(Note: I’m going to refer to Rany Jazayerli as Rany not because I know him personally but because my fingers have never been able to develop a muscle memory for his last name.)
For several years now, I’ve been pointing people to this twelve part series done by Rany Jazayerli of Baseball Prospectus as the pre-eminent work on quantifying the value of draft picks. It’s a long set of articles but the research is absolutely fantastic, and it’s something that everyone should be initiated with. To that end, I’ll spend some time reviewing each article and giving you the down and dirty on what Rany finds. I’m not sure which, if any, of these articles are available for free access so there’s a limit to how much I can present here. That’s unfortunate because the graphs and attempts at visual quantification are often very enlightening. So if you can’t access the articles, I’ll be your guide dog, blind man.
Rany begins with an interesting if brief introduction into the history of the Rule IV draft. I think that his initial suggestion that the June draft day is the single most important date of the year an interesting one if arguable. With the televising of this last year’s draft, that posit is brought to the forefront as well as this prescient quote:
Sexy, it’s not. Neither is it all that telegenic, although it certainly could be if MLB ditched the conference call for an amphitheatre with good lighting and tried to make a production out of it.
He pretty well called that one. Historically, Rany points out, that the draft emphasis was placed on high school draftees to the point that the entire first round of the 1971 draft was high schoolers. That’s quite remarkable. As with many things, the 1985 Baseball Abstract included some remarks on the draft including challenging the idea that high school players are more productive.
Rany sets out to evaluate the draft with a sample set that includes the first 100 picks from the Rule IV drafts from 1984 through 1999. As in his articles, picks 1-30 will be referenced moving forward as “first round picks”, 31-70 as “second round picks” which includes supplementals and 71-100 as “third round picks”. I really like that this study encompasses (essentially) the first three rounds since, as we somewhat painfully learned this past year, if one of those picks are not signed then the team receives a compensatory pick in the following draft. If they are selected in the a round after that, like . . . say the fourth round, teams get nothing for not signing Kyle Russell, er. . . I mean their pick. Additionally, if we’re going to examine a set of draftees the early rounds are the most vetted and talented individuals. We could reasonably guess that given those two qualities they are the most likely sample set for trends to emerge.
He selects 1984 as a starting point because the draft was streamlined in the 80’s removing an additional draft day. 1999 is selected as the end point because he’s running this analysis in 2005 and even with 6 years for the 1999 draft its reaching the point where you can’t determine whether draftees really turned into successful major leaguers. They also eliminate all players that don’t sign with their drafted team in an effort to limit the dataset. That leaves them with 1,526 draftees of a potential 1,600.
Rany then has some poor BP intern enter the WARP data for all 1,526 players. I should mention quickly that I’m not a fan of WARP for a few reasons. 1) The replacement level is set far too low making bad players seem like reasonable options. 2) It incorporates BP defensive metrics which are dubious in their own right. 3) It’s black box, which when incorporated with the other issues just leads me to trust it less. Now that’s not to say it’s a worthless stat, just not my favorite (which would be FIP for pitching or linear weights for offense).
After laying the groundwork we get to the good stuff. The first chart is a graph of what percentage of each pick made it to the majors — even if that only amounted to a short callup. I don’t want to reprint the graph because of those annoying copyright issues but I’ll hit some highlights: 80% of the first 5 picks make the majors, over 65% of the first 20, from pick 20-65 at least 40% make the majors. There’s a linearly declining relationship between draft pick and chance to make the major that is made obvious by the data. He also splits the data into a first 8 years and second 8 years set showing that the chance to make the majors has remained pretty constant in the more recent drafts compared to the older ones.
He continues:
Of course, the goal of the draft isn’t simply to find a player who will get a pinch-hit appearance in the majors a decade later; any draft measure which labels Alan Zinter a “success” is obviously incomplete. So let’s look at a different and much more telling set of data, which is the average WARP accumulated by a draft pick in the first 15 years after he was drafted.
So he proceeds with graphs that display the cumulative WARP for each draft pick (1 through 100). This is obviously a very noisy graph given that each point consists of only 16 data samples. You can see what loosely looks like an exponentially decaying curve.

When he creates data points based on grouping 5 picks the curve comes out much clearer although some variation remains. It’s important to note that there’s a significant dropoff from pick 1 to 2 compared to other ranges.
