Archive for 2024 ZiPS Projections

The Official (And Hopefully Not Too Cringe) 2024 ZiPS Projections

Kim Klement Neitzel-USA TODAY Sports

After all the rumors and money and projections, here we are, back at 0-0, with every team having at least some theoretical level of hope for the coming season. Beginning Thursday, actual games will turn these projections to shreds, but this is the best algorithmic projection I have the ability to make for 2024. Just a note that I have not committed an act of decimal cheating; ZiPS does not know that the Padres and Dodgers are 1-1.

The methodology I’m using here isn’t identical to the one we use in our Projected Standings, meaning there naturally will 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 a jumping off 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 chemin de fer like James Bond).

After that is done, ZiPS applies another set of algorithms with a generalized distribution of injury risk that changes the baseline PAs/IPs 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 (and proportionally) fills in playing time from the next players on the list to get to a full slate of plate appearances and innings. The model’s had a lot of updates since the pre-spring projections, so probabilities may have moved slightly more than you might have expected from the changes in wins.

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.

The goal of ZiPS is to be less mind-blowingly awful than any other way of predicting the future. The future is tantalizingly close but beyond our ken, and if anyone figures out how to deflect astrophysicist Arthur Eddington’s arrow of time, it’s probably not going to be in service of baseball projections. So we project probabilities, not certainties.

Over the last decade, ZiPS has averaged 19.7 correct teams when looking at Vegas preseason over/under lines. I’m always tinkering with methodology, but most of the low-hanging fruit in predicting how teams will perform has already been harvested. With one major exception, most of ZiPS’ problems now are about accuracy rather than bias. ZiPS’ year-to-year misses for teams are uncorrelated, with an r-squared of one year’s miss to the next of 0.000562. Now, correlations with fewer than 20 points aren’t ideal, but the individual franchise with the highest year-to-year r-squared is the Mariners at 0.03, which isn’t terribly meaningful. If you think that certain franchises have a history of predictive over- or underperformance, you thought wrong, and I’d bet it’s the same for the other notable projection systems.

If you want to check out the pre-spring projections, which talk about the biggest things to happen up to that point, here are the links to the AL and NL projections. Since it has been requested, for these official 2024 projections, I’ve also added 80th and 20th percentile win totals to the standings tables.

ZiPS Projected Standings – AL East
Team W L GB Pct Div% WC% Playoff% WS Win% 80th 20th
Baltimore Orioles 91 71 .562 37.2% 34.8% 72.1% 8.8% 99.0 82.2
New York Yankees 87 75 4 .537 24.1% 35.2% 59.3% 5.2% 95.8 78.7
Toronto Blue Jays 87 75 4 .537 22.4% 35.9% 58.3% 5.0% 95.3 78.7
Tampa Bay Rays 83 79 8 .512 11.9% 29.2% 41.1% 2.3% 91.1 74.4
Boston Red Sox 77 85 14 .475 4.4% 17.5% 22.0% 0.7% 85.9 69.2

Since the last set of projections, the movement here can mostly be attributed to starting pitching. Corbin Burnes provides a huge boost to the Orioles, but some of the benefit of his addition is negated because of less optimistic innings totals for the injured John Means and, more significantly, Kyle Bradish. The injury to Yankees ace Gerrit Cole diminishes their outlook a bit, though they still have the American League’s third highest playoff probability. Lucas Giolito wasn’t expected to pitch the Red Sox to the postseason, but his injury makes a Boston playoff berth even less likely.

ZiPS Projected Standings – AL Central
Team W L GB Pct Div% WC% Playoff% WS Win% 80th 20th
Minnesota Twins 86 76 .531 41.8% 15.7% 57.5% 4.5% 94.1 77.0
Cleveland Guardians 85 77 1 .525 38.4% 16.6% 55.1% 3.9% 93.3 76.7
Detroit Tigers 78 84 8 .481 13.2% 11.6% 24.8% 0.8% 85.8 69.3
Kansas City Royals 73 89 13 .451 5.9% 6.5% 12.5% 0.2% 81.4 65.0
Chicago White Sox 63 99 23 .389 0.6% 0.8% 1.5% 0.0% 71.5 54.8

People might still be shocked to see the White Sox with a 1.5% chance of making the postseason, but one of the things I’ve learned after doing this for 20 years is that people – even the most sophisticated ones – tend to underrate how often improbable things happen. Luckily, with so many years in the books, I’ve had the ability to do a lot of calibration! In most simulations, the division features a fairly tight race between the Twins and Guardians for the title and the Tigers finishing third. And because the Central is relatively weak, a Royals playoff appearance would be unlikely but not unreasonably so.

ZiPS Projected Standings – AL West
Team W L GB Pct Div% WC% Playoff% WS Win% 80th 20th
Houston Astros 88 74 .543 37.0% 26.2% 63.2% 6.3% 96.5 79.4
Texas Rangers 86 76 2 .531 28.4% 27.0% 55.5% 4.5% 94.4 77.6
Seattle Mariners 86 76 2 .531 27.4% 27.3% 54.7% 4.3% 94.0 77.6
Los Angeles Angels 77 85 11 .475 6.9% 14.7% 21.6% 0.7% 85.6 68.7
Oakland A’s 63 99 25 .389 0.2% 0.9% 1.1% 0.0% 71.6 54.7

The big change here is a slightly more negative distribution of the innings for Astros pitchers, narrowing their lead over the Rangers and Mariners. I appreciate ZiPS’ bringing the M’s just that much closer to the Seattle Mariners .540 meme. The A’s now project to finish a fraction of a win ahead of the White Sox in the AL basement, which is some kind of victory, I guess.

ZiPS Projected Standings – NL East
Team W L GB Pct Div% WC% Playoff% WS Win% 80th 20th
Atlanta Braves 95 67 .586 62.6% 21.4% 84.0% 15.2% 103.3 86.0
Philadelphia Phillies 85 77 10 .525 17.9% 33.4% 51.2% 3.7% 93.3 76.7
New York Mets 83 79 12 .512 12.9% 28.2% 41.1% 2.3% 91.2 74.0
Miami Marlins 79 83 16 .488 6.3% 20.2% 26.6% 1.0% 87.1 70.4
Washington Nationals 66 96 29 .407 0.3% 2.0% 2.3% 0.0% 74.1 57.4

ZiPS does give the Braves a 1% chance at winning 116 games! Atlanta lost a bit in the probabilities because of some changes in the generalized playing time model that fills in the backups. Even if ZiPS sees the playoffs as a bit less certain for this team than it did six weeks ago, the Braves still have the highest projected win total in the majors. The Marlins took a sizable hit after some negative injury news, a pretty big deal for them since the pitching staff is their source of upside. It sure ain’t the hitting!

ZiPS Projected Standings – NL Central
Team W L GB Pct Div% WC% Playoff% WS Win% 80th 20th
St. Louis Cardinals 83 79 .512 27.8% 16.0% 43.8% 2.6% 90.7 74.4
Chicago Cubs 82 80 1 .506 27.9% 15.6% 43.5% 2.5% 91.0 74.2
Cincinnati Reds 80 82 3 .494 20.8% 14.3% 35.1% 1.6% 89.0 71.6
Milwaukee Brewers 78 84 5 .481 14.7% 12.6% 27.3% 1.0% 86.8 70.0
Pittsburgh Pirates 75 87 8 .463 8.9% 9.0% 17.9% 0.5% 83.7 67.3

ZiPS loves Pete Crow-Armstrong and is suspicious of Cody Bellinger matching his 2023 numbers, but bringing him back was still enough to push the Cubs into a near-statistical tie in what was already projected to be a very close race. The Brewers took a hit with the loss of Burnes, and as a result, they slightly boosted the projections for the other four teams in the division.

ZiPS Projected Standings – NL West
Team W L GB Pct Div% WC% Playoff% WS Win% 80th 20th
Los Angeles Dodgers 93 69 .574 49.3% 29.7% 79.0% 11.9% 101.1 84.2
Arizona Diamondbacks 86 76 7 .531 20.5% 34.9% 55.5% 4.4% 94.4 77.8
San Francisco Giants 85 77 8 .525 17.2% 32.1% 49.4% 3.4% 93.2 76.1
San Diego Padres 83 79 10 .512 12.7% 28.5% 41.2% 2.3% 91.3 74.0
Colorado Rockies 67 95 26 .414 0.2% 1.9% 2.1% 0.0% 74.5 59.0

The NL West contenders fighting with the Dodgers – which means the three other teams that are not the Rockies – all received a boost because, since the pre-spring projections, they each added one of the top starting pitchers available, either in free agency or via trade, this offseason. The Diamondbacks, Giants, and Padres are better after having acquired, respectively, Jordan Montgomery, Blake Snell, and Dylan Cease, but the moves haven’t changed the relative positions of these teams in the projected standings. Even so, these deals — along with San Francisco’s signing of Matt Chapman — have created more scenarios in which the Dodgers can be bested for the divisional title, though they remain the favorites.

One thing you see a lot on social media, especially from sites that repost these projections, is outrage that “the best team will only have X wins.” The Orioles are projected to have the best record in the AL, at 91-71, but that doesn’t mean that ZiPS projects 91 wins to lead the AL. Those 91 wins represent Baltimore’s 50th percentile performance in those million simulations, and it is astronomically unlikely that all 30 teams hit their 50th-percentile projections. On average, you should expect three teams to hit their 90th percentile, six to hit their 80th, nine to hit their 70th, and so on and so forth. But again, it’s rarely going to be that neat. So here’s the percentile matrix for the number of wins it would take to secure each of the six playoff spots.

ZiPS Playoff Matrix
To Win 10th 20th 30th 40th 50th 60th 70th 80th 90th
AL East 89.3 92.0 94.0 95.8 97.4 99.1 100.9 103.0 105.9
AL Central 83.0 86.0 88.1 90.0 91.8 93.6 95.6 97.9 101.1
AL West 86.6 89.4 91.5 93.4 95.1 96.8 98.7 100.9 103.9
To Win 10th 20th 30th 40th 50th 60th 70th 80th 90th
AL Wild Card 1 87.4 89.2 90.6 91.8 93.0 94.2 95.5 97.0 99.2
AL Wild Card 2 84.1 85.8 87.0 88.0 89.0 90.1 91.2 92.5 94.4
AL Wild Card 3 81.6 83.1 84.3 85.3 86.2 87.1 88.2 89.4 91.1
To Win 10th 20th 30th 40th 50th 60th 70th 80th 90th
NL East 88.3 91.4 93.6 95.6 97.5 99.4 101.5 104.2 107.8
NL Central 83.5 86.2 88.1 89.8 91.4 93.0 94.8 96.8 99.6
NL West 88.8 91.7 93.8 95.7 97.5 99.2 101.1 103.3 106.4
To Win 10th 20th 30th 40th 50th 60th 70th 80th 90th
NL Wild Card 1 87.4 89.3 90.6 91.8 93.0 94.2 95.5 97.1 99.2
NL Wild Card 2 84.0 85.7 87.0 88.0 89.0 90.0 91.1 92.4 94.2
NL Wild Card 3 81.5 83.1 84.2 85.2 86.2 87.1 88.1 89.3 91.0

ZiPS 2024 Top 100 Prospects

Allan Henry-USA TODAY Sports

For the ninth time (in 10 years — it’s a long story), we’ve reached the point in the offseason where I run down the ZiPS Top 100 prospects. For those wandering in who may hear “ZiPS” and think of the University of Akron or possibly the popular Cincinnati burger spot, ZiPS is a computer projection system that crunches a lot of data about players and attempts to peer through the fog that obscures the future. You can read more about the system here or in MLB.com’s executive summary.

