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The following article is part of Jay Jaffe’s ongoing look at the candidates on the BBWAA 2025 Hall of Fame ballot. For a detailed introduction to this year’s ballot, and other candidates in the series, use the tool above; an introduction to JAWS can be found here. For a tentative schedule, and a chance to fill out a Hall of Fame ballot for our crowdsourcing project, see here. All WAR figures refer to the Baseball Reference version unless otherwise indicated.

With a foundation that centered upon the Hall of Fame triumvirate of Tom Glavine, Greg Maddux, and John Smoltz, the Braves dominated the NL West and then the NL East, reaching the postseason every year from 1991–2005 save for the ’94 strike season. Nothing lasts forever, though, and as Glavine and then Maddux departed in free agency, the team inevitably had to retool. Among the centerpieces of the next wave of Braves stars was one practically grown in their own backyard, Brian McCann.

A lefty-swinging backstop with rich baseball bloodlines, a strong arm, and a powerful bat, McCann was just 21 years old when he debuted with the Braves in June 2005. Over his first eight full seasons, he made seven All-Star teams and helped Atlanta to three postseason appearances, though the team’s success wasn’t nearly on par with the preceding dynasty. While McCann’s footwork and pitch framing wasn’t initially as polished as that of Russell Martin (who debuted with the Dodgers as a 23-year-old in 2006), he too developed into one of the game’s elite framers, that while providing stronger offense than his West Coast counterpart. Along the way, he also developed a somewhat dubious reputation as an enforcer of the unwritten rules, thanks to high-profile incidents involving José Fernández and Carlos Gómez in September 2013, though both players smoothed things over with McCann. Read the rest of this entry »

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I want to start off by saying that I was devastated — devastated — to learn that Nolan Arenado reportedly vetoed a trade to the Houston Astros. I guess it would’ve made some baseball sense, as Alex Bregman’s departure leaves a vacancy at third base, and new acquisition Isaac Paredes could easily slide across the diamond to first. Plus, Arenado is a three-time National League home run leader with a long history of hitting the ball in the air and to the pull side. Surely he’d find something to like about the Crawford Boxes.

But mostly, I wanted this to happen because I had a joke lined up. 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 Detroit Tigers.

Batters

In a year with a few surprising playoff teams, the Tigers might have the best case to be called a Cinderella story. After a June swoon dropped them nearly 10 games below .500, the Tigers looked like a team playing for 2025, to the extent that with a losing record at the trade deadline, they traded away their second-best starter, Jack Flaherty. But a funny thing happened on the way to the MLB Draft Lottery: The Tigers suddenly started winning. Going 34-19 over the final two months was just enough to squeeze into the playoffs on the season’s final weekend. While Detroit eventually lost to the Guardians in the ALDS in five games, the Tigers did prevent the Houston Astros from reaching the ALCS, the first time that’s happened since 2016.

Looking at the lineup projections, one can see a big part of why A.J. Hinch would have been my pick for AL Manager of the Year, if I had voted in that category. The Tigers basically had one good offensive performance from a full-time player on their roster: Riley Greene. To get to basically being league average in terms of runs scored, the Tigers had to juggle a lot of players of incomplete value, with lots of platoons and matchup choices, in the way that Gabe Kapler ran the 2021 San Francisco Giants — though those Giants had a much better lineup.

But when the dust clears and you look at the projections rather than the 2024 accomplishments, there’s just a lot of average here. Some of the averageness has a lot of upside; there’s a good chance that Jace Jung will be a plus major leaguer, and if he’s over his early-2024 doldrums for good, Colt Keith will be too. Parker Meadows did enough to establish himself as a viable starter in center field. Kerry Carpenter’s a great big spoon in a DH platoon. Dillon Dingler and Jake Rogers are a quietly underrated catcher tandem. But after that, there are simply a lot of questions. ZiPS has no confidence in Wenceel Pérez, and the shortstops don’t combine for a very good projection even with the computer predicting some kind of, umm, bounce back from Javier Báez. The odds that Spencer Torkelson will become a foundational part of the lineup are a lot longer than they were a year ago.

