For the 18th consecutive season, the ZiPS projection system is unleashing a full set of prognostications. For more information on the ZiPS projections, please consult this year’s introduction and MLB’s glossary entry. The team order is selected by lot, and the first team to go this year is the Boston Red Sox.

Batters

The Red Sox finished fourth in the American League in runs scored in 2022, but there’s no denying that removing Xander Bogaerts and J.D. Martinez from the roster is a giant hit for the team to take. They may replace the missing value (if not all of it), but it’s a roster with a lot of work to do to be an elite unit. The projections see the offense as being driven mostly by Rafael Devers and Trevor Story, with Story getting quite the bullish projection. Having Triston Casas on the team would be helpful on average, and he has far more upside than either Eric Hosmer or Bobby Dalbec, but as of right now, the team will unfortunately be able to find quite a lot of playing time for the latter two, at least as the roster currently stands. Read the rest of this entry »

These 2023 projections are guaranteed to be awful, wrong in many ways ranging from tragic to comic. But despite being absolutely premature and littered with horrible misses, projected standings at this point are actually quite useful, and useful is the best description any kind of predictive model can strive for. Standings at this point are a poor predictor of the 2023 season — and even the eventual 2023 projections themselves — but what they are able to do is give a “state of the union” estimate for each team. These standings represent the best estimates ZiPS can make at this point about where a team sits in the league’s pecking order, based solely on the players currently under contract with the team. It’s hard to get where you want to go if you don’t know where you’re starting.

The methodology I’m using here is the same one I use in the regular season, and as such, it isn’t identical to the one we use in our Projected Standings. So how does ZiPS calculate the upcoming 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 through 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 here is that higher-percentile projections already have more playing time baked in 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. Read the rest of this entry »

Before we get to the 2023 ZiPS projections, there’s still some unfinished work from 2022 to do. Looking at which projections went the most wayward is definitely an exercise that makes me soul cringe a bit, but in any model, being wrong is an important component of eventually being right. Calibration is a long-term project, and while chasing greater accuracy in mean projections isn’t likely to result in any huge bounty — there’s a reason projection systems are so tightly clustered — there’s still improvement to be had in things like calibrating uncertainty and long-term data.

Let’s start with how teams performed versus their projections:

Teams have gotten a bit more polarized in how they’re run in-season. Looking at the in-season ZiPS projections, roster strength has varied much more in recent years than when I started doing this. It wouldn’t be surprising to see the mean absolute error — for an exercise like this, I want to use the simplest tool that gets the point across — creep up over time. That is the case here, as the MAE of 8.3 wins is above the ZiPS historical average of 7.5 (not including 2020). ZiPS underperformed its usual matchup vs. Vegas, only going 17-13 in over/unders as of the date of release (April 6); historically, ZiPS has averaged 19-11. Read the rest of this entry »

The ghost of 18th-century statistician Thomas Bayes did not see his shadow, so we are about to launch this year’s 2023 ZiPS projections. As usual, this is a space to talk about some of the basics, answer a few common questions, and wax philosophic about the very nature of predicting baseball futures. A lot of the background can be found by reading MLB’s glossary entry for ZiPS, which gives most of the basics except for the origin story.

ZiPS is a computer projection system I initially developed in 2002–04; it officially went live for the 2004 season. The origin of ZiPS is similar to Tom Tango’s Marcel the Monkey, coming from discussions I had with Chris Dial, one of my best friends (my first interaction with Chris involved me being called an expletive!) and a fellow stat nerd, in the late 1990s. ZiPS moved quickly from its original inception as a reasonably simple projection system, and now does a lot more and uses a lot more data than I ever envisioned it would 20 years ago. At its core, however, it’s still doing two primary tasks: estimating what the baseline expectation for a player is at the moment I hit the button, and then estimating where that player may be going using large cohorts of relatively similar players.

Why is ZiPS named ZiPS? At the time, Voros McCracken’s theories on the interaction of pitching, defense, and balls in play were fairly new, and since I wanted to integrate some of his findings, I wanted my system to rhyme with DIPS (defense-independent pitching statistics), with his blessing. I didn’t like SIPS, so I went with the next letter in my last name, Z. I originally named my work ZiPs as a reference to one of my favorite shows to watch as a kid, CHiPs. I typoed ZiPs as ZiPS when I released the projections publicly, and since my now-colleague Jay Jaffe had already reported on ZiPS for his Futility Infielder blog, I decided to just go with it. I never expected that all of this would be useful to anyone but me; if I had, I would have surely named it in less bizarre fashion. Read the rest of this entry »

The National League Rookie of the Year award was announced on Monday evening, with Michael Harris II of the Braves taking home the honor. Harris earned the hardware by collecting 22 of 30 first-place votes from the BBWAA writers, convincingly beating out teammate Spencer Strider, who only collected eight (and was left off one ballot completely), including mine.

