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Friday, October 14, 2011

Jazayerli: Doctoring the Numbers: Starting Them Young, Part Two

Part One here (although I’m wondering, given this article, why we needed a “5 oldest vs. 5 youngest” article at all.)

Rather than simply looking at the youngest and oldest players in each draft year, I’ve taken all 846 players in the draft study and separated them by age into five roughly equal bins: Very Young, Young, Average, Old, and Very Old. I then calculated the combined expected value of the players in each bin based on where they were drafted and the combined Discounted WARP that they actually generated.

(If you want the technical details: “Very Young” players were less than 17 years, 296 days old on draft day; “Young” players were between 17 years, 296 days and 18 years, 38 days; “Average” players were between 18 years, 38 days and 18 years, 120 days; “Old” players were between 18 years, 120 days and 18 years, 200 days; “Very Old” players were more than 18 years, 200 days old.)

as recently as 2003, the baseball industry as a whole massively underrated the importance of age in drafting high school hitters and massively undervalued high school hitters who still needed their parents’ permission to sign their contract…. Additional studies are needed to determine whether a similar edge towards younger players exists with pitchers or at the college level; if it does, it is almost certainly a smaller one. But even a smaller edge is worth exploiting. There are fewer and fewer market inefficiencies remaining in the post-Moneyball era, and they usually require a hell of a lot more research than simply finding out a player’s date of birth. Implementing this evidence into an organization’s draft preparation is free and painless and ought to have a significant impact on where players are selected.

The District Attorney Posted: October 14, 2011 at 07:08 PM | 13 comment(s) Login to Bookmark
  Tags: sabermetrics

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   1. Walt Davis Posted: October 14, 2011 at 09:59 PM (#3963737)
Implementing this evidence into an organization’s draft preparation is free and painless and ought to have a significant impact on where players are selected.

No it shouldn't. It's actuarial information which, if you're selling millions of insurance policies, can be quite useful; if you're making a decision about an individual, it makes as much sense as using his astrological sign. Actuarial studies are useful for projecting small differences in outcomes for large populations, not small differences among a group of 5 potential #22 draft picks in early June 2012.

The lesson of the old college vs. high school debate wasn't (or shouldn't have been) "draft college players, they're better", it was "don't have a moronic bias against college players and pick who you think is the best player."

Which isn't meant as a dig at the study but at the way in which information like this tends to get cited here and elsewhere. The best you can say about this evidence is "if player A and B are otherwise identical, take the younger one as he has greater development potential."
   2. McCoy Posted: October 14, 2011 at 11:22 PM (#3963784)
I'm just trying to figure out how this actually shows that teams aren't valuing players properly. Ken Griffey Jr went #1. Yount went #3 and Jeter and Sheffield went #6 each. Teams are not only drafting positional high school players but HS pitchers and college pitchers and hitters as well.

What he should be doing is looking at players drafted in the 3rd round or lower or something like that. These are players that every single team had ample chances to draft but did not. Compare those players and we won't have to worry about the top prospects skewing the numbers.
   3. my2cents Posted: October 15, 2011 at 03:15 AM (#3964157)
Significance testing was not included here either. This article is just awful.
   4. MM1f Posted: October 16, 2011 at 03:33 AM (#3965009)
Didn't RTFA but, since the draft is held at the same time each year, doesn't this basically try to pretend that people born in August are better at baseball than people born in April?
   5. a bebop a rebop Posted: October 16, 2011 at 03:54 AM (#3965058)
Didn't RTFA but, since the draft is held at the same time each year, doesn't this basically try to pretend that people born in August are better at baseball than people born in April?

Weird way to put it. Are you dismissing the possibility that it's true?
   6. McCoy Posted: October 16, 2011 at 04:06 AM (#3965071)
Didn't RTFA but, since the draft is held at the same time each year, doesn't this basically try to pretend that people born in August are better at baseball than people born in April?

Well, not really. What it is showing is that 18.5 and older high schoolers are either getting drafted too high or do not reach the same level of heights as 17.5 and younger high schoolers who are either being drafted too low or reach higher heights. Should that really shock anyone? For the most part Rany is looking at kids that are a year or more apart. So if you got player A who is 18.5 and is hitting .340 against a mixed bag of high schoolers and player B who is 17.5 and is hitting .340 against a mixed bag of high schoolers which one do you think is actually further along in their development or higher skilled?
   7. Harold can be a fun sponge Posted: October 16, 2011 at 05:10 AM (#3965103)
Didn't RTFA but, since the draft is held at the same time each year, doesn't this basically try to pretend that people born in August are better at baseball than people born in April?

