Xavier Isaac’s game is built around damage. No. 98 on our recently-released Top 100, the 21-year-old, left-handed-hitting Tampa Bay Rays prospect has, according to our lead prospect analyst Eric Longenhagen, “some of the most exciting power in pro baseball.” Getting to it consistently will be his biggest challenge going forward. As Longenhagen also wrote in his report, “By the end of the season, [Isaac] had a sub-60% contact rate, which is not viable at the big league level… [but] if “he can get back to being a nearly 70% contact hitter, he’s going to be a monster.”

While Isaac’s 143 wRC+ between High-A Bowling Green and Double-A Montgomery was impressive, his 33.3% strikeout rate was another story. The built-to-bash first baseman knows that cutting down on his Ks will go a long way toward his living up to his lofty potential. At the same time, he’s wary of straying too far from his strengths.

“I’ve tuned up my power, and now I need to get my contact up a little bit more,” Isaac told me during the Arizona Fall League season. “It’s like a tradeoff, kind of. I’m going to strike out, but I’m also going to hit the ball a little harder. I have a lot of power, so some of it is about going up there and taking a risk. I obviously don’t want to strike out — I‘m trying to put it in play — but I also don’t want to be making soft contact.”

That’s seldom a problem when he squares up a baseball. Not only does his bat produce high exit velocities, he knows what it feels like to propel a pitch 450-plus feet. He doesn’t shy way from the power-hitter label. Asked if that’s what he is, his response was, “For sure.”

That Isaac’s bombs often go to the gaps, particularly to right-center, is by design. Read the rest of this entry »

Nobody wants to throw a backup slider. They are, definitionally, an accident. But announcers and analysts alike have noted that these unintentional inside sliders — perhaps due to their surprise factor — tend not to get hit. In 2021, Owen McGrattan found that backup sliders, defined as sliders thrown inside and toward the middle of the strike zone, perform surprisingly well.

I’ll add one additional reason these pitches are effective: They move more than any other slider.

I analyzed over 33,000 sweepers thrown by right-handed pitchers in the 2024 season. I found a clear linear relationship between the horizontal release angle of a sweeper and the horizontal acceleration, better understood as the break of the pitch. On average, as the horizontal release angle points further toward the pitcher’s arm side, the pitch is thrown with more horizontal movement.

Josh Hejka, a pitcher in the Philadelphia Phillies minor league system, told me these results corresponded with his anecdotal experience.

“I’ve often noticed — whether in game or in the bullpen — that the sliders I throw arm side tend to actually have the best shape,” Hejka said. “I believe it’s conventional wisdom across baseball that the backup sliders tend to actually be the nastiest.”

Check out all the movement Corbin Burnes gets on this backup slider from last season.

The relationship between horizontal release angle and movement also holds true for sinkers. When a sinker is aimed further to the glove side — for pitchers facing same-handed hitters, this would be a backdoor sinker — the pitch gets, on average, more horizontal movement, as is the case with this pitch from Anthony Bender.

The explanation for the relationship is straightforward enough. When sweepers are thrown to the arm side and sinkers are thrown to the glove side, the pitcher’s grip is such that maximum force is applied to the side of the baseball, allowing for more sidespin. In a 2015 interview with David Laurila, then-Royals pitching coach Dave Eiland described why sliders back up.

“They really get around it; they don’t get over the top and pull down,” Eiland said. “It’s unintentional, more of a misfire, so to speak. If you could do that intentionally, you’d have a decent pitch.”

It isn’t just sweepers and sinkers that show a relationship between release angles and movement. Back in August, I investigated the mystery of the invisible fastball. Why was a pitch like Shota Imanaga’s fastball, with its elite vertical movement and flat approach angle, so rare? I found that vertical release angles mediate the relationship between both variables. A fastball thrown with a flatter release angle gets less backspin, and so to achieve both requires outlier mechanical skills.

Release angles don’t just measure the nature of a grip, they also dictate the location of the pitch. I conclude that where the pitcher aims a pitch changes the way it moves. For fastballs, pulling down on the ball allows for more backspin. For sweepers and sinkers, getting around the ball allows for more sidespin. Analysts attempt to separate “stuff” from “location;” these findings complicate that conversation.