This is a team that really needs to add another impact bat. There are a variety of spots in the lineup they can target, and players like Alex Bregman and Teoscar Hernández remain on the board. This is a team that has two years left before its Cy Young-winning ace Tarik Skubal reaches free agency, a team that plays in a division that has no big dogs; this is the perfect time to make a push. This is a team that could sign Bregman and Hernández and still be tens of millions of dollars below a $200 million luxury tax number. Jung and eventually Max Clark and Thayron Liranzo all project to be contributors in the majors, but 2025-2026 are too soon to realistically expect them to have star-level breakouts. There’s work to be done here.

Pitchers

Skubal projects as the best pitcher in the majors, so that’s a nice place to start. ZiPS is widely a believer in the rest of the rotation’s abilities, but not so much the rest of the rotation’s attendance. There’s a lot of injury risk in Alex Cobb, Casey Mize, and Reese Olson, and even Skubal’s just over a year removed from serious injury. Olson and Cobb both project with ERAs below four, Mize isn’t that far off, and Jackson Jobe, Keider Montero, and Matt Manning all project as legitimate starters in the majors. But how many innings will they get from this crew? That was a question that Hinch had to answer back in October, and he answered it by basically relying on Skubal and having the bullpen construct a Potemkin rotation behind him. That’s not a formula that can be repeated over 162 games. Maybe other needs and the possibility of extending Skubal prevent the Tigers from going after Corbin Burnes or pursuing a Flaherty reunion, but this is a team that just begs for a middling inning-eater. Kyle Gibson anyone? Really, the Tigers should give Justin Verlander the hard sell on one last season in Detroit to cap off his Hall of Fame career.

The bullpen, of course, projects to be terrific. What a change from a few years ago! The front four of Beau Brieske, Jason Foley, Tyler Holton, and Will Vest project to be as dominating a quartet as you’ll find right now, and Alex Lange will join them at some point later in the season, depending on his progress recovering from lat surgery. ZiPS also has a surprising fancy for Brant Hurter as a reliever. The only pitcher in the pen that the projections actually hate is Kenta Maeda, who struggled in 2024 before being exiled to the bullpen, where he was more passable. Given the work to be done elsewhere, I’m not sure the Tigers need to do much with the relievers but wish them a good holiday and look forward to their February return.

Right now, ZiPS projects the Tigers to be about a .500 team. A .500 team with some upside is a team to take seriously in the AL Central, and there’s still the theoretical possibility that the Tigers do more this winter.

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. The final team projections may differ considerably from our Depth Chart playing time.

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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. It is important to remember that ZiPS is agnostic about playing time, and has no information about, for example, how quickly a team will call up a prospect or what veteran has fallen into disfavor.

As always, incorrect projections are either caused by misinformation, a non-pragmatic reality, or by the skillful sabotage of our friend and former editor. You can, however, still get mad at me on Twitter or on BlueSky.

Below is an analysis of the prospects in the farm system of the Chicago Cubs. Scouting reports were compiled with information provided by industry sources as well as our own observations. This is the fifth year we’re delineating between two anticipated relief roles, the abbreviations for which you’ll see in the “position” column below: MIRP for multi-inning relief pitchers, and SIRP for single-inning relief pitchers. The ETAs listed generally correspond to the year a player has to be added to the 40-man roster to avoid being made eligible for the Rule 5 draft. Manual adjustments are made where they seem appropriate, but we use that as a rule of thumb.

A quick overview of what FV (Future Value) means can be found here. A much deeper overview can be found here.

All of the ranked prospects below also appear on The Board, a resource the site offers featuring sortable scouting information for every organization. It has more details (and updated TrackMan data from various sources) than this article and integrates every team’s list so readers can compare prospects across farm systems. It can be found here. Read the rest of this entry »

Right after FanGraphs published my piece on the Kirby Index, the metric’s namesake lost his touch. George Kirby’s trademark command — so reliable that I felt comfortable naming a statistic after him — fell off a cliff. While the walk rate remained under control, the home run rate spiked; he allowed seven home runs in May, all on pitches where he missed his target by a significant margin.

Watching the namesake of my new metric turn mediocre immediately following publication was among the many humbling experiences of publishing this story. Nevertheless, I wanted to revisit the piece. For one, it’s December. And writing the story led me down a fascinating rabbit hole: While I learned that the Kirby Index has its flaws, I also learned a ton about contemporary efforts to quantify pitcher command.