Getting inappropriately annoyed with year-end awards — more specifically in 1995, the year Mo Vaughn beat Albert Belle in the AL and Dante Bichette confusingly finished second in the NL — was one of the things that got me reading Usenet. A high schooler at the time, I had little idea that it was the start of an astonishing career path. And even back then, I was frustrated that the writers who voted for these awards didn’t always make convincing arguments about their picks and, occasionally, offered no justifications at all. I still believe that this kind of transparency is crucial for the legitimacy of any type of award. This is ostensibly an expert panel — if it’s not, there’s no purpose for the award to exist — and as such, a secret ballot is not appropriate the way I believe it is for, say, a presidential or parliamentary election.

In my previous Rookie of the Year ballots, I gave my first-place votes to Corey Seager, Pete Alonso, and Trevor Rogers. The last one basically ruined my social media for a week. I had expected more writers to pick Jonathan India, but I felt (and still do) that Rogers had a slightly stronger case for the award. While it wouldn’t have changed my vote, I freely admit that I would have preferred to be one of three or five Rogers voters rather than end up being alone!

As usual, I will now endeavor to explain why I voted for the players I voted for. Read the rest of this entry »

Another solid pitcher has come off the free agent list on Thursday evening, as Clayton Kershaw is apparently close to a return to the Dodgers on a one-year contract. No financial terms have yet been revealed, but I would expect that the bottom-line figure is similar to the $17 million he made last year, or just a few million dollars more. The team didn’t extend him a qualifying offer, but that may reflect less on what the dollar figure is and more on the fact that he is Los Angeles’ longest-tenured player and a crucial part of the franchise’s history. Bouncing back from an elbow injury that ended his 2021 before the playoffs, Kershaw returned to his usual late-career form, with a 2.28 ERA and 2.57 FIP over 22 starts, good enough for 3.8 WAR and to make him the starting pitcher for the National League in the All-Star Game.

Kershaw has attained the service time and respect with the organization that he’s now one of those players who, as long as he wants to keep returning, can likely receive endless contracts, a status similar to that earned by players such as Adam Wainwright and David Ortiz in recent years. While he avoided a recurrence of the dreaded flexor tendon soreness from 2021, his ongoing back problems limited him to 126 1/3 innings, an expectation that seems likely to repeat going forward. Since leading the league with 232 2/3 innings in 2015, he has only been healthy enough to qualify for the ERA title twice in the last seven seasons. The bigger question wasn’t whether Kershaw would be back in Dodger blue but whether he would be back at all; the general consensus has been that he would either return to the Dodgers, go to his hometown Rangers, or retire.

ZiPS suggests a one-year, $17.6 million contract or a two-year, $31.8 million deal, so the projection is likely in the same zip code, if not the same neighborhood. Read the rest of this entry »

One of this winter’s top free agents crossed himself off the list over the weekend, as Edwin Díaz signed a five-year, $102 million contract to remain the Mets’ closer. Díaz was absolutely dominant this season, striking out nearly two batters an inning, resulting in a FIP under 1.00, and avoiding any of the walk or home run flurries that occasionally have marred his résumé. While I’m not particularly a fan of the save stat or the conclusions drawn as a result, him only blowing three saves in 2022 accurately reflects his dominance; he only allowed multiple runs in a single appearance all year, and all three of his blown saves occurred with one-run leads. The deal comes with a $12 million signing bonus, a team option at $20 million for a sixth season, a no-trade provision, and an opt-out after 2025.

Star closer Edwin Díaz and the New York Mets are in agreement on a five-year, $102 million contract, pending physical, sources familiar with the deal tell ESPN. There’s an opt-out and a full no-trade clause, plus a sixth-year option. The best closer in baseball stays in New York.

— Jeff Passan (@JeffPassan) November 6, 2022

Generally speaking, when a pitcher has a microscopic ERA, there’s some measure of luck involved; nobody’s long-term baseline expectation is an ERA of 1.31. So it naturally amuses me that Díaz arguably underperformed his peripherals this season. How often does a pitcher with an ERA that excellent actually have a FIP nearly half a run lower? Not very.

Going back to the start of 1901, there have been only 35 player-seasons in which a pitcher had an ERA under 2.00 and had a FIP lower than their ERA (out of 796 possible player-seasons). Only Gagne and Kimbrel had lower ERAs in seasons during which they failed to match their FIP; the average FIP for a pitcher with an ERA between 1.01 and 1.51 is 2.30. Read the rest of this entry »

We live in an era where every team, to some degree or another, embraces modern analytics when assessing itself and the rest of the league. Wanting to know about the numbers that drive baseball has filtered into fandom as well, which is why you’re here on this very website! But despite all the progress the stathead crowd has made over the last quarter-century, when it comes to the playoffs and playoff results, many fans seem more inclined to defenestrate the numbers and attribute the losses to all sorts of causative elements beyond a surplus or dearth of players just happening to have particularly good games that week.

