Everyone missed on Mike Trout. Don’t get me wrong: Trout was a well-regarded player headed into the 2009 draft, a certain first-round talent. But he wasn’t—yet—a phenom. Everyone liked Trout; it’s just that no one loved him. Baseball America ranked him as the 22nd-best player in the draft. No one doubted his athleticism or his work ethic; a lot of people doubted the level of competition he faced as a high school player from rural New Jersey. The Angels drafted him with the 25th pick overall, and they’ll tell you today that they knew he was destined to be a special player. What they won’t tell you is that they had back-to-back picks at #24 and #25, and they announced Randal Grichuk’s name first.

It didn’t take Trout long to prove to everyone that he had been underestimated. He hit .360 in rookie ball that summer, which was just an appetizer for his 2010 season, when he hit .341/.428/.490 with 56 steals in A-ball, and was ranked as the #2 prospect in the minor leagues by Baseball Prospectus. In 2011 he jumped to Double-A and hit .326/.414/.544, was named Baseball America’s Minor League Player of the Year, and debuted in the major leagues at the age of 19.

Any time a team misses on a player the way almost every organization in baseball missed on Trout, there’s bound to be some soul-searching: what did we miss? Many times, there’s no satisfactory answer to that question. Albert Pujols was a 13th-round pick in 1999, and less than two years later was one of the best baseball players in the world—and even today, no one has been able to adequately explain why every organization in baseball misjudged him so badly.

In Trout’s case, there’s one astoundingly obvious reason why he was underrated going into the draft. It’s one of the most basic pieces of information we have about a player, a piece of information that precisely because of its ubiquity is almost always ignored: his date of birth.

Mike Trout was born on August 7, 1991. This is relevant because, unlike most players drafted out of high school, Trout was still just 17 years old when he was picked. His performance as a high school senior came at an age when many of his fellow draftees were still in their junior year; he played as well as he did without the benefit of an extra year of development.

Baseball’s aging curve is fairly well known by now. Most hitters peak at or around the age of 27, and their performance usually proceeds along a parabolic curve, rapidly improving in their late teens and early 20s, then a more gradual improvement in their mid 20s, before a gradual decline in their late 20s that accelerates in their 30s. The following chart simulates the aging curve for players in the aggregate, plotting talent as a function of age:

(Note that the chart is an approximation not based on actual data, which is hard to come by for 14-year-old major leaguers.)

The implication of the aging curve is that, the younger a player is, the more likely he is to improve over a short period of time. Take two players who are equally valuable today; if one of them is 25 and the other is 26, the difference between their long-term projections is minor. If one of them is 20 and one of them is 21, the differences can be massive, and much greater than you would intuitively expect.

Nearly a quarter-century ago, Bill James addressed this very point in the 1987 edition of his Abstract:

Suppose that you have a 20-year-old player and a 21-year-old player of the same ability as hitters; let’s say that each hits about .265 with ten home runs. How much difference is there in the expected career home run totals for the two players?”

As best I can estimate, the 20-year-old player can be expected to hit about 61% more home runs in his career. That’s right—61%.

The list of 20-year-olds who perform well as everyday major-league hitters is small, and they almost all go on to have stellar careers. This is what made Jason Heyward’s rookie season so promising. His .277/.393/.456 performance isn’t particularly noteworthy for a rookie, but for a 20-year-old rookie it was almost unprecedented, which is why—despite his sophomore struggles—he has almost limitless upside.

Incidentally, Heyward was born in August, and like Trout, was also just 17 on his draft day.

As you can see from the above chart on Baseball’s Aging Curve, the younger the player, the greater the slope of the curve—meaning the greater the rate at which he improves. So if there is such a substantial difference in the expectations between 20-year-old and 21-year-old players, it stands to reason that the difference between 17-year-old and 18-year-old players would be even more massive. At such a young age, a difference of even eight or nine months—the difference between an 18-year-old born in September and an 18-year-old born in May—might move the needle.

