Dan Szymborski: Since it decided not to post in the queue for some reason at 11:40 and I had to force it at 11:57, there’s no queue built up, so feel free to ask any question, no matter how ridiculous
12:02
Dan Szymborski: And there’s a good chance I’ll be able to answer it, since asking me about how I evaluate pretzels or how many weird AIs I’ve made of Lindsey Graham is way better than awkward silence
12:02
Kate: Would Grant Holmes projection be worse if he was projected in the rotation? In terms of how ZiPS projects his role, is that something you do manually?
12:03
Dan Szymborski: As a default, ZiPS will project a player based on their recent usage. It looks at the last four years and VERY heavily weights recent play.
12:04
Dan Szymborski: However, I have clickboxes for “full-time starter” or “full-time reliever” which will cause ZiPS to go back and translate the past baselines for a pitcher into full-time starter or full-time reliever lines, and then make a new projections
For the 21st 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 Atlanta Braves.
Batters
This past year was a bit of a trainwreck for the Braves, but an unusual one, in that with so much going wrong, they still won 89 games and made the playoffs, if by the skin of their teeth. The lineup still looks fundamentally similar to the one that everyone liked going into the season, just one that’s a bit riskier with an extra year of age and an extended recent history of significant injuries. The depth chart graphic below is a bit too generous for my taste with some of the playing time for the injured players, with Austin Riley, Michael Harris II, and Ozzie Albies all with current projections above 650 plate appearances and Ronald Acuña Jr. just below 600. However, even with being a bit more conservative about health, Atlanta should have a lot of dangerous weapons. In other words, even if expectations should be tempered slightly, there’s nothing fundamentally broken about this offense.
ZiPS shares Steamer’s optimism when it comes to Harris. I was a bit surprised by the projection too – and surprised to see Steamer also very high on him – but it’s easy to forget that Harris only turns 24 in March, so there’s still a realistic possibility that he improves, perhaps significantly, and projections do have to account for that. Interestingly enough, both projection systems think there’s some power upside remaining from him, too.
Honestly, there’s not much in the way of actual surprises in the offensive projections. The only significant loss is Sean Murphy’s catching sidekick, Travis d’Arnaud, but this is one of the places where the Braves could afford to let someone walk. Drake Baldwin and Chadwick Tromp may sound like 80s movies antagonists who head up the rich kids’ summer camp across the lake, but they’re more than suitable caddies for Murphy. Baldwin in particular didn’t come out of nowhere, either; he was the no. 30 prospect in baseball in our updated 2024 prospect rankings.
Nacho Alvarez Jr. already projects as a viable replacement for Orlando Arcia when the time comes (probably after 2025), but the projections aren’t bullish on the farm once you get past Baldwin and Alvarez. Atlanta would be smart to be active in the non-roster invitee sweepstakes this winter.
Pitchers
Subtracting Max Fried is a pretty big deal, so adding a pitcher – perhaps Fried himself – should be the team’s top priority. Chris Sale returned to form in a big way in 2024, winning the NL Cy Young award and leading all pitchers with 6.4 WAR, but one can’t be too confident in his health; he turns 36 at the end of March, and the 177 2/3 innings he pitched this year were easily his most in a season since 2017. Reynaldo López as a full-time starter went better than anyone could have reasonably expected, but he’s probably going to give back some of that ERA in 2025. With Strider returning from internal brace surgery sometime early in 2025 and Schwellenbach getting a surprisingly spectacular projection, the Braves should be pleased that Spencers will make up two of the top four in their rotation. I’m just not excited, especially given the injury concerns in the rotation, about not having another option better than Griffin Canning or Ian Anderson for the last slot. From a prospect standpoint, ZiPS doesn’t see a whole lot of upside in the minors beyond AJ Smith-Shawver.
The problems that faced Atlanta elsewhere this season mostly spared the bullpen, which finished the season ranked third in WAR and second in FIP in the majors. It’s still a unit that’s in pretty good shape, with five of its six relievers used in the highest leverage situations already under contract for 2025. (A.J. Minter is the free agent.) But free agent departures have thinned out the bottom half of the ’pen considerably, and Joe Jiménez will miss most, if not all, of the 2025 season, so the Braves are probably going to have to do more than stand pat here. That said, because they’re looking for depth, they don’t necessarily have to fish in the deep end of the free agent pool.
