Archive for Essential Prospects

In November, I published the results of my research attempting to put a value on minor league prospects. It seems only natural that a similar study on draft picks should follow.

As with prospect valuations, considerable work has preceded mine in the area of valuing draft picks. Sky Andrechuk, Victor Wang, Matthew Murphy, Jeff Zimmerman, and Anthony Rescan and Martin Alonso have all done similar studies.

The work below is less a replacement of the work already done and is more of a continuation of, and addition to, the study of the subject matter. As to why we might want to know this information, creating an expected value for a draft pick helps us to understand and manage our expectations of draftees’ performance. More practically, teams regularly give up draft picks to sign free agents, receive extra draft picks when they lose free agents or reside in a smaller media market, and drop slots when they exceed the highest competitive balance tax payroll threshold, not to mention that some picks can be traded. Determining a value for these picks helps us better understand the decisions teams make regarding those picks.

In some ways, determining draft pick value is a little more complicated than figuring out prospect value. When determining prospect value, players are placed within the constraints of the current CBA, which provides for a minimum salary for roughly three seasons and suppressed arbitration salaries for another three years after that before a player reaches free agency. Draft picks are confined to the same system, but there is also a signing bonus to consider, not to mention slotting rules that are often manipulated in order to move money around to different picks.

Due to signing bonuses and bonus slots, to arrive at an appropriate value for a draft pick, it isn’t enough to determine the present value of players’ WAR in the majors without getting to a dollar figure. We also have to account for the present value in dollars and then subtract the expected bonus.

Before explaining the methodology for draft picks, we can look at the very similar framework used to get to the present value of minor league prospects. From my “Update to Prospect Valuation”:

To determine surplus value for players, I used WAR produced over the first nine seasons of a career, including the season in which a prospect was ranked. Why nine years? In today’s game, most players don’t hit free agency until after their seventh major-league season. By examining nine seasons, it’s possible to account for prospects who were still a couple years away from the majors when they appeared on a top-100 list — as well as late-bloomers who might have bounced up and down between the majors and minors for a full season.

Of course, not all prospects continue to develop in the minor leagues after appearing on a top-100 list. Some debut in the majors right away. Due to the methodology outlined above, such players might be in a position to receive greater credit for their first nine seasons simply because they were closer to the majors when they were ranked. To accommodate this issue, I’ve spread out a player’s WAR over the final seven seasons of the period in question, distributing 10% of it to years three and four before slightly gradually increasing that figure up to 20% by year nine. To calculate surplus value, I’ve discounted WAR by 3% in years No. 3 through 5 (to approximate the impact of the league-minimum salary) and then 15% in year six, 32% in year seven, 48% in year eight, and 72% in year nine. Spreading out the WAR in this way not only mimics a sort of generic “development curve” but also ensures that arbitration discounts aren’t too heavy.

After that, I applied an 8% discount rate for present value. For players immediately ready to play, the extra value they get from the eighth and ninth year is minimized by removing value they actually provided from the first two years and spreading into later seasons. This similarly ensures that the controllable years of players who take longer to develop or reach the majors aren’t treated the same way as those produced by players who contribute right away. A two-win season in 2019 is more valuable than a two-win season in 2021; and this method helps to strike that balance.

Draft picks aren’t as close to the majors as most minor league prospects are. To combat this problem, I used 10 years for college draftees and 11 years for those drafted out of high school, but kept the rest the same as above.

The other difficult issue for draft picks is one of sample size. When I looked at 15 years of prospect lists, it meant we were looking at hundreds of prospects at nearly every single prospect grade. If we did the same for draft picks over 15 years, we only have 15 players at every pick, which isn’t much of a sample. To compensate for this issue, I took a large percentage of the pick in question, and then a smaller percentage on a sliding scale of the next 12 picks. After all, having the third pick in the draft isn’t just an opportunity to take the third-best player; it is the opportunity to choose between a whole host of players. The Astros taking Mark Appel ahead of Kris Bryant doesn’t make the second pick in the draft better than the first. The Astros could have had Kris Bryant, and factoring in the picks that follow helps represent that challenge.