***

Before we go any further, it’s important to know what exactly is a release angle. Release angles measure — or, in this case, approximate — the angle at which the ball comes out of the pitcher’s hand. For vertical release angles, anything above zero degrees suggests the ball is pointing upward at release; most vertical release angles, particularly for four-seam fastballs, are negative, meaning that the pitcher is aiming the ball downward at release.

Horizontal angles work the same way, but in the x-dimension. Positive values mean the ball is pointed toward the pitcher’s left; negative values point toward the pitcher’s right. (This is a feature of the original Pitch F/X coordinate system, when it was determined that x-dimension pointed to the catcher’s right.) In any case, release angles, both horizontal and vertical, attempt to capture the exact position of the ball at release. Because they capture the position of the ball at release, they contain information about the pitcher’s aim and, it turns out, the force they’re applying to the ball.

My research finds that there is a relationship between horizontal release angles and horizontal acceleration. In simpler terms, the way the ball is released out of the hand, and therefore where it is aimed, impacts the movement of the pitch.

There are some confounding variables in this specific relationship. The Hawkeye cameras (and, in earlier times, the Pitch F/X technology) report accelerations in three dimensions. These accelerations are measured relative to a fixed point on the field, which happens to be right in front of home plate. Because these accelerations are fixed to one point, the reported values can be biased by the position of release in space. This is far from intuitive, so it might be helpful to consider an example.

Remember that Burnes sweeper from the introduction? It accelerated at roughly 16 feet per second squared in the x-dimension. Imagine that instead of throwing his sweeper from the mound, Burnes threw it from the third base dugout. It’s the exact same pitch as before — same velocity, same horizontal break — but the release point has completely changed. On a fixed global coordinate system of movement measurement, the acceleration in the x-dimension no longer describes the pitch’s relevant movement; all that sideways movement would instead be measured in the y-dimension.

This is an extreme example to illustrate the point, but on a smaller scale, this fixed point measurement system biases acceleration measurements. In order to fix this bias, accelerations can be recalculated to be relative to the pitch’s original trajectory, removing the influence of the release point on the acceleration value. These calculations come courtesy of Alan Nathan; Josh Hejka rewrote them as Python code, making my job easy.

A slight nuance:

The accelerations given by MLB (ax, ay, az) are biased by pitch location.

To make these values location-agnostic, we need to adjust the acceleration vector to be relative to the initial trajectory (i.e. the initial velocity vector vx0, vy0, vz0).

?: https://t.co/CVVNKtG3YM

— Josh Hejka (@hedgertronic) November 14, 2024

Even after accounting for these confounding variables, the relationship between release angles and movement is still present. As the plot shows, it isn’t a particularly strong relationship — when modeled, a two-degree change in horizontal release angle is associated with roughly a foot per second increase in transverse acceleration. But while the relationship is not as strong as that between four-seam fastballs and vertical release angle, it is nonetheless meaningful.

Alternatively, the relationship can be measured using good old-fashioned “pfx_x,” or horizontal movement, which is also measured relative to the pitch’s original trajectory. Why go through all this effort to transform the accelerations? For one thing, I had a good time. And also, isn’t it fun to imagine Burnes throwing sweepers from the dugout?

The plot of horizontal location and horizontal movement, with each pitch colored by its horizontal release angle, illuminates the ostensible lack of a relationship between pitch location — measured by “plate_x” on the plot below — and movement. Draw your attention to the patch of dark blue dots around the -2 line of the x-axis. There are two potential ways for a sweeper to end up two feet off the plate inside. It can be thrown with a horizontal release angle around zero and little sideways movement, or it can be thrown with a negative horizontal release angle and lots of sideways movement.

The same relationship holds true for horizontal release angles and two-seam fastballs after the aforementioned adjustments.