So, where are the Braves now? They ought to enter the season with one of the best win projections in baseball, somewhere in the mid-90s. A healthy Strider and Acuña alone would have been more than enough to get the relatively disappointing 2024 squad to that level. This is a top franchise, but there’s a little more risk this time around.
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.
Players are listed with their most recent teams wherever possible. This includes players who are unsigned or have retired, players who will miss 2025 due to injury, and players who were released in 2024. So yes, if you see Joe Schmoe, who quit baseball back in August to form a Norwegian Ukulele Dixieland Jazz band that only covers songs by The Smiths, he’s still listed here intentionally. ZiPS is assuming a league with an ERA of 4.11.
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.
For the 21st 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 Seattle Mariners.
Batters
For the third time in the last four years, the Mariners hung around the playoff race for most of the 2024 campaign, before falling just short by the season’s denouement. When it comes to winning 85 to 90 games a year like clockwork, Jerry Dipoto’s ship is airtight and his sailors always on the ball. But so far, St. Louis Cardinals: Pacific Northwest hasn’t been quite as effective as the original series — the Mariners aren’t going to have as easy a time stealing division titles as the Cardinals did, playing as they do in a harder division. While the Mariners aggressively add players and make trades, there’s a basic conservatism here that limits the team’s ultimate upside; despite seven winning seasons in the last 11 years, Seattle has maxed out at just 90 wins. This isn’t a new thing either — the franchise only has one 95-win-or-better season in its history (the 116-win 2001 season, of course). Read the rest of this entry »
Baseball’s awards season is in full swing this week. Tonight, the National League Rookie of the Year award, officially known as the Jackie Robinson award since 1987, was awarded to Paul Skenes, who was impressive enough to also be a finalist in the NL Cy Young award voting. Skenes finished with 23 first-place votes to Jackson Merrill’s seven.
I’m not here to praise or criticize the results. Instead, I’m here to perform what I see as my journalistic duty. I was an NL Rookie of the Year voter this year (my sixth time voting for the award), and I have always felt that it’s important to give a detailed explanation of the reasoning behind my choice. As usual, I spent most of the final weekend of the season agonizing over my choices, because while being asked to vote for one of these awards is admittedly really cool, it’s also a weighty responsibility that demands care as well as candor. Offering a breakdown of my vote hasn’t always been fun — in 2021,my decision to vote forTrevor Rogers over Jonathan India resulted in my social media mentions being inundated with a combination of threats and insults — but I think I owe it to the fans and the players involved to explain myself. (OK, some of the brouhaha in 2021 was fun, like the suggestion that the Cincinnati Reds should fire me, a notion that still amuses me on many levels.) Read the rest of this entry »
For the 21st 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
Are the Rockies a good team? No, they are not. Are the Rockies even a middling team? Again, no. But things may slowly be getting better. Colorado will still have a lousy offense in 2025, but you can at least see the light at the end of a (very) long tunnel, most obviously when looking at the lineup. No one would confuse the Rockies with the Rays in terms of the cleverness with which they construct their roster, but the utter disaster that is the Kris Bryant signing does appear to have to had some kind of effect on their organizational decision-making. Since the start of 2023, they’ve done some very un-Rockies things. Jumping on the opportunity to snatch up an upside play like Nolan Jones isn’t something this team would have done in the late 2010s. The old Rockies would have found a way to play a mediocre veteran over Ezequiel Tovar, and there’s no way Brenton Doyle would have been given anywhere near enough rope to stick around for a possible breakout. Can you imagine past Rockies teams being patient with fringy prospects like Michael Toglia, giving an opportunity to a veteran journeyman like Jake Cave, or releasing Elias Díaz, a veteran catcher who made the All-Star Game the year prior, to find playing time for a prospect? Now, it hasn’t all worked out, but it at least represents some movement away from the strategies that slammed the competitive window of the last good Rockies team closed. You can’t get out of a hole until you stop digging. Read the rest of this entry »
For the 21st consecutive season, the ZiPS projection system is unleashing a full set of prognostications. For more information on the ZiPS projections, please consult this year’s introduction and MLB’s glossary entry. The team order is selected by lot, and the first team up is the Arizona Diamondbacks.