ZiPS prospect projections aren’t an attempt to supplant scouting. Rather, they try to be a supplement to scout-generated lists. There’s a lot of uncertainty in lower-level minor league stats that isn’t present at the upper levels. As such, non-statistical information about players takes on added value. ZiPS doesn’t seek to be the one-ring-to-bind-them-all-unified-field-theory-giant-Katamari-Damacy-ball of prognostication; it aims to give the very best data-generated predictions possible, for people to use, ignore, mock, or worship according to their personal tastes and worldview. Read the rest of this entry »


The 2024 Pre-Spring Training ZiPS Projected Standings: National League

Jeff Curry-USA TODAY Sports

With the Dodgers reporting for pitchers and catchers today, this week seems like a good time to run ZiPS projections for all 30 teams. I covered the American League projections yesterday, so today is all about the National League. Let’s be clear up front: These are not the final preseason projections, but they’re the best expression of how ZiPS sees the NL right now. After all, several marquee free agents remain unsigned and rosters will surely change between now and the start of the 2024 season.

These standings are the result of a million simulations, not results obtained from binomial, or more competently, beta-binomial magic. The methodology isn’t identical to the one we use for our playoff odds, which were released Wednesday, meaning there naturally will be some notable 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 a jumping off 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 — though it would be fun to don a tuxedo and play chemin de fer like James Bond).

After that is done, ZiPS applies another set of algorithms with a generalized distribution of injury risk that changes the baseline plate appearances or innings pitched for each player. ZiPS then automatically and proportionally “fills in” playing time from the next players on the list to get to a full slate of PAs and innings.

The result is a million different rosters for each team and an associated winning percentage for each million of them. 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. I promise, this is much less complex than it sounds.

The goal of ZiPS is to be less awful than any other way of predicting the future. The future is tantalizingly close but beyond our ken, and if anyone figures out how to deflect the astrophysicist Arthur Eddington’s arrow of time, it’s probably not going to be in the form of baseball projections. So we project probabilities, not certainties.

Over the last decade, ZiPS has averaged 19.6 correct teams when looking at Vegas preseason over/under lines. I’m always tinkering with methodology, but most of the low-hanging fruit in predicting how teams will perform has already been harvested. ZiPS’ misses for teams from year to year are uncorrelated, with an r-squared of one year’s miss to the next of 0.000562. In other words, none of the year-to-year misses for individual franchises has told us anything about future misses for those franchises.

2024 ZiPS Projected Median Standings – National League East
Team W L GB Pct Div% WC% Playoff% WS Win%
Atlanta Braves 95 67 .586 71.3% 21.4% 92.7% 17.4%
Philadelphia Phillies 85 77 10 .525 13.9% 41.2% 55.0% 3.6%
New York Mets 83 79 12 .512 8.9% 34.4% 43.3% 2.3%
Miami Marlins 81 81 14 .500 5.9% 28.4% 34.3% 1.5%
Washington Nationals 66 96 29 .407 0.0% 0.8% 0.9% 0.0%

That ZiPS likes the Atlanta Braves can hardly be considered a surprise considering they won 104 games last year, all projection systems everywhere love them, and I’ve been warning non-Braves fans that this would be the likely result all winter. What else is there to say? They’re a great team and there’s no scary number two in the division.

The Phillies project just slightly worse than last year, partially due to some aging risk in their prime offensive players, but more likely than not — really, unless they lose either Zack Wheeler or Aaron Nola to injury — they are going to be a playoff team. Catching the Braves isn’t a futile gesture — we’re talking a roughly one-in-seven chance — but they’ll need some help from Atlanta to win the division.

ZiPS doesn’t think the Mets did enough to patch up their rotation, but if their starting pitchers are better than expected, they should contend for a wild card. The Marlins project a little worse than New York, but they have a high variance in their projected outcomes; their pitching is elite, and that could be enough to make a pretty lousy offense almost unimportant — as was the case last year when they snagged a wild card berth.

Last year, the Nationals remained within bullhorn distance of .500 for much of the late summer, but they aren’t good enough to take a big step forward in 2024. Washington has the worst ZiPS projection for any National League team.

2024 ZiPS Projected Median Standings – National League Central
Team W L GB Pct Div% WC% Playoff% WS Win%
St. Louis Cardinals 83 79 .512 33.2% 15.7% 48.9% 2.9%
Chicago Cubs 81 81 2 .500 23.5% 15.2% 38.6% 1.9%
Milwaukee Brewers 80 82 3 .494 20.5% 14.3% 34.8% 1.5%
Cincinnati Reds 79 83 4 .488 16.0% 12.7% 28.8% 1.1%
Pittsburgh Pirates 75 87 8 .463 6.8% 7.1% 13.9% 0.3%

While it may seem like a relief that ZiPS is hedging enough here that I won’t get blamed too badly, no matter what happens, I also won’t get much credit! Fans have a tendency to overrate teams when things are going well and underrate teams when they’re not, and I think the Cardinals are a good example of this. The additions outside of Sonny Gray don’t send a tingle down your spine, but they did successfully patch up the rotation, which was a gaping wound for most of last season. Paul Goldschmidt, 36, and Nolan Arenado, who turns 33 in April, might not be as good as they once were, but if they age gradually instead of all at once, St. Louis should have the necessary depth in its lineup to score enough runs to compete in such a weak division. ZiPS isn’t alone here.

Shota Imanaga is my favorite signing this winter, but the Cubs are probably still one more starting pitcher away from being the favorite in this division. I’d certainly like more ambitious solutions at first base or catcher. In recent weeks, the Brewers patched some of their roster holes, signing first baseman Rhys Hoskins, starting pitcher Jakob Junis, and backup catcher and DH Gary Sánchez, but they also opened up a larger, newer one when they traded ace right-hander Corbin Burnes for infield prospect Joey Ortiz and left-hander DL Hall. Ortiz should get the chance to play every day, and Hall could be the latest dominant arm fermented by Milwaukee’s reliever brewery, but the Brewers will feel the absence of Burnes in 2024.

There’s a lot to like about the Reds’ future, but they haven’t done much this offseason to address their shortcomings. They have a logjam of guys who get a lot of their value playing third base, but instead of using some of those players as trade pieces to upgrade elsewhere, the Reds are going to shove them all into the lineup at various other positions, such as first base, DH and corner outfield. That isn’t a particularly lucrative plan. Cincinnati’s starting pitching could be very good, but there is a quite a bit of variance with this group due to consistency and/or injury concerns. A few bad “rolls” here and the rotation could become awful quickly.

The Pirates aren’t a depressing team and have some interesting players to watch, like shortstop Oneil Cruz, outfielder Bryan Reynolds, and third baseman Ke’Bryan Hayes. But they do have some holes to fill at other positions, and their starting pitching staff probably peaks at OK. ZiPS is a bigger fan of their bullpen.

2024 ZiPS Projected Median Standings – National League West
Team W L GB Pct Div% WC% Playoff% WS Win%
Los Angeles Dodgers 93 69 .574 66.2% 21.8% 88.1% 13.9%
Arizona Diamondbacks 84 78 9 .519 16.6% 34.8% 51.4% 3.3%
San Francisco Giants 82 80 11 .506 11.2% 29.9% 41.1% 2.1%
San Diego Padres 79 83 14 .488 5.9% 21.2% 27.1% 1.0%
Colorado Rockies 67 95 26 .414 0.1% 1.0% 1.1% 0.0%

The Dodgers are clearly the best team in the NL West, but they’re not invincible. The team’s pitching plan to have about 15 really talented pitchers and hope nine or so are healthy at any given time could work out tremendously – as it has in recent years – but there’s certainly some risk there. It’s hard to capture in preseason projections, but the Dodgers will likely be aggressive in making trades to remedy flaws that pop up with their pitching staff during the season.

Arizona is a good team, but as is the case with the Rangers, there’s a serious risk of overrating a team because of a World Series appearance. The Diamondbacks were an 84-win team last year and their outlook for 2024 would’ve been about the same if the Brewers had eliminated them in the first round rather than vice-versa. That said, Arizona made several moves this offseason and, as a result, appears to be a better team overall than it was last year (and they were a team I talked up quite a bit).

The Giants are underwhelming, in part because they’ve missed out on most of the big free agents they’ve gone after, but that doesn’t mean they are bad. They are solid enough that they could make a wild card push, and their floor is higher than many think. But they need some more production in their lineup, and behind Logan Webb, there are a lot of moving parts in the rotation.

Replacing Juan Soto is a nearly impossible task, so it’s unsurprising that the Padres are projected to take a step back this season. ZiPS projects both the offense and the pitching to rank somewhere in the 17 to 21 range, depending on playing time assumptions. And while San Diego has repaired its farm system quicker than many (including this writer) expected, that doesn’t exactly help much for 2024.

The Rockies aren’t going to the postseason and will probably be well out of the playoff picture by mid-April. But at least they didn’t do anything this offseason to make their long-term outlook worse, which is kind of an improvement. I’m mildly hopeful that they take the proper lesson from the Nolan Jones trade and make it an organizational priority to acquire every interesting 25-year-old from a team that is unsure what to do with him.

2024 ZiPS Projected Playoff Wins – National League
To Win 10th 20th 30th 40th 50th 60th 70th 80th 90th
NL East 88.7 91.2 93.0 94.6 96.2 97.8 99.5 101.7 104.7
NL Central 82.8 84.9 86.4 87.7 89.0 90.3 91.7 93.4 95.9
NL West 87.2 89.6 91.4 92.9 94.4 96.0 97.7 99.8 102.7
To Win 10th 20th 30th 40th 50th 60th 70th 80th 90th
NL Wild Card 1 86.1 87.6 88.7 89.7 90.7 91.6 92.7 94.0 95.9
NL Wild Card 2 83.3 84.6 85.6 86.5 87.3 88.1 89.0 90.0 91.5
NL Wild Card 3 81.2 82.5 83.4 84.2 84.9 85.7 86.5 87.4 88.7

And here we have the simple chart – which I’ve been including in all of these ZiPS projected standings, except the times I forget – to show what win totals likely will make the playoffs, rather than the highest median win projection.


The 2024 Pre-Spring Training ZiPS Projected Standings: American League

Reggie Hildred-USA TODAY Sports

With the Dodgers reporting for pitchers and catchers on Friday, this week seems like a good time to do run ZiPS projections for all 30 teams. Let’s be clear up front: These are not the final preseason projections – and an ancient curse I saw suggests that if you quote them as such, ghosts will eat your lymphatic system – but they’re the best expression of how ZiPS sees the league right now. After all, several marquee free agents remain unsigned and rosters will surely change between now and the start of the 2024 season.

These standings are the result of a million simulations, not results obtained from binomial, or more competently, beta-binomial magic. The methodology isn’t identical to the one we use for our playoff odds, which were released yesterday, meaning there naturally will be some notable 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 a jumping off 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 chemin de fer like James Bond).

After that is done, ZiPS applies another set of algorithms with a generalized distribution of injury risk that changes the baseline plate appearances or innings pitched for each player. ZiPS then automatically and proportionally “fills in” playing time from the next players on the list to get to a full slate of PAs and innings.

The result is a million different rosters for each team and an associated winning percentage for each million of them. 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. I promise, this is much less complex than it sounds.

The goal of ZiPS is to be less awful than any other way of predicting the future. The future is tantalizingly close but beyond our ken, and if anyone figures out how to deflect the astrophysicist Arthur Eddington’s arrow of time, it’s probably not going to be in the form of baseball projections. So we project probabilities, not certainties.