Smoothing things out a bit helps make sure a small sample doesn’t create a bias around a pick. For example, in the years I studied (1993-2007), the third overall pick often performed poorly, but Eric Hosmer, Manny Machado, and Trevor Bauer were taken with the third pick in the three of the four drafts that followed. It wasn’t bad to have the third pick from 1993-2007. It just happened that those picks didn’t work out well.

First round picks were then adjusted upwards slightly so that the actual WAR of the picks and the adjusted value using the method above matched. The values were then smoothed out to ensure the value of the picks moved downward. The smoothing stopped mattering after the second round. After finding the present-value WAR for each pick (I used $9M/WAR), I then subtracted the slot amount for each pick to come up with a current value.

This is what the first 70 picks look like:

The values at the very top of the draft are going to be context heavy. Sometimes, the top pick is a solid 55, like Casey Mize was a season ago. Other years, it might be Bryce Harper. For context, here is how the first round played out last season in terms of bonuses and slots for the pick.

The draft reveals just how important it is for teams to receive a compensation pick the following season when they fail to sign a pick in the current year. While there is certainly lost developmental time and opportunity in losing a pick for one year, losing that pick permanently would be a major loss, and provide considerably more leverage to the players when negotiating contracts.

Moving down, this is what the picks in the third round and below are worth. For the 11th round and below, the median value is used instead of the average given the potential for a few really good picks out of thousands to distort the value beyond what would be a reasonable expectation for that pick.

In practical terms, that means that for the picks in round 20 or later, you might come up with one average player every three years. For picks in rounds 11-20, a team can expect an average player every two or three seasons. The same is true for rounds three and four combined. It’s hard to find good players in the draft after the first round. There’s as much value in the first 100 picks as in the entire rest of the draft. Teams might opt to pay a third round pick a $3,000 bonus to save money and use it elsewhere. That doesn’t mean that we should expect the same performance from that pick as we would a typical third rounder, but we should expect that the slot money the team uses elsewhere will have a value somewhere close to $4 million.

When considering how teams sometimes shift money around from the second or third round to the sixth and seventh round (and vice versa) or use money to sign players above $125,000 after the 10th round, it helps to know how to properly value every dollar spent. For the first 100 picks, where the bonuses are the highest, every dollar spent generally yields five dollars in value. In rounds 4-5, every dollar should yield about six dollars in value, and in rounds 6-10, every dollar spent should yield 10 dollars in value due to the talent available and the small signing bonuses. Given this information, it appears teams might be better off paying slightly less money in the first few rounds while still getting good talent, and shifting some of that money elsewhere in the first 10 rounds. If teams are shifting money from the first 10 rounds to the back of the draft, they need to feel pretty confident in that player’s ability.

In terms of comp picks in this year’s draft, the Arizona Diamondbacks will receive a pick at the end of the first round for losing Patrick Corbin to the Nationals. That pick is worth something close to $10 million. The six small-market teams will receive picks between rounds one and two that are worth $8 million to $9 million each. The other eight small-market picks after the second round are worth around $4 million each, and the same is true for the free agent compensation picks like the one the Dodgers will receive for losing Yasmani Grandal.

Teams signing free agents who have received a qualifying offer generally lose their second pick, and that pick is worth somewhere between $4 million and $10 million depending on where in the draft the team is picking. The Red Sox’s top pick drops down 10 spots this year because they were more than $40 million over the competitive balance tax. That penalty is only worth around $2 million.

There’s further analysis to be done based on whether a player is coming out of high school or college, as well as whether he is a position player or pitcher, but that work will be left to a later date. For now, I hope this is a useful starting point for further study, and for gaining a greater understanding of draftees’ expected production and teams’ decision making.

FanGraphs has obtained bonus figures for over 90% of all the international signings in baseball history. We have all of the most significant bonuses, every big leaguer, notable current prospects, and everything in the mid-six figure range and above, along with many years for which we have every single signing.

This provides us with a pretty complete picture of the distribution and trends of these bonuses, also allowing us to estimate how many players we’re missing. Those players are overwhelmingly names you wouldn’t recognize, guys who played for a couple of years before being released, signing as filler for a five-figure bonus.