Batters
Last year in this space, ZiPS was optimistic about the Diamondbacks bettering their 2023 win total. A big part of that was the computer predicting that the offense would be somewhere around average or (mostly) better everywhere except designated hitter. That’s generally what happened, and they even improved on that projection a bit, signing Joc Pederson at the end of January. The Snakes did, in fact, improve on their won total, going from 84 to 89 wins even though that wasn’t enough to squeeze into the postseason this go-around. Arizona actually led baseball in runs scored, edging out the Dodgers, and the team wasn’t even really aided by Chase Field, which is a much more neutral offensive environment than it used to be. Read the rest of this entry »
Well, it’s that time of the year again. When the last gasps of summer weather finally die and everybody starts selling pumpkin spice everything, that’s when I make the magical elves living in the oak in my backyard start cranking out the E.L.fWAR cookies. Szymborski shtick, Szymborski shtick, pop culture reference, and now, let’s run down what the ZiPS projections are, how they work, and what they mean. After all, you’re going to be seeing 30 ZiPS team articles over the next two months.
ZiPS is a computer projection system I initially developed in 2002–04. It officially went live for the public in 2005, after it had reached a level of non-craptitude I was content with. The origin of ZiPS is similar to Tom Tango’s Marcel the Monkey, coming from discussions I had in the late 1990s with Chris Dial, one of my best friends (our first interaction involved Chris calling me an expletive!) and a fellow stat nerd. ZiPS quickly evolved from its original iteration as a reasonably simple projection system, and now does a lot more and uses a lot more data than I ever envisioned it would 20 years ago. At its core, however, it’s still doing two primary tasks: estimating what the baseline expectation for a player is at the moment I hit the button, and then estimating where that player may be going using large cohorts of relatively similar players.
So why is ZiPS named ZiPS? At the time, Voros McCracken’s theories on the interaction of pitching, defense, and balls in play were fairly new, and since I wanted to integrate some of his findings, I decided the name of my system would rhyme with DIPS (defense-independent pitching statistics), with his blessing. I didn’t like SIPS, so I went with the next letter in my last name, Z. I originally named my work ZiPs as a nod to CHiPs, one of my favorite shows to watch as a kid. I mis-typed ZiPs as ZiPS when I released the projections publicly, and since my now-colleague Jay Jaffe had already reported on ZiPS for his Futility Infielder blog, I chose to just go with it. I never expected that all of this would be useful to anyone but me; if I had, I would have surely named it in less bizarre fashion.
ZiPS uses multiyear 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. And research is a big part of ZiPS. Every year, I run 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 2013, while data derived from Statcast has been included in recent years as I’ve gotten a handle on its predictive value and the impact of those numbers on existing models. I believe in cautious, conservative design, so data are only included once I have confidence in their improved accuracy, meaning 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 guessed.
How does ZiPS project future production? First, using both recent playing data with adjustments for zStats, and other factors such as park, league, and quality of competition, ZiPS establishes a baseline estimate for every player being projected. To get an idea of where the player is going, the system compares that baseline to the baselines of all other players in its database, also calculated from the best data available for the player in the context of their time. The current ZiPS database consists of about 145,000 baselines for pitchers and about 180,000 for hitters. For hitters, outside of knowing the position played, this is offense only; how good a player is defensively doesn’t yield information on how a player will age at the plate.
Using a whole lot of stats, information on shape, and player characteristics, ZiPS then finds a large cohort that is most similar to the player. I use Mahalanobis distance extensively for this. A few years ago, Brandon G. Nguyen did a wonderful job broadly demonstrating how I do this while he was a computer science/math student at Texas A&M, though the variables used aren’t identical.
As an example, here are the top 50 near-age offensive comparisons for World Series MVP Freddie Freeman right now. The total cohort is much larger than this, but 50 ought to be enough to give you an idea:
Top 50 ZiPS Offensive Player Comps for Freddie Freeman
Ideally, ZiPS would prefer players to be the same age and play the same position, but since we have about 180,000 baselines, not 180 billion, ZiPS frequently has to settle for players at nearly the same age and position. The exact mix here was determined by extensive testing. The large group of similar players is then used to calculate an ensemble model on the fly for a player’s future career prospects, both good and bad.