Over the last decade, ZiPS has averaged 19.6 correct teams when looking at Vegas preseason over/under lines. I’m always tinkering with methodology, but most of the low-hanging fruit in predicting how teams will perform has already been harvested. ZiPS’ misses for teams from year to year are uncorrelated, with an r-squared of one year’s miss to the next of 0.000562. In other words, none year-to-year misses for individual franchises has told us anything about future misses for those franchises.

2024 ZiPS Projected Median Standings – American League East
Team W L GB Pct Div% WC% Playoff% WS Win%
Baltimore Orioles 90 72 .556 36.4% 38.5% 74.9% 8.4%
New York Yankees 88 74 2 .543 25.5% 40.0% 65.6% 5.9%
Toronto Blue Jays 88 74 2 .543 24.5% 39.1% 63.6% 5.6%
Tampa Bay Rays 83 79 7 .512 9.7% 29.9% 39.5% 2.1%
Boston Red Sox 79 83 11 .488 3.9% 18.1% 22.0% 0.8%

I’m from Baltimore, but I would hope last year’s projection miss would disavow anyone of the notion that I weight these team standings toward my personal preferences. The Orioles – and last year’s Orioles – do a bit better in my methodology than others, I suspect because of the weight I deal with depth. In those seasons in which they lose players, especially offensive ones, the team’s depth keeps the falloff from being too dire. Even in simulation no. 452,331, in which the O’s lose both Gunnar Henderson and Adley Rutschmann to season-ending injuries before the first game, the team still finished 84-78!

The Yankees have significant downside given how much of their punch is tied up in a handful of players, but the reports of their death are quite premature. Juan Soto will provide a huge offensive boost this year, even if they don’t re-sign him after the season. They also added two other outfielders, Alex Verdugo and Trent Grisham, who are better than everybody they ran out there last year, with the exception of Aaron Judge.

ZiPS likes Toronto’s rotation and expects the return of Kevin Kiermaier to help, but without Matt Chapman, it sees third base as a major downgrade from last year. The Rays almost always get the most out of their depth, but ZiPS isn’t sure how much production they will get from their DH spot or how they will cobble together their rotation without Tyler Glasnow.

The Red Sox aren’t a dreadful team, but they’re merely OK in a division that has four good-to-great teams. That being said, they’re just good enough that they still have slightly better than a one-in-five chance of making the playoffs.

2024 ZiPS Projected Median Standings – American League Central
Team W L GB Pct Div% WC% Playoff% WS Win%
Cleveland Guardians 85 77 .525 42.4% 13.6% 56.0% 3.9%
Minnesota Twins 85 77 .525 42.1% 13.6% 55.7% 3.8%
Detroit Tigers 77 85 8 .475 10.3% 7.3% 17.7% 0.5%
Kansas City Royals 74 88 11 .457 4.7% 3.9% 8.6% 0.2%
Chicago White Sox 66 96 19 .407 0.4% 0.4% 0.7% 0.0%

ZiPS projects Cleveland to be relatively even with Minnesota, in large part because it likes the rotation trio of Shane Bieber, Triston McKenzie, and Tanner Bibee more than other projection systems do. ZiPS doesn’t see the Guardians as significantly below average at any position — Andrés Giménez remains a ZiPS favorite — and it thinks their bullpen is underrated. The Twins won the division fairly comfortably last year, but remember, they won only 87 games and just lost the AL Cy Young runner-up, Sonny Gray, in free agency. The Jorge Polanco trade came from a surplus of infield talent, but the additions of Anthony DeSclafani and Justin Topa won’t compensate for Gray’s loss to the rotation. If you like Carlos Santana, the team’s “big” offseason signing, I’d recommend you not look at the projection for him.

The projections still see more upside for Detroit’s pitching than its hitting, though after Spencer Torkelson’s surge last summer, ZiPS does expect him to keep improving in his third big league season. The Tigers are good enough that they can make a serious run at .500, but they’ll need some good fortune to get enough offense.

The Royals get credit for being active in free agency this offseason, signing veteran starting pitchers Seth Lugo and Michael Wacha, reliever Will Smith, slugger Hunter Renfroe, and utility man Adam Frazier, among other players. That said, those are the types of moves a team makes when it already has a strong core in place and is ready to contend, and, at least as ZiPS sees it, the Royals aren’t quite there yet. That’s not the worst thing in the world, considering they just signed shortstop Bobby Witt Jr. to the longest, most valuable extension in franchise history.

ZiPS has the White Sox as one of the worst teams in baseball, with little to look forward to outside of Dylan Cease, Luis Robert Jr., and the hope that Yoán Moncada and Eloy Jiménez get back on track. This organization is in a very dangerous position in that, like the Rockies a few years ago, I’m not sure it truly understands where it stands.

2024 ZiPS Projected Median Standings – American League West
Team W L GB Pct Div% WC% Playoff% WS Win%
Houston Astros 89 73 .549 43.3% 26.5% 69.8% 7.3%
Texas Rangers 86 76 3 .531 28.0% 28.4% 56.4% 4.5%
Seattle Mariners 85 77 4 .525 23.0% 27.4% 50.4% 3.5%
Los Angeles Angels 79 83 10 .488 5.6% 13.2% 18.9% 0.6%
Oakland A’s 63 99 26 .389 0.0% 0.1% 0.1% 0.0%

ZiPS still sees the Astros as the class of the AL West, thanks to the massive concentration of talent in the heart of their lineup. It wasn’t a busy winter for Houston, but the big addition, Josh Hader, gives a boost to the bullpen. The Astros, though, are not unstoppable. They have a lot of viable arms in the rotation, but the upside isn’t what it was three or four years ago, even if Justin Verlander has another strong season left in his arm.

The Rangers are a well-built team, but a lot of their offensive talent is on the wrong side of 30, and last year was probably the best case scenario for a few of their hitters. Their starting pitching is weaker now than it was at the end of 2023. ZiPS did account for the late-season returns of Max Scherzer, Jacob deGrom, and Tyler Mahle to reinforce the rotation, but all those games without them count, too, and as of this writing, Texas has not re-signed or replaced Jordan Montgomery.

ZiPS likes a lot of what the Mariners did this offseason. It projects Jorge Polanco as a moderate plus at second base and Luis Urías to be an effective replacement for Eugenio Suárez. Gregory Santos is in the top tier of projected relievers, though his projection will come down just a tad once a fixed error in the ZiPS database propagates to our player pages.

It will be nice for the Angels to get full seasons from Zach Neto and Nolan Schanuel, and the team has spent its offseason quietly beefing up its bullpen. But losing Shohei Ohtani is going to hurt.

I believe I have talked about all the major league teams in the AL West and surely did not forget anyone.

2024 ZiPS Projected Playoff Wins – American League
To Win 10th 20th 30th 40th 50th 60th 70th 80th 90th
AL East 89.2 91.4 93.0 94.4 95.7 97.1 98.5 100.2 102.7
AL Central 82.7 85.1 86.8 88.3 89.7 91.2 92.8 94.7 97.3
AL West 86.7 89.0 90.6 92.1 93.5 94.9 96.4 98.2 100.8
To Win 10th 20th 30th 40th 50th 60th 70th 80th 90th
AL Wild Card 1 87.3 88.8 89.9 90.9 91.8 92.8 93.9 95.1 97.0
AL Wild Card 2 84.6 86.0 86.9 87.8 88.6 89.5 90.3 91.4 92.9
AL Wild Card 3 82.5 83.8 84.7 85.5 86.2 87.0 87.8 88.8 90.2

One thing that drive me nuts about the discourse of the ZiPS projections is when someone looks at the top median projection and gets very angry with me that some division can be won with 89 or 90 wins. Since most of the tweets on this subject have an aspect for Mature Audiences Only, I’ve translated an example into something suitable for polite company.

Verily, Szymborski, thou art bereft of wit! How dare thee proclaim that a mere tally of 89 victories shall secure the Astros dominion over the AL Wast! Thy discourse betrays a lamentable ignorance, akin to that of a common dullard. Thy prognostications, I dare say, are as worthless as the dregs of a shire-reeve’s larder after Michaelmas!

Yes, the Astros have the best median projection in the AL West at 89 wins, but that doesn’t mean 89 wins will actually win the AL West. This last chart shows the probabilities that X number of wins will take the division or wild card spot in question. So, 89 wins might win the AL West, but only about 20% of the time. The Orioles project to 90 wins, but in the 36.4% of scenarios in which they won the AL East, they averaged 95.3 wins.


2024 ZiPS Projections: Chicago White Sox

For the 20th 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 final team is the Chicago White Sox.

Batters

In a certain way, this team is a bit more depressing than the Oakland Athletics. No, they’re not trying to peace out of Chicago, but I think the A’s front office is probably more realistic about the team’s immediate chances of winning than the Southsiders are. It’s always hard to gauge exactly what a team truly thinks of their outlook, but I get the impression that the White Sox think they will be at least halfway competitive in 2024. Realistically, though, a lot of things would have to go their way, even in a weak division like the AL Central.

The Pale Hose have a one-dude offense in a sport where that isn’t a thing. Luis Robert Jr. is in his prime right now, probably at his peak form. If the White Sox were actually rebuilding, he’s the guy they could trade to start meaningfully restocking the farm system. Instead, he’ll be a key cog in their quest to win 70 games. Read the rest of this entry »


2024 ZiPS Projections: New York Yankees

For the 20th 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 penultimate team is New York Yankees.

Batters

If I’ve learned anything from two decades of projection work (and people getting mad at that projection work), it’s that fans are way too optimistic about teams when everything goes great and way too pessimistic after things have gone awry.
The 2022 Yankees were a good example of this. After a first half in which they went 58-23, good for a .716 winning percentage and a Pythagorean win percentage that nearly matched, ZiPS only projected the team to go 45-36 the rest of the way. “Knave!” they shouted at me on Twitter. “Vagabond! Miscreant!” OK, maybe they didn’t use those words exactly, but there was bewilderment and more than a touch of anger that I would disrespect the Bronx Bombers so. In the end, the Yankees actually were four wins worse than that projection in the second half.

And just like things are never as amazing as they seem when everything goes your way, the reverse is true, with the Yankees again being a good example. They had their worst season since 1992 in 2023, and based on how they’ve been talked about over the last six months or so, you’d think they were a glorified Triple-A team. But the Yankees weren’t that bad — they went 82-80 in a division without a true doormat team to beat up on. That record was all of seven wins below their preseason projection, a miss that barely merits raising an eyebrow, and certainly doesn’t justify floating Brian Cashman out to sea (or bunting more). Read the rest of this entry »


2024 ZiPS Projections: Oakland Athletics

For the 20th 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 next team up is the Oakland Athletics.

Batters

The A’s are the most depressing team in baseball, hands down. They actually have some competition if we’re talking about the worst team in baseball, but neither the Colorado Rockies nor the Chicago White Sox feel quite as miserable as the A’s. How often does a player take the opportunity to go all-out on the team’s owner when they announce their retirement? Read the rest of this entry »


2024 ZiPS Projections: Texas Rangers

For the 20th 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 next team up is the Texas Rangers.

Batters

The Rangers led the American League in runs scored in 2023, and most of the players who contributed to that success are set to return to the lineup in 2024. Sure, Corey Seager might not repeat his monster season, but his performance should still land comfortably in star territory, and the Rangers could get a little luckier with his health. Evan Carter isn’t likely to post an OPS around 1.000 again, but that’s balanced by the fact that the team ought to get a lot more than 23 regular season games from him this year. Read the rest of this entry »


2024 ZiPS Projections: Colorado Rockies

For the 20th 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 next team up is the Colorado Rockies.

Batters

Let’s get the obvious out of the way: the Colorado Rockies will almost certainly be one of the worst teams in baseball in 2024, a prime candidate to get the top pick in next year’s draft lottery. There’s just no way around that conclusion, as the Rockies don’t have the same young depth or big league-quality role players that the Baltimore Orioles had on hand to make their initial burst in 2022 possible.

