Below is an analysis of the prospects in the farm system of the Kansas City Royals. 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 »

Home teams don’t win enough in extra innings. It’s one of the most persistent mysteries of the last five years of baseball. Before the 2020 season, MLB changed the extra innings rules to start each half of each extra frame with a runner on second base. (This only occurs during the regular season, which means the 18-inning ALDS tilt between the Mariners and the Astros in the picture above didn’t actually feature zombie runners, but the shot was too good to pass up.) They did so to lessen the wear and tear on pitchers, and keep games to a manageable length. Almost certainly, though, they weren’t planning on diminishing home field advantage while they were at it.

In recent years, Rob Mains of Baseball Prospectus has extensively documented the plight of the home team. Connelly Doan measured the incidence of bunts in extra innings and compared the observed rate to a theoretical optimum. Earlier this month, Jay Jaffe dove into the details and noted that strikeouts and walks are a key point of difference between regulation frames and bonus baseball. These all explain the differing dynamics present in extras. But there’s one question I haven’t seen answered: How exactly does this work in practice? Are home teams scoring too little? Are away teams scoring too much? Do home teams play the situations improperly? I set out to answer these questions empirically, using all the data we have on extra innings, to get a sense of where theory and practice diverge.

The theory of extra inning scoring is relatively simple. I laid it out in 2020, and the math still works. You can take a run expectancy chart, start with a runner on second and no one out, and figure out how many runs teams score in that situation in general. If you want to get fancy, you can even find a distribution: how often they score one run, two runs, no runs, and so on. For example, I can tell you that from 2020 to 2025, excluding the ninth inning and extra innings, teams that put a runner on second base with no one out went on to score 0.99 runs per inning. Read the rest of this entry »

Way, way, way back in December 2023, the Rangers signed Tyler Mahle to a two-year contract. Absolutely nobody cared at the time, mostly because the news dropped the same day as Shohei Ohtani’s first press conference with the Dodgers. But also because Mahle, then recovering from Tommy John surgery, was expected to play a trivial part, at best, in the 2024 season.

Mahle had been a bit of a hipster favorite as an upper-mid-rotation starter in Cincinnati and then briefly in Minnesota — far from a household name, but from 2020 to 2022, he’d been quite good, and in high volume. Over those three seasons, he’d averaged 27 starts, 146 innings, and 3.0 WAR per 162 team games, with an ERA- of 90. For two years and $22 million, the Rangers were conceding that he’d rehab on their dime for most of 2024. But he would’ve been available for the 2024 playoffs if they’d made it that far, and if everything worked according to plan, they’d have a workhorse no. 3 starter under contract for 2025 at a fraction of what that kind of production usually goes for.

At least in 2025, everything has been working according to plan. Mahle has made 11 starts so far this season, with the past 10 lasting at least 80 pitches and five innings. Until his most recent outing, he hadn’t allowed more than two runs in any start. Even then, he allowed only three runs in his season-worst outlier. His 1.80 ERA is fifth best among all qualified starters. Read the rest of this entry »

Earlier this month, an article titled “Executive’s View: How Have Analytics Impacted a General Manager’s Job?” was published here at FanGraphs. Featured were Ross Atkins, Brian Cashman, Jerry Dipoto, and John Mozeliak, with the foursome sharing their perspectives on this ever-evolving aspect of their shared position.

Shortly after the piece ran, two people suggested David Stearns as a followup interview subject. That’s understandable. Now in his second season as the president of baseball operations for the New York Mets, the 40-year-old Ivy League product has two decades of experience within the industry, almost all of it in front offices.

A summer intern with the Pittsburgh Pirates prior to graduating from Harvard University in 2007, Stearns subsequently worked in MLB’s central office, then served as co-director of baseball operations with Cleveland, became an assistant general manager with the Houston Astros, and, in 2015, was hired by the Milwaukee Brewers as general manager. His data-driven approach was a common thread throughout. Moreover, he has remained true to his analytic bent since assuming his current position following the 2023 season.

Stearns was at Fenway Park this past week when the Red Sox hosted the team whose front office he now leads, so I took the opportunity to get his perspective on the subject at hand. Here is our conversation, lightly edited for clarity.

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David Laurila: I’ll start with the question I asked Atkins, Cashman, Dipoto, and Mozeliak: How has the continued growth of analytics impacted the job?

