“If something cannot go forever, it will stop.” Credited to economist Herb Stein, this tautology, sometimes known as Stein’s law, has broad application past the field of economics: the Earth will end, the sun will end, the ability of the universe to sustain life will end, all the non-Top Chef shows on Bravo will end (hopefully), and we’ll end. Joey Votto does not exist outside of the space-time universe, and his 2022 season so far makes it look like his career will end before all of these things. Or will it?

Votto’s career has looked shaky at times before, but he has made comebacks before: from a leg injury that cost him half a season, a mid-career power outage, and a huge dropoff in play at age 35. He’s had enough successful comebacks to become a rarity in baseball: a highly paid star first baseman who doesn’t make his team regret a very large contract covering his 30s. But while he’s gotten off to slow starts before, a .122/.278/.135 line is something else.

Perhaps even worse is that so many of his non-baseball card stats look abysmal as well. Votto is striking out at nearly triple the rate of his 2017 peak. His soft-hit and hard-hit percentages of 22% and 20%, respectively, are closer to Ben Revere than a slugger, and those numbers are twice and half his career rates, respectively. Votto’s average exit velocity of 86.4 mph is six ticks off last year’s 92.9 mark, and his 70% contact rate is the lowest of his career. Read the rest of this entry »

The Los Angeles Angels are off to a 13-7 start. A couple of the big reasons for that are not unexpected. Mike Trout, who hadn’t played in a regular-season game in 11 months, is off to a blazing start even by his robust standards, sporting an OPS north of 1.200 and already nearing the sort of WAR we expect a league-average player to post over six months. Shohei Ohtani isn’t torching the league to quite the same degree but he’s also on a 6-WAR pace when you combine his hitting and pitching. Still, in the past, the team has struggled even with two superstars at the top of their game. What’s working for Los Angeles now is truly unusual compared to recent years: getting lots of contributions from the other guys. And none of “the other guys” have stood taller so far than Taylor Ward.

I’m always one of the first to yell “April!” about small-sample-size stars, but Ward’s performance has still been stunning. His .381/.509/.762 line calculates out to a 269 wRC+, besting his teammate Trout and everyone else with at least 50 PA this season. What makes it even more impressive is that some of the numbers fueling that line are of the sort that are meaningful in a small sample.

There’s a bit of a fallacy with extreme data in small samples (if it has a name, I don’t know it). In baseball, when a .280 hitter hits .300, people accept it as normal, but when a .280 hitter hits .500, it is generally written off as a fluke. But while the “hitting .500” part is, the .280 hitter who is hitting .500 is more likely to have improved than the one posting .300. Read the rest of this entry »

12:02

Dan Szymborski: Good afternoon to people with righteous time zones! Good morning to the rest of you.

12:03

Dan Szymborski: Nick Martinez?

12:03

Dan Szymborski: Certainly more than three games. Especially because his plate discipline numbers suggest a lower walk rate.

12:03

Dan Szymborski: I’d actually be more worried that he’s not throwing as hard as he was overseas.

Read the rest of this entry »

12:01

Dan Szymborski: April.

12:01

Dan Szymborski: April.

12:01

Dan Szymborski: A good one?

12:01

Dan Szymborski: No

Read the rest of this entry »

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:

• Corey Kluber: 3.83 ERA, 3.85 FIP, 1.5 WAR in 80 IP
• Patrick Corbin: 5.82 ERA, 5.41 FIP, 0.2 WAR in 171.2 IP
• Jon Lester: 4.71 ERA, 5.41 FIP, 0.0 WAR in 141.1 IP
• Martín Pérez: 4.74 ERA, 5.54 FIP, 0.6 WAR in 114 IP
• Marco Gonzales: 3.96 ERA, 5.02 FIP, 0.6 WAR in 143.1 IP
• Johnny Cueto: 4.08 ERA, 4.95 FIP, 1.5 WAR in 114.2 IP
• Rich Hill: 3.86 ERA, 4.34 FIP, 1.7 WAR in 158.2 IP
• Dustin May: 2.74 ERA, 3.30 FIP, 0.5 WAR in 23 IP

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:

• José Abreu: .261/.351/.481, 126 wRC+, 2.9 WAR
• Jackie Bradley Jr.: .163/.236/.261, 35 wRC+, -0.8 WAR
• Justin Upton: .211/.296/.409, 92 wRC+, 0.3 WAR
• Kyle Lewis: .246/.333/.392, 107 wRC+, 0.3 WAR
• Charlie Blackmon: .270/.351/.411, 94 wRC+, 1.5 WAR
• Robbie Grossman: .239/.357/.415, 114 wRC+, 2.7 WAR
• Josh Bell: .261/.347/.476, 118 wRC+, 1.4 WAR
• Joey Votto: .266/.375/.563, 140 wRC+, 3.6 WAR

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:

• Jordan Montgomery: 3.83 ERA, 3.69 FIP, 3.3 WAR in 157.1 IP
• Joe Musgrove: 3.18 ERA, 3.70 FIP, 3.3 WAR in 181.1 IP
• Zach Eflin: 4.17 ERA, 3.68 FIP, 2.2 WAR in 105.2 IP
• John Means: 3.62 ERA, 4.62 FIP, 2.5 WAR in 146.2 IP
• Ryan Tepera: 2.79 ERA, 2.73 FIP, 1.6 WAR in 61.1 IP
• Julio Urías: 2.96 ERA, 3.13 FIP, 5.0 WAR in 185.2 IP
• Dylan Cease: 3.91 ERA, 3.65 FIP, 4.4 WAR in 165.2 IP
• Jeffrey Springs: 3.43 ERA, 3.91 FIP, 0.2 WAR in 44.2 IP

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.

• Vladimir Guerrero Jr.: .311/.401/.601, 166 wRC+, 6.7 WAR
• Alex Kirilloff: .251/.299/.423, 93 wRC+, 0.3 WAR
• Kyle Schwarber: .266/.374/.554, 145 wRC+, 3.1 WAR
• Gary Sánchez: .204/.307/.423, 99 wRC+, 1.5 WAR
• J.P. Crawford: .273/.338/.376, 103 wRC+, 3.1 WAR
• Nathaniel Lowe: .264/.357/.415, 115 wRC+, 1.6 WAR
• Ryan McMahon: .254/.331/.449, 95 wRC+, 2.5 WAR
• Luis Urías: .249/.345/.445, 111 wRC+, 2.7 WAR

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 »