Still, the team has at least shown signs of life in their decision-making processes over the year or so. Gone — as far as I’m aware — is the ownership talk of winning 94 games. And young players are seeing the field. There were times Ezequiel Tovar struggled mightily at the plate during his rookie season, especially in the first month, but for once, the team avoided panicking and blocking a top prospect with the Chris Owings/José Iglesias-type veterans who hang around Triple-A looking for a big league job. And while it took the franchise a month to get him onto the roster, the Rockies looked for excuses to play Nolan Jones rather than searching for reasons not to. That paid off, with Jones enjoying an excellent rookie season that saw him improve more in the outfield than most expected. A lot of Colorado’s marginal prospects in their 20s struggled, but at least they played. None of Elehuris Montero, Alan Trejo, or Michael Toglia did all that well, but their playing time demonstrated a (possible) change in organizational thinking, as players like them have been marginalized in the past. Think back to Tom Murphy or Mike Tauchman during their time with the Rockies — the team could not have seemed less interested in seeing what they had.

Brenton Doyle rightly won a Gold Glove award, and the defense is probably mostly real; the ZiPS probability-based method for minor league performance had him at 11 runs better than average in 2022 despite this method being intentionally conservative because of its uncertainty. (For those curious, the highest-ranked outfielders in 2023 were Jorge Barrosa and Ceddanne Rafaela.) Doyle’s bat leaves much to be desired, but there’s a real argument to be made that given the size of the Coors outfield, this is a spot where the team should be willing to swap offense for defense.

ZiPS doesn’t expect Jones to regress too much in 2024 — the Sophomore Slump is a myth, but regression toward the mean is not — and projects considerable growth from Tovar, who was extremely young in his debut. ZiPS also expects a typical season from Ryan McMahon, and while it appears that Brendan Rodgers no longer has star upside, he’s a roughly league-average player when healthy.

That isn’t to say that this team has completely avoided mistakes. The Rockies actually made some trades near the deadline, which they almost never did in the past, but if they were ever going to get anything for Elias Díaz, it would have been last July. Coco Montes deserved a longer look, and I think he’d be an interesting pickup for someone (though he did pass through waivers in September).

As for the rest of the lineup… well, things don’t look great. At this point, neither Kris Bryant nor Charlie Blackmon project particularly well. This team is going to have one of the worst offenses in baseball again, and any long-term turnaround is going to require greater creativity than simply waiting for Zac Veen and Drew Romo, both of whom had disappointing 2023 seasons for a variety of reasons, to come and save the day.

Pitchers

Losing Germán Márquez and Antonio Senzatela to Tommy John makes this group look even worse than it is, but let’s be honest: their arm trouble isn’t what’s keeping the Rockies from being playoff relevant in 2024. Márquez seems the more likely to appear towards the end of the season, having had his procedure a couple months earlier than Senzatela in 2023. Of the pickups the team has made to eat some innings, Dakota Hudson is a groundball pitcher in front of what should be a solidly above-average infield defense, while Cal Quantrill typically isn’t crushable when he struggles. Along with Austin Gomber and Ryan Feltner, they’re basically here so that the games will end and the bullpen doesn’t have to throw 1,100 innings.

As for the bullpen… yeah. ZiPS likes Justin Lawrence, Jake Bird, and Jalen Beeks to be around average, with the rest of the relief corps — including any possible interesting minor leaguers — set to be off that standard. But again, like the Orioles from 2019-2021, the 2024 record isn’t the point. Instead of paying too much for relievers who were good two or three years ago, the Rockies should try to accumulate any halfway interesting young thrower under 25, even if they don’t have a clue, and see who has an epiphany. This is a method only lousy teams have the luxury to adopt.

Right now, ZiPS has Colorado in the 64-68 win range, depending on the assumptions you make about who will play, where, and when. The problem with the Rockies hasn’t just been that they’ve been a bad team, but that they’ve been a bad team with no idea of how bad teams become good ones. Being a bad team with a vision would be a tremendous upgrade over the club that frittered away the prime years of some good young talent during the disastrous Jeff Bridich era. And I’m not one who tends toward optimism with this franchise!

Ballpark graphic courtesy Eephus League. Depth charts constructed by way of those listed here. Size of player names is very roughly proportional to Depth Chart playing time.

Batters – Standard
Player B Age PO PA AB R H 2B 3B HR RBI BB SO SB CS
Nolan Jones L 26 LF 552 480 77 133 29 3 22 84 65 155 14 2
Ryan McMahon L 29 3B 588 521 73 125 27 2 22 74 61 167 5 3
Ezequiel Tovar R 22 SS 588 552 75 143 32 4 17 76 28 142 11 4
Brendan Rodgers R 27 2B 417 383 49 107 21 2 12 51 27 81 0 1
Coco Montes R 27 2B 514 465 61 114 27 3 14 65 40 144 6 3
Brenton Doyle R 26 CF 484 451 59 106 17 4 14 61 25 163 18 4
Adael Amador B 21 SS 434 385 60 105 19 2 10 50 39 61 10 5
Jimmy Herron R 27 LF 484 429 63 108 21 3 11 59 44 101 17 4
Ryan Ritter R 23 SS 536 475 70 108 22 3 18 74 41 177 10 3
Sean Bouchard R 28 RF 333 294 45 75 19 3 12 46 34 88 5 2
Jacob Stallings R 34 C 312 276 27 67 15 0 5 32 29 69 0 1
Braxton Fulford R 25 C 354 310 46 73 17 1 7 45 27 94 4 2
Kris Bryant R 32 RF 402 356 53 96 23 1 13 47 38 84 2 1
Alan Trejo R 28 2B 366 340 43 85 22 2 10 47 20 86 4 3
Elehuris Montero R 25 1B 484 442 59 113 21 2 20 71 30 132 0 1
Hunter Stovall R 27 2B 419 386 45 100 18 3 5 43 26 78 9 4
Yonathan Daza R 30 CF 347 321 38 93 16 2 3 33 18 55 3 3
Hunter Goodman R 24 1B 541 495 68 124 33 2 24 87 34 149 1 1
Willie MacIver R 27 C 336 298 31 64 14 1 6 35 29 99 8 3
Charlie Blackmon L 37 RF 440 394 53 107 21 4 9 52 34 72 3 1
Braiden Ward L 25 CF 318 271 49 63 9 2 1 38 20 77 25 5
Ronaiker Palma R 24 C 251 234 25 60 8 1 1 20 10 33 2 2
Sterlin Thompson L 23 3B 398 363 44 95 22 1 9 51 24 84 8 2
Roman Quinn B 31 CF 158 133 19 26 6 2 2 14 16 58 9 2
Jack Blomgren R 25 2B 240 208 28 48 8 2 3 29 17 73 8 3
Jordan Beck R 23 LF 548 496 61 121 29 1 15 65 50 148 10 3
Connor Kaiser R 27 SS 331 296 43 62 13 2 6 33 31 98 7 1
Elias Díaz R 33 C 417 384 41 99 20 1 12 52 28 88 0 1
Aaron Schunk R 26 3B 479 444 51 107 21 3 9 53 27 129 6 4
Zach Kokoska L 25 1B 337 304 40 73 15 2 9 44 22 87 9 4
Cole Tucker B 27 CF 359 322 42 76 15 3 4 34 33 89 5 3
Drew Romo B 22 C 413 379 41 97 18 4 7 48 23 83 5 4
Warming Bernabel R 22 3B 390 368 45 95 21 1 8 45 15 77 3 2
Yorvis Torrealba R 26 LF 151 138 21 35 7 0 1 14 10 29 6 3
Daniel Cope R 27 DH 173 156 15 38 7 1 3 19 14 50 0 1
Julio Carreras R 24 SS 459 417 52 99 23 3 5 46 27 113 8 3
Jameson Hannah L 26 RF 257 234 28 62 13 2 2 25 18 57 6 1
Kyle Datres R 28 2B 380 333 42 75 12 2 9 44 35 103 11 2
Bret Boswell L 29 3B 293 265 27 55 13 2 6 30 25 98 2 1
Yanquiel Fernandez L 21 RF 545 513 60 126 27 3 17 70 25 146 1 1
Zac Veen L 22 RF 389 351 41 84 16 3 7 39 32 96 22 4
Niko Decolati R 26 RF 333 293 42 65 11 1 3 31 30 90 8 3
AJ Lewis R 26 1B 251 214 23 42 11 0 3 28 23 77 1 2
Trevor Boone R 26 RF 200 184 20 35 8 3 5 23 12 95 1 1
Grayson Greiner R 31 C 167 152 14 28 6 0 3 16 12 68 0 1
Jonathan Morales R 29 1B 366 338 31 84 14 0 7 35 22 63 0 1
Michael Toglia B 25 1B 530 476 59 105 22 3 17 63 48 155 4 2
Nic Kent R 24 2B 422 381 42 93 19 1 5 41 23 83 5 4
Eddy Diaz R 24 2B 378 344 44 81 12 3 2 35 19 88 12 9
Bladimir Restituyo R 22 CF 470 444 56 113 19 3 9 53 7 96 12 6
Colin Simpson L 27 DH 287 263 28 59 14 2 6 31 21 86 1 1
Harold Castro L 30 2B 349 329 34 91 16 1 5 42 14 73 1 1
Taylor Snyder R 29 3B 347 320 39 67 14 2 10 38 23 119 7 2
Grant Lavigne L 24 1B 517 458 48 106 19 4 9 55 49 146 2 2
Benny Montgomery R 21 CF 481 443 51 103 20 3 7 49 31 151 9 3
Daniel Montano L 25 LF 413 368 40 85 16 5 6 40 39 123 3 2