The two best high school hitters selected with the #1 overall pick in the draft,  Alex Rodriguez and Ken Griffey Jr, were both 17 on draft day. Griffey was born in November—making him one of the youngest first-round picks ever. Meanwhile, the oldest high school hitter selected #1 overall, Shawon Dunston (who was already 19 at the time), spent his entire career leaving people wanting more.

Here’s my point: I don’t think anyone would argue that, all things equal, a 17-year-old player is likely to develop into a better player than an 18-year-old player. But I wondered if the baseball industry as a whole has underestimated the importance of age. I wondered if, given two players taken at the same slot in the draft, the younger player returned greater value. In other words, even accounting for the fact that teams took age into consideration—presumably, a player who is particularly young for his draft class might get picked earlier—I wondered if those players were still undervalued. So I decided to do a study.

So far, all I’ve presented to you are anecdotes, and the plural of anecdote is not data. For instance, the youngest hitter drafted #1 overall wasn’t Griffey, it was Tim Foli, who in 16 years in the majors hit a total of 25 home runs. We need some data.

Fortunately, this is what BP interns were created for. With the help of Bradley Ankrom, Paige Landsem, and Clark Goble, I compiled a list of every high school hitter selected in the first 100 picks of every draft from 1965 through 1996. I stopped the data set at 1996 because I wanted to look at how these players performed over the course of their careers—I defined “careers” as the 15 years after they were drafted.

I avoided college players because the impact of age, if there is one, would be much more likely to show up in evaluating players who are 17 or 18 rather than players who are 20 or 21. I avoided pitchers because the aging curve for pitchers is much less predictable than it is for hitters, as many pitchers never throw as hard again as they did in high school. I figured that, if there were an effect to be seen, it would be most obvious in high school hitters. If it turned out that age on draft day did have an impact on a high school hitter’s future prospects, we could expand the study later to look at other draft groups.

Roughly 10 percent of the players in the data set have no date-of-birth information available—most of these players flamed out of pro ball too quickly to leave an impression. Those players were eliminated from the study, as were three players who were listed as being drafted out of high school but were 21 or older on draft day. (I’m assuming these players had extenuating circumstances; perhaps they had a stint in the army before they started their pro careers.) That left us with a data set of 846 players.

What I wanted to find out is whether players who were younger than average on draft day tended to return more value than expected. In order to determine that, the first step was to figure out what “expected value” was for each player. First, I had to define “expected value.” Fortunately, WARP is an incredibly handy tool whose express purpose is to estimate a player’s value. So I took the WARP generated by each draft pick for the 15 years after he was selected. However, I also applied a discount factor of 8%—meaning that 1 Win Above Replacement Player generated the year after a player was selected was worth 0.92 WARP in the year he was selected. By Year 15, 1 WARP was as valuable as only 0.29 WARP generated in his draft year. That seems fair, given that by Year 15 the player would almost certainly have become a free agent and likely as not moved on to another team.

I also “zeroed out” any seasons in which a player generated negative WARP. Given that most draft picks don’t reach the major leagues at all, it would be misleading to penalize a player who was good enough to reach the majors for having a negative-WARP season, relative to a player who might never have gotten out of rookie ball.

Using the data, I tried to determine the best formula to predict a player’s expected value in discounted WARP based on when he was picked. As I showed in my draft study from 2005, the expected value of a draft pick is highly dependent on when he was picked, and it isn’t a linear relationship—the expected value of a draft pick drops quickly from the #1 to the #2 pick in the draft and gradually levels out so that the difference in expected value between picks #99 and #100 is miniscule.

I looked at a number of different formulas to determine which would best fit the data, and the most accurate correlation I came up with was a linear relationship between “expected value” and 1/SQRT(PK). That is to say, the value of a draft pick correlates with the reciprocal of the square root of the pick number.

An easier way to look at it is this: the square root of the pick number is a measure of how much more valuable the #1 overall pick is relative to that pick. By this formula, the #1 overall pick is three times more valuable than the #9 pick, four times more valuable than the #16 pick, and so on. It also means that the #4 pick is three times more valuable than the #36 pick, and the #25 pick is twice as valuable as the #100 pick.