We’ve taken out all of the major league deals (think older, high profile Japanese and Cuban players), and we have incomplete data for all of the Mexican players, as MLB notes all of them as receiving a $0 bonus (it’s an easy workaround for a convoluted system that’s mostly cleaned up now). We’ve filled in correct bonuses for players where we have it, mostly among the high profile Mexican signings, like Luis Urias and Julio Urias (no relation).

We could do a lot of things with this data — and we will, including listing it on the player pages and THE BOARD — but the thing that interests me today is combining this bonus data with our asset value research, and the dollars-per-WAR framework to get a better idea of what a dollar invested in an international amateur player returns. We’ll start with some of the meta data:

2017 was the first season of hard-capped bonus pools, which explains why bonuses declined and also why they spiked the year prior. These figures don’t include the pool overage payments made to MLB from 2013 to 2016. We estimate those figures to add up to about $250 million over those four years, with about $100 million paid to MLB in 2016 alone. (The CBA says that this money was to be spent on international operations and initiatives.)

Since the international market changes and matures so rapidly, it makes sense to start with the early 2000s signing classes as a baseline for a similar era to today. Most of the players who signed 15 years ago are now in their early 30s and have either played out their entire careers or are into their seventh year of major league service time. We can grab the dollar-per-WAR figures from the years that spanned their controlled years and turn that historical WAR into a dollar amount of value created. I used seven seasons since we don’t have comprehensive service time data, which, from some spot-checking, appears to do the trick. We have the FV of the most recent signings that are current prospect on THE BOARD, which maps to an asset value.

The most interesting players to analyze signed in the last 5-10 years, are in the big leagues, and are in the middle of their control years, so I had to do some work to peg their value. I quantified what they’ve already produced the same way I did with the older players, then estimated or figured out by hand their current service time situation. I then used our various projections to fill in what those players are expected to produce in the rest of their controlled years.

In short, it’s not perfect, but as with filling in the holes in the bonus data, it’s fairly accurate and any mistakes appear to cancel each other out in the aggregate. There’s some noise in the data year-to-year, but it appears that right around 2004, the market improved its output and has held mostly steady to today. Here’s the production (a combination of produced WAR, projected WAR, and minor league asset values) over this period:

I included up to the 2017 class, but it would appear that we need three full seasons in the system — with players having signed on July 2, 2015, and played in 2016, 2017, 2018 — before the class as a whole has developed enough to reveal how much value it could create. As such, a dozen years (2004-2015) appears to be our usable sample.

We could use the above figures to create a simple return on investment calculation, but a true ROI would compute what a team is making on the average dollar spent, so we also have to consider the expense to operate the department that signs the players. Building or renting an academy, feeding and housing the players, running a DSL team, paying coaches, trainers, scouts, and administration and travel expenses are all facets of an international operation that are essential to signing and developing these players, so they have to be considered alongside the bonus expenditures. After consulting with some international directors, I’ve estimated those costs for all 30 teams combined and added that to the bonuses, before arriving at an ROI figure that represents something close to what MLB clubs can expect a bonus pool dollar to return. I used a rolling figure to smooth out any noise in the yearly results.

This gives us an idea of what a club’s accounting department would say their ROI was running an international operation in these years. There are a couple of other ways to look at this data. Going forward, we know that overages won’t exist. We also know the maximum that can be spent with hard caps in place. If we were to take the historic spending of 2016 and keep those signing rules, while also imagining that the talent of 2018 demanded the same outlay in bonuses and overages as the group in 2016, we could compare the two realities owners were considering in the most recent completed CBA negotiations:

You can see that there’s still a solid positive return even with historic spending levels, but owners negotiated to add a hard bonus cap to the international market, essentially doubling their ROI. The 2016 class was unique in that clubs were motivated to spend wildly in anticipation of the caps and because of that, a great class of Cuban players that couldn’t be duplicated today (four of our top 132 prospects are Cuban players from this class) drove much of that spending. That roughly $315 million expenditure may be the closest figure we’ll get to what clubs think the true value of a historically-talented class is in an open market with multiple motivated bidders. The market is now capped at half that figure.