Batters – Advanced
Player PA BA OBP SLG OPS+ ISO BABIP Def WAR wOBA RC
Nolan Jones 552 .277 .368 .488 121 .210 .366 6 3.1 .367 90
Ryan McMahon 588 .240 .321 .426 93 .186 .310 11 2.3 .324 74
Ezequiel Tovar 588 .259 .300 .424 87 .165 .321 10 2.2 .311 74
Brendan Rodgers 417 .279 .333 .439 100 .159 .328 1 1.4 .334 56
Coco Montes 514 .245 .311 .406 86 .161 .326 3 1.0 .312 61
Brenton Doyle 484 .235 .280 .384 71 .149 .336 11 0.9 .287 54
Adael Amador 434 .273 .345 .410 97 .138 .303 -6 0.9 .330 59
Jimmy Herron 484 .252 .331 .392 88 .140 .306 7 0.9 .317 61
Ryan Ritter 536 .227 .303 .400 82 .173 .321 0 0.9 .306 61
Sean Bouchard 333 .255 .336 .463 106 .207 .325 1 0.9 .344 48
Jacob Stallings 312 .243 .319 .351 76 .109 .307 5 0.9 .299 31
Braxton Fulford 354 .235 .319 .365 79 .129 .316 2 0.8 .304 38
Kris Bryant 402 .270 .351 .449 108 .180 .320 -5 0.6 .347 57
Alan Trejo 366 .250 .296 .415 83 .165 .307 2 0.5 .306 44
Elehuris Montero 484 .256 .310 .448 95 .192 .321 3 0.5 .325 62
Hunter Stovall 419 .259 .308 .360 74 .101 .314 5 0.4 .293 46
Yonathan Daza 347 .290 .330 .380 86 .090 .342 0 0.4 .311 42
Hunter Goodman 541 .251 .307 .471 99 .220 .311 -2 0.3 .330 72
Willie MacIver 336 .215 .296 .329 63 .114 .301 4 0.3 .279 32
Charlie Blackmon 440 .272 .339 .414 96 .142 .313 -2 0.3 .328 56
Braiden Ward 318 .232 .334 .292 66 .059 .321 0 0.2 .291 34
Ronaiker Palma 251 .256 .290 .312 58 .056 .295 5 0.2 .266 22
Sterlin Thompson 398 .262 .322 .402 88 .140 .319 -6 0.2 .315 49
Roman Quinn 158 .195 .299 .316 61 .120 .329 3 0.2 .278 15
Jack Blomgren 240 .231 .315 .332 70 .101 .341 1 0.1 .291 25
Jordan Beck 548 .244 .312 .397 84 .153 .318 2 0.1 .310 65
Connor Kaiser 331 .209 .288 .328 61 .118 .292 3 0.1 .274 30
Elias Díaz 417 .258 .309 .409 86 .151 .306 -10 0.0 .311 49
Aaron Schunk 479 .241 .288 .363 69 .122 .320 5 0.0 .283 49
Zach Kokoska 337 .240 .307 .391 81 .151 .308 3 0.0 .306 40
Cole Tucker 359 .236 .307 .339 69 .102 .314 1 -0.1 .287 36
Drew Romo 413 .256 .301 .380 77 .124 .311 -6 -0.2 .295 46
Warming Bernabel 390 .258 .295 .386 76 .128 .307 -2 -0.2 .295 43
Yorvis Torrealba 151 .254 .313 .326 68 .072 .315 0 -0.3 .286 17
Daniel Cope 173 .244 .312 .359 75 .115 .340 0 -0.3 .297 18
Julio Carreras 459 .237 .292 .343 66 .106 .314 -2 -0.4 .280 45
Jameson Hannah 257 .265 .320 .363 79 .098 .343 -2 -0.4 .301 29
Kyle Datres 380 .225 .313 .354 75 .129 .299 -7 -0.4 .296 40
Bret Boswell 293 .208 .280 .340 61 .132 .304 1 -0.4 .274 26
Yanquiel Fernandez 545 .246 .283 .409 78 .164 .311 3 -0.5 .295 60
Zac Veen 389 .239 .306 .362 74 .123 .310 -2 -0.5 .294 45
Niko Decolati 333 .222 .307 .297 60 .075 .310 4 -0.5 .275 30
AJ Lewis 251 .196 .308 .290 58 .093 .291 2 -0.6 .277 20
Trevor Boone 200 .190 .250 .348 54 .158 .357 2 -0.7 .260 17
Grayson Greiner 167 .184 .253 .283 40 .099 .309 -2 -0.7 .241 11
Jonathan Morales 366 .249 .295 .352 69 .104 .287 2 -0.8 .284 36
Michael Toglia 530 .221 .294 .387 76 .166 .289 1 -0.8 .296 56
Nic Kent 422 .244 .296 .339 66 .094 .300 -3 -0.8 .280 41
Eddy Diaz 378 .235 .290 .305 56 .070 .311 1 -0.8 .266 37
Bladimir Restituyo 470 .255 .271 .372 66 .117 .307 -2 -0.9 .276 50
Colin Simpson 287 .224 .282 .361 67 .137 .310 0 -0.9 .279 27
Harold Castro 349 .277 .305 .377 77 .100 .343 -10 -0.9 .297 39
Taylor Snyder 347 .209 .265 .359 61 .150 .298 -4 -1.0 .272 33
Grant Lavigne 517 .231 .314 .349 74 .118 .320 0 -1.0 .295 51
Benny Montgomery 481 .233 .291 .339 64 .106 .337 -5 -1.0 .279 47
Daniel Montano 413 .231 .304 .351 71 .120 .331 -3 -1.0 .289 41

Batters – Top Near-Age Offensive Comps
Player Hit Comp 1 Hit Comp 2 Hit Comp 3
Nolan Jones Rick Monday Willie Crawford Norm Siebern
Ryan McMahon Greg Norton Jeff Larish Jack Howell
Ezequiel Tovar Oswaldo Cabrera Alex Gonzalez Corey Hart
Brendan Rodgers Jake Noll Ramon Martinez Julio Gotay
Coco Montes Jeff Moronko Jim Command Casey Blake
Brenton Doyle Reggie Abercrombie Dick Smith César Hernández
Adael Amador Luis Alicea Geraldo Perdomo Mike Woodard
Jimmy Herron Phillip Ervin Eric Owens Terry Bradshaw
Ryan Ritter Josh Fields Billy Consolo Alex Gonzalez
Sean Bouchard Paul Jernigan Fred Rico John Briggs
Jacob Stallings Jeff Reed Chris Gimenez Bob Swift
Braxton Fulford Jayhawk Owens Edwin Marquez Jakson Reetz
Kris Bryant Clyde Barnhart Orlando Merced Ira Flagstead
Alan Trejo Scott Kingery Omar Infante Foster Castleman
Elehuris Montero Rich Murray Gino Kinchen Rogelio Alvarez
Hunter Stovall Ray Olmedo Brendan Ryan Pedro Chavez
Yonathan Daza Juan Delis Ken Woods Stan Johnson
Hunter Goodman Mike Fitzgerald Gail Harris Richie Sexson
Willie MacIver Barry Winford Dennis Pelfrey Joe Pignatano
Charlie Blackmon Jack Tobin Max Flack Charlie Jamieson
Braiden Ward Dan Motl Antoan Richardson William Ray
Ronaiker Palma Carlos Ruiz Jack Bowen Tom Zimmer
Sterlin Thompson J.P. Roberge Billy Smith Jared Triolo
Roman Quinn Chris Powell Milt Cuyler Dick Smith
Jack Blomgren Jason Brett David Dalton Kris Goodman
Jordan Beck Chris Knabenshue Glenn Owens Marvin Garrison
Connor Kaiser Brett King Jeremy Sy Juan Bell
Elias Díaz Harry Saferight Pat Borders Matt Wieters
Aaron Schunk Francisco Martinez Eddie Pye Kristopher Negrón
Zach Kokoska Anthony Seratelli Sil Campusano Kyle Colligan
Cole Tucker Jason Maas Evan Marzilli Aaron Cain
Drew Romo Phil Avlas John Hicks Wilkin Castillo
Warming Bernabel Robert Shelton Jim Pamlanye Shane Letterio
Yorvis Torrealba Kevin Reynolds Scott Stetson Kit Putnam
Daniel Cope Ronnie Farkas Wally Backman Gerardo Avila
Julio Carreras Blake Davis Johnnie LeMaster Tommy Manzella
Jameson Hannah Steve Bieser Julio Peguero Robert Belford
Kyle Datres Scott Earl Luis Guance Dave Hirtz
Bret Boswell Bob Frazier Harry Riconda Vic Harris
Yanquiel Fernandez Mike McDonald Don Dillard Nelson Gardner
Zac Veen Larry Shaw Corey Adamson Duane Walker
Niko Decolati Carlton Steele Larry Blackwell Scott Buss
AJ Lewis Jeremy Schied Carlos Lopez Doak Jones
Trevor Boone Runey Davis Danny Simpson Logan Wood
Grayson Greiner John Orton Alan Probst Marc Sullivan
Jonathan Morales Brian Traxler Freddie Thon Tommy Peterman
Michael Toglia Charles Howard Jim Koranda Glen Merklen
Nic Kent Dan Kaczrowski Eric King Chris Barnwell
Eddy Diaz Robbie Hudson David Rivera Demetrius Heath
Bladimir Restituyo Johan Rojas Rick Bosetti Doug Glanville
Colin Simpson Steven Caseres Brian Turner Gabe Snyder
Harold Castro Marco Hernández Jimmy Jordan Jim Glover
Taylor Snyder Matt Hagen Aaron Sisk Tye Waller
Grant Lavigne Jason Turner Ryan Aguilar Mark Chasey
Benny Montgomery Johnny Jeter Edward Ovalle Alfred Facchini
Daniel Montano Dusty Rhodes Jameson Fisher Ross Jones

Batters – 80th/20th Percentiles
Player 80th BA 80th OBP 80th SLG 80th OPS+ 80th WAR 20th BA 20th OBP 20th SLG 20th OPS+ 20th WAR
Nolan Jones .304 .394 .543 139 4.3 .248 .340 .434 101 1.7
Ryan McMahon .265 .347 .484 113 3.6 .212 .293 .372 75 0.9
Ezequiel Tovar .285 .325 .484 107 3.6 .234 .276 .377 69 0.9
Brendan Rodgers .311 .363 .492 122 2.4 .253 .306 .384 82 0.4
Coco Montes .271 .334 .461 102 2.0 .215 .281 .356 66 -0.2
Brenton Doyle .264 .308 .438 91 2.1 .207 .255 .335 53 -0.2
Adael Amador .301 .371 .463 116 1.9 .242 .313 .364 77 -0.1
Jimmy Herron .280 .358 .444 107 2.1 .225 .301 .343 71 -0.1
Ryan Ritter .258 .332 .465 106 2.4 .201 .280 .347 64 -0.3
Sean Bouchard .283 .364 .526 127 1.7 .233 .314 .417 89 0.2
Jacob Stallings .274 .352 .405 97 1.7 .208 .290 .305 58 0.2
Braxton Fulford .269 .350 .421 99 1.6 .203 .292 .313 59 0.0
Kris Bryant .297 .377 .503 127 1.5 .235 .321 .394 84 -0.5
Alan Trejo .280 .325 .476 103 1.4 .222 .268 .367 64 -0.3
Elehuris Montero .285 .335 .511 117 1.7 .229 .283 .398 76 -0.6
Hunter Stovall .288 .339 .409 94 1.5 .228 .278 .319 55 -0.5
Yonathan Daza .323 .362 .424 104 1.2 .258 .300 .339 69 -0.4
Hunter Goodman .280 .335 .532 119 1.6 .225 .280 .414 78 -1.1
Willie MacIver .249 .331 .379 85 1.2 .184 .264 .279 43 -0.6
Charlie Blackmon .305 .369 .472 116 1.3 .241 .309 .367 75 -0.8
Braiden Ward .265 .362 .330 82 0.9 .205 .311 .252 52 -0.4
Ronaiker Palma .293 .328 .359 79 0.9 .220 .255 .273 39 -0.4
Sterlin Thompson .288 .348 .455 107 1.1 .228 .291 .354 68 -0.7
Roman Quinn .227 .331 .375 84 0.6 .166 .263 .266 40 -0.2
Jack Blomgren .263 .340 .388 89 0.6 .202 .285 .291 52 -0.4
Jordan Beck .270 .339 .448 103 1.3 .216 .281 .344 63 -1.4
Connor Kaiser .237 .318 .392 82 0.9 .179 .259 .281 42 -0.8
Elias Díaz .288 .338 .470 107 1.0 .228 .282 .350 65 -1.1
Aaron Schunk .273 .319 .418 92 1.3 .214 .258 .317 52 -1.1
Zach Kokoska .272 .339 .452 103 0.9 .214 .281 .343 63 -0.8
Cole Tucker .264 .335 .385 86 0.6 .208 .278 .295 52 -0.9
Drew Romo .286 .328 .439 100 0.9 .225 .271 .330 58 -1.2
Warming Bernabel .287 .322 .439 96 0.8 .229 .269 .343 59 -1.0
Yorvis Torrealba .281 .340 .370 85 0.0 .227 .287 .290 52 -0.6
Daniel Cope .272 .341 .410 93 0.0 .206 .275 .302 50 -0.8
Julio Carreras .265 .321 .384 83 0.7 .209 .269 .296 49 -1.3
Jameson Hannah .293 .347 .408 96 0.2 .235 .288 .325 61 -1.0
Kyle Datres .253 .345 .408 94 0.5 .193 .284 .302 54 -1.4
Bret Boswell .240 .313 .411 86 0.5 .174 .250 .290 40 -1.1
Yanquiel Fernandez .272 .308 .459 96 0.7 .219 .258 .356 60 -1.8
Zac Veen .272 .334 .424 97 0.5 .209 .276 .313 57 -1.4
Niko Decolati .254 .336 .339 76 0.1 .190 .277 .252 40 -1.3
AJ Lewis .232 .339 .343 79 -0.1 .163 .277 .244 38 -1.2
Trevor Boone .222 .285 .416 78 -0.1 .157 .223 .284 32 -1.2
Grayson Greiner .217 .286 .337 61 -0.3 .154 .221 .235 21 -1.1
Jonathan Morales .280 .327 .400 88 0.1 .221 .269 .309 51 -1.6
Michael Toglia .251 .320 .435 96 0.4 .192 .267 .332 58 -2.1
Nic Kent .275 .325 .386 85 0.2 .216 .268 .294 49 -1.7
Eddy Diaz .261 .320 .350 74 0.0 .206 .262 .265 39 -1.6
Bladimir Restituyo .283 .300 .414 85 0.3 .227 .244 .333 51 -1.7
Colin Simpson .253 .310 .421 87 -0.1 .198 .253 .320 50 -1.5
Harold Castro .307 .336 .421 96 0.0 .248 .276 .332 58 -1.7
Taylor Snyder .238 .293 .429 85 0.1 .175 .229 .306 40 -1.9
Grant Lavigne .258 .343 .402 91 0.2 .201 .288 .306 56 -2.1
Benny Montgomery .262 .318 .387 83 0.1 .207 .264 .295 48 -2.0
Daniel Montano .259 .335 .399 90 -0.1 .200 .275 .302 52 -2.0