David Stearns: “Over the span of my career, we’ve been inundated with more and more sources of information — information sources that are increasingly granular in nature, increasingly have to do with the processes of playing baseball, and not necessarily the results or outcomes of playing baseball. Those lead towards more and more complex algorithms and models that require greater numbers of analysts, and really smart, creative people to have in a front office. So, one of the greatest changes is just the size of the departments within baseball. The information has grown to such a enormous extent that we need more and more people to manage the information. That’s the first thing that comes to my mind. And then we need to make all that information actionable.” Read the rest of this entry »

Spencer Schwellenbach had just two big-league games under his belt when he was featured here at FanGraphs early last June. The most recent of them had come a few days earlier at Fenway Park, where he’d allowed six runs and failed to get out of the fifth inning. Two starts into his career, the Atlanta Braves right-hander was 0-2 with an 8.38 ERA.

Those initial speed bumps quickly became a thing of the past. Schwellenbach allowed three runs over his next two outings, and by season’s end he had made 21 appearances and logged a 3.35 ERA and a 3.29 FIP. Counting this years’s 10 starts, the 24-year-old Saginaw, Michigan native has a 3.41 ERA and a 3.41 FIP over 185 innings. Moreover, he has a 23.5% strikeout rate and just a 4.7% walk rate. Relentlessly attacking the zone with a six-pitch mix, Schwellenbach has firmly established himself as a cog in Atlanta’s rotation.

On the eve of his returning to the mound in Boston last Sunday, I asked the 2021 second-round pick out of the University of Nebraska what has changed in the 11-plus months since we first spoke.

“Honestly, when we talked last year I was just throwing the ball to the catcher,” claimed Schwellenbach, who was a shortstop in his first two collegiate seasons and then a shortstop/closer as a junior. “It was really only my second year as just a pitcher, so I was very young-minded with how I pitched. Now that I’ve got 30 or so starts, I have an idea of what I’m trying to do out there. Being around guys like Max Fried, Charlie Morton, and Chris Sale last year was obviously big, too. I learned a lot from them, as well as from [pitching coach] Rick Kranitz.”

Morton, who is now with the Baltimore Orioles, helped him improve the quality of his curveball. Their mid-season conversation was the genesis of a more efficient grip. Read the rest of this entry »

He doesn’t look like he’s riding a horse so much as he looks like he’s pretending to ride a horse. I have been thinking about it for a while now, and that is as well as I can describe the way Jacob Wilson gets ready for the pitch. He looks like he’s pretending to ride a horse. I say this with love.

Baseball is hard. The ball is small and very dense. A big, strong man stands not very far away and repeatedly throws it pretty much right at you with a great deal of force. The ball performs all sorts of twists and turns on its short journey toward almost breaking your fragile human body, and not only are you expected to not run away, you’re expected to hit it with a stick. So if the only way that you can manage to do all that is by pretending to ride a horse for a few seconds while you’re waiting for the missile to be launched, then by all means, pretend to ride a horse for a few seconds:

(I started writing this article the day after the Statcast team released all their new fancy bat tracking information, including wireframe models of every player’s average swing. Naturally, I offered MLB.com’s Mike Petriello $20 to find me a few seconds of wireframe footage of Wilson doing his bouncy pre-pitch routine. He declined like a principled jerk, even after I upped the offer to $23.) Read the rest of this entry »

Yesterday, I wrote an introduction to Statcast’s latest round of bat tracking metrics. MLB.com’s Mike Petriello wrote a real primer, so I tried to build on that by analyzing how the different metrics work together using a couple common pitch types. We’re still figuring out how to use these new toys, but today I’d like to explain how my first dive into the bat tracking metrics led me to one particular player who is doing something weird, which led me to learn something small about the way swings work. After all, that’s why we’re here exploring all these strange new numbers in the first place.