We can also answer the question of what an international pool dollar is worth going forward. If we assume that the overhead of running a department is fixed, how should clubs think about the value of each additional dollar added to their bonus pool? We could take the table just above this one and use the projected actual 2018 row to figure out the ROI from $150 million in bonuses and the estimated $1.125 billion in value that will be created by the signees. The result is a staggering 650%. It appears that it takes about three years for the an investment in the international market to mostly mature in terms of trade value, though there’s a way to read this data where there’s further value gained in a 5-7 year horizon for full maturity.

This sort of analysis can get too close to quantifying the worth of humans in purely dollar terms, although going through the exercise in this way also helps to define what a fair market price is for someone’s service. 650% is a pretty abstract number to consider, so let’s compare it to an standard investment for wealthy individuals such as baseball club owners: investing in the stock market. An owner can invest roughly $5 million into international market each year and expect a median return of 650% after three years, while a strong 10% yearly compounded return in the stock market over that period would return a 35% return. That sort of return makes clear both the appeal for ownership of signing international players, and capping their bonuses. It also points to how wide a gap exists between the value these players generate for their clubs and their compensation relative to that value.

In the next part of this series, I’ll take a look at some of the best and worst signing classes, if we were to grade out every club’s international signing class over the last 30 years using the framework rolled out today.

We got a hold of the bonus slot values and, it follows, each team’s total pool amount for the upcoming 2019 MLB Draft. The PDF we acquired from an industry source was missing Washington’s comp pick for Bryce Harper at 138 overall, so we added that and manually recalculated each team’s pool total (which were incorrect on the PDF because of this missing pick).

(Update: After receiving additional clarification, it appears that Washington will not receive a comp pick for Harper; the pick that would have received as Harper compensation became the pick they gave up to sign Patrick Corbin.)

What follows is, first, the total draft bonus pool amounts for all thirty teams, followed by the individual slot values for each pick in the first ten rounds of the draft. Picks labeled “COMP” are compensatory selections for players lost via free agency or from last year’s unsigned draft picks. Picks labeled “CBA” or “CBB” are competitive balance picks (rounds A and B) allocated to teams in the spirit of parity. These can be traded, and several have been. In both the compensatory and traded competitive balance picks, we note the players for which the picks were received. The tables here will be updated if competitive balance picks change hands or if teams receive comp for a yet-to-sign free agent who received a qualifying offer, like Dallas Keuchel or Craig Kimbrel.

Below is our list of the top-100 prospects in baseball. The scouting summaries were compiled with information provided by available data, industry sources, as well as from our own observations.

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 rankings. For example, the gap between prospect No. 5 on this list, Victor Robles, and prospect No. 35, Sean Murphy, is 30 spots, and there’s a substantial difference in talent there. The gap between Travis Swaggerty (No. 56) and Adrian Morejon (No. 86), meanwhile, is also 30 numerical places, but the difference in talent is relatively small. You may have noticed that there are more than 100 prospects in the table below, and more than 100 scouting summaries. That’s because we have also included 50 FV prospects who didn’t make the 100; their reports appear below, under the “Other 50 FV Prospects” header. The same comparative principle applies to them.

As a quick explanation, variance means the range of possible outcomes in the big leagues, in terms of peak season. If we feel a prospect could reasonably have a best big league season of anywhere from one to five WAR, that would be “high” variance, whereas someone like Colin Moran, whose range is something like two to three WAR, would be “low” variance. High variance can be read as a good thing, since it allows for lots of ceiling, or a bad thing, since it allows for a lower floor. Your risk tolerance could lead you to sort by variance within a given FV tier if you feel strongly about it. Here is a primer explaining the connection between FV and WAR. For further explanation of the merits and drawbacks of Future Value, read this.