One of the tenets of projections that I follow is that no matter what the ZiPS projection says, that’s what the projection is. Even if inserting my opinion would improve a specific projection, I’m philosophically opposed to doing so. ZiPS is most useful when people know that it’s purely data-based, not some unknown mix of data and my opinion. Over the years, I like to think I’ve taken a clever approach to turning more things into data — for example, ZiPS’ use of basic injury information — but some things just aren’t in the model. ZiPS doesn’t know if a pitcher wasn’t allowed to throw his slider coming back from injury, or if a left fielder suffered a family tragedy in July. Those sorts of things are outside a projection system’s purview, even though they can affect on-field performance.
It’s also important to remember that the bottom-line projection is, in layman’s terms, only a midpoint. You don’t expect every player to hit that midpoint; 10% of players are “supposed” to fail to meet their 10th-percentile projection and 10% of players are supposed to pass their 90th-percentile forecast. This point can create a surprising amount of confusion. ZiPS gave .300 batting average projections to two players in 2024: Luis Arraez and Ronald Acuña Jr. But that’s not the same thing as ZiPS thinking there would only be two .300 hitters. On average, ZiPS thought there would be 22 hitters with at least 100 plate appearances to eclipse .300, not two. In the end, there were 15 (ZiPS guessed high on the BA environment for the second straight year).
Another crucial thing to bear in mind is that the basic ZiPS projections are not playing-time predictors; by design, ZiPS has no idea who will actually play in the majors in 2025. Considering this, ZiPS makes its projections only for how players would perform in full-time major league roles. Having ZiPS tell me how someone would hit as a full-time player in the big leagues is a far more interesting use of a projection system than if it were to tell me how that same person would perform as a part-time player or a minor leaguer. For the depth charts that go live in every article, I use the FanGraphs Depth Charts to determine the playing time for individual players. Since we’re talking about team construction, I can’t leave ZiPS to its own devices for an application like this. It’s the same reason I use modified depth charts for team projections in-season. There’s a probabilistic element in the ZiPS depth charts: Sometimes Joe Schmo will play a full season, sometimes he’ll miss playing time and Buck Schmuck will have to step in. But the basic concept is very straightforward.
What’s new in 2025? Outside of the myriad calibration updates, a lot of the additions were invisible to the public — quality of life things that allow me to batch run the projections faster and with more flexibility on the inputs. One consequence of this is that I will, for the first time ever, be able to do a preseason update that reflects spring training performance. It doesn’t mean a ton, but it means a little bit, and it’s something that Dan Rosenheck of The Economistdemonstrated about a decade ago. Now that I can do a whole batch run of ZiPS on two computers in less than 36 hours, I can turn these around and get them up on FanGraphs within a reasonable amount of time, making it a feasible task. A tiny improvement is better than none!
The other change is that, starting with any projections that run in spring training, relievers will have save projections in ZiPS. One thing I’ve spent time doing is constructing a machine learning approach to saves, which focuses on previous roles, contract information, time spent with the team, and other pitchers available on the roster. This has been on my to do list for a while and I’m happy that I was able to get to it. It’s just impractical to do with these offseason team rundowns because the rosters will be in flux for the next four months.
Have any questions, suggestions, or concerns about ZiPS? I’ll try to reply to as many as I can reasonably address in the comments below. If the projections have been valuable to you now or in the past, I would also urge you to consider becoming a FanGraphs Member, should you have the ability to do so. It’s with your continued and much appreciated support that I have been able to keep so much of this work available to the public for so many years for free. Improving and maintaining ZiPS is a time-intensive endeavor and reader support allows me the flexibility to put an obscene number of hours into its development. It’s hard to believe I’ve been developing ZiPS for nearly half my life now! Hopefully, the projections and the things we’ve learned about baseball have provided you with a return on your investment, or at least a small measure of entertainment, whether it’s from being delighted or enraged.
When it comes to throwing shade in the playoffs in recent years, nothing has caught as much – not even your least favorite broadcaster – than the concept of home field advantage. The reason for the negative feelings isn’t surprising. Other than a possible first-round bye, home field advantage is the main reward for playoff teams that win more regular-season games than other playoff teams.
It’s true that home teams have struggled in recent postseasons, but they actually haven’t been too bad this year. The 19-18 record of home teams isn’t the most scintillating of tallies, but their .513 winning percentage across 37 games is not exactly a stunning departure from the .522 winning percentage for home teams during the 2024 regular season. The most games a team can possibly play in a single postseason is 22, and nine points of winning percentage works out to only 0.2 wins per 22 games.
