborski: April.
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| 12:01 |
: I know it’s still early, but are we starting to worry about Jared Kelenic again?
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| 12:01 |
Dan Szymborski: April.
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| 12:01 |
: Can you give the devil’s advocate explanation for a lineup where Niko Goodrum bats third and Kyle Tucker 6th?
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| 12:01 |
Dan Szymborski: A good one?
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| 12:01 |
Dan Szymborski: No
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On Wednesday, I looked at the hitters I’m bearish on, so it’s time to finish the series for 2022 with the pitchers that are causing me worries. (If you’re wondering about my breakout picks, wonder no more.)
So what exactly is a bust? I don’t take it to mean that a player is awful or has no value. For me, a bust is a player who will step down a tier in performance or who is in a down cycle and has passed the window to get back to what they used to be. None of the players involved are literally without value, and some of them are still really good. But they’re all players I think will be well below their best, usually in a manner that makes me sad as a baseball fan.
Before getting to the 2022 candidates, here are my ’21 bust choices and how they performed:
Nobody really shone here, but by the same token, nobody was legendarily awful (I had expected Lester to go down that route and he didn’t really). We obviously didn’t get a ton of Kluber, but he was definitely much more effective than I expected. My concern with May was that he was still rather awkward at punching out batters, despite the explosiveness of his stuff, so I was happy that he spent April proving me very wrong about where he was as a pitcher — then very unhappy as he tore his UCL in early May and required Tommy John surgery.
As a reminder, I selected all of these players by Opening Day, so there’s no knowledge of anything that happened after Opening Day. It would have been really awkward if someone on my list had surprise Tommy John surgery this week! Read the rest of this entry »
Earlier this week, I ran down my favorite breakout candidates. Now it’s time for the darker side: the busts.
So what exactly is a bust? I don’t take it to mean that a player is awful or has no value. For me, a bust is a player who will step down a tier in performance or who is in a down cycle and has passed the window to get back to what they used to be. None of the players involved are literally without value, and some of them are still really good. But they’re all players I think will be well below their best, usually in a manner that makes me sad as a baseball fan.
Let’s start things off with a look at last year’s list of possible hitter busts and check how things worked out:
As you can see, I did much worse here than with the pitcher breakouts. I’m especially happy to have been wrong about Votto last year — my feeling was that there wasn’t another comeback left in him, but there was! I’m also quite pleased that Abreu didn’t slump back to league-average as I expected, staying a bit above instead, though well off his MVP performances. Lewis gets a pass since he was injured most of the year, and Grossman remained legitimately good, if below his 2020 rates. Read the rest of this entry »
One of my favorite yearly preseason pieces is also my most dreaded: the breakout list. I’ve been doing this exercise since 2014, and while I’ve had the occasional triumph (hello, Christian Yelich), the low-probability nature of trying to project who will beat expectations means that for every time you look smart, you’re also bound to look dumb for some other reason. Yesterday, I highlighted my breakout candidates among the league’s hitters. Today, I consider the pitchers.
Let’s start things off with a brief look at last year’s breakout pitcher list and see how they fared:
Seven of the eight players here either tied (Musgrove) or beat their previous career best in WAR, so it would be greedy to complain that Means only had a good bounce rather than finding a truly new plateau. While I’d like to attribute this showing to some brilliance on my part, I’d also call these results luckier than average and certainly above any reasonable mean expectation of my perceptiveness. Read the rest of this entry »
One of my favorite yearly preseason pieces is also my most dreaded: the breakout list. I’ve been doing this exercise since 2014, and while I’ve had the occasional triumph (hello, Christian Yelich), the low-probability nature of trying to project who will beat expectations means that for every time you look smart, you’re also bound to look dumb for some other reason.
Let’s start things off with a brief look at last year’s breakout hitter list and see how they fared.
On the plus side, nobody really embarrassed me. Alex Kirilloff came closest, but in his defense, he was playing with a wrist injury that eventually required surgery. Read the rest of this entry »
It arrived stressfully, chaotically, and slightly late, but the 2022 season is here. And that means it’s time for one last important sabermetric ritual: the final ZiPS projected standings that will surely come back and haunt me multiple times as the season progresses.
The methodology I’m using here isn’t identical to the one we use in our Projected Standings, so there will naturally be some important differences in the results. So how does ZiPS calculate the season? Stored within ZiPS are the first through 99th percentile projections for each player. I start by making a generalized depth chart, using our Depth Charts as an initial starting point. Since these are my curated projections, I make changes based on my personal feelings about who will receive playing time, as filtered by arbitrary whimsy my logic and reasoning. ZiPS then generates a million versions of each team in Monte Carlo fashion — the computational algorithms, that is (no one is dressing up in a tuxedo and playing baccarat like James Bond).
After that is done, ZiPS applies another set of algorithms with a generalized distribution of injury risk, which change the baseline PAs/IPs selected for each player. Of note is that higher-percentile projections already have more playing time than lower-percentile projections before this step. ZiPS then automatically “fills in” playing time from the next players on the list (proportionally) to get to a full slate of plate appearances and innings.
The result is a million different rosters for each team and an associated winning percentage for each of those million teams. After applying the new strength of schedule calculations based on the other 29 teams, I end up with the standings for each of the million seasons. This is actually much less complex than it sounds. Read the rest of this entry »
It arrived stressfully, chaotically, and slightly late, but the 2022 season is here. And that means it’s time for one last important sabermetric ritual: the final ZiPS projected standings that will surely come back and haunt me multiple times as the season progresses.