You’ll also notice that this year, we’ve added probable FV outcome distribution graphs for each prospect on our list. This is our attempt to graphically represent how likely each FV outcome is for each prospect. Using the work of Craig Edwards, we found the base rates for each FV tier of prospect (separately for hitters and pitchers), and the likelihood of each FV of outcome. For example, based on Craig’s research, the average 60 FV hitter on a list becomes a perennial 5+ WAR player over his six controlled years 26% of the time, and a 27% chance of accumulating, at most, a couple WAR during his six controlled years. We started with these base rates for every player, then manually tweaked them to reflect how we think the player differs from the average player in that FV tier, since a player in rookie ball and a player in Triple-A with the same FV grade obviously don’t have exactly the same odds of success. So, these graphs are based on empirical findings, but with the subjectivity of our opinions included to more specifically reflect what we think the odds are of various outcomes. This is just a concept we’ve been kicking around for a while, one we hope to continue to refine to try to better communicate things about prospects.

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What follows is simply a list of prospects who exceeded rookie-eligibility requirements in 2018. They are sorted into FV tiers (as listed at time of graduation) but not ranked within those tiers. Note the players whom we missed in the “Not Ranked” section at the bottom of the list. Lou Trivino (dominant reliever, probably should have been a 45 FV) and Niko Goodrum (a multipositional bat, typically a 45 FV for us) are prominent whiffs. Several of the 35 FV players were graded more highly in previous years (Fletcher, Laureano, O’Hearn, Palka) and they are a lesson in impatience and over-correction. Also take a gander at this piece from last winter, which I think has aged pretty well, especially the list of middle infielders.

It’s been a while since we posted anything comprehensive and transparent about how we draw our conclusions about prospects. Player evaluation and development are changing very quickly in the game, and those changes obviously influence how we think about prospects here at FanGraphs, enough to merit a refreshing primer before we start publishing this offseason’s organizational lists. In addition to teeing up the lists, this post is meant to act as a central hub that can serve to answer commonly asked questions about prospects and how they’re evaluated, specifically for those in the near future who want to start swimming in the deep end of the prospect pool. As we continue to augment our thinking and methodology, so too will we update this document, which will live in The Essentials section of the Prospects Coverage landing page. Feel free to direct any applicable correspondence to prospects@fangraphs.com. Common queries sent our way may find their way onto this webpage.

What information drives your opinions on prospects?

We see a lot of players ourselves. We talk to scouts from amateur, pro, and international departments about players they’ve seen. We talk to in-office analysts, front-office executives, and people in player development. We also use publicly available data we think is relevant. Some combination of these things fuels each player’s evaluation.

What are some of your shortcomings as far as information is concerned?

Increasingly, teams are using proprietary data as part of the player-evaluation process. TrackMan and Yakkertech aid evaluations on many different components of pitching and hitting, high-speed video of players from Edgertronic cameras allows clubs to better understand and alter hitting and pitching mechanics, and Motus sleeves and Rapsodo are used in pitch engineering. The mere existence and demonstrable efficacy of this stuff has altered the way we’re projecting players, but we don’t have access to the data generated by these devices across the entire population or prospects.

What is FV?

FV stands for Future Value, and it’s the way we distill each player’s scouting evaluation into a single expression. Broadly stated, Future Value is a grade on the 20-80 scale that maps to anticipated annual WAR production during the player’s first six years of service. But there’s also quite a bit of nuance underlying that definition, so let’s break down its components.

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There are 6,000 or so minor-league baseball players at any given moment. By definition, meanwhile, there are only 100 minor-league ballplayers on any given top-100 prospect list. That means there are also around 6,000 minor leaguers not on top-100 lists — all 6,000 of them still intent on reaching the major leagues.

And many of them do reach the majors. For half-a-dozen years, Carson Cistulli has highlighted a number of prospects who failed to make a top-100 list by means of his Fringe Five series, and some of those players — like Mookie Betts and Jose Ramirez — have gone on to become stars. There should be little doubt that prospects outside the standard top-100 lists have value. Determining how much value, however, is a different and more involved question.

When I attempted to determine a value for prospects who’d appeared on top-100 lists, I was working with a relatively small pool of players. Even 15 years’ worth of lists equates to 1,500 players at most. Attempting to determine the value for every prospect, meanwhile, would appear to be a much larger task. Does one look at the roughly 90,000 minor-league seasons over the same period? That seems daunting. Looking at Baseball America’s team-level prospects lists, which feature 10 players per organization, would provide a more manageable 200 prospects per season outside the top-100 list, but that wouldn’t quite get us where we need to be, either.