Postseason Winning Percentage at Home, 1995-2024
Year
Wins
Losses
Winning Percentage
2023
15
26
.366
2010
13
19
.406
1996
14
18
.438
2019
17
20
.459
1998
14
16
.467
2003
18
20
.474
2016
17
18
.486
2012
18
19
.486
1997
17
17
.500
2024
19
18
.514
2001
18
17
.514
2018
17
16
.515
2000
16
15
.516
2015
19
17
.528
2005
16
14
.533
2020
29
24
.547
2002
19
15
.559
2008
18
14
.563
2014
18
14
.563
2006
17
13
.567
2022
23
17
.575
2004
20
14
.588
2011
23
15
.605
2013
23
15
.605
2007
17
11
.607
1995
19
12
.613
2021
24
14
.632
2009
19
11
.633
1999
20
11
.645
2017
27
11
.711
Naturally, the data are noisy given the relatively small number of postseason games, even under the current format, but the recent issues with home field advantage seem to mostly be a 2023 thing, when home teams went 15-26, comfortably their worst year. Smoothing out the data a bit doesn’t really do much, either.
Postseason Winning Percentage at Home, Five-Year Periods, 1995-2024
Five-Year Period
Winning Percentage
1995-1999
.532
1996-2000
.513
1997-2001
.528
1998-2002
.540
1999-2003
.538
2000-2004
.529
2001-2005
.532
2002-2006
.542
2003-2007
.550
2004-2008
.571
2005-2009
.580
2006-2010
.553
2007-2011
.563
2008-2012
.538
2009-2013
.549
2010-2014
.537
2011-2015
.558
2012-2016
.534
2013-2017
.581
2014-2018
.563
2015-2019
.542
2016-2020
.546
2017-2021
.573
2018-2022
.547
2019-2023
.517
2020-2024
.526
You can always find an oddity if you shave data paper-thin like prosciutto, but with data as volatile as this, you’ll mostly end up with bleeps and bloops that don’t really mean anything. Like, sure, teams are 29-31 since 1995 at home in Game 7s and Game 5s, but that’s primarily the odd blip of NLDS home teams going 4-12 in their rubber matches.
Returning to 2023 one more time, I went back and looked at the projections, both from ZiPS and regular-season record or Pythagorean record. Using each team’s actual 2023 record, the average home team in the playoffs had a .562 regular-season winning percentage; it was .551 for the road teams. It’s a .564/.553 split using the Pythagorean records. But I still have all the projected matchups and rosters at the start of the playoffs saved, so I re-projected the results of every actual game that was played. ZiPS thought on a game-by-game basis, with home field advantage completely removed from the equation, the road teams were actually slightly stronger, projecting the average home team at .545 and the average road team at .556. Facing off against each other, ZiPS expected home teams to have a .489 record in the 31 actual playoff games, with an 8% chance of going 15-26 or worse.
Looking at the Wild Card era as a whole, home teams have gone .540 over 1,045 playoffs games. In the regular season over the same era, home teams have a .537 winning percentage. In other words, the playoffs just aren’t that different from the regular season. (ZiPS assumes a .535 playoff winning percentage for the home team in a game of exactly equal teams.) So why does it feel so bad? I suspect one reason can be found in the charts above. Home teams had a pretty good run in the mid-2010s, on the heels of the expansion from eight to 10 playoff teams, peaking at a .581 winning percentage from 2013 to 2017. In that context, it conveys the feeling that home field advantage is working as intended, and the five-year runs stayed slightly above the historical trend until the 2023 home field crash.
Since that crash feels especially bad, it’s natural that people search for deeper meaning in data that don’t really have a lot to give. One common cry was blaming the long layoffs from the bye round. This argument doesn’t hold up, as Ben Clemens pointed out last postseason.
It also doesn’t have much to do with modern baseball or modern players, either. Home field advantage has been relatively stable in the regular season throughout baseball history.
Regular Season Winning Percentage by Decade
Decade
Winning Percentage
1900s
.551
1910s
.540
1920s
.543
1930s
.553
1940s
.544
1950s
.539
1960s
.540
1970s
.538
1980s
.541
1990s
.535
2000s
.542
2010s
.535
2020s
.531
There’s been some long-term decline, but nothing earth-shattering.