Performing a linear regression on the data leads to this formula:

XP = 11.21/SQRT(PK)—0.04.

XP refers to a player’s eXPected value. By this formula, the #1 overall pick is expected to bring back 11.17 Discounted WARP (henceforth known as DW). The #10 pick has an expected value of 3.50 DW; the #100 pick would be valued at 1.08 DW. The correlation between DW and 1/SQRT(PK) is highly statistically significant; the p-value was essentially zero. And if you’ve made it to the end of this paragraph, you need to get out more.

Here’s a graph that plots out the value generated by every player in the study and where they were taken in the draft:

The data shows an enormous amount of variation—not surprising, given the boom-or-bust nature of the draft—but you can sense that on average, the higher a player is drafted, the more he is worth.

I also looked at whether there was any correlation between a player’s expected value and the year he was drafted. My thought was that perhaps, as teams have done a better job of identifying players over time, players from more recent drafts might be expected to do better. On the other hand, it was possible that, with more and more major-league talent coming from foreign lands not subject to the draft, players from more recent drafts might be less valuable than those drafted in the 1960s and 1970s. It turns out these two factors must have canceled each other out, as there was no statistically significant relationship between draft year and DW.

Now that we have a simple formula for calculating what a particular draft pick “should” be worth, we can evaluate whether players who were particularly young or old were likely to return more or less value than expected on their investment. For example, we can look at the very first draft in 1965, when 25 high school hitters were selected among the top 100 picks. The oldest of them was a shortstop named Carl Richardson. Richardson was born on June 2nd, 1946. For the sake of standardization, we set “draft day” as occurring on June 1st for every year, so in our system, Richardson is listed as a day shy of 19 years old on his draft day.

Richardson was selected #77 overall by the Cincinnati Reds. The expected return of that draft slot was 1.32 DW. Richardson never made the major leagues, so his actual DW was zero.

On the other hand, the youngest high school hitter selected in the 1965 draft was a catcher from Oklahoma who was born on December 7th, 1947, making him more than 18 months younger than Spencer. He was also selected by the Reds, with the #36 overall pick, which has an expected value of 1.91 DW. As it turned out, that hitter—Johnny Bench—was worth considerably more that. (Bench ranks fifth among all the players in our study with 34.05 DW, behind only Alex Rodriguez, Rickey Henderson, George Brett, and Ken Griffey Jr.)

Now that you get the idea, here’s some data. I took the five youngest players from every draft from 1965 through 1996 and compared them to the five oldest players from the same draft. Here’s that data in chart form:


Young XP

Young DW

Young Return

Old XP

Old DW

Old Return








































































































































































































































“Young XP” refers to the eXPected value of the five youngest high school hitters in that year’s draft, based on where they were selected. “Young DW” refers to the total Discounted WARP those five players actually earned. “Old XP” and “Old DW” refer to the same for the five oldest high school hitters in that year’s draft. “Return” refers to the return on investment above or below expectations; +100% would mean that those five players returned, in total, 100% more than (i.e. double) what was expected from them.

Now that the explanations are out of the way: wow. Over the 32 years combined, the youngest players in each year’s draft were expected to produce slightly less value than the oldest players, because on average they were taken with slightly later draft selections. Despite that, the five youngest players in each year returned MORE THAN TWICE AS MUCH VALUE as the five oldest players. If you adjust for the fact that the older group had a slightly higher expected value on Draft Day, the younger group had a return that was 117% higher than the older group.

Let me repeat that: a team that drafted one of the five youngest high school hitters selected among the top 100 picks could expect MORE THAN TWICE AS MUCH VALUE from him as a team that selected one of the five oldest high school hitters. And that’s not a small sample size fluke; that’s a result derived from 32 years of the draft, looking at 160 players from both camps.

Here’s a graph that displays the data. The bars indicate the return for both young and old players in each season, while the lines measure a rolling average of the return over the previous five years:

The take-home from the graph is that the red line—which indicates the five-year return from the youngest players in the draft class—is above the yellow line (the five-year return for the oldest players in the draft class) for virtually the entire length of the study. And in most years the gap between the lines is substantial.

