Throughout years of playing fantasy baseball and being enamored with sabermetrics, I’ve come to develop a set of indicators that is helpful in identifying potential prospect breakout candidates in the MLB. These indicators are based around a combination of qualitative/quantitative analysis, and are generally focused on two broad categories: Quantitative Statistical Production and Qualitative Trait-Based Projectability. Indicators are helpful in identifying prospects to watch that could potentially break out, either making the leap from the minors to a true Major League star or rising quickly through prospect rankings and the minor leagues.

All projections and statistics in this article are sourced from FanGraphs, my favorite site for sabermetric analysis and data.

Quantitative Statistical Production#

There are a few key tenets of my philosophy towards measuring statistical production in prospects.
-Statistical production should be adjusted for age and level, in order to clearly depict a picture of a prospect’s production relative to their potential peak.
-Sample size is extremely important for measuring production across different levels of the minor leagues and eventually the majors.
-In the same vein, high BABIP levels or other “red flags” in a profile should be accounted for, and either explained via qualitative analysis or used as a filter mark against a prospect.
-Choosing the right stats for indicators is extremely important and allows us to identify which statistical production traits in a prospect most clearly correspond to eventual major league production.

With all of this being considered, here are a list of quantitative, statistical-based production indicators, both positive and negative, to identify in prospects. I try to focus on the “stickiest” identifiers for both positive and negative traits, with a general focus around the averaged stats of MLB-level hitters and trying to identify prospects that are performing above-average in their respective level, then cross checking this production with their age and performance history to identify how far away each prospect is from their peak. In simpler words, we want to measure truly impressive production, and to do so, we need to adjust player production for what we expect from players at their age and level.

The individual metrics that I’ve identified as “stickiest” and most tied to MLB production, in my research, are listed below. I prefer rate-based metrics paired with an established sample size to measure prospect performance, in conjunction with age and level adjustments.

Hitters: K%, BB%, K/BB, ISO, BABIP, OPS+, wRC+, Spd, through a minimum of 60 AB
Pitchers: K/9, BB/9, BB/K, HR/9, FIP, xFIP, with a minimum of 40 IP

-I highly recommend reading the FanGraphs Library if you’re curious about any of these metrics.
-Regarding the sample sizes I’ve chosen, I refer you to this FanGraphs article on sample size.

We will use these metrics, in combination with the quantitative analysis of different projection systems, in order to identify MLB prospect breakouts through a quantitative lens.

Qualitative Trait-Based Projectability#

There is an old adage supporting the unbiased nature of statistical analysis, the adage being “Stats are blind”. This very fact ushered in the age of sabermetrics into baseball, and eventually, the now analytic-based world. This is a positive in regards to analyzing value, but it can also be a negative, as stats are inherently flawed, seeing as they can always be mathematically improved upon to further adjust for the context of each stat. It is imperative, then, to use qualitative trait-based indicators in combination with quantitative indicators in order to most clearly project and “see” the value in a prospect. Some prospects are late bloomers, not statistically producing at their full potential until a rapid breakout occurs. High-variance, boom or bust prospects often require a swing change, adding a new pitch to their mix, or other various “tweaks” that can cause them to unlock the full physical potential that they embody. FanGraphs does a terrific job at scouting prospect traits, so we can use their qualitative data for our analysis here, in combination with my own observations of each prospect’s tape and profile.

It is important to weight qualitative observations relative to their importance. Qualitative measures are best employed when focused on finding value in the “blind spots” of quantitative analysis. In the qualitative analysis of baseball prospects, these blind spots manifest themselves in regards to untapped production, or performance that hasn’t manifested itself statistically yet, but is an observable trait or phenomenon surrounding a player.

Classic examples of this stem from a somewhat common prospect profile: the boom-or-bust player with a ++ frame, ++ velocity, and/or ++ power, who can’t seem to produce in spite of their otherworldly physical traits. These traits don’t show up in the box score, but they represent some intangible value that hasn’t been converted into baseball performance yet. These prospects are often late bloomers, and have massively high ceilings, while also having equally abyssal floors. Some examples of this include players like Aaron Judge, who struggled at points in his minor league career and in his rookie debut, but eventually evolved and bloomed into the best baseball player on the planet.

It’s important to not get obsessed with seeing upside in every prospect that fits this profile. There are a lot of toolsy players who never reach their full upside. I’ll get into this more in the next section.