Batters – Projected Splits
Player BA vs. L OBP vs. L SLG vs. L BA vs. R OBP vs. R SLG vs. R
Nolan Jones .269 .352 .469 .281 .376 .497
Ryan McMahon .227 .304 .407 .245 .329 .434
Ezequiel Tovar .270 .309 .446 .253 .295 .411
Brendan Rodgers .293 .348 .488 .273 .326 .415
Coco Montes .250 .323 .422 .242 .304 .396
Brenton Doyle .244 .290 .406 .229 .273 .369
Adael Amador .272 .339 .417 .274 .348 .406
Jimmy Herron .253 .335 .400 .251 .328 .387
Ryan Ritter .227 .304 .407 .228 .303 .397
Sean Bouchard .260 .348 .472 .251 .328 .456
Jacob Stallings .245 .327 .362 .242 .315 .346
Braxton Fulford .237 .318 .368 .235 .320 .362
Kris Bryant .277 .358 .479 .267 .348 .439
Alan Trejo .252 .300 .439 .249 .293 .398
Elehuris Montero .264 .319 .483 .250 .304 .425
Hunter Stovall .269 .323 .372 .253 .298 .353
Yonathan Daza .294 .339 .394 .288 .326 .373
Hunter Goodman .266 .324 .509 .242 .298 .451
Willie MacIver .221 .304 .344 .210 .289 .318
Charlie Blackmon .269 .333 .387 .273 .342 .425
Braiden Ward .226 .337 .286 .235 .333 .294
Ronaiker Palma .271 .308 .306 .248 .280 .315
Sterlin Thompson .252 .316 .369 .265 .324 .415
Roman Quinn .205 .300 .364 .191 .298 .292
Jack Blomgren .240 .321 .347 .226 .311 .323
Jordan Beck .250 .326 .410 .241 .306 .391
Connor Kaiser .208 .300 .330 .211 .281 .326
Elias Díaz .262 .316 .418 .256 .306 .405
Aaron Schunk .243 .295 .379 .240 .284 .353
Zach Kokoska .229 .302 .354 .245 .310 .409
Cole Tucker .243 .305 .336 .233 .308 .340
Drew Romo .259 .301 .385 .254 .301 .377
Warming Bernabel .267 .307 .397 .253 .288 .380
Yorvis Torrealba .259 .328 .310 .250 .302 .338
Daniel Cope .250 .333 .357 .240 .300 .360
Julio Carreras .242 .297 .354 .234 .289 .336
Jameson Hannah .256 .306 .372 .269 .327 .359
Kyle Datres .235 .321 .391 .220 .309 .335
Bret Boswell .207 .278 .354 .208 .281 .333
Yanquiel Fernandez .238 .271 .384 .249 .288 .422
Zac Veen .239 .306 .359 .239 .306 .364
Niko Decolati .224 .311 .290 .220 .305 .301
AJ Lewis .207 .316 .293 .189 .303 .288
Trevor Boone .194 .256 .347 .188 .246 .348
Grayson Greiner .184 .245 .286 .184 .257 .282
Jonathan Morales .254 .312 .365 .245 .285 .344
Michael Toglia .216 .284 .389 .223 .301 .385
Nic Kent .248 .303 .349 .242 .292 .333
Eddy Diaz .248 .302 .321 .227 .281 .295
Bladimir Restituyo .262 .278 .372 .250 .266 .371
Colin Simpson .217 .267 .361 .228 .289 .361
Harold Castro .260 .286 .342 .281 .311 .387
Taylor Snyder .215 .278 .380 .206 .257 .347
Grant Lavigne .224 .299 .314 .235 .322 .368
Benny Montgomery .244 .306 .359 .226 .283 .328
Daniel Montano .224 .286 .336 .235 .314 .359

Pitchers – Standard
Player T Age W L ERA G GS IP H ER HR BB SO
Germán Márquez R 29 7 10 4.75 24 24 136.3 135 72 18 46 117
Cal Quantrill R 29 6 8 4.93 26 23 127.7 140 70 19 42 83
Dakota Hudson R 29 7 9 4.51 25 21 123.7 129 62 15 48 75
Anthony Molina R 22 5 7 5.09 27 26 120.3 143 68 17 36 71
Kyle Freeland L 31 7 10 5.24 26 26 137.3 161 80 22 41 87
Tanner Gordon R 26 7 10 5.14 24 23 117.3 137 67 19 32 77
Jeff Criswell R 25 5 8 5.22 25 23 112.0 118 65 17 49 93
Carson Palmquist L 23 5 7 5.01 20 20 93.3 93 52 14 38 90
Joe Rock L 23 5 7 5.14 22 22 98.0 105 56 14 40 77
Antonio Senzatela R 29 4 6 5.09 19 19 99.0 117 56 13 29 61
Gabriel Hughes R 22 6 8 4.99 18 18 83.0 89 46 12 30 65
Austin Gomber L 30 6 9 5.32 24 21 118.3 130 70 20 37 86
Ryan Feltner R 27 5 6 5.15 20 19 92.7 96 53 14 38 83
Nick Bush L 27 3 5 5.28 15 15 75.0 84 44 13 19 56
Chris McMahon R 25 3 4 5.31 17 16 78.0 92 46 12 24 51
Connor Seabold R 28 4 6 5.36 27 18 100.7 112 60 16 28 87
Connor Van Scoyoc R 24 4 8 5.56 20 19 102.0 116 63 16 42 68
Peter Lambert R 27 4 6 5.38 27 17 95.3 103 57 15 38 78
Justin Lawrence R 29 5 5 4.54 65 0 69.3 63 35 8 32 76
Jalen Beeks L 30 3 4 4.97 42 7 63.3 68 35 10 25 61
Jake Bird R 28 3 4 4.75 61 2 77.7 79 41 10 29 67
Ty Blach L 33 3 5 5.48 26 13 87.0 107 53 14 22 56
Mitchell Kilkenny R 27 4 6 5.47 18 15 72.3 87 44 11 22 40
Case Williams R 22 4 9 5.80 22 22 104.0 124 67 17 45 62
Ryan Rolison L 26 2 4 5.62 13 13 57.7 67 36 10 23 42
Andrew Quezada R 27 4 7 5.75 21 17 87.7 107 56 14 34 49
Karl Kauffmann R 26 5 9 5.87 25 21 112.0 131 73 16 52 69
Josh Rogers L 29 5 9 5.80 25 13 94.7 114 61 18 32 48
Will Ethridge R 26 3 5 5.81 18 13 66.7 77 43 10 27 41
Lucas Gilbreath L 28 2 4 5.40 29 7 53.3 55 32 8 32 51
Noah Davis R 27 3 7 5.92 21 20 89.7 99 59 14 44 67
Tyler Kinley R 33 2 2 5.06 38 1 37.3 37 21 6 15 36
Blake Goldsberry R 27 2 2 5.17 31 2 38.3 41 22 6 15 31
Dylan Spain R 26 2 2 5.11 25 2 37.0 42 21 6 11 26
Victor Vodnik R 24 2 3 5.24 37 2 46.3 47 27 7 26 44
Gavin Hollowell R 26 1 2 5.02 43 0 52.0 51 29 8 21 53
Dugan Darnell R 27 3 5 5.07 39 0 49.7 51 28 7 21 46
Nick Mears R 27 2 3 5.03 39 0 39.3 37 22 6 24 44
Colten Schmidt L 28 1 2 5.93 12 5 30.3 38 20 5 10 16
Thomas Ponticelli R 27 3 5 5.68 36 6 63.3 72 40 10 29 43
Seth Halvorsen R 24 1 2 4.96 17 0 16.3 18 9 2 5 10
Austin Kitchen L 27 3 4 5.31 37 2 57.7 69 34 9 17 34
Chase Anderson R 36 2 5 6.21 18 14 66.7 76 46 13 31 51
Reagan Todd L 28 3 4 5.12 40 0 38.7 38 22 6 21 40
Matt Koch R 33 2 5 5.58 48 3 59.7 68 37 10 21 48
Chance Adams R 29 1 2 5.77 26 4 43.7 51 28 7 16 28
Mike Ruff R 26 3 5 6.25 22 15 72.0 83 50 12 37 46
Geoff Hartlieb R 30 3 4 5.52 34 1 44.0 48 27 7 20 35
Eli Lingos L 28 2 3 5.62 30 2 49.7 58 31 8 22 32
Kyle Johnston R 27 2 4 5.88 32 6 52.0 58 34 8 30 38
Stephen Jones R 26 2 4 5.25 48 0 58.3 62 34 9 26 51
Daniel Bard R 39 3 4 5.44 46 0 44.7 43 27 7 28 46
Adam McKillican R 26 1 2 5.67 25 0 33.3 37 21 5 14 23
Ben Braymer L 30 3 5 6.39 16 14 62.0 76 44 11 34 35
PJ Poulin L 27 2 4 5.40 38 0 50.0 54 30 7 24 38
Phillips Valdez R 32 2 6 6.33 25 9 54.0 63 38 9 30 36
Alec Barger R 26 2 4 5.66 36 1 47.7 52 30 8 26 41
Matt Carasiti R 32 1 3 5.86 36 1 43.0 49 28 7 20 36
Brendan Hardy R 24 1 2 5.79 24 0 28.0 26 18 4 22 31
Jacob Kostyshock R 26 0 1 6.14 22 0 22.0 25 15 4 11 16
Evan Justice L 25 2 5 5.79 45 0 42.0 40 27 7 29 48
Will Gaddis R 28 2 4 6.41 25 5 53.3 70 38 9 25 22
Michael Petersen R 30 1 3 6.27 38 0 37.3 40 26 7 24 33
Bryce McGowan R 24 1 2 6.21 39 0 42.0 45 29 7 30 36
Riley Pint R 26 2 4 5.90 43 0 50.3 46 33 8 39 57
Nick Kennedy L 28 1 2 6.17 37 0 42.3 49 29 7 24 27
Kyle Wilcox R 30 2 6 6.21 41 0 42.0 42 29 7 34 42
Nick Kuzia R 28 3 5 6.04 40 0 47.7 53 32 8 27 35
Shelby Lackey R 26 0 2 7.33 24 0 23.3 24 19 4 22 20