The other thing the chart reveals is that the five-year return for the oldest players in a draft class has been at or below 0% in every year of the study. There has never been a time when old high school hitters generated a positive return.

While the advantage enjoyed by younger players ebbs and flows from year to year, it doesn’t appear to grow or diminish over time. If we combine the draft years into four year bins (i.e. 1965-1968, 1969-1972, etc.), we can see that:


Young XP

Young DW

Young Return

Old XP

Old DW

Old Return

























































With the exception of the four-year span from 1981-1984—thank you, Shawn Abner—the young players beat their expected return (and beat the pants off of the older players) every time. Hedge funds would kill to beat the market this consistently.

Young high school hitters are simply much more likely to develop into stars, particularly players who weren’t elite picks. I already mentioned Johnny Bench, who went from the second round to the Hall of Fame. In 1972, Chet Lemon was selected with the #22 overall pick; Lemon was 17 years, 3 months, and went on to a fantastic career.

The following year, amazingly, two of the five youngest high school hitters went on to the Hall of Fame. Maybe it’s not a surprise that Robin Yount did, given that he was the #3 overall pick and was starting at shortstop in the majors the following year—the only 18-year-old to play regularly in the majors in the last 75 years—but it was a surprise that Eddie Murray, drafted as a catcher/first baseman with the #63 overall pick, went on to find the success he had. It shouldn’t have been; Murray was two weeks younger than Lemon had been. Murray and Lemon, in fact, were both among the six youngest players in the entire study.

The youngest player in our study from 1976 was taken with the #96 overall pick.  Rickey Henderson was a month younger than Mike Scioscia, drafted #19 overall that year. In 1980, the #71 overall pick was used on a young high school second baseman named Danny Tartabull. In 1986, the Brewers had the 6th overall pick and didn’t screw it up, using it to select Gary Sheffield. And in 1987, the Mariners selected Ken Griffey Jr 1st overall.

In 1992, Derek Jeter was selected #6, and Jason Kendall, born on the same day, was selected #23. And in the last year of the study, 1996, the youngest player selected in the Top 100 was Jimmy Rollins, who was drafted #46 overall. Meanwhile, the best players in the entire study selected from among the five oldest players in their draft class were Willie Wilson, Johnny Damon, and Richie Hebner.

This is, all modesty aside, quite possibly the most impressive and significant finding of my career. When it comes to the drafting of high school hitters, even slight differences in age matter. At least when it comes to high school hitters, young draft picks are a MASSIVE market inefficiency.

In The 1985 Bill James Abstract, James published the results of his study which showed that “The rate of return on players drafted out of college is essentially twice that of high school players.” That is considered to be one of James’ most important findings, and in fact it was more than a little surprising when, in 2005, I found that the advantage for college players had almost disappeared over the years.

Based on the data above, the advantage the youngest high school hitters in a draft class have on the oldest high school hitters is just as great as the advantage college hitters once enjoyed. And this advantage does not appear to be diminishing over time.

These numbers are so dramatic that they cry out for corroboration. In Part Two tomorrow, I’ll delve into the data a little more.

An expanded version of this article will appear in the forthcoming book Extra Innings: More Baseball Between the Numbers from Baseball Prospectus.

