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.”
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.
———
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.”
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 »
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 »
Overview:
The Los Angeles Angels are seeking an Analyst to join the Baseball Operations’ Research & Development team. This position will focus on analyzing baseball-related data and researching baseball topics to help inform decisions. The ideal candidate has a strong background of technical skills with an understanding of baseball research concepts and modern gameplay and development strategies.
This position is also benefit-eligible including: medical, dental and vision insurance, 401K eligibility; employee contributions after 3 months, employer matching and safe harbor after 1 year and 1000 hours of employment and additional perks not listed above. The expected salary for this position can range from $80,000-$90,000. Final offers for this role will be made within the parameters of the salary range provided. Years of experience, skills, and other factors are considered when determining the salary offered.
Responsibilities:
Assist in creating and improving models to help forecast various areas of baseball
Write code and implement systems that increase the efficiency of the Baseball Operations department
Perform ad-hoc research projects as requested and present results in a concise manner
Required Qualifications:
Intellectual curiosity and a desire to learn and grow as an analyst and member of a baseball operations team
Strong foundation in the application of statistical concepts to baseball data and the translation of data into actionable baseball recommendations
Ability to communicate concepts to individuals with diverse baseball backgrounds, including coaches, scouts and executives
Strong capabilities in R and/or Python
Familiarity with popular data science and visualization libraries such as tidyverse, pandas, scikit-learn, xgboost, and others
Proficiency in or clear ability to learn SQL
Ability to work flexible hours including evenings, weekends and holidays as dictated by the baseball calendar
Preferred Qualifications:
Demonstrable independent baseball research
Bachelor’s degree in Mathematics, Statistics, Computer Science, Economics or equivalent experience
Ability to relocate to Anaheim, CA strongly preferred
Physical Demands:
Ability to frequently sit for extended periods of time
Ability to occasionally work in inclement weather (when in stadium)
Ability to traverse from office to stadium frequently
Ability to occasionally lift up to 20 lbs.
The above statements are intended to describe the general nature and level of work being performed by individuals assigned to this position. They are not intended to be an exhaustive list of all duties, responsibilities, and skills required of personnel so classified.
The Angels believe that diversity contributes to a more enriched collective perspective and a better decision-making process. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status, or any other characteristic protected by law.
Ben Lindbergh and Meg Rowley banter about the Cody Bellinger trade, the Yankees’ and Cubs’ next moves, Guaranteed Rate Field changing to Rate Field, and the results of MLB’s inquiry into pitcher injuries. Then (31:39) they bring back A’s slugger Brent Rooker to discuss his monster 2024, his work as a World Series correspondent, misconceptions about hitting, how Sutter Health Park will play, leaving the Coliseum, his future with the A’s, unreported injuries, ballplayer parental leave, his favorite meals of the year, learning chess, his 2025 goals, and much more.
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.
Russell Martin was sneaky good. At the plate he combined a compact swing and mid-range power with strong on-base skills and (early in his career, at least) the ability to steal the occasional base. Behind the plate, he was exceptional. Shifted from third base after his first professional season, he took to the new position with the zeal of a convert. Martin combined outstanding athleticism — a strong arm, extraordinary lateral mobility, and elite pitch framing — with an intense competitive drive, an off-the-charts baseball IQ, and a natural leadership ability that was already apparent during his 2006 rookie season with the Dodgers.
The 23-year-old Martin’s arrival went a long way toward turning that squad around. In his first four seasons, he helped the Dodgers to three playoff appearances, including their first two trips to the National League Championship Series since their 1988 championship run. When the tight-fisted team nonsensically non-tendered him after an injury-wracked 2010 season, Los Angeles missed the playoffs in each of the next two years. Meanwhile, the nomadic Martin helped spur his subsequent teams — the Yankees (2011–12), Pirates (2013–14), and Blue Jays (2015–18) — to a total of six straight postseasons.
That wasn’t a coincidence. The general managers of those three teams (New York’s Brian Cashman, Pittsburgh’s Neal Huntington, and Toronto’s Alex Anthopoulos) all recognized that in addition to the softer factors that made Martin such a great catcher and leader, he was consistently among the game’s best at the newly quantifiable and highly valuable art of turning borderline pitches into strikes — an area that landed in the public spotlight with Mike Fast’s 2011 Baseball Prospectus article, “Removing the Mask.” Building on previous research by Dan Turkenkopf and others using PITCHf/x data, Fast showed that the difference between a good framer and a bad one could amount to something on the order of four wins per year, and identified Martin as having accrued more value via framing over the 2007–11 span (71 runs) than any backstop besides Jose Molina. Read the rest of this entry »
Junfu Han, Kim Klement Neitzel, Junfu Han, and Matt Krohn via Imagn
The Detroit Tigers and the Minnesota Twins were two of the teams I focused on during last week’s Winter Meetings in Dallas. As such, I attended media sessions for the managers and top executives of both clubs, asking questions alongside reporters who cover the AL Central rivals on a regular basis. Here are some highlights from those exchanges.
“That’s a good question,” Minnesota’s president of baseball operations said when asked about the possibility of Jax, who logged a 1.94 FIP over 71 innings out of the Twins’ bullpen, becoming a starter. “It’s a conversation we had during the season [and] it carried through to the offseason. It’s a two-way dialogue. Griff has expressed some interest in exploring the idea, but at the same time, he wants to think about what the right next steps are for him and his career. We remain in contact with his agent, and with Griffin, about that… It remains to be determined.” Read the rest of this entry »
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