On the individual pitcher level, the relationship is slightly weaker; on average, the r-squared is roughly 0.04 for sweepers, with variation between pitchers on the strength of this relationship. Zack Wheeler’s sweeper movement, for example, appears to be particularly sensitive to release angles:

***

Ultimately, analysts attempt to separate “stuff,” defined as the inherent quality of a pitch, from “location,” defined as where the pitch ends up. But what this research suggests is that, to some degree, these two qualities are inseparable. (I wrote about this a bit on my Pitch Plots Substack last September.) Certain pitches generate their movement profiles because of where they’re aimed out of the hand.

These findings naturally lead to deeper questions about the interaction between biomechanics and pitch movement. While there are variables (arm angle, release height, etc.) that are commonly understood to influence movement, these findings suggest that there are even more granular factors to explore.

Is the angle of the elbow flexion at maximum external rotation the most influential variable? Is it hip-shoulder separation? Torso anterior tilt? Pelvis rotation at foot plant? How much do each of these components contribute to pitch shapes?

Thanks to data from Driveline’s OpenBiomechanics Project, it’s easy to model the relationship between dozens of biomechanical variables and the velocity of the pitch. There are about 400 pitches in the database; by attaching markers to a pitcher moving through space, points of interest can be calculated and then compared to the pitch’s velocity.

In this public dataset, Driveline does not provide the movement characteristics of the pitch. But if the force applied to the ball based on the direction of its aim affects the movement of the pitch, it follows that these variables could be measured in a detailed manner. On the team side, KinaTrax outputs provide the markerless version of these data, providing a sample of hundreds of thousands of pitches from a major league population. Imagine the possibilities.

Correction: A previous version of this article misstated the units of acceleration of Burnes’ backup sweeper. It is feet per second squared, not feet per second.

Editor’s Note: An updated version of the Top 100, which incorporates Eric’s spring looks through the end of March, is available to read here. As always, full scouting reports and tool grades for every ranked prospect can be found on The Board.

Below is my list of the top 100 prospects in baseball. The scouting summaries were compiled with information provided by available data and my own observations. 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 I use that as a rule of thumb.

All of the prospects below also appear on The Board, a resource the site offers featuring sortable scouting information for every organization. It has more details than this article and integrates every team’s list so readers can compare prospects across farm systems. It can be found here.

And now, a few important things to keep in mind as you’re perusing the Top 100. You’ll note that prospects are ranked by number but also lie within tiers demarcated by their Future Value grades. The FV grade is more important than the ordinal ranking. For example, the gap between Sebastian Walcott (no. 4) and Quinn Mathews (no. 32) is 28 spots, and there’s a substantial difference in talent between them. The gap between Chase Petty (no. 42) and Cam Smith (no. 70), meanwhile, is also 28 numerical places, but the difference in talent is relatively small. Read the rest of this entry »

Today is the first day of the 2025 college baseball season, and to celebrate, we’re cutting the ribbon on our 2025 Draft prospect rankings and scouting reports. They’re now live on The Board, so head over there for all these players’ tool grades and reports. In this piece, I’ll touch on several individual players who I think are among this year’s best and most interesting prospects, and discuss the class as a whole from a talent standpoint, as well as which teams are in position to have a huge draft.

First, some quick housekeeping on the rankings. I’ve got just shy of 100 players on The Board right now. I’ve hard-ranked the players with a 40+ FV and above, while the 40 FV players are clustered by demographic below them. At this stage in the draft process, players are more in “neighborhoods” or clusters. It’s too early to have hundreds of players ordinally ranked, because the deeper you go, the more those rankings will change between now and draft day. On this update, I’ve tried to include players who have the best chance to take a leap during this season and climb The Board. This is definitely a ceiling-heavy list at this stage, in part because so many of the higher-floored players tend to reveal themselves during the college season. New prospect contributor David Gerth, whose debut piece will run later today, helped produce the reports on the players in the Big Ten conference. Obviously, there will be much more to come in the next few months as guys separate themselves from their peers, and new standouts emerge. Read the rest of this entry »

It’s Valentine’s Day, and instead of being out on the town with your beloved, you’re sitting on the sofa bingewatching the latest installment of a streaming entertainment institution. Not the new season of Love is Blind; the new season of college baseball.