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congratulations Rany - this is pretty fascinating.
Rany -- fascinating stuff. Can you please explain a little more the discount factor? First, why 8%? Second, why a discount at all? I see the reason you gave in the text, that a 15th-year player is likely to have moved on to another team, but is the point of this study to find the characteristics of players who give the most value to the teams that draft them or simply to find the best players? Do teams draft with an eye toward who will provide them the most value, or do they, by and large, simply try to find who will be the best players when they reach the majors? Thanks!
First off, I'm happy to see that everyone likes the article, and finds the data as compelling as I do. The reason I used a discount factor of 8% is simple - that's the discount factor I used for my original study of the draft in 2005, and I wanted to be consistent. I don't know that 8% is better or worse than any other discount factor. I know that when economic analysis is done of, say, long-term baseball contracts, a discount factor of somewhere between 5% and 7% is usually applied. I decided to use a slightly higher factor simply because once a player becomes a free agent, the value his original team gets from him after that point is miniscule. You could use a 10% or even 15% discount factor and get no argument from me. In any case, I doubt changing the discount factor would change the results in an appreciable way.
Well I hope you're happy, Rany. You've kept me up till 2am reading this article. Now I'm going to be a mess tomorrow. It was totally worth it though. Great work. Sorry I don't have anything more substantive to add now (at 2am), but I hope this marks the beginning of more contributions from you at BP.
This is the best article BP has published in several years. Mind-boggling piece of analysis. Many thanks.
Absolutely agree
So if I'm to understand this properly, historical data suggests that Brandon Nimmo has a better chance of becoming a star than Josh Bell does because he's 7 months younger? That is pretty interesting.
Though it is fascinating stuff, it's difficult to know how to apply this to current (or future) draftees. Was Bubba Starling a poor pick at #6 despite all those tools? Like Josh Bell, Starling was an old 18, nearly 19 when drafted. Color me confused, but fascinated.
I get into this a little in Part 2, but suffice it to say that as a Royals fan, this study has me at least somewhat concerned about what Bubba Starling's ultimate upside is.
As someone who analyzes cost of capital for use in discount rates, I am also curious how you came up with 8%. I would have guessed higher since salaries increase exponentially as service time increases.
Also, did you do any sensitivity analysis using various discount rates? thanks.
Wow ... great stuff Rany!
This is similar to what I've read about the best hockey players. Because hockey divisions are broke out by birth year, a vast majority of the best players were born early in their birth years. Theoretically a player born 1 Jan 1997 would be at same level as a player born 31 Dec 1997, despite the year in age difference, which at earlier levels (Bantam for example) is HUGE. I know there are always exceptions (Crosby was born in August, August 7 1987 to be exact, thus his number 87) but just on the surface this does make sense. Off topic a bit, I know, apologize.
Actually, this study is looking at a very different phenomenon, one that works in the *opposite* direction than what you're referring to. The evidence about hockey players - which went mainstream in Malcolm Gladwell's book "Outliers" - is that young hockey players born at the beginning of the calendar year, who (because January 1st is the cutoff to determine what age group a kid plays in) are the oldest players in their league, tend to be slightly better than their younger teammates, which means they're more likely to make All-Star teams, get expert coaching, make travel teams, etc - meaning they're more likely to improve over time, and more likely to reach the NHL. A similar but smaller effect occurs in baseball, only the cutoff in baseball is August 1st - there are far more American-born baseball players born in August than in July. (The age cutoff has recently been moved from August 1st to May 1st, I believe.) But my study reveals sort of the opposite. In this case, every player selected in the draft has already shown elite talent, but in this case it's the *younger* player who simply has more time and physicial maturation on his side, and is likely to improve more. This effect is NOT an artifact of some artificial cut-off date; you could hold the draft on any day of the year, and some draft picks are always going to be younger than others. I hope that makes sense.