From that data, Rany arrives at his first draft rule. Draft Rule #1: The greatest difference in value between consecutive draft picks is the difference between the first and second picks in a draft. While this appears to be extremely evident from the data I’m not sure how helpful it is to us as draft evaluators. If MLB were to implement a system where picks were tradeable (and what a headache of analysis that would create), then this would be critical information as you would want a much higher return for the #1 draft pick than subsequent picks.
The second draft rule is much more helpful, imo. Draft Rule #2: There is surprisingly little difference in value between second-round and third-round draft picks. Rany notes that the WARP plot for draft picks “essentially flatlines” after pick 40. The curve above shows what he’s talking about. There’s a significant drop off in the earlier picks but past pick 40 the decay doesn’t significantly alter the value of the pick. In part we’re equating the value of supplemental picks to third round picks. That shouldn’t be taken as a devaluing of supplemental picks but rather the importance that you’re getting another pick that has nearly a 30% change to reach the majors. Would you rather have two picks with a 30% chance each or three? Obvious, quantity of picks within the first 100 is significant.
Filed under: Sabermetrics, analysis













Thanks for leading this blind man through the wilderness. good stuff!
I don’t know if this gets covered elsewhere in this series but is there an accepted $$ value per WARP?
And just to be clear, this graph is saying that the average 10th pick in the draft will accumulate approximately 22 WARP’s over his career?
Big thanks, for this one and the rest to come!
“I don’t know if this gets covered elsewhere in this series but is there an accepted $$ value per WARP?”
That all depends on a teams needs and payroll. If they have a lot of young players, they can accept a lower $$/WARP value from their free agents. The key is to set your win goal (probably about 90-92 wins gets it done for the Cardinals), calculate what you expect from you cost controlled players and then you can find how many wins you need to be getting from your free agent signings.
thanks az, i know somebody must have done this. all the more puzzling that we didn’t offer eck arb. the value of a supplemental pick is nothing to sneeze at regardless of what dollar value you assign. it is an opportunity and it is up to the team to optimize it.
it is also an interesting backdrop or context against which the cardinals drafts can be judged, i.e., another league average comparison. luhnow has to outperform the league if we are to succeed by focusing more on the draft. FA signings can modify the index of a team’s true success, but the draft will remain the major talent base for a long time. hopefully the cards front office can get on the same page next year.
Az - I guess I wasn’t clear as your answer didn’t get to my point. I’m trying to get a ball park figure of how much is a draft pick that will provide 10 Warps over his career worth or, with regard to FA’s, how much is a player that will add 3 WARPS this year worth.
It points to the thought that the number of picks in the top 40 is a very important number. The question I wonder is if it is always (almost always) the best idea to offer Type A players arbritration based on risk reward of a player you don’t want accepting arbritration versus picking up another pick. Probably a lot of grey area here, but the Eckstien case is a good example to examine.
cariocacardinal - Sorry, wasn’t quite following you initially. Tango uses this salary scale for WAR (wins above replacement) but that has a different replacement level than WARP from BP.
I don’t know what the exact difference is but I’d be willing to guess it’s something on the order of 2-2.5 wins in general. So if a player projects for 5 WARP in 2008, I think that it’s something closer to 2.5 WAR. But I’m really not confident about that . . . . .
I don’t know of any salary data specific to WARP since everything at BP is tied to VORP now I think.
Regarding Solano, if he isn’t striking out or walking that means he is getting wood on the ball most times he is up. The ability to hit the ball in the gaps is much easier attained than the ability to not strike out. It seems to me that just a little bit of tightening in in pitch selection could pay much higher dividends than it would for most players.
I propose a different approach: the first parameter to be used to evaluate a player is the number of PA/IP in the major, this is stating his major league likeness related to the era he played in. A threshold must be put on this parameter, to discriminate between a real major leaguer and a AAAA player (I would say an equivalent of 800PA or 300IP for starters or 70IP for relievers). Then their WARP/OPS+/ERA+/whatever can be evaluated to discriminate the real value.
Moreover, the tail of the distribution should be analyzed in a different way: due to the flattening of the average, it is more the peaks that should be studied, because the situation will go from a “generally good” player selected in the first two rounds, to the “diamond in the rough” player selected later. What it is important is to understand how to predict who will be the diamond.
GO CARDS!!!