Baseball is like football and basketball, in that a large part of the appeal of the college game is its abundance. Not every game is worth watching, but with some 300 Division I schools to choose from, there’s a good chance that somewhere out there, there’s a close game in the bottom of the ninth, or a pitchers’ duel between top prospects, or a rivalry matchup with postseason implications. It’s borderline-impossible to remember the names of all 300 teams, much less any useful information about them. So in the interest of efficiency, here are seven schools I’ll have my eye on this season, because I think they’ll have an outsize influence on the shape of this season as a whole.

Oregon State
I’m not going to say this is the most excited I’ve ever been for a college team, ever. But it’s the most excited I’ve been for a college team without multiple contenders for the no. 1 overall pick, like the Kumar Rocker/Jack Leiter Vandy team, or Paul Skenes and Dylan Crews at LSU. Read the rest of this entry »

Now that the dust has settled on teams’ pursuit of Roki Sasaki, and clubs have signed most of their 2025 international prospects, it is time to turn our attention to the international pros whose 2025 seasons will soon get underway and to the tippy top of the 2026 international amateur class. All of my top 2025 international prospects have now signed. Twins outfielder Carlos Taveras was the last from that group to put pen to paper, signing a couple of days ago for a shade over $1 million. The players and rankings from that class have been archived on their own page of The Board, including the couple of Japanese pros who came over from NPB this offseason. Remaining on the active International Players page (which you’re going to want to open in a new tab) are the foreign pros I think readers should know about and follow for this season and beyond, as well as a couple of amateur players from the upcoming 2026 class (more on those lads in a few paragraphs). Read the rest of this entry »

The Seattle Mariners currently have one of baseball’s best farm systems, and its strength differs markedly from that of the big league roster. Pitching-rich at the major league level, it’s Mariners position player prospects who populate the top tier of our rankings. That’s welcome news — at least on paper — for a Seattle team that has recently excelled at keeping runs off the board, but has too often struggled to score.

Justin Toole is front and center in the organization’s quest to graduate productive bats into the parent club’s lineup. Brought on as director of player development following the 2022 season, the 38-year-old Council Bluffs, Iowa native has both the background and the acumen to help make that happen. Prior to coming to Seattle, Toole played seven professional seasons, then served four years as a minor league hitting coach, followed by three as a major league hitting analyst. All of his pre-Mariners experience came with Cleveland.

Toole discussed several of the system’s most promising prospects prior to heading to Arizona for the start of spring training.

———

David Laurila: What is the current strength of the system?

Justin Toole: “From a player development standpoint, I think the strength is the individuality with how we handle our players. When we get people into our system, we figure out their strengths, we figure out their weaknesses, we help them understand their identity. We work with our players to get a feel for where they think they are, and where they want to go.

“Our group has done an unbelievable job of creating good player plans that are clear, that are are easy to follow. They’re simple. I think that’s kind of been the strength of our player development group. Of course, any good player development group is going to be good because of the scouting group. They bring in good players, players that fit what we want to do, and who we want to be.” Read the rest of this entry »

I was hired as FanGraphs’ Lead Prospect Analyst just after the 2016 draft and took my first run at evaluating the entirety of the minor leagues on my own the following winter. Enough time has now passed that many of the players from that era of prospecting have had big league careers unfold (or not). Hindsight allows me to have a pretty definitive idea of whether my call on a player was right or wrong in a binary sense, and gauge any gap that may exist between my evaluation and what the player ultimately became. Looking back allows me to assess my approach to grading and ranking players so that I might begin to establish some baselines of self-assessment and see how I perform compared to my peers at other publications. Last offseason, I began compiling the various Top 100 prospect rankings from seven years ago for the purposes of such a self-assessment, an exercise that culminated in the “How’s My Driving?” piece that ran during Prospect Week 2024. This winter, I turned my attention to the 2018 Top 100, which I co-authored with Kiley McDaniel. Below are the results of that audit and my thoughts on them.