Your assumption is that whatever beneficial effect of growing up as older than most teammates on little league teams has totally evaporated by the time the player is 17. In fact, the month-of-year-born effect (surmised to result in superior coaching during formative years, higher self-confidence, etc.) could very well have a permanent imapct and still be at work after a player is drafted.
How much of a monopoly on organized ball does Little League have in the U.S.? Other organizations could have different cut-off dates. Toronto Playgrounds, for example up here in Canada, uses January 1 - just as the school year. High Schools in the U.S. could have different cut-off dates depending on the state. When my family moved from New Jersey to Connecticut, I had to repeat kindergarten, because I was born in October.
Little League and Cal Ripken youth baseball account for the vast majority of organized leagues, and both do use May 1. PONY, probably the next biggest, uses it as well.
"But my study reveals sort of the opposite" Actually, it goes along with that result very well. Your study shows that older players are playing at a higher level relative to their potential and are more likely to under perform their indicators if drafted. The hockey player study shows that being older puts you over a threshold of performance for getting drafted. Imagine that players have two scores: (1) current performance rating and (2) future performance potential. If you hold 2 constant, as you increase age 1 increases. When drafting teams use 1 to determine if a player is worth taking. In hockey there is a cutoff value for 1. In baseball the draft is larger and there is a lower cutoff value for 1 - large enough that over time the higher 2 value of a same 1 value in a baseball player makes a huge difference. Your study suggests that hockey teams are making the exact same mistake and that there are better hockey players that are undrafted.
Rany, This article was fantastic. Can't wait for part 2.
Was Francisco Lindor the youngest HS hitter in this year's draft?
He was the youngest (born 11/14/93) high school hitter that signed, yes. The younger ones that didn't sign: OF Tyler Scott (41st round by Texas, born on 5/16/94) OF Garrett Brown (43rd round by Colorado, born on 3/16/94) SS Morgan Phillips (17th round by Cincinnati, born on 12/11/93) OF Waldyvan Estrada (50th round by San Francisco, born on 12/11/93) SS Ahmad Christian (46th round by Milwaukee, born on 11/23/93)
Wow amazing stuff and glad to see you back here at BP. Intuitively this seems right and I personally use this theory when playing Baseball Mogul without ever thinking about why.
Great stuff Rany! Kind of puts international signings in perspective as well when kids are getting signed at 16. Guys, I why are you quibbling about the 8% discountrate? Tweaking that doesn't really change the overall results. Personally, I've been thinking that HS hitters in general are a large market inefficiency. I haven't done any studies, but I'd be really interested to see how many HS hitters that are initially ranked in the top 200 or so talents in the draft but don't sign because they want over-slot bonuses end up vastly improving their draft stock (i.e. the over-slot bonus should have been paid)? The thought really occurred to me this year as I watched the Mariners decide not to sign the younger Cron brother. But I've also been curious watching guys like Pedro Alvarez and Anthony Rendon improve in college. Are teams missing out on opportunties for HS hitters in general by not going a little further over-slot?
It's hard to know how changing the discount rate would affect things. A higher discount rate would dramatically decrease the value of later years, when the players are entering their decline phase. Since the older players would reach their decline phase much sooner, this puts them at a disadvantage. I wonder how this would look with a different period looked at as well (Say 10 years, 4 for development and 6 years of major league service time?).
"Tweaking that doesn't really change the overall results." If that's true, then that's fine. But is that true? For my part, I'm not quibbling. I just want to know what the justification is. I can't quibble until I know the "why" of it.
Great article. I have one question though, at the end of the article you compare this finding to Bill James' finding that the rate of return on college players was much higher, but you seem to only be looking at the outliers (comparing the five oldest to the five youngest). Isn't there some way to look at how much a change in age affects the rate of return, so then a team could look at two players and say well we think that high school hitter A is 10% better than player B today, but since high school hitter B is two months younger we think that he will be better in the future?
