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 »

We passed the halfway mark of the 2021 season over the long holiday weekend, providing a convenient spot to take a break, look back over the preseason projections, and hopefully not cringe too much about how the predictions are shaking out. Since this is the big midseason update, I used the full-fat ZiPS model for individual players in addition to the normal depth chart reconfiguring, with all the high-fructose algorithms rather than the leaner one used for daily updates.

I went through the American League on Wednesday, so now it’s the Senior Circuit’s turn.

The Mets only averaging 89 wins in the update might feel a bit disappointing, but that negative inclination is misplaced. ZiPS actually likes the team’s talent slightly more than it did in March, with the difference being that the injury situation has been worse than expected. Use the preseason playing time predictions with the up-to-date player projections, and ZiPS believes that New York would have a 93-win roster, good enough to be the third-best team in the National League.

Read the rest of this entry »

MLB passed the halfway mark of the 2021 season over the long holiday weekend, providing a convenient spot to take a break, look back over the preseason projections, and hopefully not cringe too much about how the predictions are shaking out. Since this is the big midseason update, I used the full-fat ZiPS model for individual players in addition to the normal depth chart reconfiguring, with all the high-fructose algorithms rather than the leaner one used for daily updates.

Let’s start with the American League standings.

I was making a “do not panic” argument on behalf of the Yankees back when they were 5–10 and some people were digging for their doomsday preparedness kits, and while it might not be time to find where you left those water purification tablets, the situation is bleaker now than it was three months ago. Not that the team is actually worse; New York has been on an 88-win pace in the games since that reference point. But an 88-win pace isn’t nearly enough to get out of an early-season hole in a division where there are three other teams with more than detectable pulses. Even projected to play solid baseball the rest of the season, the Yankees have gone from the favorite to the projected fourth-place team.

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Earlier this year, we released our team expected win totals and playoff odds for the 2021 season. These projections are based on the FanGraphs Depth Charts, which blend Jared Cross’ Steamer projections with those of Dan Szymborski’s ZiPS, with playing time allocated by our own Jason Martinez of RosterResource fame; the playoff odds then simulate the season 20,000 times, taking strength of schedule into account.

We thought it’d be interesting to duplicate that process, instead using the Average Total Cost (ATC) Projections.

The ATC player projections have been available on the pages of FanGraphs since 2017. Similar to the Depth Charts model, ATC is an aggregation of other projections. While most other accumulation models typically apply equal weight to all their underlying data sources, ATC assigns weights based on historical performance. The method is similar to what Nate Silver does with his political forecasting model at FiveThirtyEight.com. You can read more about how ATC works in the introductory article here.

Over the past few years, ATC has consistently been one of the most accurate baseball forecasting models. Indeed, according to FantasyPros, the ATC projections have been the most accurate baseball projections over the past two seasons. The success of ATC stems from minimizing parameter risk across the player pool. You can read more about inter-projection volatility, how to use the ATC projections on the FanGraphs site, and what is new for ATC in 2021 here. Read the rest of this entry »

After having typically appeared in the hallowed pages of Baseball Think Factory, Dan Szymborski’s ZiPS projections have now been released at FanGraphs for nine years. The exercise continues this offseason. Below are the projections for the New York Yankees.

Batters

The possible loss of DJ LeMahieu is a real hit to the Yankees, but even if they don’t come to an agreement with him, I don’t expect the club to actually be cruising with Tyler Wade and Thairo Estrada come April. But even if they did, as long as the team remains healthy — a big condition given their recent experiences — it’s still an extremely potent lineup even if they tank a position or two. Brett Gardner is a free agent as well after the team declined his option, but I expect him to return anyway. After all, Gardner didn’t attract a ton of interest in last year’s free agent market, and with him being a year older and coming off a worse season, and with baseball’s economics, I doubt he gets more phone calls this time around. Like Mitch Moreland in Boston, Gardner appears to have a de facto arrangement where he can just show up at some point and the team will give him a one-year contract for $X million.