Qualitative indicators that we seek to identify in prospects that can represent the chance at potential production that hasn’t been realized yet include:

-Frame
-Levers
-Form (Swing/pitching/throwing/running motion)
-Positional Eligibility
-Platoon Splits
-Tool Scouting Grades
-Statistical Projections

In searching for these prospects, we will focus on these identifying indicators by using FanGraphs tool scouting grades, film review of their mechanics, and any “X-Factor” phenomenon that exist around a prospect.

An easy example of this “X-Factor” would be Shohei Ohtani, but he’s already a success story, and the two-way player factor is well researched at this point, so instead we’ll use Jurrangelo Cijntje as an example. Cijntje is interesting because he’s a switch pitcher. There is no statistical way to quantitate the potential value of this yet, because there’s never been a pitcher like him, and there’s no way of knowing what the Mariners will choose to do with him yet. Will he start as a RHP (his better side, where he grades out as a + pitcher already), and make bullpen appearances in between his starts as a LHP? Will he switch handedness between batters to play righty-lefty advantages better? Will the Mariners choose to just develop him as a RHP, choosing the high floor of a controllable young pitcher over the potential ceiling of a unicorn? There’s no way of knowing, but qualitatively, I can ask these questions and clearly identify that there is potential untapped value here.

We will use all of the above methods in order to identify potential MLB prospect breakouts through a quantitative lens.

Mixed-Methods Analysis: Blending Quantitative and Qualitative Analysis#

After identifying both quantitative and qualitative indicators, we can use them in combination to fully identify breakout candidates. Prospects with multiple indicators, both within quantiative/qualitative lenses and collectively, are more likely to break out.

We can also use a combination of quantiative/qualitative analysis to develop new metrics that are a blended version of the two. A metric that I’ve developed that’s extremely helpful in evaluating the potential production of a prospect qualitatively is one that I call “TSS”, short for Tool Super Score, that attempts to weight prospect tools in relation to other prospects to quantitate the measured tools of different prospects. I’ve used OTSS, or Offensive Tool Super Score, to identify prospects that are potentially valuable in fantasy baseball in combination with or beyond their minor league production.

The formula for OTSS is simple: (Hit Tool + Game Power Tool) + (Speed Tool / 2)

We weight the speed tool here in order to account for its decreased importance when compared to hit tool or power tool in regards to offensive production, while still accounting for it in order to measure hitters who gain offensive value via their potential production on the basepaths.

We will use OTSS, as well as “sub tool” metrics like FanGraphs grades for plate discipline and bat control, or their evaluations of player frames, versas, and profile, to clearly identify blended metrics that allow for us to further evaluate potential breakouts.

The key for identifying these breakouts is providing a proof of a prospect’s potential value. This can be done individually via either quantitative or qualitative analysis, but using the two in tandem is imperative in finding imminent breakouts, or potential breakouts that have gone underacknowledged. The ideal prospect shines both quantitatively, qualitatively, and in mixed-methods analysis that combines the two.

Prospects that do this are traditionally considered “blue-chip” prospects, usually have an FV grade of 60 or higher, and are nearly impossible to acquire outside of international free agency or the draft. Teams covet these prospects and are rightly unwilling to move them for anything. This applies even more so for pitching prospects. Blue chip pitching prospects are by far the most valuable asset in the sport, as elite young controllable starting pitching is extremely valuable and can create championship-caliber teams out of nothing (see: Paul Skenes).

Because of this, identifying these players, both hitters and pitchers, before the industry evaluates them as such is the most valuable thing a scout can do. I’ll get into this more in a later article on Baseball Alchemy (Creating Value With Positional Changes, The Conversion of RPs to SPs, And Quantified Player Roles), which might actually end up being multiple articles, but for now I’ll just say that the key to finding value in the industry that invented sabermetrics isn’t to ignore sabermetrics, but to evolve them.

To tie it all together before we get into individual player analyses - stats are blind. We will use the stats to see what we cannot, and use ourselves to help the stats see what they cannot.

In summary, we will use quantitative, qualitative, and mixed-methods lenses of analysis to identify indicators for prospect breakouts in 2026, then analyze each player according to a combination of these lens to further filter and identify breakout candidates. This will be an article series where, upon now establishing our criteria for indicators, we go through farm systems one-by-one to identify potential prospect breakouts within each system, and evaluate these breakouts based on a holistic overview of each prospect’s profile.