Very enjoyable article.....makes me glad to be a regular "" reader even though I'm a Tigers' fan!
I wonder which team (if any) had this data already and had been exploiting it. Rany are there any teams that have tended to draft "younger" hitters
That's an excellent question, and while I didn't look at that issue in this column, it's something I hope to look at soon, possibly for the expanded version of this article we're planning for the next edition of Baseball Between The Numbers.
I was also going to suggest looking for teams that consistently draft younger-than-average high schoolers to see if anyone has grabbed onto this result already.
Keith Law said in his chat today that Rany's findings were not entirely new, and that some teams (he mentioned the Cards and the As) were already taking this into account. That might be a good place to start looking.
The A's took Yordy Cabrera last year and he was one of the oldest, if not the oldest high school hitter in the top 100.
Welcome back, Rany! Tremendous article. Between this and Mike Fast's article on catcher framing (as well as a bunch of other excellent content), business has certainly picked up here on BP lately. Kudos to Steven Goldman and the whole BP crew!
A few more recent examples, looking at top 50 picks for sake of brevity): 2002: B.J. Upton 17.78 Price Fielder 18.06 Scott Moore 18.46 Jeremy Hermida 18.34 James Loney 18.07 Jeff Francoeur 18.40 Sergio Santos 18.91 Matt Whitney 18.33 Micah Schilling 19.43 Jason Pridie 18.64 Joey Votto 18.72 Corey Shafer 19.46 Adam Donachie 18.25 Brent Clevlen 18.60 2003: Delmon Young 17.71 Chris Lubanski 18.19 Ryan Harvey 18.76 Ian Stewart 18.15 Lastings Milledge 18.15 (not a typo, same day) Matt Moses 18.26 Brandon Wood 18.25 Eric Duncan 18.48 Daric Barton 17.79 Jarrod Saltalamacchia 18.08 Adam Jones 17.83 2004: Matt Bush 18.31 Chris Nelson 18.74 Neil Walker 18.72 Billy Butler 18.12 Trevor Plouffe 17.96 Greg Golson 18.70 Blake Dewitt 18.78 Reid Brignac 18.37 2005 Justin Upton 17.77 Andrew McCutchen 18.64 Jay Bruce 18.16 Brandon Snyder 18.52 CJ Henry 19.00 Colby Rasmus 18.81 Hank Sanchez 18.51 2006: Billy Rowell 17.72 Travis Snider 18.32 Chris Marrero 17.91 Chris Parmelee 18.27 Max Sapp 18.27 Cody Johnson 17.79 Hank Conger 18.34 Jason Place 18.06 Preston Mattingly 18.76 Kyler Burke 18.11 Adrian Cardenas 18.64 Jason Taylor 18.38 David Christensen 18.30 So... 5 youngest: Delmon Young 17.71 Billy Rowell 17.72 Justin Upton 17.77 B.J. Upton 17.78 (four days before Justin; weird, right?) Daric Barton 17.79 Cody Johnson 17.79 5 oldest: Corey Shafer 19.46 Micah Schilling 19.43 CJ Henry 19.00 Sergio Santos 18.91 (flamed out as a SS) Adam Jones 18.83 Five youngest had more hits obviously, but also Billy Rowell (who was also from NJ, like Trout) and Cody Johnson.
You were right the first time on Adam Jones. He was only 17.83, which puts him close to the 5 youngest.
Wow, bad error on my part. Sub Colby Rasmus in for Adam Jones on the 5 oldest.
So I ran the numbers. Looking at last year's top 11 picks, the 5 oldest were: 1) 26 Swihart, Blake V Sue Cleveland HS (NM) C 4/3/1992 2) 81 Jerez, Williams Grand Street Campus (NY)CF5/16/1992 3) 5 Starling, Bubba Gardner Edgerton HS (KS)OF8/3/1992 4) 61 Bell, Josh Jesuit College Prep (TX)RF8/14/1992 5) 82 Hedges, Austin Junipero Serra HS (CA) C8/18/1992 The five youngest were: 5) 84 Rosa, Gabriel Colegio Hector Urdaneta CF7/2/1993 4) 60 Harris, James Oakland Tech Sr. HS (CA)OF8/7/1993 3) 73 Santana, Alex Mariner HS (FL) 3B8/21/1993 2) 38 Martin, Brandon Santiago HS (CA)SS8/24/1993 1) 8 Lindor, FranciscoMontverde Academy (FL) SS11/14/1993 That brings me back to today's cover boy - Josh Bell. He is older, but in your study, you would be comparing him to his draft number, as opposed to his signing bonus, which should be more reflective of his true perceived talent level. Wouldn't that skew things in your study?
I like the idea behind your question, but aren't some draft picks paid more simply to pull them away from the players' desire to play football or go to college etc as opposed to just perceived talent level?
I know in the recent past (although it seems to be changing a little bit...) the best players weren't always selected ahead of lesser players due to bonus demands. Has this always been the case? Were bonus demands much of an issue in the '70s and '80s? I'd love to hear Rany address this. It must have made a big enough difference that Nate Silver worked signing bonus into PECOTA instead of draft position. Would it be better if Rany made this distinction as well?
I'd like to see the signing bonus issue addressed as well, but my instinct is that it's something that would even itself out over the years. Unless either older or younger high school players are overwhelmingly more likely to ask for insane bonuses I would assume they sign over slot at roughly the same rate. I could certainly see it having some minor effects on the numbers, but I would be shocked if it dramatically changed the results.