Did you really think that ZiPS would fall out of love with Gleyber Torres after his power went missing for six weeks? I do think he’s at the point at which I’d try to get him moved to third base. If the Yankees don’t re-sign LeMahieu and instead go after one of the players in the surprisingly deep shortstop pool, could Gio Urshela theoretically play second base? He’s likely a better third baseman than Torres, who hasn’t been great at second, and the team did work him out some at the position in summer training. How the infield gets shuffled will be one of the more interesting questions for them this offseason. Read the rest of this entry »

After having typically appeared in the hallowed pages of Baseball Think Factory, Dan Szymborski’s ZiPS projections have now been released at FanGraphs for nine years. The exercise continues this offseason. Below are the projections for the Oakland Athletics.

Batters

Marcus Semien’s BABIP-aided regression to the mean was unwelcome, but Oakland received surprising production elsewhere from sources such as Robbie Grossman. That being said, the loss of Semien to free agency does create a bit of a vacuum, as a fair amount of the team’s depth at shortstop from the last few years (Franklin Barreto, Jorge Mateo, Jurickson Profar theoretically) has moved on to other organizations. Chad Pinder is likely the de facto shortstop if the season started today, but there’s a good chance that Oakland’s starter in 2021 is not in this set of projections, unless Semien returns. Normally I’d think a player of his caliber would be loath to sign a one-year deal, but given the circumstances of baseball in 2020, who knows if a multi-year deal is in his future. Suffice it to say, it would have been highly useful for the minor leagues to exist last season so that the A’s could have seen more of Vimael Machín or Nick Allen.

Oakland’s offense will go as far as their current Big Three — Matt Chapman, Ramón Laureano, and Matt Olson — take them. Second base and right field do show up as weaknesses in the projections, and this is another place where the lack of a minor league season hurts the A’s; they don’t sign free agents to big contracts, so getting to look at some of that Quadruple-A talent is a valuable exercise. ZiPS is sort of optimistic about Khris Davis, but after a second down season, the ceiling has been lowered farther than that early scene in the Wonka factory. Oakland’s top-level talent still keeps it in the high-80s in wins without a single move, but I’m quite uneasy about the team’s overall depth. Read the rest of this entry »

The first ZiPS team projection for 2021 goes live on Wednesday, and as usual, this is a good place to give reminders about what ZiPS is, what ZiPS is trying to do, and — perhaps most importantly — what ZiPS is not.

ZiPS is a computer projection system, developed by me in 2002–04 and which officially went live for the ’04 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 and a fellow stat nerd, in the late 1990s (my first interaction with Chris involved me being called an expletive!). ZiPS moved quickly from its original inception as a fairly simple projection system: It now does a lot more and uses a lot more data than I ever envisioned 20 years ago. At its core, however, it’s still doing two basic 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.

ZiPS uses multi-year statistics, with more recent seasons weighted more heavily; in the beginning, all the statistics received the same yearly weighting, but eventually, this became more varied based on additional research. Research is a big part of ZiPS, and every year, I run literally hundreds of studies on various aspects of the system to determine their predictive value and better calibrate the player baselines. What started with the data available in 2002 has expanded considerably: Basic hit, velocity, and pitch data began playing a larger role starting in ’13; and data derived from StatCast has been included in recent years as I got a handle on the predictive value and impact of those numbers on existing models. I believe in cautious, conservative design, so data is only included once I have confidence in improved accuracy; there are always builds of ZiPS that are still a couple of years away. Additional internal ZiPS tools like zBABIP, zHR, zBB, and zSO are used to better establish baseline expectations for players. These stats work similarly to the various flavors of “x” stats, with the z standing for something I’d wager you’ve already figured out! Read the rest of this entry »

When looking at the differences between midseason and original projections, it’s always fun to see where reality shredded expectations the most. The American League in 2019, on the other hand, is fairly boring. We have one big surprise, bordering on the edge of truly affecting the playoff hunt, and a relatively mild switcheroo in the AL Central leader. Sure, the White Sox are a bit better than projected and the Angels a bit worse, but it’s generally a league in which most teams are at least in the same time zone as their preseason win prognostications.

So how do the ZiPS in-season projections work? For the Big Official ones, I use the full-on ZiPS model rather than the comparatively simple in-season one, to try to get the best estimates possible. Each player gets a percentile projection, with ZiPS randomly selecting from each player’s distribution to get a range of the expected roster strength for each individual team. Then each team is projected against every other team in their schedule a million times for the rest of the year. All this has the benefit of getting more accurate tails as opposed to the binomial distribution when you’re working with an assumed roster strength; one of the most important things in ZiPS is that on all layers, it’s designed to be skeptical about its own accuracy.