In my first shot at playing with the metrics, I tried to establish something simple. I pulled the overall bat tracking data for all qualified players, and I focused on Attack Direction, which tells you the horizontal angle of the bat at the moment of contact (or, in the case of a whiff, at the moment when the bat is closest to the ball). That seemed pretty straightforward to me. As with most bat tracking metrics, it’s also a timing and location metric. You generally need to meet inside pitches further out in front of home plate. If you’re behind the pitch, your bat will be angled toward the opposite field, and you won’t pull the ball. If you’re out in front of the pitch, your bat will be angled toward the pull side, and you’ll pull it. A player’s average Attack Direction should correlate pretty well with their pull rate, and the numbers pretty much bear that out. Attack Direction and pull rate have a .60 correlation coefficient:

Most of the dots are clustered around that very clear trendline. Players who pull the ball more tend to have their bats angled toward the pull side just as you’d expect. What interested me was that green dot way at the bottom. It belongs to Leody Taveras. I guess it is Leody Taveras, if we really believe in our graphs, which we probably should at this particular website.

Taveras has a moderately low Attack Direction, four degrees to the pull side, but he’s got the third-lowest pull rate of any player on this chart. I couldn’t help wondering how exactly he was doing that. Before I dug into it too deeply, I was reminded that the fact that he’s a switch-hitter might have something to do with it. So I pulled the data again, this time separating out all players by handedness. On the chart below, switch-hitters will appear twice:

The correlation isn’t quite as strong, because switch-hitters are now broken into two different players with two smaller samples (that’s how small-sample right-handed Patrick Bailey got way down at the bottom). But there are two green dots now! And they’re both Leody Taveras! From both sides of the plate, Taveras looks like he should have a pull rate that’s a bit above average, and instead has one of the very lowest pull rates in the game. At this point, I was officially curious, so I started poking around.

First, I specifically looked at Attack Direction on balls hit to the opposite field. Since the start of bat tracking midway through the 2023 season, when Taveras hits the ball the other way, his average Attack Direction is three degrees toward the opposite field. Only four players in baseball have an average Attack Direction that’s less oriented toward the opposite field. Oddly, they’re all sluggers. Salvador Perez, Yordan Alvarez, Aaron Judge, and José Ramírez are all at two degrees toward the opposite field, and Austin Riley is tied with Taveras at three degrees. Taveras is definitely not a slugger. He could not be more different from these five guys. So not only is he doing something way different from most hitters when he goes the opposite way, but the only players out on that ledge with him have completely different swings than he does. There really is something weird about him.

Next, I tried looking specifically for balls hit to the opposite field even though the bat was angled toward the pull side at the moment. Just 21% of balls hit to the opposite field have the bat angled toward the pull side at all. I ran a Baseball Savant search, setting the minimum Attack Direction at seven degrees toward the pull side. Since bat tracking started, 5.5% of Taveras’s batted balls have fallen into this category. Among the 375 players with at least 200 BIP over that period, that’s the 11th-highest rate. Elehuris Montero is the champion at a shocking 10.5%, but no player who has put as many balls in play as Taveras has run as high a rate as he has.

At that point, I decided to look at individual balls that fell into this category: balls that go to the opposite field even though the bat is angled toward the pull side at contact. How exactly does this happen? Try to picture it in your mind. If the bat is angled toward the pull side, and it’s being swung in that direction anyway, how does the ball end up going in the opposite direction? There are two main answers. Here’s the less common way:

That’s Taveras way, way out in front of a curveball, hitting it off the very end of his bat. He cued it up so perfectly that if the end of his bat were cupped, the ball might have just gotten stuck in there. So that’s one way to do it. In fact, 21% of the balls we’re looking at, hit to the opposite field even though the Attack Direction is seven or more degrees to the pull side, are squibbers hit off the end of the bat below 80 mph. That’s one way to do it.

The other way is much more common, and it looks like this:

Of those same balls, hit to the opposite field even though the Attack Direction is seven or more degrees to the pull side, 50% are classified as popups, and 65% have a launch angle above 38 degrees. Basically, when you hit a ball in that weird manner, it’s almost always going to be either a cue shot or a popup. Leody Taveras taught me that.

This has a lot to do with Attack Angle. If your bat were perfectly parallel to the ground, but angled toward the pull side, it would be pretty much impossible to hit the ball the other way. But when you pop the ball up, you’re not hitting it flush. You’re getting under it. And regardless of the situation, your bat is almost never parallel to the ground. According to Statcast, the bat is angled downward on more than 80% of swings. If you just look at popups, that number is up above 90%. About half of popups come on four-seamers and cutters, where the batter has trouble catching up and swings just under the pitch. The rest come on softer stuff, and those pitches are usually low in the zone. I need you to do some 3D visualization in your head here, because my diagram is not very good:

On the left is a perfectly level bat, parallel with the front of the plate. Now imagine you’re angling your bat downward and you get just underneath the ball. If your bat is angled toward the opposite field or, as in the middle example, straight toward center field, you’ll likely just foul the ball off behind you or into the opposite field stands. Once you angle it toward the pull side, however, it can stay fair, bouncing up and toward the opposite field. Please imagine that the bat on the right looks so funky because it’s foreshortened, pointed out toward the first baseman. Taveras can show us what that looks like in the real world:

If his Attack Direction were zero, he would’ve fouled the ball up and into the stands down the third base line. He only kept it fair because of his Attack Direction of 18 degrees.

