The baseball world suffered a brief moment of shock on Tuesday night. Mike Trout, who needs no introduction within this interrupting clause to emphasize his greatness, was replaced by Peter Bourjos in the fourth inning of the Angels-Brewers game. Afterwards, Trout was diagnosed with a mild groin strain, and baseball collectively breathed a large sigh of relief. It appears that he plans to miss just a single game with the injury.

Before Trout exited on Tuesday, however, he had already gone 2-for-2 at the plate, adding yet another game to his hot start; in 49 plate appearances this season, Trout is slashing .406/.592/.938. Even in a brief outing, his WAR total still managed to increase, moving from 1.2 to 1.3. As of Wednesday, he is tied with Cody Bellinger for the major league lead in WAR rounded to one decimal place.

As we all know, Trout is the WAR king. He’s already put up 66.2 WAR in his career, making him the most valuable player in baseball since 2006, even though his career didn’t even start until 2011 and his first full season didn’t even come until 2012. I could go on and on about how great Mike Trout is at producing WAR, but a lot of those articles have already been written here and at other places.

One article that did catch my attention, however, was this 2015 piece from August Fagerstrom titled “Getting Mike Trout to 168.4 WAR.” In this piece, Fagerstrom outlined a potential career curve for Trout to hit 168.4 total WAR, a mark that would tie him with Babe Ruth for the most WAR produced by a single player in baseball history.

In this piece, I plan to do something similar but different. As in Fagerstrom’s piece, I want Trout to tie Ruth, but on a different WAR leaderboard: the all-time single-season mark. Read the rest of this entry »

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.

Sure, he’s won seven straight batting titles in Japan, but it’s telling that, in English, “Ichiro Suzuki” roughly translates to “Can’t hit Pedro.”

– The Utah Chronicle, March 30th, 2001

It is late afternoon in Seattle, and it is the beginning of April, and it is quite cold. The Mariners are going to play the Oakland A’s. Today, the baseball starts counting. Across the infield dirt, just behind second base, a few faint letters mark the time: 2001.

More than 45,000 people are here, the most that have ever crowded into this still-new stadium. There’s less team spirit on display than you might expect. Most of the attendees aren’t flaunting jerseys; they’re bundled up, hands tucked into coats. The fading sunlight falls over the stadium from behind the pale, high clouds, and as a few Mariners take the field, running sprints across the outfield grass, a hearty cheer rises up to greet them. The men in white stretch, pulling arms and bouncing in lunges, before trotting back to the dugout. Not much longer, now. Not much longer.

High up on a view level fence, in front of a kid and their dad, you can see a white posterboard, letters painted in amateurish block text: “WELCOME ICHIRO.”

Many of them know only what the numbers can tell them, the list of achievements that made him worth tens of millions. Seven straight batting titles and a lifetime .353 average. Some may have gone down to spring training, gathering in the Arizona heat, and seen it for themselves: 26 hits, catching batting practice fly balls behind his back, throwing runners out at third with seemingly effortless throws from deep right. The speed — the Mariners said they’d clocked his home-to-first time at 3.7 seconds. (The fastest average home-to-first time among major leaguers in 2018 was 3.86.) Read the rest of this entry »

Among the proposals exchanged by MLB and the MLBPA was the idea of studying the mound. More specifically, baseball is interested in studying what might happen were the mound to be lowered, or were the mound to even be moved back. In a sense, adjusting the mound might seem radical, but of course, the mound has been lowered before, and baseball wants to see if it might be able to combat the ever-increasing strikeout rates. The league-average strikeout rate in 1998 was 16.9%. A decade later, it was 17.5%. Yet a decade later than that, it was 22.3%. That’s a 27-percent increase in strikeouts over the course of ten years. You can see why people might want to nip this in the bud.

Why have strikeouts been on the rise? How might you explain all the swinging and missing? I suppose there are the people who might just grumble the term “launch angle” and leave it at that, but a more compelling explanation might be the league-wide increase in velocity. It’s been hard not to notice — as they say, now every bullpen has a half-dozen guys who come out throwing 95 miles per hour. Billy Wagner used to throw 96. Now everybody throws 96. And the less time hitters have to react, the more often they’re going to whiff. Case closed! We’ve all solved it, together.

Except for the part where velocity has ceased increasing. The velocity surge was something I think a lot of us just took for granted. Teams like velocity, and players are training harder than ever before. But the surge has slowed, if not stopped. You don’t need to dig too deep to find evidence.

Read the rest of this entry »

You’ve visited this website and clicked on this article, so chances are, you’re not only familiar with new forms of baseball data, but with the impact that data has had on various branches of the game, including and especially scouting. Kiley and I have each written about some of the ways that new data and technology are transforming player evaluation, but all you really need to know for the purposes of this article is that these developments have funneled in-person scouting resources down to lower levels of baseball, both amateur and professional.

There are several reasons for this. For one, the majors and the upper levels of the minors (Double- and Triple-A) are more stable competitive environments, and thus teams are more comfortable with statistical performance accumulated at those tiers of play. Individuals who reach those heights almost always have sufficient talent, technical proficiency, or some combination of the two, to play competitive baseball there, whereas the on-field competency of lower-level pro baseball talent (think teenagers in the DSL, AZL, Pioneer League, etc.) is more variable player to player.

As a result, statistical performance is much more reliable the further up the pro ladder a player climbs, allowing teams to more confidently incorporate it into their player evaluations. This, combined with the proliferation of TrackMan and Statcast metrics in pro baseball (almost every minor league park in the country has a TrackMan unit now), means that a growing number of teams feel that they have a firm grasp on upper-level players even if those players are not seen as much by scouts, and some organizations have even begun to de-emphasize in-person scouting at these levels. This frees up scouts to sift through the growing bodies and developing athletes at the lower levels, where statistical performance is almost meaningless. Read the rest of this entry »

Some of you might not realize that, if you hover over the search bar up there, you’ll see which player pages have recently been the most popular. Let’s give it a spin, shall we?

Willians Astudillo. Between Astudillo and Vlad, I don’t know which has been more popular, but I strongly suspect it’s the former, and it’s definitely the former among major-league players. People have been losing their minds over Astudillo of late. Now, I did write about him last week. Playing winter ball down in Venezuela, Astudillo has performed like a deserving MVP candidate. But also, there’s a clip that’s been making the rounds. Willians Astudillo hit a home run.

Read the rest of this entry »