In the worst case, failing to win two of three games or three of five is attributed to some kind of character flaw. At best, the loss is because of some fundamental flaw in a team’s construction, typically something that sabermetrics is to blame for, no matter whether the team is sabermetrically inclined or not. Here’s one example of very common thinking along these lines just from the last couple of days. It’s from a fan on Reddit, so I’m not specifically attributing them, mainly because I don’t want to risk social media pile-ons:

The postseason is the real season from now on, so the focus shouldn’t be on sabermetrics as much as it has been. With the playoffs being expanded, 111 wins doesn’t mean squat. Shift some of the focus to bringing in guys who play with fire and aggressiveness and will situationally hit rather than live and die by the long ball. If it costs us some wins during the regular season, so what?? It’s the little things that win the most important games.

With three of MLB’s four 100-win teams already out of the playoffs and the 99-win team pushed to the brink but ultimately surviving, these types of incriminations will be common this season. The Dodgers didn’t lose a 60/40 matchup (the ZiPS projection for the series) because they were simply outplayed over four games, but because something was broken in how the team was built. Depending on who you listen to, you can hear the same type of grumbling about the Mets and Braves.

Since questions should be explored rather than dismissed, let’s look at playoff overperformers and underperformers over the Wild Card era (starting in 1995) and examine if there really are consistent patterns behind which teams are overperforming or underperforming in the postseason. And since this is illustrative more than anything, I’m trying to keep it as simple as possible, within reason. Read the rest of this entry »

It wouldn’t be unreasonable to say that no matter what happens, the 2022 Padres ultimately had a fine season. Despite losing their best player for the entire season (and a chunk of the next one), they won 89 games and made the playoffs, and also acquired one of the best young players ever available via trade. They excised the worst of 2021’s demons in avoiding a repeat of the sudden, stunning collapse that transformed them from a top-tier contender to a sub-.500 squad. And most recently, they went to New York and ended the season of the 101-win Mets. But the season would still not feel like a triumph if they now fell to their biggest rival, the Los Angeles Dodgers (sorry, Giants fans, you have to share your bête noire). That’s easier said than done.

The rivalry between the Dodgers and Padres over the last few years has largely been a mismatch. San Diego went a miserable 5–14 against the Dodgers in 2022, didn’t even take a single series against them this season, and haven’t had a winning record in this matchup since 2010. To add injury to insult, the only time the Padres have made the playoffs during the A.J. Preller era, the weird 2020 season, it was the Dodgers that sent them packing in a 3–0 sweep in the NLDS.

Let’s start things out with the ZiPS game-by-game projection.

The Dodgers are keeping it close, as of press time, whether Clayton Kershaw or Julio Urías will be the Game 1 starter (though reportedly, they already know). While Kershaw has seniority in the rotation, the team has regularly not started him in the first game of a series when he’s available, with both Urías and Walker Buehler among the pitchers getting the nod in recent years among others. ZiPS would slightly favor Kershaw as the Game 1/Game 5 starter, bumping the projected probability of the Dodgers advancing from 60.2% to 61.6%. While Urías won his first ERA crown in 2022, his peripherals are down slightly from ’21. Read the rest of this entry »

Of the 12 teams in the playoffs in 2022, only one was projected by both ZiPS and FanGraphs in the preseason as a sub-.500 team: the Cleveland Guardians. But this lone Cinderella in a sea of mean stepsisters toppled the White Sox handily this year, pulling away from the pack late to finish with an 11-game cushion in the AL Central. As the league’s No. 3 seed by virtue of winning the division, Cleveland now hosts the Tampa Bay Rays in the three-game Wild Card Series.

Broadly speaking, there are broad similarities between the Guardians and the Rays. Both play in smaller markets and, depending on how you look at the issue, have a payroll attitude somewhere on the spectrum from admirably thrifty to Ebenezer Scrooge on tax deadline day. However they got there, these teams embraced modern analytics early on, long before it was de rigeur in baseball, and have seen advantages. The Rays were the league doormat during the early, very non-sabermetric days of the franchise, but after an abrupt change in direction, they have the fourth-most wins in baseball over the last 15 years. The Guardians are not far behind.

Despite both teams regularly making the playoffs, they’ve only met in the postseason once before, in the 2013 AL Wild Card Game. Things didn’t go Cleveland’s way then, as Alex Cobb and Tampa’s bullpen combined for a shutout, causing a quick exit from October. Now Cleveland has a three-game series to get its revenge. Read the rest of this entry »