Look, I don’t have a big takeaway here. I just think this interesting. I think it highlights the way that the angle of the bat informs even the most mundane batted balls. If you’d asked me yesterday whether it’s possible to go the opposite way while your bat was angled toward the pull side, I would’ve had to think about it, but my first reaction would’ve been to say no. The bat and ball move through space so quickly that they can be hard to track, but the bat tracking metrics help explain why exactly Taveras pops out so very, very often, and how it’s even possible to hit a ball like that in the first place.

Aaron Nola is having a truly awful season: Through nine starts, he’s 1-7 with a 6.16 ERA, which is bad for any pitcher. For the putative no. 2 starter on a big-market team whose fans are getting pretty tetchy about not having won a World Series in a while, it’s disastrous. Especially when said pitcher is in year two of a seven-year, $172 million contract. In fact, you’d have to say Nola has been surpassed in the pecking order by Cristopher Sánchez at the very least, and possibly by newcomer Jesús Luzardo.

Everyone’s got their theories as to what’s gone wrong. Davy Andrews tried to figure out Nola’s deal last month. Timothy Jackson of Baseball Prospectus speculated earlier this week that there’s something off with his fastball, and that lefty-heavy opposing lineups might be to blame. The Phillies, for their part, just put Nola on the (non-COVID) IL for the first time in almost eight years. The stated reason is an ankle injury Nola says is messing up his mechanics, but a player in a slump this bad can almost always use some time off to clear his head as well. Read the rest of this entry »

Below is an analysis of the prospects in the farm system of the Cleveland Guardians. Scouting reports were compiled with information provided by industry sources as well as my 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 »

At one point or another, most of us have done the thing where we go to the refrigerator in search of a snack, decide nothing looks appealing, close the door, then come back 15 minutes later to check again and somehow feel annoyed when the contents remain unchanged. It’s a near-universal experience despite the illogical nature of the whole thing. And when we relate this experience to others, it’s always the refrigerator, even though we could just as easily choose to re-check a cabinet or the pantry. But I think this is where we do get some credit for being slightly logical. The contents of a refrigerator are far more transient than the dry and canned goods stored elsewhere in the kitchen. The fridge is where we keep the perishables, the food that by definition isn’t meant to last long. Food in the refrigerator comes and goes, rots and gets tossed, all at a much faster rate than elsewhere in the kitchen.

Park factors work a little like a refrigerator. They present a single value that contains within it the influence of several different components that vary from park to park, much in the way my refrigerator is two-thirds beverages and cheese, while yours probably has fruits and veggies and maybe some leftover ham from Easter that you should definitely throw away. Some of the components captured by park factors are static and easily measured, like surface dimensions and wall height. They’re the condiments that remain consistently stocked in the fridge door.

But sometimes you throw open the door to a park’s refrigerator and get whacked in the face with a stench of unknown origin. And that stench becomes all the more potent as it mingles with a to-go box of leftover Thai and a carton of milk growing more questionable by the day. Likewise, wind speeds, the daily dew point, and the angle of the sun at different points relative to the solstice all fluctuate and interact in a way that a scientist with the right expertise could tease out and quantify, but that remain a bit fuzzy to the casual observer.

It was these squishier components of park factors, the ones that ebb and flow as weather cycles in and out and the seasons change, that sparked my curiosity about how park factors might vary over the course of such a long season. Traditionally, park factors are calculated over multiple full seasons of data (though sometimes single-season park factors are useful for capturing more recent trends), and that’s not just a sample size consideration. A full season of data is needed to ensure a balanced schedule where every opponent faced on the road is also faced at home and vice versa. This ensures that when comparing runs per game at home to runs per game on the road, the team quality is consistent in both subsets. Read the rest of this entry »