Introducing an Updated Method for Prospect Valuation

Seven years ago, Craig Edwards published a landmark study on prospect valuation. Craig’s work built on previous studies by Victor Wang, Scott McKinney, Kevin Creagh, Steve DiMiceli, and our own Jeff Zimmerman, as well as a few prior ad hoc attempts here at FanGraphs; subsequent work on the subject was done by the team at Driveline Baseball. These studies have been hugely important both for FanGraphs’ own evaluation of prospects — among other things, Craig’s work has helped to feed the Farm System Rankings over on The Board — and for the broader public study of the minor leagues.

The reasoning behind these studies is clear and simple. If you want to evaluate a prospect-for-big-leaguer trade, you’ll need to know the expected value of the prospect in the trade. If you want to evaluate how much help is waiting in a given team’s farm system, a quantitative assessment of the talent there is necessary. Even if you’re just wondering how likely your team is to find the next big thing, again, you’ll need some type of framework to understand how often that’s happened in the past.

The previous studies of prospect valuation are still excellent, but they’re all very much of their time. Since Craig published his study in November 2018, the league has changed significantly. The COVID-abbreviated 2020 season changed minor league timelines across the board. The league contracted the number of minor league franchises significantly in 2021. A new CBA, signed before the 2022 season, changed compensation structures and competitive balance tax levels, and introduced the Prospect Promotion Incentive. The cost of a win in free agency has skyrocketed; league-wide payrolls are up more than 30%, and free agent salaries are up by more than that.

With help from my colleagues, I set out to update those previous studies for the modern era. I followed the lead of prior research and divided my evaluation between pitchers and hitters, split between each Future Value tier we use to grade prospects, and estimated values across three different possible measures of value.

What We Measure
The simplest way to think about what a prospect is “worth” is to work out what their production would cost in free agency and subtract what they project to make in their team control years. This surplus value, to use the industry term, is particularly useful when it comes to evaluating trades. Comparing major leaguers and minor leaguers with different team control profiles, skill sets, and positions is much easier if you reduce them all to a single dimension, and surplus value does a good job of tracking real-life trades. It’s one of the things that teams care about most in evaluating transactions, and as such, it’s historically been the most common measure of prospect value. We’ve updated our surplus value calculations to include the modern-day cost of a win in free agency, which is increasingly non-linear at the top end. That works out to roughly $7 million per win for 0-1 WAR players, $8.5 million per win for 1-2 WAR players, and $13 million per win for players who rack up more than 2 WAR. Teams will pay up for stars, and as a result, prospects more likely to turn into stars command extra surplus value.

That’s not the only useful way to think about prospect value, however, so we’ve included two other metrics. First, we calculated the un-discounted WAR that each tier of prospect projects to accumulate during their team control years. If you’re hoping to use a prospect to shore up a weak spot in your lineup instead of trading that prospect for a veteran, surplus value isn’t a sufficient metric. A player producing $100 million worth of on-field value and getting paid $99 million and a player producing $10 million of value and getting paid $9 million each have $1 million in surplus value, but it wouldn’t make sense to treat those guys the same in team-building.

And mean WAR isn’t the only way of looking at potential outcomes, either. That number is an average of all the hits, semi-hits, semi-misses, and outright busts that carried a similar evaluation in each prospect tier in the past. Sometimes, though, you don’t want to know the average; you want to know your odds of hitting a good outcome. To that end, we also calculated the odds that a prospect will turn out to be a star. For this study, we’ve defined a star as a player who posts two or more seasons of 4 or more WAR during their team control years.

None of these three metrics is a perfect encapsulation of the value of a prospect, but each has merit, and looking at all three in conjunction provides a good holistic picture. And it’s particularly useful to aggregate these numbers up to the team level.

Methodology
If you’re interested in a deep dive on the methodology used to produce these valuations, you’ll find an detailed explainer here. What follows is a top-level overview of what data we used, how we used it, and how that turns into each of our outputs.

First, we took historical prospect rankings from 2005-2018, using Baseball America’s Top 100 for 2005-2016, and our own rankings for 2017 and 2018. We transformed the ordinal rankings (nos. 1-100) from 2005-2016 into FV grades based on the current distribution of FV grades that our prospect team gives to players, assigning grades to pitchers and hitters separately. For each player, we then calculated how much WAR they accrued during their team control years. We also noted whether or not a player achieved two four-win seasons during their team control years.

To create a per-player surplus value, we then valued that WAR at 2026 levels with adjustments for the time value of money and the rising cost of a win over time, using our own study of the cost of a win in free agency. We then subtracted out pre-arbitration and estimated arbitration salaries to produce a by-year surplus value in present-day dollars. We didn’t ignore any prospect rankings in doing so, and didn’t remove nulls – if a 50-FV player didn’t make the majors, for example, we left in a row of zero WAR and zero surplus value.

That handled the valuation of the 50-FV and higher prospects. Given that we have no consistent historical record of lower-ranked prospects, some approximation was in order to complete the next step. To estimate the total value of prospects outside of the Top 100, we measured team control WAR accumulated by players who debuted in each year and separated them into two groups: Top 100 players and players who were not ranked in a Top 100.

From there, we estimated the value accrued by 50-FV players outside the Top 100 in each year. Top prospect lists have to end somewhere, but ranking exactly 100 players is a somewhat arbitrary endpoint; on this year’s preseason Top 100 list, for example, our prospect team ranked 110 players, with the number of 50-FV and above players growing to 123 over the course of this list cycle. Since the historical Baseball America lists ranked exactly 100 players in every year, it seemed likely that ascribing all of the value outside the Top 100 to players with a grade below a 50 FV would mis-assign some of that value. So we attributed some value to those players who were outside of the strict historical Top 100 list but nonetheless would carry a grade of 50 FV or higher using modern grading methods, and subtracted that from the value assigned to prospects with a grade below a 50 FV. We approximated the distribution of this remaining WAR based on the early-career returns of players graded as a 35+, 40, 40+, 45, or 45+ on FanGraphs lists starting in 2019, taking care to subtract from our estimate based on the share of players who make major league contributions without being ranked by the team before their debut.