But first, what is the Kirby Index? I found that release angles, in concert with release height and width, almost perfectly predicted the location of a pitch. If these two variables told you almost everything about the location of a pitch, then a measurement of their variation for individual pitchers could theoretically provide novel information about pitcher command.

This got a few people mad on Twitter, including baseball’s eminent physicist Alan Nathan and Greg Rybarczyk, the creator of the “Hit Tracker” and a former member of the Red Sox front office. These two — particularly Rybarczyk — took issue with my use of machine learning to make these predictions, arguing that my use of machine learning suggested I didn’t understand the actual mechanics of why a pitch goes where it goes.

“You’re spot on, Alan,” wrote Rybarczyk. “The amazement that trajectory and launch parameters are strongly associated with where the ball ends up can only come from people who see tracking data as columns of digits rather than measurements of reality that reflect the underlying physics.”

While the tone was a bit much, Rybarczyk had a point. My “amazement” would have been tempered with a more thorough understanding of how Statcast calculates the location where a pitch crosses home plate. After publication, I learned that the nine-parameter fit explains why pitch location could be so powerfully predicted by release angles.

The location of a pitch is derived from the initial velocity, initial release point, and initial acceleration of the pitch in three dimensions. (These are the nine parameters.) Release angles are calculated using initial velocity and initial release point. Because the location of the pitch and the release angle are both derived from the 9P fit, it makes sense that they’d be almost perfectly correlated.

This led to a reasonable critique: If release angles are location information in a different form, why not just apply the same technique of measuring variation on the pitch locations themselves? This is a fair question. But using locations would have undermined the conclusion of that Kirby Index piece — that biomechanical data like release angles could improve the precision of command measurements.

Teams, with their access to KinaTrax data, could create their own version of the Kirby Index, not with implied release angles derived from the nine-parameter fit, but with the position of wrists and arms captured at the moment of release. The Kirby Index piece wasn’t just about creating a new way to measure command; I wanted it to point toward one specific way that the new data revolution in baseball would unfold.

But enough about that. It’s time for the leaderboards. I removed all pitchers with fewer than 500 fastballs. Here are the top 20 in the Kirby Index for the 2024 season:

And here are the bottom 20:

A few takeaways for me: First, I am so grateful Kirby got it together and finished in the top three. Death, taxes, and George Kirby throwing fastballs where he wants. Second, the top and bottom of the leaderboards are satisfying. Cody Bradford throws 89 and lives off his elite command, and Joe Boyle — well, there’s a reason the A’s threw him in as a piece in the Jeffrey Springs trade despite his otherworldly stuff. Third, there are guys on the laggard list — Seth Lugo and Miles Mikolas, in particular — who look out of place.

Mikolas lingered around the bottom of the leaderboards all year, which I found curious. Mikolas, after all, averages just 93 mph on his four-seam fastball; one would imagine such a guy would need to have elite command to remain a viable major league starter, and that league-worst command effectively would be a death sentence. Confusing this further, Mikolas avoided walks better than almost anyone.

Why Mikolas ranked so poorly in the Kirby Index while walking so few hitters could probably be the subject of its own article, but for the purposes of this story, it’s probably enough to say that the Kirby Index misses some things.

An example: Mikolas ranked second among all pitchers in arm angle variation on four-seam fastballs, suggesting that Mikolas is intentionally altering his arm angle from pitch to pitch, likely depending on whether the hitter is left-handed or right-handed. This is just one reason why someone might rank low in the Kirby Index. Another, as I mentioned in the original article, is that a pitcher like Lugo might be aiming at so many different targets that it fools a metric like the Kirby Index.

So: The Kirby Index was a fun exercise, but there are some flaws. What are the alternatives to measuring pitcher command?

Location+

Location+ is the industry standard. The FanGraphs Sabermetric library (an incredible resource, it must be said) does a great job of describing that metric, so I’d encourage you to click this hyperlink for the full description. The short version: Run values are assigned to each location and each pitch type based on the count. Each pitch is graded on the stuff-neutral locations.

Implied location value

Nobody seems particularly satisfied with Location+, including the creators of Location+ themselves. Because each count state and each pitch type uses its own run value map to distribute run value grades, it takes a super long time for the statistic to stabilize, upward of hundreds of pitches. It also isn’t particularly sticky from year to year.

