Last offseason, the Diamondbacks were in search of a marquee starter to pair with Zac Gallen atop their rotation. The market was thin at the top – Yoshinobu Yamamoto and Shohei Ohtani were probably never available to them, so their best options were Sonny Gray, Blake Snell, Eduardo Rodriguez, Shota Imanaga, and Jordan Montgomery. They signed two of those guys, and neither delivered the rotation-stabilizing performance they had expected. But instead of waving their hands in the air and raving at the injustice of variance, the Diamondbacks got right back on the horse:
BREAKING: Corbin Burnes to Diamondbacks, $210M, 6 years. opt out after 2 years
Corbin Burnes was the best free agent pitcher available. In each of the last five seasons, he’s been one of the top pitchers in the game, racking up a 2.88 ERA, 3.01 FIP, and 816 innings pitched. He’s second in WAR (21.7) over that time frame, second in RA9-WAR (23.2), second in strikeouts (946), and third in innings pitched. In other words, he’s been a capital-A Ace, a set-it-and-forget-it choice at the top of the starting rotation. He’ll receive $35 million a year for six seasons, with an opt-out after the second year of the deal, which also includes a $10 million signing bonus.
With Gallen also on their dance card, the Diamondbacks have one of the best one-two combinations in the majors. That doesn’t even include Merrill Kelly, a borderline All-Star when healthy, or Brandon Pfaadt, who looked like he was finally breaking out before a rough final two months of the season. Add in Montgomery and Rodriguez, and Arizona goes six deep with plausible playoff starters. That’s how you injury-proof a rotation – sheer depth. Read the rest of this entry »
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.
Even as an amateur, Ian Kinsler spent most of his career in someone else’s shadow. At Canyon Del Oro High School in Tucson, Arizona — where he played on two state champion teams — and then at Central Arizona Junior College, he played alongside players who were picked much higher in the draft. After transferring to Arizona State, he lost the starting shortstop job to Dustin Pedroia, who had initially moved to second base to accommodate his arrival. With the Rangers, for whom he starred from 2006–13, he was a vital cog on two pennant winners but took a back seat to MVP Josh Hamilton, future Hall of Famer Adrian Beltré, and perennial All-Star shortstop Michael Young. Even after being dealt to the Tigers, he drew less attention than Miguel Cabrera, Justin Verlander, or Max Scherzer.
Particularly in the developmental phase of his career, those slights and oversights left Kinsler with a chip on his shoulder, but also a drive to improve — and improve he did. He starred at his third collegiate stop, the University of Missouri, helped the Rangers emerge as an American League powerhouse while making three All-Star teams, added another All-Star selection in Detroit and won two belated but well-earned Gold Gloves. His 48 leadoff home runs ranks sixth all-time. Twice he combined 30 homers and 30 steals in the same season, making him one of just 16 players with repeat membership in the 30-30 club. For the 2007–16 period, he ranked among the game’s most valuable players by WAR via a combination of excellent defense, very good baserunning, and above-average hitting. Read the rest of this entry »
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.
Because of his size — officially 5-foot-9 and 170 pounds, but by his own admission, a couple inches shorter — Dustin Pedroia was consistently underestimated. Though he took to baseball as a toddler and excelled all the way through high school and Arizona State University, scouts viewed him as having below-average tools because of his stature. He barely grazed prospect lists before reaching the majors, but once he settled in, he quickly excelled. He won American League Rookie of the Year honors while helping the Red Sox win the 2007 World Series, then took home the MVP award the next year, when he was just 24.
Over the course of his 14-year career, Pedroia played a pivotal role in helping the Red Sox win one more World Series, made four All-Star teams, and banked four Gold Gloves. Understandably, he became a fan favorite, not only for his stellar play but because of the way he carried himself, radiating self-confidence to the point of cockiness, and always quick with a quip. “Pedie never shuts up, man,” Manny RamireztoldESPN Magazine’s Jeff Bradley for a 2008 piece called “170 Pounds of Mouth.” Continued Ramirez, “He’s a little crazy. But that’s why we love him. He talks big and makes us all laugh.” Read the rest of this entry »
Which player had a better career, Dustin Pedroia or David Wright? I asked that question in a Twitter poll a few days ago, with the erstwhile Boston Red Sox second baseman outpolling the former New York Mets third baseman by a measure of 58.8% to 41.2%. Results aside, how they compare in historical significance has been on my mind. Both are on the Hall of Fame ballot I will be filling out in the coming days, and depending on what I decide to do with a pair of controversial players that have received my votes in recent years, each is a strong consideration for a checkmark. More on that in a moment.
It’s no secret that Pedroia and Wright were on track for Cooperstown prior to injuries sidetracking their seemingly clear paths. Rather than having opportunities to build on their counting stats, they finished with just 1,805 and 1,777 hits, and 44.8 and 51.3 WAR, respectively. That said, each has a resumé that includes an especially impressive 10-year stretch (Wright had 10 seasons with 100 or more games played. Pedroia had nine).
To wit:
From 2007-2016, Pedroia slashed .303/.368/.447 with an 118 wRC+ and 45 WAR. Over that span, he made four All-Star teams, won four Gold Gloves, and earned both Rookie of the Year and MVP honors. Moreover, he was an integral part of two World Series-winning teams.
From 2005-2014, Wright slashed .298/.379/.492 with a 134 wRC+ and 48.1 WAR. Over that span, he made seven All-Star teams and won two Gold Gloves. Unlike his Red Sox contemporary, he captured neither a Rookie of the Year or MVP award, nor did he play for a World Series winner. That said, as Jay Jaffe wrote earlier this month, “Wright is the greatest position player in Mets history.” Read the rest of this entry »
The Astros’ facelift continues. One week after trading star outfielder Kyle Tucker to Chicago, Houston has dived into the free agent market and come up with a replacement: first baseman Christian Walker, now the beneficiary of a brand spanking new three-year, $60 million contract.