Before we get to a couple of big, fun tables and my notes, I want to quickly go over why I’ve taken the approach I have here and discuss its flaws. There are absolutely deeper avenues of retrospective analysis that can be done with prospect lists than what I have attempted below, approaches that could educate us about prospects themselves, and probably also about prospect writers. (Last year, in the first edition of this piece, I proposed a few such potential methods of evaluation and included my thoughts on their limitations. For the sake of brevity, I’ve cut that discussion from this year’s edition, but if you’re curious about that stuff specifically, you’ll want to go back and read the paragraph that begins, “Eventually, someone could pool the lists…”) Read the rest of this entry »

Hello, and welcome to Prospect Week! (Well, closer to Prospect Fortnight — as you can probably tell from the navigation widget above, the fun will continue well into next week, including the launch of our Top 100.) I’m not your regular host – that’d be Eric Longenhagen – but not to worry, you’ll get all the Eric you can handle as he and the team break down all things minor leagues, college baseball, and MLB draft. I’m just here to set the stage, and in support of that goal, I have some research to present on prospect grades and eventual major league equivalency.

When reading coverage of the minor leagues, I often find myself wondering what it all means. The Future Value scale does a great job of capturing the essence of a prospect in a single number, but it doesn’t translate neatly to what you see when you watch a big league game. Craig Edwards previously investigated how prospect grades have translated into surplus value, but I wanted to update things from an on-field value perspective. Rather than look at what it would cost to replace prospect production in free agency, I decided to measure the distribution in potential outcomes at each Future Value tier.

To do that, I first gathered my data. I took our prospect lists from four seasons, 2019-22, and looked at all of the prospects with a grade of 45 FV or higher. I separated them into two groups — hitters and pitchers — then took projections for every player in baseball three years down the line. For example, I paired the 2019 prospect list with 2022 projections and the 2022 prospect list with 2025 projections. In this way, I came up with a future expectation for each player.

I chose to use projections for one key reason: They let us get to an answer more quickly. In Craig’s previous study, he looked at results over the next nine years of major league play. I don’t have that kind of time – I’m trying to use recent prospect grades to get at the way our team analyzes the game today. If I used that methodology, the last year of prospect lists I could use would be 2015, in Kiley McDaniel’s first term as FanGraphs’ prospect analyst.

Another benefit of using projections is that they’re naturally resistant to the sample-size-related issues that always crop up in exercises like this. A few injuries, one weird season, a relatively small prospect cohort, and you could be looking at some strange results. Should we knock a prospect if his playing time got blocked, or if his team gamed his service time? I don’t think so, and projections let us ignore all that. I normalized all batters to a 600 plate appearance projection and all pitchers to a 200 innings pitched projection.

I decided to break future outcomes down into tiers. More specifically, I grouped WAR outcomes as follows. I counted everything below 0.5 WAR per season as a “washout,” including those players who didn’t have major league projections three years later. Given that we project pretty much everyone, that’s mostly players who had either officially retired or never appeared in full-season ball. I graded results between 0.5 and 1.5 WAR as “backup.” I classified seasons between 1.5 and 2.5 WAR as “regular,” as in a major league regular. Finally, 2.5-4 WAR merited an “above average” mark, while 4-plus WAR got a grade of “star.” You could set these breakpoints differently without too much argument from me; they’re just a convenient way of showing the distribution. There’s nothing particularly magical about the cutoff lines, but you have to pick something to display the data, and a simple average of WAR projections probably isn’t right.

With that said, let’s get to the results. My sample included 685 hitters from 45-80 FV. Allowing for some noise at the top end due to small sample size, the distribution looks exactly like you’d hope:

Consider the 55 FV line for an explanation. Of the players we graded as 55 FV prospects, 17% look washed three years later – Jeter Downs, a 2020 55 FV, for example. Another 17% have proven to be backup-caliber, like 2022 55 FV Curtis Mead, or 2019 55 FV Taylor Trammell if you don’t think Mead’s trajectory is set just yet. Continuing down the line, 30% look like big league regulars – 2021 55 FV Alek Thomas, perhaps. A full 31% appear to be above-average major league contributors three years later, like 2019 55 FV Sean Murphy or 2021 55 FV Royce Lewis. Finally, 6% project as stars three years later – Jackson Merrill, a 55 FV in 2022, feels appropriate as an example.