As Rany said, anecdotes are not data, but two of the 5 oldest last year, Starling and Bell, got two of the largest bonuses. I think Swihart and Hedges were also paid handsomely. Of the youngins, only (to my knowledge) Lindor was significantly above slot.
I'm sure we can pull up any random year in the data set and see the draft bonuses skewed, sometimes heavily, in favor of either the younger or the older players. In order for it to contradict Rany's work though, you would have to consistently see the opposite of this year with higly talented, bonus demanding youngsters dropping in draft slot and then outperforming because of their superior talent.
Rany, a very very sincere welcome back. What a great piece. Age is my favorite topic, and this is my catnip.
Great article. I can see what will happen. Michael Lewis and Rany will write a book about young high schoolers being a draft inefficiency. All the major league clubs start reaching for young high schoolers. Older high schoolers soon become a draft inefficiency. I think in 50 years an interesting study would be to see if there's a cyclical nature to draft inefficiencies based on the results of sabermetric research.
"Michael Lewis and Rany will write a book about young high schoolers being a draft inefficiency." Rany, what actor would you like to see play you in the movie version?
Excellent, thank you. OK, then, when are we going to study rural vs. suburban vs. urban? northern climate vs. southern climate? everything else that could be studied?
This is a great piece. The writing, subject, approach, significance of findings. Awesome. I'll get the book, and I hope to see more BP pieces like this.
Just adding my voice to the chorus of praise.. It *is* wonderful to see you back, and this kind of writing always has been the essence of BP for me. More, please (and not just Part 2, although I'm really looking forward to it).
Great work Rany. I had been working on something involving the draft and age as well. In my research, age compared to league is one of the biggest correlations to future success from high school all the way to the majors. I even have a list of ages of almost every minor leaguer and a large amount of amateurs at my blog. I think it is a huge market inefficiency. Thank you for delving into this and interpreting it in a understandable way. My only question is where it evens out. Is there a smaller difference when you look at the top 10 oldest/youngest? 20?
Awesome stuff. I think it is important to make clear that what makes this phenomenon so striking is that it occurs among differently aged players at the SAME experience level (i.e., as high school seniors, with (almost) everyone having the same number of years of baseball experience under their belts). If, for instance, high school juniors were also eligible for the draft, then the best players would tend to get drafted in their junior years; thus we would of course expect the younger draftees to have more value over the course of their careers than the older draftees. However, only high school seniors are eligible, so the only players who would violate this assumption would be either older high schoolers who may have been held back (Rany cuts off at 21 and up, but maybe he should try cutting off at 20), and on the other end, someone like Bryce Harper who effectively jump-started his draft eligibility by a year.
it is interesting, too, the way international free agency has worked down to kids barely legally old enough to work in the states being made millionaires, and how some of the greatest baseball talent has come from that pool of players. it'd be a lot of work, but to see how the draft to age considerations here compare cost benefit wise to those of international signings. this was one of the better reads in quite some time here. thank you
Great work. Nice having you here again, even if it is only a guest spot.
Speaking of Harper, he was two months younger than Trout when he was drafted and had already played and dominated against players two and even three years younger. As a Nationals fan, I can't help but be excited about the implications of your study for his performance.
"In 1986, the Brewers had the 6th overall pick and didn’t screw it up, using it to select Gary Sheffield." I think every Brewers fan on the planet would argue this statement as it relates to the Brewers organization.
I'm another reader who ended up sitting up past 2:00 a.m., transfixed--damn you, Rany, and your thought-provoking writing! :)
i love it. im not sure what else I could spend $5 a month on and get thhe level of satisfaction BP provides. Great article.

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