And yet, as I’ve noted, these prospects have value. On THE BOARD, for example, there are currently 689 prospects with grades (a) of at least 40 but (b) less than 50 (the lowest grade earned by players on a top-100 lists, typically). It’s these prospects in whom I’m interested. What follows represents my attempt to place a value on them, as well.

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Today, I’ve published a pair of posts in which I attempt to estimate the present-day value of prospects, both in terms of WAR and dollars. With that work complete, the logical next step is to turn away from the value of specific prospects and towards farm systems as a whole.

One can get a sense of the stronger and weaker systems just by eyeballing the rankings produced by Eric Longenhagen and Kiley McDaniel. What the prospect-valuation data allows us to do, however, is to place a figure on baseball’s top-800 or so prospects, creating a more objective ranking based on the grades assessed to each player here at FanGraphs.

These rankings provide a current snapshot of the farm systems before Longenhagen and McDaniel embark on their winter-long reveal of team prospect rankings. (The first post in their offseason series will appear this week.) As noted, the methodology for valuing prospects based on their grades is explained in my last two posts on the subjects:

• An Update to Prospect Valuation
• Putting a Dollar Value on Prospects Outside the Top 100

Keep in mind, these values are based on the current CBA, where players receive a minimum salary for roughly three years and then have three (or four) years of arbitration before reaching free agency after six full MLB seasons. Players are generally underpaid compared to their value on the field during these seasons, which is what creates the high present-day values and partially justifies the high value placed by teams on prospects when executing trades.

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Over the years, a good deal of effort has been put into determining the value of prospects. Victor Wang, Scott McKinney (updated here), Kevin Creagh and Steve DiMiceli together, and Jeff Zimmerman have all published work on the subject, roughly in that order.

The reasoning behind such efforts is fairly obvious: teams trade prospects for proven players all the time. Finding an objective way to evaluate those trades is useful to better understanding how the sport operates. Indeed, FanGraphs has benefited from those prospect-valuation studies on multiple occasions.

With another year having passed, I’ve attempted to build on the work of others and produce updated valuations of my own. Previous efforts have been very helpful in the process, while the input of prospect analysts Eric Longenhagen and Kiley McDaniel has helped me find results that would be most useful.

In building this study, I set out with the following aims:

• To separate players into as many useful tiers as possible without creating unnecessary distinctions.
• To use as much data as possible so long as it was useful and likely still relevant today.
• To make the valuations as forward-looking as possible.
• To recognize that player development is not linear and that players appearing on prospect lists vary from major-league-ready to raw, Rookie-level talents.

To those various ends, here are some of the parameters of this study:

1. Baseball America’s top-100 lists from 1996 to 2010 serve as the foundation for prospect grades.
When I started the study, I looked at the lists dating back to 1990, separating out position players from pitchers and organizing by year. I found that the evaluations from the earlier part of the 90s — especially those for pitchers — had considerably worse outcomes than those that came after. I debated whether or not to throw out the data. Eventually, though, I decided that since 15 years of prospect numbers were showing decidedly different results, and that there was considerable turmoil occurring within the sport during that time — expansion, a strike, and a lockout — it seemed reasonable to toss the earlier years and go with the assumption that the 1996-2010 lists more accurately represented prospect evaluation today and going forward than the rankings of 25 years ago.

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The 2018 trade deadline has passed and, with it, dozens of prospects have begun a new journey toward the major league with a different organization. We have the prospects traded since the Manny Machado deal ranked below, with brief scouting snippets for each of them. Players highlighted in blue are not technically prospects, having exhausted rookie eligibility, but we felt they fell under our umbrella of evaluation anyway as they’ve spent a lot of time up and down in the minors this year. Plus, it’s just interesting to think about where they fit. Scouting info comes from both in-person looks and also a combination of scouts and front-office personnel to whom we are eternally grateful.

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