So let’s dive right into the American League. Read the rest of this entry »

The ZiPS projection system will update these tables after every game and as the starting-pitcher probables change. They are based on the up-to-date ZiPS projections of the strengths of the teams and the projected starting pitchers. They are different than the playoff odds that appear elsewhere at this site. The FanGraphs playoff probabilities are based on 10,000 simulations using the updated projections in the depth charts. Where ZiPS differs is by guessing the game-by-game starting-pitcher matchups and using the ZiPS projections, including split projections.

First, here are the game-by-game probabilities:

And here are the overall series probabilities.

Eric Longenhagen and Kiley McDaniel published their top-100 list on Monday. Other outlets have released similar lists, as well, recently — outlets including Baseball America, Baseball Prospectus, Keith Law, and MLB Pipeline. I submitted my own contribution yesterday with KATOH’s top-100 prospects. All of these lists attempt to accomplish the very same goal: both to identify and rank the best prospects. But KATOH goes about it in a very different way than the others. While most others rely heavily on scouting, KATOH focuses on statistical performance.

On the whole, there’s a good deal of agreement between KATOH and the more traditional rankings. Many of KATOH’s favorite prospects have also received praise from real-live human beings who’ve watched them play. Ronald Acuna, Vladimir Guerrero, Jr., Brent Honeywell, Michael Kopech, and Kyle Tucker all fall within this group. In general, there is a lot of agreement. However, there are other KATOH favorites who’ve received little public consideration from prospect analysts. The purpose of this article is to give these prospects a little bit of attention.

For each position, I’ve identified the player, among those excluded from all top-100 lists, who’s best acquitted by KATOH. These players have performed in the minors in a way that usually portends big-league success. Yet, for one reason or another, each has been overlooked by prospect evaluators.

Of course, the fact that these players missed every top-100 list suggests that their physical tools are probably underwhelming. That’s very important information! Often times, the outlook for players like this is much worse than their minor-league stats would lead you to believe. There’s a reason people in the industry always say “don’t scout the stat line.” Although KATOH scouts the stat line in an intuitive fashion, it still overlooks important inputs that can predict big-league success.

Still, the stat-line darlngs sometimes pan out. I performed this  exact same exercise last year, as well, and I’m proud to say there were some big successes. Rhys Hoskins, Jake Faria, Ben Gamel, Chad Green, and Brandon Nimmo have each blossomed into productive big leaguers just one year out. Zach Davies and Edwin Diaz also appeared in this space two years ago. Of course, others haven’t worked out so well. Clayton Blackburn, Dylan Cozens, Ramon Flores, and Garrett Stubbs: none of them were particularly useful major leaguers in 2017. There will be hits, and there will be misses, especially when you’re dealing with non-elite prospects.

*****

C – Jake Rogers, Detroit (Profile)

Why KATOH Loves Him
Rogers hit a respectable .261/.350/.467 across two levels of A-ball last year, pairing an 11% walk rate with encouraging power. Most impressive of all, however, is that he did so as a catcher — a position where good hitters are few and far between. Rogers isn’t just any catcher, either: Clay Davenport’s defensive numbers graded him out as elite. Elite defensive catchers who can also hit a little are exceptionally valuable.

Why Scouts Don’t (J.J Cooper)

He has a big leg kick to start his swing, and takes a ferocious cut with a pull-heavy approach. When his swing works, he has the power to deposit pitches in the left-field bleachers. When it doesn’t, he rolls over ground outs or hits a number of harmless pop outs. Evaluators generally see Rogers as a below-average hitter with a lot of swings and misses and average bat speed.

My Thoughts
Usually, KATOH’s catcher crushes are good hitters who are questionable behind the plate. Rogers is the exact opposite, as his offense is the questionable piece. Eric Longenhagen called him “best defensive catching prospect I’ve seen, a polished receiver and cat-like ball-blocker with a plus arm” over the summer. Even if Rogers’ A-ball numbers ultimately don’t translate, he could still be a solid regular given how little catchers hit. For example, Martin Maldonado defended his way to 1.1 WAR last year in spite of a 73 wRC+.

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