Two things immediately jump out to me when looking at this data. First, the “above average” and “star” columns increase at every tier break, and the “washout” column decreases at every tier break. In other words, the better a player’s grade, the more likely they are to be excellent, while the worse their grade, the more likely they are to bust. That’s a great sign for the reliability of our grades; they’re doing what they purport to do, essentially.

Second, each row feels logically consistent. The 45 FV prospects are most likely to bust, next-most-likely to end up as backups, and so on. The 45+ FVs look like the 45 FVs, only with a better top end; their chances of ending up above average are meaningfully better. The 50 FVs are a grab bag; their outcomes vary widely, and plenty of those outcomes involve being a viable major leaguer. By the time you hit the 55 and 60 FV prospects, you’re looking at players who end up as above-average contributors a lot of the time. The gap between 55 and 60 seems clear, too; the 60 FVs are far more likely to turn into stars, more or less. Finally, there are only six data points above 60 FV, so that’s mostly a stab in the dark.

This outcome pleases me greatly. Looking at that chart correlates strongly with how I already perceived the grades. For a refresher, roughly 30 prospects in a given year grade out as a 55 FV or above, give or take a few. Something like three quarters of those tend to be hitters. That means that in a given year, 20-ish prospects look like good bets to deliver average-regular-or-better performance. The rest of the Top 100? They’re riskier, with a greater chance of ending up in a part-time role and a meaningfully lower chance of becoming a star. But don’t mistake likelihood for certainty – plenty of 55 and 60 FVs still end up at or below replacement level, and 45 FVs turn into stars sometimes. Projecting prospect performance is hard!

How should you use this table? I like to think of Future Value in terms of outcome distributions, and I think that this does a good job of it. Should a team prefer to receive two 50 FV prospects in a trade, or a 55 FV and a 45 FV? You can add up the outcome distributions and get an idea of what each combination of prospects looks like. Here are the summed probabilities of those two groups:

Another way of saying that: If you go with the two-player package that has the 55 and 45 FV prospects, you’re looking at a higher chance of developing a star. You’re also looking at a greater chance of ending up with at least one complete miss, and therefore lower odds of ending up with two contributors. Adding isn’t exactly the right way to handle this, but it’s a good shorthand for quick comparisons. If you want to get more in depth, I built this little calculator, which lets you answer a simple question: For a given set of prospects, what are the odds of ending up with at least X major leaguers of Y quality or better? You can make a copy of this sheet, define X and Y for yourself, and get an answer. In our case, the odds of ending up with at least one above-average player (or better) are 40.7% for the two 50s and 41.4% for the 45/55 split. The odds of ending up with two players who are at least big league regulars? That’d be 28.1% for the two 50 FVs, and 16.1% for the 45/55 pairing. Odds of at least one star? That’s 4% for the two 50 FVs and 6% for the 45/55 group. In other words, the total value is similar, but the shape is meaningfully different.

For example, you’d have to add together a ton of 50 FV prospects to get as high of a chance of finding a star as you would from one 60 FV. On the other hand, if you have three 50 FVs, the odds of ending up with at least a solid contributor are quite high. Meanwhile, even 60 FV prospects end up as backups or worse around a quarter of the time. That description of the relative risks and rewards makes more sense to me than converting players into some nebulous surplus value. Prospects are all about possibility, so representing them that way tracks analytically for me.

Take another look at the beautiful cascade of probabilities in that table of outcomes for hitting prospects, because we’re about to get meaningfully less pretty. Let’s talk about pitching prospects. Here, the outcomes are less predictable:

I have tons of takeaways here. First, there are substantially fewer pitching prospects ranked, particularly as 50 FVs and above. Clearly, that’s a good decision by the prospect team, because even the highest-ranked pitchers turn into backups at a reasonable clip. Pitching prospects just turn into major league pitchers in a less predictable way, or so it would appear from the data.

Second, there are fewer stars among the pitchers than the hitters. That’s true if you look at 2025 projections, too. There are only six pitchers projected for 4 WAR or higher, while 42 hitters meet that cutoff. It’s also true if you look at the results on the field in 2024; 36 hitters and 12 pitchers (22 by RA9-WAR) eclipsed the four-win mark. You should feel free to apply some modifiers to your view of pitcher value if you think that WAR treats them differently than hitters, but within the framework, the relative paucity of truly outstanding outcomes is noticeable.