Walker didn’t establish himself as a major league starter until he was almost 30; he spent the mid-2010s stuck behind Chris Davis, Freddie Freeman, Joey Votto, and Paul Goldschmidt, in that order. But since claiming the Diamondbacks’ first base job after Goldschmidt got traded, Walker has established himself as one of the most consistent players at the position. Over the past three seasons, he’s had wRC+ marks of 122, 119, and 119, and posted WAR totals of 3.9, 3.9, and 3.0. That downturn in 2024 was informed by an oblique strain that cost Walker the month of August. If he’d played 162 games, he would’ve been right back up around 3.9 WAR again.
The former South Carolina star is 33, a bit old for a big free agent signing, especially a first baseman, and even more especially a right-handed first baseman. But he’ll be a tremendous asset to the Astros, and sorely missed by the Diamondbacks. Read the rest of this entry »
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:
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.”
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 »
Seiya Suzuki has been in the news as a trade candidate all offseason — partially because the Cubs can’t stop shipping outfielders in and out — and at the Winter Meetings, his agent, Joel Wolfe, sprinkled some enlightening details into a massive throng of onlooking reporters. Cubs president of baseball operations Jed Hoyer and Wolfe have had conversations about the 30-year-old outfielder’s future. The Cubs aren’t desperate to trade a player who hit .283/.366/.482 in 2024, but Suzuki apparently isn’t particularly keen on being a full-time DH, which is the most natural landing spot for him after the Cubs traded for Kyle Tucker.
If the Cubs were to trade Suzuki, they’d have to have a pretty good idea of how valuable he is. In fact, they would have to have a firm belief in Suzuki’s value, and a good idea of the rosiest possible picture they could sell to a potential trade partner, as well as the difference between those two numbers. 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 Boston Red Sox.
Batters
I have mixed feelings about the Red Sox. As a baseball fan, it galls me to see Mookie Betts in another uniform, and their complacency these past few trade deadlines has been frustrating. Yes, it’s good that they extended Rafael Devers to a long-term deal, but more often than not their forays into free agency end up in the We Tried bin.
But at the same time, even though Boston isn’t throwing its weight around like most large-market teams, this is a highly competent organization that makes smart moves. The Red Sox have developed a significant number of players internally – and more are on the way – and they’ve put those players positions to succeed. I have to admit that Jarren Duran and Wilyer Abreu have become far better than I expected, and though the experiment of moving Ceddanne Rafaela back to shortstop in the majors didn’t really work out, his upside was worth the gamble.
If anything, the Red Sox now look a lot like a 2010s St. Louis Cardinals roster. Not a single player in the lineup is projected to be an MVP candidate – no, ZiPS is not that high on Duran – but by the same token, almost every player is projected to be average or better, with decent depth at most positions. Even at catcher, which is projected to be their worst position (now that they’ve traded Kyle Teel), the Red Sox should get an acceptable level of mediocrity.
ZiPS holds out hope for Rafaela being just good enough offensively for his glove to play, and his WAR projection is a full win higher in center field than it would be at shortstop. A Trevor Story revival would be nice, but ZiPS isn’t particularly taken with him these days, and David Hamilton actually has a similar projection. It’s not something they’d announce, but I suspect the Red Sox would be happy to see Marcelo Mayer seize the shortstop job soon. At second base, ZiPS thinks Kristian Campbell would be one of the most accomplished offensive players to debut in the majors in 2025. Campbell and Roman Anthony project to be Boston’s third- and fourth-best offensive players, respectively.
Pitchers
Naturally, ZiPS doesn’t expect Garrett Crochet to carry an ace’s workload, but if he throws only his projected 135 innings, he should still be the best member of Boston’s rotation, which also features Tanner Houck, Kutter Crawford, and Brayan Bello. That group looks like one of the better starting staffs in baseball, though it’s a tier below the elite rotations of the Phillies and Dodgers.
I’m higher than ZiPS on Crawford, but I think it’s right about Houck as a borderline ace and Bello as a solid no. 2 or 3 starter. ZiPS is a bit of concerned with how Lucas Giolito will perform coming back from internal brace surgery, but he and Richard Fitts both project as reasonable fifth starters. Quinn Priester and Garrett Whitlock also project to be decent fifth starters, but Whitlock is also returning from an internal brace procedure, and I expect the Red Sox will use him conservatively once he’s healthy. It seems likely that he’ll see more innings out of the bullpen than in the rotation next season.
The bullpen projects to be solid, though ZiPS doesn’t rank them quite as highly as Steamer does. There’s some natural skepticism about Michael Fulmer coming off injury, and ZiPS is down on Justin Wilson and Luis Guerrero. But after these players, the projections see pretty much everyone else as either good or very good — but not elite — even edge options like Zach Penrod or Priester.
Like those Cardinals teams, these Red Sox can’t do much upgrading unless they get a superstar to raise their ceiling. The big problem here is the Red Sox play in the AL East, not the NL Central. ZiPS projects them to finish with a win total in the mid-80s. That’d be good enough to contend for a playoff spot, but it probably won’t cut it if they want to win the division. Still, considering the Yankees are trying to figure out how to fill the Juan Soto-sized hole in their lineup and the Orioles could be without Corbin Burnes, the Red Sox have improved enough to make those two teams sweat at least a little bit.
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.
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.