No, not at all; it means that those people are underrated at the time of the draft.
   8. LionoftheSenate (Brewers v A's World Series) Posted: October 16, 2011 at 05:38 AM (#3965111)
It seems the Packers know that younger players = better players.
   9. McCoy Posted: October 16, 2011 at 06:04 AM (#3965116)
No, not at all; it means that those people are underrated at the time of the draft.

Well, it means they might be underrated. Ken Griffey Jr. went #1 and he is one of the 8 spikes in Rany's timeline that cause much of the very young gain. I would think Rany should have seen the flaw in his metric when that popped up and done something about it but he didn't. When you go #1 it is impossible to be underrated. Even if it is true that Griffey performed better than a typical #1 draftee the point is still moot since nobody can draft him any higher than first. So he shouldn't be a datapoint in this study. Just like if the oldest player is the 100th drafted high schooler and he never plays a game in the major that shouldn't be a datapoint either. Or at the very least they shouldn't be used to develop a drafting strategy.
   10. Harold can be a fun sponge Posted: October 16, 2011 at 06:15 AM (#3965120)
Well, it means they might be underrated.

Yes, you're right. I just meant that the article implied "underrated" rather than "better"; I tried to edit my post to clarify and to give you a coke, but the site was going through one of its two nightly blips.
   11. Harold can be a fun sponge Posted: October 16, 2011 at 06:22 AM (#3965124)
Even if it is true that Griffey performed better than a typical #1 draftee the point is still moot since nobody can draft him any higher than first.

I agree with you, but I do think it's worth pointing out that the Mariners could've drafted somebody else. Or to think of it another way, perhaps not all 26 teams would've drafted him #1; perhaps if we could consider all possible draft orders, Griffey's average draft position would be 1.1 or 1.25 or something. The Mariners (and the pool of younger players) deserve credit for that value, even out of the #1 slot. (Of course, since we *don't* know how the draft would've played out with different orders, we really don't know how much credit. It might be very close to zero, especially in the specific case of Griffey, rather than a theoretical case.)
   12. McCoy Posted: October 16, 2011 at 06:33 AM (#3965127)
I agree with you, but I do think it's worth pointing out that the Mariners could've drafted somebody else.

They certainly could have gone the Sam Bowie route but they didn't so you can't really use Griffey to say that young high schoolers are under drafted as compared to old high schoolers. As for considering all possible draft orders I mentioned something similar in the other thread. My opinion was that it would probably be beneficial if compiled the data based on all of the pre draft rankings of players that are out there. Obviously we couldn't go very far back and perhaps it would be something we could only do going forward since we might only find a baseball america list and who knows if that is representative of what the scouts think.


One other thing I would like to add. When I looked up Chet Lemon I noticed that that draft didn't have a lot of positional players rack up a lot of WAR. AS I mentioned in the other thread Chet Lemon was the only hitter that really produced that year from the first round and it wasn't until I believe the third round that you got another good producer. So this study doesn't really factor in the possibility of a weak crop or a good crop popping up. Something that I believe Misirlou or Walt talked about. For instance if you have to pick between Mark Prior or Joe Mauer and you pass on Mark Prior because of money concerns that doesn't really show that college pitchers are underrated if Mark then goes on and puts up a 300% return compared to expected out of the second slot. Obviously this example is just a hypo. It appears a ton of value for young players comes from players drafted in the first round with a very very small group of players providing value from later rounds. I'm not sure how Johnny Bench getting drafted in the 5th round or whenever he got drafted can create a viable draft strategy nowadays.
   13. Zach Posted: October 16, 2011 at 10:19 AM (#3965141)
As I said in the previous thread, doing a linear regression is terrible here.

Seriously, what looks linear in this graph? Is there any difference between these two lines at all? The two very high points at x=1 for the red graph look like they're responsible for the entire "effect."

A lot of sabermetric studies get away with using questionable statistical methods because they're looking for results which are very significant and have huge sample sizes. For a study like this, you probably want to crack open the ol' statistical textbook and do some reading before you do anything else.

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