Another thing worth mentioning here is that pitchers don’t develop the same way that hitters do. Sometimes one new pitch or an offseason of velocity training leads to a sudden change in talent level in a way that just doesn’t happen as frequently with hitters. Tarik Skubal was unmemorable in his major league debut (29 starts, with a 4.34 ERA and 5.09 FIP). Then he made just 36 (very good) starts over the next two years due to injuries. Then he was the best pitcher in baseball in 2024. Good luck projecting that trajectory. Perhaps three-year-out windows of pitcher performance just aren’t enough thanks to the way they continue to develop even after reaching the majors.

There’s one other limitation of measuring pitchers this way: I don’t have a good method for dealing with the differential between reliever and starter valuation. Normalizing relievers to 200 innings pitched doesn’t make a ton of sense, but handling them on their own also feels strange, and I don’t have a good way of converting reliever WAR to the backup/regular/star scale that I’m using here. A 3-WAR reliever wouldn’t be an above-average player, they’d be the best reliever in baseball. I settled for putting them up to 200 innings and letting that over-allocaiton of playing time handle the different measures of success. For example, a reliever projected for 3.6 WAR in 200 innings would check in around 1.2 for a full season of bullpen work. That’s a very good relief pitcher projection; only 20 players meet that bar in our 2025 Depth Charts projections.

In other words, the tier names still mostly work for relievers, but you should apply your own relative positional value adjustments just like normal. A star reliever is less valuable than a star outfielder. A star starting pitcher might be more valuable than a star outfielder, depending on the degree of luminosity, but that one’s much closer. This outcome table can guide you in terms of what a player might turn into. It can’t tell you how to value each of those outcomes, because that’s context-specific and open to interpretation.

This study isn’t meant to be the definitive word on what prospects are “worth.” Grades aren’t innate things, they’re just our team’s best attempt at capturing the relative upside and risk of yet-to-debut players. Being a 60 FV prospect doesn’t make you 17% likely to turn into a star; rather, our team is trying to identify players with s relatively good chance of stardom by throwing a big FV on them. And teams aren’t beholden to our grades, either. They might have better (or worse!) internal prospect evaluation systems.

With those caveats in mind, I still find this extremely useful in my own consumption of minor league content. The usual language you hear when people discuss prospect trades – are they on a Top 100, where do they rank on a team list, what grade are they – can feel arcane, impenetrable even. Breaking it down in terms of likelihood of outcome just works better for me, and I hope that it also provides valuable information to you when you’re reading the team’s excellent breakdown of all things prospect-related this week.

Robert Hassell III has encountered bumps in the road, but he’s confident that he’s finally heading in the right direction. Health and a better understanding of his left-handed stroke are two reasons why. Added to the Washington Nationals’ 40-man roster over the offseason, the 2020 first-rounder — he went eighth overall to the San Diego Padres — is also still just 23 years old. While his path to the big leagues has been anything but smooth, Hassell is far from over the hill in terms of prospect status.

Injuries have hampered his progress. Since turning pro, Hassell has incurred a pair of wrist injuries, including a broken hamate bone, and strained a groin muscle. As a result, he’s played in just 428 games over four seasons. Seldom at full strength for an extended period of time, he’s slashed an uninspiring .260/.350/.385 with 36 home runs and a 105 wRC+.

Hassell didn’t want to dwell on his past injury issues when I spoke to him during the Arizona Fall League season, although he did acknowledge that he “needs to be healthy and on the field” in order to allow his true talent to play. And he definitely has talent. While power has never been part of his profile, Hassell’s combination of bat-to-ball skills, speed, and outfield defense helped make him a primary piece in the multi-player trade that sent Juan Soto from Washington to San Diego in August 2022.

The conversation I had with Hassell in Arizona centered on his development as a hitter — something he views as a work-in-progress in need of nuance, not one that requires an overhaul. Read the rest of this entry »