The newest version of Location+, which will debut sometime in the near future, will use a similar logic to PitchProfiler’s command model. Essentially, PitchProfiler calculates a Stuff+ and a Pitching+ for each pitcher, which are set on a run value scale. By subtracting the Stuff+ run value from the Pitching+ run value, the model backs into the value a pitcher gets from their command alone.

Blobs

Whether it’s measuring the standard deviation of release angle proxies or the actual locations of the pitches themselves, this method can be defined as the “blob” method, assessing the cluster tightness of the chosen variable.

Max Bay, now a senior quantitative analyst with the Dodgers, advanced the Kirby Index method by measuring release angle “confidence ellipses,” allowing for a more elegant unification of the vertical and horizontal release angle components.

Miss distance

The central concern with the Kirby Index and all the blob methods, as I stated at the time, is the single target assumption. Ideally, instead of looking at how closely all pitchers are clustered around a single point, each pitch would be evaluated based on how close it finished to the actual target.

But targets are hard to come by. SportsVision started tracking these targets in the mid-2010s, as Eno Sarris outlined in his piece on the state of command research in 2018. These days, Driveline Baseball measures this working alongside Inside Edge. Inside Edge deploys human beings to manually tag the target location for every single pitch. With these data in hand, Driveline can do a couple of things. First, they created a Command+ model, modifying the mean miss distances by accounting for the difficulty of the target and the shape of a pitch.

Using intended zone data, Driveline also shows pitchers where exactly they should aim to account for their miss tendencies. I’m told they will be producing this methodology in a public post soon.

Catcher Targets (Computer Vision)

In a perfect world, computers would replace human beings — wait, let me try that sentence again. It is expensive and time-intensive to manually track targets through video, and so for good reason, miss target data belong to those who are willing to pay the price. Computer vision techniques present the potential to produce the data cheaply and (therefore) democratically.

Carlos Marcano and Dylan Drummey introduced their BaseballCV project in September. (Drummey was hired by the Cubs shortly thereafter.) Joseph Dattoli, the director of player development at the University of Missouri, offered a contribution to the project by demonstrating how computer vision could be used to tag catcher targets. The only limitation, Joseph pointed out, is the computing power required to comb through video of every single pitch.

There are some potential problems with any command measurement dependent on target tracking. Targets aren’t always real targets, more like cues for the pitcher to throw toward that general direction. But Joseph gets around this concern by tracking the catcher’s glove as well as his center of mass, which is less susceptible to these sorts of dekes. Still, there’s a way to go before this method scales into a form where daily leaderboards are accessible.

The Powers method

Absent a raft of public information about actual pitcher targets, there instead can be an effort to simulate them. In their 2023 presentation, “Pitch trajectory density estimation for predicting future outcomes,” Rice professor Scott Powers and his co-author Vicente Iglesias proposed a method to account for the random variation in pitch trajectories, in the process offering a framework for simulating something like a target. (I will likely butcher his methods if I try to summarize them, so I’d encourage you to watch the full presentation if you’re interested.)

The Powers method was modified by Stephen Sutton-Brown at Baseball Prospectus, who used Blake Snell as an example of the way these targeting models can be applied at scale to assess individual pitchers. First, Sutton-Brown fit a model that created a global target for each pitch type, adjusting for the count and handedness of each batter. Then, for each pitcher, this global target was tweaked to account for that pitcher’s tendencies. Using these simulated targets, he calculated their average miss distance, allowing for a separation of the run value of a pitcher’s targets from the run value of their command ability.

“Nothing”

On Twitter, I asked Lance Brozdowski what he saw as the gold standard command metric. He answered “Nothing,” which sums up the problem well. This is a challenging question, and all the existing methods have their flaws.

There are ways that the Kirby Index could be improved, but as far as I can tell, the best way forward for public command metrics is some sort of combination of the final two methods, with active monitoring of the computer vision advancements to see if consistent targets can be established.

But one would imagine the story is completely different on the team side. By marrying the KinaTrax data with miss distance information, these methods could potentially be combined to make some sort of super metric, one that I imagine gets pretty close to measuring the true command ability of major league pitchers. (In a video from Wednesday, Brozdowski reported on some of the potential of these data for measuring and improving command, as well as their limitations.) The public might not be quite there, but as far as I can tell, we’re not that far off.