The larger problem is simply that fundamentally, home field advantage just isn’t a big deal in baseball. It’s not as big a deal in other sports as some think, but unlike in the other major sports, the difference in baseball between a great team, a good team, a lousy team, and the Chicago White Sox is not that large. Other sports don’t need home field advantage to be as much of a differentiator, especially in the playoffs. A few years back, Michael Lopez, Greg Matthews, and Ben Baumer crunched some numbers and estimated that to match the better-team-advances rate of the NBA playoffs, MLB teams would need to play best-of-75 playoff series. I certainly love me some baseball, but I can’t imagine I’d still watch World Series Game 63 with the same intensity as I do every Fall Classic game now. Besides, the MLBPA wouldn’t be on board, and the calendar would make that a practical impossibility anyway.
Even giving the team with more wins home field advantage in every single game doesn’t drastically weight the dice. Assuming a .535 home winning percentage and evenly matched teams, the home team would require a best-of-13 series to become a 60/40 favorite; to increase its odds to 2-to-1, we’d have to make it a best-of-39 series. Just to experiment, I simulated series with the normal postseason distribution of home field advantage (one extra game) between two teams, the one in which the home team is .020 wins better than its opponent (just over three wins in a season). I then ran the numbers for how often the better team would be expected to win, based on series length.
Playoff Simulation, Better Team’s Series Win Probability
Series Length (Maximum Games)
Win Probability
3
54.7%
5
55.1%
7
55.5%
9
55.9%
11
56.3%
13
56.6%
15
57.0%
17
57.3%
19
57.7%
21
58.0%
23
58.3%
25
58.6%
27
58.8%
29
59.1%
31
59.4%
33
59.6%
35
59.9%
37
60.1%
39
60.4%
41
60.6%
43
60.8%
45
61.0%
47
61.3%
49
61.5%
51
61.7%
53
61.9%
55
62.1%
57
62.3%
59
62.5%
61
62.7%
63
62.8%
65
63.0%
67
63.2%
69
63.4%
71
63.6%
73
63.7%
75
63.9%
77
64.1%
79
64.2%
81
64.4%
So what does this all mean? In all likelihood, home field advantage in the playoffs hasn’t changed in any meaningful way. And isn’t really all that big of a deal in the first place. Without altering the very nature of the postseason significantly — aggressive changes such as requiring the lower-seeded team sweep in the Wild Card series to advance — baseball has a very limited ability to reward individual playoff teams based on their regular-season results. Home field advantage isn’t broken; it’s working in the extremely limited way that one should expect. If the Dodgers beat the Yankees in the World Series this year, it probably won’t be because they were rewarded one more possible home game.
Once considered the natural successor to Clayton Kershaw as The Man in the Dodgers’ rotation, Walker Buehler’s career hit a rocky stretch in 2022. Coming off arguably his best season in the majors, Buehler was pulled from a June start with elbow pain, starting a journey that ended with a Tommy John surgery, the second of his career, two months later. After some unrelated injury setbacks this spring, Buehler returned to the Dodgers, but as a shadow of his former self. He finished 2024 with a 5.38 ERA and a 5.54 FIP, and might not have even made the postseason roster if not for the fact that most of the organization’s other plausible starters don’t currently have working throwing arms. His no-strikeout, six-run outing against the Padres in Game 3 of the NLDS wasn’t an inspiring sign that he’d turn things around in the playoffs.
And yet, in Game 3 of the NLCS against the Mets at Citi Field, Buehler had opposing batters flailing at his shockingly nasty repertoire in a short but effective four-inning start. He left with a two-run lead, but after the Los Angeles offense kept tacking on and the bullpen threw five scoreless innings, the Dodgers left the ballpark Wednesday night with an 8-0 win and a 2-1 advantage in the best-of-seven series.
One of the problems with Buehler in his return this year was that he was just so darn hittable at times. Before 2022, his four-seamer was the foundation that his out-pitches were built around, but even before his elbow surgery, the effectiveness of the pitch had practically disappeared. From 2021 to 2022, he bled about 200 rpm off his fastball’s average spin rate. Batters apparently took notice, suddenly slugging .618 as his heater lost some of its rise. Buehler returned from surgery, but the four-seamer’s effectiveness did not, and the pitch became a smaller part of his toolset. Read the rest of this entry »
Dan Szymborski: Welcome!