Pitchers – Advanced
Player IP K/9 BB/9 HR/9 BB% K% BABIP ERA+ FIP ERA- WAR
Germán Márquez 136.3 7.7 3.0 1.2 7.8% 19.9% .293 100 4.33 100 1.6
Cal Quantrill 127.7 5.9 3.0 1.3 7.5% 14.8% .294 96 5.06 104 1.3
Dakota Hudson 123.7 5.5 3.5 1.1 8.8% 13.8% .285 93 4.95 107 1.2
Anthony Molina 120.3 5.3 2.7 1.3 6.7% 13.2% .310 93 4.86 107 1.2
Kyle Freeland 137.3 5.7 2.7 1.4 6.8% 14.4% .307 91 5.10 110 1.1
Tanner Gordon 117.3 5.9 2.5 1.5 6.2% 14.9% .307 92 4.94 108 1.0
Jeff Criswell 112.0 7.5 3.9 1.4 9.8% 18.5% .301 91 4.99 110 1.0
Carson Palmquist 93.3 8.7 3.7 1.4 9.3% 22.1% .300 95 4.85 106 0.9
Joe Rock 98.0 7.1 3.7 1.3 9.2% 17.6% .302 92 4.95 108 0.9
Antonio Senzatela 99.0 5.5 2.6 1.2 6.6% 14.0% .313 93 4.76 107 0.9
Gabriel Hughes 83.0 7.0 3.3 1.3 8.2% 17.9% .302 95 4.72 105 0.9
Austin Gomber 118.3 6.5 2.8 1.5 7.2% 16.7% .297 89 5.01 112 0.9
Ryan Feltner 92.7 8.1 3.7 1.4 9.3% 20.3% .304 92 4.76 108 0.9
Nick Bush 75.0 6.7 2.3 1.6 5.9% 17.3% .303 90 4.82 111 0.6
Chris McMahon 78.0 5.9 2.8 1.4 6.9% 14.7% .311 89 4.99 112 0.6
Connor Seabold 100.7 7.8 2.5 1.4 6.3% 19.7% .316 88 4.70 113 0.6
Connor Van Scoyoc 102.0 6.0 3.7 1.4 9.0% 14.6% .303 85 5.46 117 0.6
Peter Lambert 95.3 7.4 3.6 1.4 9.0% 18.4% .304 88 5.05 113 0.6
Justin Lawrence 69.3 9.9 4.2 1.0 10.6% 25.1% .301 104 4.25 96 0.5
Jalen Beeks 63.3 8.7 3.6 1.4 8.8% 21.6% .317 95 4.71 105 0.5
Jake Bird 77.7 7.8 3.4 1.2 8.6% 19.8% .301 100 4.59 100 0.4
Ty Blach 87.0 5.8 2.3 1.4 5.7% 14.5% .320 87 4.92 116 0.4
Mitchell Kilkenny 72.3 5.0 2.7 1.4 6.8% 12.4% .308 87 5.20 115 0.4
Case Williams 104.0 5.4 3.9 1.5 9.4% 13.0% .307 82 5.63 122 0.4
Ryan Rolison 57.7 6.6 3.6 1.6 8.8% 16.1% .310 84 5.35 118 0.3
Andrew Quezada 87.7 5.0 3.5 1.4 8.4% 12.2% .310 83 5.51 121 0.3
Karl Kauffmann 112.0 5.5 4.2 1.3 10.1% 13.4% .308 81 5.46 124 0.3
Josh Rogers 94.7 4.6 3.0 1.7 7.6% 11.3% .296 82 5.85 122 0.2
Will Ethridge 66.7 5.5 3.6 1.4 8.9% 13.5% .303 82 5.46 122 0.2
Lucas Gilbreath 53.3 8.6 5.4 1.4 13.0% 20.7% .309 88 5.26 114 0.2
Noah Davis 89.7 6.7 4.4 1.4 10.8% 16.4% .304 80 5.70 125 0.2
Tyler Kinley 37.3 8.7 3.6 1.4 9.1% 22.0% .298 94 4.70 107 0.1
Blake Goldsberry 38.3 7.3 3.5 1.4 8.8% 18.2% .302 92 5.00 109 0.1
Dylan Spain 37.0 6.3 2.7 1.5 6.7% 16.0% .305 93 5.09 108 0.1
Victor Vodnik 46.3 8.5 5.1 1.4 12.2% 20.7% .303 90 5.13 111 0.1
Gavin Hollowell 52.0 9.2 3.6 1.4 9.3% 23.5% .303 95 4.72 106 0.1
Dugan Darnell 49.7 8.3 3.8 1.3 9.5% 20.8% .308 94 4.62 107 0.1
Nick Mears 39.3 10.1 5.5 1.4 13.4% 24.6% .304 94 4.98 106 0.1
Colten Schmidt 30.3 4.7 3.0 1.5 7.4% 11.8% .314 80 5.53 125 0.1
Thomas Ponticelli 63.3 6.1 4.1 1.4 9.9% 14.7% .304 83 5.51 120 0.1
Seth Halvorsen 16.3 5.5 2.8 1.1 7.0% 14.1% .296 96 4.54 104 0.0
Austin Kitchen 57.7 5.3 2.7 1.4 6.6% 13.3% .309 89 5.20 112 0.0
Chase Anderson 66.7 6.9 4.2 1.8 10.2% 16.8% .304 76 5.84 131 0.0
Reagan Todd 38.7 9.3 4.9 1.4 12.0% 22.9% .305 93 5.07 108 0.0
Matt Koch 59.7 7.2 3.2 1.5 7.9% 18.0% .314 85 5.08 118 0.0
Chance Adams 43.7 5.8 3.3 1.4 8.2% 14.4% .308 82 5.36 122 0.0
Mike Ruff 72.0 5.8 4.6 1.5 11.0% 13.7% .302 76 6.10 132 -0.1
Geoff Hartlieb 44.0 7.2 4.1 1.4 9.9% 17.3% .304 86 5.30 116 -0.1
Eli Lingos 49.7 5.8 4.0 1.4 9.7% 14.1% .307 84 5.45 118 -0.1
Kyle Johnston 52.0 6.6 5.2 1.4 12.2% 15.5% .305 81 5.81 124 -0.1
Stephen Jones 58.3 7.9 4.0 1.4 9.9% 19.5% .306 90 5.11 111 -0.1
Daniel Bard 44.7 9.3 5.6 1.4 13.7% 22.5% .298 87 5.45 115 -0.1
Adam McKillican 33.3 6.2 3.8 1.4 9.3% 15.2% .302 84 5.36 119 -0.1
Ben Braymer 62.0 5.1 4.9 1.6 11.6% 11.9% .308 74 6.22 135 -0.1
PJ Poulin 50.0 6.8 4.3 1.3 10.7% 16.9% .303 88 5.18 114 -0.1
Phillips Valdez 54.0 6.0 5.0 1.5 11.8% 14.2% .307 75 6.20 133 -0.2
Alec Barger 47.7 7.7 4.9 1.5 11.8% 18.6% .308 84 5.54 119 -0.2
Matt Carasiti 43.0 7.5 4.2 1.5 10.0% 18.0% .318 81 5.30 123 -0.2
Brendan Hardy 28.0 10.0 7.1 1.3 16.2% 22.8% .301 82 5.68 122 -0.2
Jacob Kostyshock 22.0 6.5 4.5 1.6 10.9% 15.8% .304 77 6.35 129 -0.2
Evan Justice 42.0 10.3 6.2 1.5 14.6% 24.2% .306 82 5.71 122 -0.3
Will Gaddis 53.3 3.7 4.2 1.5 9.9% 8.7% .314 74 6.09 135 -0.3
Michael Petersen 37.3 8.0 5.8 1.7 13.6% 18.8% .303 76 6.17 132 -0.4
Bryce McGowan 42.0 7.7 6.4 1.5 14.7% 17.6% .304 76 6.06 131 -0.4
Riley Pint 50.3 10.2 7.0 1.4 16.0% 23.4% .295 80 5.86 124 -0.4
Nick Kennedy 42.3 5.7 5.1 1.5 12.0% 13.5% .302 77 6.11 130 -0.4
Kyle Wilcox 42.0 9.0 7.3 1.5 16.3% 20.2% .302 76 5.99 131 -0.4
Nick Kuzia 47.7 6.6 5.1 1.5 12.1% 15.6% .302 79 6.05 127 -0.4
Shelby Lackey 23.3 7.7 8.5 1.5 18.5% 16.8% .294 65 7.24 154 -0.5

Pitchers – Top Near-Age Comps
Player Pit Comp 1 Pit Comp 2 Pit Comp 3
Germán Márquez Mike Foltynewicz Jeremy Guthrie Sonny Gray
Cal Quantrill Jason Hammel Roberto Hernandez Jeremy Guthrie
Dakota Hudson Chi Chi González Steve Sparks Tom Brewer
Anthony Molina Buddy Carlyle 카라이어 Dave Borkowski Ariel Jurado
Kyle Freeland Ken Holtzman Joe Saunders Kirk Rueter
Tanner Gordon Daniel Mengden Ariel Jurado Kyle Lohse
Jeff Criswell Dave Freisleben Allen Webster 웹스터 Chuck Locke
Carson Palmquist Patrick Sandoval Mike Connolly Brad Havens
Joe Rock Ronnie Driver Kevin Morton Henry Werland
Antonio Senzatela Ivan Nova Chien-Ming Wang Mark Bomback
Gabriel Hughes Troy Bradford Edgar Ramos Leonardo Gonzalez
Austin Gomber Dennis Rasmussen John Danks Mike McCormick
Ryan Feltner Claudio Vargas Tom Griffin Patrick Johnson
Nick Bush Evan Grills Joe Rogers Anthony Boughner
Chris McMahon Jamie Brown 브라운 Jim Farr Daryl Thompson
Connor Seabold Seth Lugo Brad Lincoln Paolo Espino
Connor Van Scoyoc Jacob Turner 터너 Kyle McGowin Brad Salmon
Peter Lambert Parker Bridwell Roberto Rodriguez Scott Gardner
Justin Lawrence Curt Leskanic Ike Delock Don Robinson
Jalen Beeks Lance Painter Al Yaylian Mike Paul
Jake Bird Adam Warren Jim Johnson Vladimir Nunez
Ty Blach Greg Smith Adam Pettyjohn Don Rudolph
Mitchell Kilkenny Ismael Castillo Joe Niekro Dave Osteen
Case Williams Deolis Guerra Andrew Sopko Clayton Bittinger
Ryan Rolison Mike Skane Austin Sodders Tommy MacLane
Andrew Quezada William Hall Brian Barber Tanner Anderson
Karl Kauffmann Chris Jensen Jim Stump Merrill Kelly 켈리
Josh Rogers Ryan Rowland-Smith Michael Roth Allan Anderson
Will Ethridge Beiker Graterol Rickey Keeton Jimmy Whisman
Lucas Gilbreath Angel Miranda Dave Geisel Ryan Wing
Noah Davis Allen Webster Jake Thompson John Leister
Tyler Kinley Ryan Tepera Dave Tobik Gregg Olson
Blake Goldsberry Joe Cotton Bruce Thompson Pete Della Ratta
Dylan Spain Craig Glassco Chad Schroeder Alberto Montes
Victor Vodnik Bob Blyth Chris Lemp Trevor Hurley
Gavin Hollowell Phil Hennigan Kyle Martin Josh Martin
Dugan Darnell Gene Stechschulte Federico Castaneda Not that Edgar Martinez
Nick Mears Josh Sharpless Mark Acre Brad Lesley
Colten Schmidt Robert Warren Jeff Huber Jamie Walker
Thomas Ponticelli James Sprankle Douglas Gentry Theodore Ellis
Seth Halvorsen Dick Coffman Charles Giddens Pedro Borbon
Austin Kitchen Bryan Gore Trevor Enders David Speer
Chase Anderson Joe Niekro Joe Bowman Ken Ray 레이
Reagan Todd Josh Edgin Jaime Cerda Steve Sinclair
Matt Koch Daniel McCutchen David Shepard Tom McCarthy
Chance Adams Deryk Hooker Scott Huntsman Cody Evans
Mike Ruff Nick McCully Ralph Treuel Craig Chamberlain
Geoff Hartlieb Jim Winn Victor Marte Hank Behrman
Eli Lingos Lou Marone Ron Locke Mark Hendrickson
Kyle Johnston A.J. Morris Gary Parmenter Edgar Martinez
Stephen Jones Steve Mintz Jason Arnold Chris Bennett
Daniel Bard Don McMahon Turk Lown Jose Valverde
Adam McKillican Ray Soff Dale Hrovat Dan Brown
Ben Braymer Joe Magrane Tommy Shirley Bryan Oelkers
PJ Poulin Kevin Hickey Mariano Gomez Mike Dalton
Phillips Valdez Craig McMurtry Seth Simmons Thomas Arruda
Alec Barger Kevin Vance Derek Diaz Bo Schultz
Matt Carasiti Victor Marte Wes Gardner Darryl Scott
Brendan Hardy Steven Lovins Lon Morton Mike Barba
Jacob Kostyshock Jeff Brueggemann Eddie Moore Ron Kaufman
Evan Justice Lee Stoppelman Omar Duran Ryan Buchter
Will Gaddis Hector Ramirez Ricky Brooks Ryan Cummings
Michael Petersen Doug Bochtler Jesus Colome Derek Eitel
Bryce McGowan Amilcar Correa Jhondaniel Medina Eduardo Sierra
Riley Pint Todd Jones John Morlan Gene Pentz
Nick Kennedy Colin Young Chris Petrini Len Whitehouse
Kyle Wilcox Calvin Jones Ryan Bukvich Roger Salkeld
Nick Kuzia Frank Kamfonik Ryan Perry James Thornton
Shelby Lackey Brian Kolbe Vaughn Kovach Richard Negrette