Editor’s Note: This story has been updated to include Vicente Iglesias as a co-author on the 2023 presentation, “Pitch trajectory density estimation for predicting future outcomes.”

Brooks Lee embraces the art of hitting. The son of longtime Cal Poly head baseball coach Larry Lee, the 23-year-old Minnesota Twins infielder approaches his craft diligently. Drafted eighth overall by the Twins in 2022 after putting up a healthy 1.073 OPS across three years in college — he played for his father — Lee logged a 148 wRC+ over 114 plate appearances with Triple-A St. Paul last season prior to receiving his July call-up. The start to the switch-hitter’s minor league season had been delayed by nearly two months due to a herniated disc, which was diagnosed in early April.

Assigned a 50 FV and a no. 3 ranking when our 2024 Minnesota Twins Top Prospect list came out last June, Lee slashed .221/.265/.320 with three home runs and a 62 wRC+ over 185 plate appearances in his initial opportunities against big league pitching. He sat down to talk hitting when the Twins visited Fenway Park in the penultimate weekend of the season.

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David Laurila: How would you describe yourself as hitter? Moreover, how do you view yourself going forward?

Brooks Lee: “Ultimately, I want to evolve into a pure hitter and be able to hit all pitches in all zones. I want to hit for average. I think I can drive the ball, but most importantly, I want to get hits.”

Laurila: A lot of people will argue that batting average isn’t all that important. Why is it important to you?

Lee: “I’ve just always loved people that hit .300. As a switch-hitter, I want to be able to get on base at all times, from both sides of the plate. I really enjoy getting hits. That’s probably my favorite part of the game. For me, hitting over .300 is a benchmark. If you do that, everything kind of takes care of itself.”

Laurila: Being able to hit all pitches in all zones is an admirable trait, but at the same time, it can mean putting balls in play that you aren’t able to drive. You might be better off taking those pitches.

Lee: “Yes. That is something I’m learning, too. Sometimes you have strikes that aren’t necessarily good pitches to hit, even though they’re in the zone. For me, the pitch has to be elevated in order to drive it, because of the way my swing works, and the way I see the ball. So, when it’s up, then I go. Most likely, it’s a good pitch for me to hit.”

Laurila: How does your swing work, and does it differ from one side to the other? Read the rest of this entry »

On Monday, Statcast took its the latest step toward the goal of consolidating all baseball data into one website so unimaginably massive that not even Joey Gallo’s batting average can escape its gravitational pull. Baseball Savant unveiled enhanced baserunning leaderboards, supplementing its leaderboard for extra bases taken with a separate leaderboard for basestealing, and also adding one that combines the two into an overall baserunning value leaderboard. (In a much quieter move that could end up being even more consequential for the super-duper data dorks in your life, Baseball Savant also introduced toggles for the first and second halves of the season into its search function.) I’ve spent the past couple days looking around at the numbers to see how this new information might change our understanding of the craft of baserunning, and I’d like to share my initial thoughts.

I think the big benefit of these data is they will teach us a lot about how particular players do what they do. MLB.com’s David Adler broke down some of the fun features of the new leaderboards, and if that’s your thing, there are indeed plenty of fun features to marvel at. If you surf around the leaderboard, you can see that on-base machine Juan Soto unsurprisingly led all players with 1,324 opportunities to steal a base this season. You can see that Mookie Betts gets excellent jumps when he’s stealing, traveling 6.1 feet between the moment of the pitcher’s first move and the moment of their release, the largest distance in the game. You can see just how anachronistic Lane Thomas’s 26-for-40 stolen base season really was. 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 Chicago Cubs.

Batters

To get this out of the way, ZiPS absolutely adores Chicago’s lineup, from top to bottom and every which way around. ZiPS and the Cubs have been on the same page before — the projections for Shota Imanaga last winter had me proclaiming that his deal was the offseason’s best signing — but the projections haven’t been this high on the lineup since the team’s World Series contention days. Now, a lot of that is defense, with Nico Hoerner, Dansby Swanson, and Pete Crow-Armstrong each having elite defensive projections. But there’s a lot of bat in there as well, with six starters projected for a 100 OPS+ or better, and two of the three who aren’t — Swanson and PCA — bolstered by their aforementioned defense. Read the rest of this entry »