To turn that yearly debut number into the value of all current minor leaguers, whether they debut this year or not, we estimated the time spent in the minors by the average player with a grade below a 50 FV. In this estimation, we used only data from the last 10 years; the shortening of the draft, the contraction of the minor leagues, and the Prospect Promotion Incentive mean that players play in the minor leagues for less time on average, and our numbers account for that change. Finally, we calculated the odds that a prospect currently graded below a 50 FV would be upgraded to a 50 FV or higher. We worked out the estimated value of each tier as a weighted average: the odds of being upgraded times the value we’d calculated earlier for a 50-FV or higher player plus the expected major league contribution of a player conditional on not being upgraded times one minus the odds of an upgrade.

I’ve described the methodology for calculating surplus value, but the WAR and star odds methods follow closely from this one. In addition to calculating surplus value, we also calculated the total team control WAR for each player, and measured how frequently each tier of player turned in a star-level team control span. In that way, we were able to use the same method of dividing value to measure these variables as well.

That’s a ton of words for an abbreviated description of the methodology, but what can I say? This is a complicated thing to measure. Dealing with limited historical data and approximating value over decade-long spans for players on prospect lists means that we’re limited to working off of the past and making educated decisions about how to apply that information to current and future outcomes. The methodology piece contains some sensitivity analysis, but in broad terms, we calculated that the value of the minor leaguers we currently assign grades to is between $10.5 billion and $13.75 billion across the affiliated minor leagues, with our best-calibrated guess at $12 billion.

Results

We used this method to calculate the value of each Future Value grade, both for hitters and pitchers. The following table displays those results:

As you can see here, 70- and 65-FV prospects are phenomenally valuable to teams, whether you’re interested in surplus value, expected WAR, or just the odds of them developing into a star. That’s something that arises from how rarely we hand out 65- and 70-FV grades to pitchers these days. We haven’t given out a 70 FV to a pitcher since 2020 (MacKenzie Gore), and our grading has changed meaningfully since then, to the point where I’m confident that the same evaluation wouldn’t produce a 70-FV grade today. The last three 65s? Paul Skenes, Roki Sasaki, and Nolan McLean. The only other 70-FV pitcher in the history of our rankings? That would be Shohei Ohtani, and that grade was meant to also encompass his contributions as a hitter. Pitching prospects this elite are less common than hitting prospects of the same caliber, but the few who do exist perform just as well on a per-player basis.

By the time we get to more “normal” top prospects, the standard split, with hitting prospects being more valuable than pitching prospects with the same grade, is still borne out by the data. That pattern continues even outside the prospects who populate the Top 100; for every Future Value below 65, pitchers deliver less WAR on average and are less likely to turn into stars than hitters with the same grade.

Notably, there’s plenty of value to be had outside the Top 100. Across the entire population of ranked minor leaguers, we project prospects with a grade of 50 FV or higher to accumulate $5.7 billion in surplus value during their team control years, while prospects graded below a 50 FV project to accrue an aggregate $6.3 billion in their team control years. Sure, one individual 40-FV prospect might not be that likely to turn into a star, but if the past is a reliable indicator, a good number of players who we currently rank below a 50 FV will end up making that leap. Prospect evaluation is hard and talent levels aren’t stationary. This makes good sense, and now we have the numbers to show it.

We compared these new rankings to Craig’s methodology from 2019 to get a sense of how different the two approaches are. We also compared our rankings to Craig’s methodology after adjusting for inflation; the cost of a win is meaningfully higher than it was in 2019, so placing those numbers in 2026 dollars is another useful comparison. Here are the results by prospect grade:

As mentioned above, the new rankings place more value on prospects outside of the Top 100. Accordingly, our Farm System Rankings, the 2026 edition of which you can now view on The Board, will look different using these new numbers, because teams with more prospects graded below a 50 FV come out looking better by our new approach. Here’s an overview of how our Farm System Rankings come out using the old and new prospect valuation methods:

This is a good time to remind everyone that the Farm System Rankings on The Board update in real time as our prospect team adjusts the grades of individual players. As key risers move up (like some of the young hitters in Atlanta’s system) or rookies graduate and fall off team lists (like Konnor Griffin and Kevin McGonigle), you’ll see changes to where various systems rank. The current snapshot includes any ranked prospects from this list cycle who were rookie eligible to begin the season.

As we discuss in the methodology post, the individual assumptions used in this prospect model move the final value of the minor leagues around, but the rough contours of these numbers hold across a broad range of possible assumptions. It’s inarguable that the future big league contributors currently in the minor leagues provide a huge amount of surplus value to their teams.

We think that this approach to prospect valuation does a good job of approximating how much value today’s prospects are likely to return to teams over the course of their team control years. Importantly, these estimates reflect the current structure of the minor leagues and player compensation; should the next collective bargaining agreement change the rules of early-career compensation, or even change free agency such that the cost of a win moves meaningfully, these surplus values will move as well. The WAR estimates and the odds of a player being a star are more robust to changes in compensation structure, but if the length of team control changes, those will need recalibration too. But none of those things have happened yet, and should they transpire, we’ll simply use the new state of the world and update this model to create new estimates. In other words, we think both that this is the best way to approximate the value of prospects today, and that this method will allow us to approximate the value of prospects well into the future.