Pitchers – Splits and Percentiles
Player BA vs. L OBP vs. L SLG vs. L BA vs. R OBP vs. R SLG vs. R 80th WAR 20th WAR 80th ERA 20th ERA
Germán Márquez .270 .338 .431 .235 .287 .404 2.3 0.8 4.23 5.44
Cal Quantrill .265 .336 .451 .281 .335 .454 1.8 0.6 4.52 5.60
Dakota Hudson .272 .352 .443 .255 .319 .400 1.8 0.6 4.10 4.98
Anthony Molina .298 .352 .483 .281 .324 .449 1.7 0.6 4.60 5.68
Kyle Freeland .280 .329 .455 .288 .342 .481 1.8 0.3 4.71 5.99
Tanner Gordon .277 .329 .459 .292 .333 .488 1.7 0.5 4.59 5.63
Jeff Criswell .262 .345 .437 .267 .336 .458 1.5 0.2 4.74 5.95
Carson Palmquist .248 .336 .385 .256 .340 .450 1.5 0.2 4.42 5.78
Joe Rock .261 .338 .417 .271 .344 .451 1.4 0.4 4.64 5.71
Antonio Senzatela .286 .347 .457 .290 .335 .459 1.3 0.4 4.61 5.67
Gabriel Hughes .282 .343 .468 .256 .318 .420 1.3 0.4 4.44 5.64
Austin Gomber .250 .299 .397 .282 .338 .497 1.5 0.2 4.70 6.02
Ryan Feltner .259 .337 .440 .264 .330 .443 1.4 0.3 4.58 5.77
Nick Bush .278 .327 .467 .277 .317 .479 1.0 0.1 4.71 6.02
Chris McMahon .307 .367 .484 .269 .315 .461 1.0 0.2 4.81 5.94
Connor Seabold .272 .343 .461 .278 .325 .458 1.2 0.1 4.69 6.02
Connor Van Scoyoc .278 .366 .460 .282 .350 .475 1.0 0.1 5.18 6.07
Peter Lambert .272 .356 .478 .267 .330 .431 1.1 0.1 4.82 5.92
Justin Lawrence .246 .347 .413 .227 .317 .355 1.1 -0.2 3.81 5.50
Jalen Beeks .257 .325 .446 .272 .343 .450 1.1 -0.1 4.12 5.97
Jake Bird .264 .348 .451 .253 .322 .395 0.9 -0.2 4.18 5.53
Ty Blach .274 .320 .416 .306 .348 .516 0.8 -0.1 4.93 6.16
Mitchell Kilkenny .276 .333 .448 .307 .357 .503 0.7 0.0 5.05 6.03
Case Williams .286 .365 .500 .293 .358 .472 0.8 -0.2 5.34 6.34
Ryan Rolison .286 .354 .471 .283 .348 .482 0.6 0.0 5.11 6.34
Andrew Quezada .307 .380 .524 .284 .341 .452 0.8 -0.1 5.24 6.35
Karl Kauffmann .308 .397 .524 .268 .337 .416 0.7 -0.3 5.47 6.44
Josh Rogers .250 .297 .406 .313 .375 .550 0.6 -0.4 5.36 6.48
Will Ethridge .295 .378 .488 .273 .340 .448 0.5 -0.2 5.37 6.40
Lucas Gilbreath .240 .348 .440 .272 .371 .434 0.6 -0.2 4.74 6.17
Noah Davis .285 .392 .517 .265 .350 .407 0.6 -0.4 5.39 6.69
Tyler Kinley .232 .312 .449 .273 .337 .442 0.5 -0.3 4.19 6.23
Blake Goldsberry .257 .333 .429 .277 .344 .470 0.4 -0.2 4.52 6.10
Dylan Spain .265 .324 .485 .293 .352 .463 0.3 -0.1 4.61 5.83
Victor Vodnik .259 .370 .435 .255 .339 .439 0.4 -0.3 4.61 6.00
Gavin Hollowell .263 .358 .463 .241 .314 .398 0.5 -0.4 4.14 5.97
Dugan Darnell .253 .333 .451 .267 .339 .419 0.4 -0.3 4.40 6.05
Nick Mears .257 .373 .457 .232 .333 .402 0.4 -0.3 4.34 6.06
Colten Schmidt .275 .341 .400 .314 .368 .547 0.2 -0.1 5.32 6.80
Thomas Ponticelli .281 .362 .446 .279 .357 .493 0.4 -0.4 5.19 6.43
Seth Halvorsen .281 .343 .438 .265 .297 .412 0.2 -0.1 4.17 5.86
Austin Kitchen .250 .315 .375 .312 .363 .522 0.3 -0.3 4.80 5.93
Chase Anderson .250 .343 .435 .306 .376 .551 0.3 -0.5 5.56 7.15
Reagan Todd .260 .351 .360 .248 .353 .475 0.3 -0.3 4.29 6.06
Matt Koch .268 .346 .455 .290 .343 .481 0.4 -0.4 4.76 6.38
Chance Adams .286 .362 .476 .287 .346 .479 0.2 -0.4 5.11 6.54
Mike Ruff .282 .387 .504 .284 .374 .463 0.3 -0.5 5.73 6.95
Geoff Hartlieb .282 .358 .494 .261 .349 .424 0.2 -0.4 4.86 6.57
Eli Lingos .284 .355 .418 .287 .355 .500 0.2 -0.4 5.06 6.40
Kyle Johnston .268 .379 .423 .283 .374 .487 0.2 -0.5 5.31 6.69
Stephen Jones .269 .358 .481 .264 .342 .419 0.3 -0.5 4.65 6.05
Daniel Bard .253 .373 .462 .241 .357 .398 0.3 -0.7 4.48 7.18
Adam McKillican .290 .380 .484 .264 .333 .444 0.0 -0.3 5.08 6.46
Ben Braymer .280 .362 .463 .303 .391 .520 0.2 -0.5 5.91 7.16
PJ Poulin .254 .338 .388 .278 .362 .466 0.2 -0.5 4.68 6.10
Phillips Valdez .296 .402 .510 .276 .375 .447 0.1 -0.5 5.70 7.17
Alec Barger .286 .371 .484 .257 .356 .446 0.1 -0.5 5.09 6.42
Matt Carasiti .304 .396 .506 .260 .333 .438 0.1 -0.6 5.10 6.94
Brendan Hardy .264 .400 .472 .218 .371 .364 0.0 -0.5 5.09 6.71
Jacob Kostyshock .273 .385 .500 .289 .389 .489 -0.1 -0.4 5.57 6.87
Evan Justice .216 .355 .314 .259 .391 .491 0.1 -0.6 4.96 6.73
Will Gaddis .320 .398 .515 .303 .363 .516 -0.1 -0.6 5.90 7.13
Michael Petersen .257 .373 .429 .278 .389 .519 -0.1 -0.7 5.39 7.49
Bryce McGowan .266 .402 .456 .270 .371 .461 -0.1 -0.7 5.46 7.09
Riley Pint .239 .369 .446 .235 .397 .402 0.0 -1.0 5.10 7.09
Nick Kennedy .254 .362 .407 .298 .391 .509 -0.2 -0.7 5.59 7.04
Kyle Wilcox .266 .408 .468 .244 .365 .430 0.0 -0.9 5.16 7.66
Nick Kuzia .314 .412 .581 .245 .352 .377 -0.2 -0.9 5.38 6.90
Shelby Lackey .273 .429 .455 .250 .422 .479 -0.3 -0.7 6.49 8.79

Here are how the ZiPS percentiles worked out in 2023 for pitchers and hitters in in 2023. Percentiles are based on the projected PA or TBF.

Players are listed with their most recent teams wherever possible. This includes players who are unsigned or have retired, players who will miss 2024 due to injury, and players who were released in 2023. So yes, if you see Joe Schmoe, who quit baseball back in August to form a Belgian Death Metal Skiffle Band that only plays songs by Franz Schubert, he’s still listed here intentionally. ZiPS is assuming a league with an ERA of 4.33.

Hitters are ranked by zWAR, which is to say, WAR values as calculated by me, Dan Szymborski, whose surname is spelled with a z. WAR values might differ slightly from those that appear in the full release of ZiPS. Finally, I will advise anyone against — and might karate chop anyone guilty of — merely adding up WAR totals on a depth chart to produce projected team WAR.

As always, incorrect projections are either caused by flaws in the physical reality of the universe or by the skillful sabotage of our friend and former editor. You can, however, still get mad at me on Twitter.


2024 ZiPS Projections: Chicago Cubs

For the 20th 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 next team up is the Chicago Cubs.

Batters

Are the Cubs back to their mid-2010s form? That’s a bit of a stretch, but these Cubs showed they have a live pulse, not falling out of playoff contention until the final week of the 2023 season. Cody Bellinger, one of the reasons the offense was so potent (third in the National League in runs scored), turned out to be one of the best pillow-contract signings of all time. Perhaps a little too good, as it was enough for Bellinger to decline his side of a $25 million mutual option for 2024. The Cubs could still sign Bellinger, but even if they do, it’s not a great bet they’ll get the same production as last year. ZiPS is a bit more optimistic than Steamer, but it still projects a returning Bellinger to “give back” some of his contact and a significant chunk of his power. Read the rest of this entry »