The methodology I’m using here isn’t identical to the one we use in our Projected Standings, so there will naturally be some important differences in the results. So how does ZiPS calculate the season? Stored within ZiPS are the first through 99th percentile projections for each player. I start by making a generalized depth chart, using our Depth Charts as an initial starting point. Since these are my curated projections, I make changes based on my personal feelings about who will receive playing time, as filtered by arbitrary whimsy my logic and reasoning. ZiPS then generates a million versions of each team in Monte Carlo fashion — the computational algorithms, that is (no one is dressing up in a tuxedo and playing baccarat like James Bond).
After that is done, ZiPS applies another set of algorithms with a generalized distribution of injury risk, which change the baseline PAs/IPs selected for each player. Of note is that higher-percentile projections already have more playing time than lower-percentile projections before this step. ZiPS then automatically “fills in” playing time from the next players on the list (proportionally) to get to a full slate of plate appearances and innings.
The result is a million different rosters for each team and an associated winning percentage for each of those million teams. After applying the new strength of schedule calculations based on the other 29 teams, I end up with the standings for each of the million seasons. This is actually much less complex than it sounds. Read the rest of this entry »
If you thought the Padres were done tinkering with the starting rotation, you thought wrong. On Sunday morning, San Diego agreed to a deal with the Athletics, acquiring pitchers Sean Manaea and Aaron Holiday in exchange for pitcher Adrian Martinez and infielder Euribiel Angeles.
In a season in which teams continued to be conservative at stretching out their starting pitchers, the A’s were more aggressive with the injury-prone Manaea last year. Over the short-term, that proved to be successful, as he remained healthy and threw 179 1/3 innings, his most as a professional. The plaudits aren’t just quantity; he set career-highs all over the place, from WAR (3.3) to full-season FIP (3.66) to strikeout rate (25.7%).
How did Manaea do it? The likeliest reason is velocity. No, he didn’t suddenly become Aroldis Chapman or Brusdar Graterol, but every pitcher — with the possible exception of knuckleballers — requires some level of velocity to achieve effectiveness. In Manaea’s career there’s nearly a 100-point delta in batter slugging percentage between his sinkers going 91 mph or slower (.491) and those traveling 92 mph or faster (.397). On a player-to-player comparison, I get in the neighborhood of 40 points of SLG being the norm.
Similarly, Manaea also gets a lot more strikeouts when he’s throwing harder. More than three-quarters of his career strikeouts from sinkers come from the harder ones despite less than half of his sinkers being in the category. It’s not just chicken-and-egg; this pattern existed prior to this season. Sinkers aren’t traditionally used to punch out batters, but for Manaea, they’re an important weapon. Statcast credits him with 114 strikeouts on sinkers in 2021, the third-most of the Statcast era and the most since Chris Sale had 124 in 2015 (David Price has the crown with 125 in 2014).
It’s not even that Manaea’s sinker is a particularly nasty pitch, such as a splitter in fastball’s clothing; he actually gets less break on his than the average pitcher. It’s that his deceptive delivery and his excellent control basically function as a force multiplier to his velocity, effectively reducing the distance between the mound and home plate. Even a 30-mph fastball can strike out Juan Soto if you get to determine the sliver of time he has to make a decision. Read the rest of this entry »
Earlier today, Eric Longenhagen kicked off our reliever rankings. Now we’ll take a look at the bullpens projected to be baseball’s best.
If you were hoping to see the rise in bullpen innings start to really reverse itself in 2021, you were no doubt disappointed. The percentage of all innings thrown by relievers did tick below 2020’s 44.5%, dropping to 42.7% last year, but it remained higher than in previous seasons. The differences from the past are even larger when you take into account that the zombie runner rule of 2020 and ’21 (and ’22, grrrrr) lopped off some reliever innings, artificially holding down the percentages. Don’t expect the trend to meaningfully reverse itself any time soon. Teams have extensive relief corps, and short of a dramatic rule/roster change, there’s little incentive for them to revert back to an older style of bullpen usage.
That doesn’t mean that things will always stay the same, however. Just as the Ace Reliever era eventually translated into the Modern Closer era, the idea of the closer as a superhuman entity at the front of the bullpen has and will continue to erode. That’s not to say there won’t still be elite relievers who get tons of save opportunities, just that the meaning of the word “closer” will continue to shift away from describing veterans like Todd Jones or Joe Borowski, among a multitude of others, who got high-leverage opportunities their performances didn’t warrant. Baseball’s top 20 closers combined for just 570 saves in 2021, the lowest number in a full season since 1987. The elite closer peaked around 20 years ago, with the top 20 closers combining for 788 saves in 2002. Save totals aren’t dramatically down (1,191 total last season vs. 1,224 in 2002), we’ve just seen the sanctity of the role of those collecting them fade. Read the rest of this entry »
| 12:04 |
Dan Szymborski: Welcome to SzymChat! Now, we should have had baseball this week, but at least we’ll have it next Thursday instead of , say, nothing until 2023!
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| 12:04 |
: What’s for lunch?
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| 12:04 |
Dan Szymborski: Probalby some leftover pizza.
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| 12:04 |
: Daaaan !!!
How worried are you about Bellinger not being able to hit ? |
| 12:04 |
Dan Szymborski: Quite. There were already reasons to be worried
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| 12:05 |
Dan Szymborski: And while spring training doesn’t mean a LOT, Rosenheck showed some years ago that it means a LITTLE of something
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