I’ve spent a good chunk of time this winter updating and reworking my draft research. There’s a lot of stuff to get out and I’ve just been slow getting over the hump and really writing it up in reasonably final form. A couple weeks ago I participated in my strat league draft and of course a lot of players from the June 2005 draft were selected. Someone asked about the probabilities of these players actually panning out and I thought that was a good opening to at least knock out a quick summary of some of my research just based on those players that were selected in our draft. I was originally just going to send out it to the league, but I really liked the idea of putting specific names to the generic draft slots. Instead of looking at draft slot #4 we can look at draft slot #4, Ryan Zimmerman. I think that adds a nice put of context both for the draft slots and for the players many of whom are popping up on prospect lists this winter.
I started to think of this as a nice summary of some of the work I’ve done, so I’ve added additional sections to that framework and it became much longer than I intended, but that’s life.
Part I: What does an X WARP3 player look like?
This work is designed to show the frequency of different types of players in the draft. The most important “type” of player distinction that I make is quantified by the player’s career WARP3 total. I’ve looked at these players so much that it’s very easy for me to refer to an X WARP3 player and know exactly what type of career that player has had, but they may be just numbers for other people. In order to familiarize people with what I mean by certain WARP3 types of careers I’ve pulled a number of players with Red Sox connections from my dataset. After running through these examples you should have a pretty good idea of what a 20-40 WARP3 player looks like.
60+ WARP3: the very good to HoF great
Rd Pick Team Player Pos School WARP3 4 109 Bos Jeff Bagwell* 3B C 125.3 3 79 Tor John Olerud* 1B-LHP C 111.6 1 13 Cle Manny Ramirez* OF HS 100.2 1 12 Bos N Garciaparra* SS C 63.6 8 200 Pit Tim Wakefield* 1B C 61.7 1s 35 KC Johnny Damon* OF HS 61.4I actually have this range split into three smaller intervals, but there are so few players in each one that it makes it easier to just collapse them into one. Wakefield, naturally, is a bit of an oddball member of this group, but basically a 60+ WARP3 player is a very significant contributor to his team over a period of many years. A 60+ WARP3 player is in the top ~5% of expected value for every draft slot except #1 overall.
40-60 WARP3: good players
Rd Pick Team Player Pos School WARP3 1 23 Bos Mo Vaughn 1B C 56.4 5 121 Bos John Valentin SS C 56.1 5 127 Tor Mike Timlin* RHP C 50.2 8 215 Sea Derek Lowe* RHP HS 49.9 1 23 Bos Aaron Sele* RHP C 47.8 5 127 NYY JT Snow* 1B C 45.5 7 207 Mil Mark Loretta* SS C 44.7 6 161 Bos Paul Quantrill* RHP C 43.4 9 256 SF Keith Foulke* RHP C 43.2 15 414 SF Bill Mueller* SS C 42.3 1 10 NYY Carl Everett* OF HS 41.9I think 40 WARP3 works reasonably well as a cutoff line for “good” players. Vaughn and Valentin were better than merely “good”, but injuries cut short their ability to accumulate enough WARP3 to get up into the next category. Wherever you chose to draw these kinds of lines there are going to be a few players on the edges of each interval that probably belong in the adjacent one. To me players like Mueller and Lowe typify good, solid players that may win a batting title (Mueler in 03) or contend for the Cy Young (Lowe in 02) in a fluke year but mostly contribute solid performances for several years.
One issue to always keep in the back of your mind is that if two players have similar career WARP3 totals, then the one with the shorter career was most likely much more valuable because most of his production occurred prior to free agency. For example, Valentin and Timlin are going to end up with very similar career totals, but over 90% of Valentin’s production was concentrated in his first six years. Timlin is a WARP3 accumulator who will end up with ~70% of his production in his post free agency years. Valentin was much more valuable to the team that drafted him, then Timlin.
Foulke is arguably better than a “good” player, but his role makes it difficult to accumulate very high WARP3 totals. That’s something to keep in mind in the discussion about Hansen.
20-40 WARP3: useful players
Rd Pick Team Player Pos School WARP3 2 49 Bos Jeff Suppan* RHP HS 37.7 1 2 Det Tony Clark* OF HS 35.1 1 3 Atl Steve Avery LHP HS 33.7 1 7 Bos Trot Nixon* OF HS 33.2 1 8 Min Todd Walker* 2B C 31.5 6 140 Cub Frank Castillo* RHP HS 29.5 13 330 Mil Troy O’Leary OF HS 29.3 6 158 Stl Rheal Cormier* LHP CC 29.1 18 463 Oak Darren Lewis OF C 27.7 15 386 SF Pat Rapp RHP C 27.5 3 86 SD Matt Clement* RHP HS 27.3 1s 29 NYM Jay Payton* OF C 24.7 1 20 Cin Pokey Reese* SS HS 24.4 13 355 Bos Carl Pavano* RHP HS 23.5 1 26 Det Rico Brogna 1B HS 22.0 2 56 NYM Pete Schourek LHP HS 21.8 1s 43 Bos Scott Hatteberg* C C 21.7 3 85 Sea Jim Mecir* RHP C 21.1 10 256 Atl T Graffinino* SS HS 21.1 30 765 Tex Jeff Frye 2B C 20.9 8 199 Bos Tim Naehring SS C 20.8 5 144 SF Bobby Howry* RHP C 20.3I generally use the 20 WARP3 line to denote players who’ve had “useful” careers. The best players in this group – Suppan, Nixon, Clement – should end up in the 40-60 WARP3 group. Any time you’re labeling career totals of active players you’re going to run into some that are characterized a little lower than they should be. For the most part this isn’t a big problem with the players in the study. Players like O’Leary, Lewis and Brogna typify the “useful” player type. They do represent good value on their draft position and they are good contributors to their teams for a few years, but they’re generally just complementary players on good teams.
<20 WARP3: not quite useful players
Rd Pick Team Player Pos School WARP3 25 641 Bos John Flaherty* C C 19.6 23 640 Hou John Halama* LHP C 19.1 11 291 Bos Phil Plantier 3B HS 18.6 25 657 Sea Matt Mantei* RHP HS 15.6 4 120 SF Mike Myers* LHP C 15.5 17 454 NYM Brian Daubach* 1B HS 15.3 5 123 Cle Alan Embree* LHP HS 14.4 28 781 Det Dave Roberts* OF C 14.0 5 131 SF Doug Mirabelli* C C 13.1Just to make my life simpler I lumped every pick that doesn’t produce at least a 20 WARP3 player into one catchall category. Although the vast majority of picks in this category never make the majors at all, some are certainly positive contributors. You find career backups like Flaherty – who was over 20 WARP last year, boy I’m glad the Sox signed a 35 yr old moving backward down the career ladder! – and Mirabelli. There are decent niche relievers like Myers and Embree and fringe swingmen like Halama. The most interesting players are the one-year wonders like Plantier. However, most of the MLB players in this group or more like Tim VanEgmond or Donnie Sadler. Generally, these players just aren’t quite good or durable enough to hold down regular jobs long enough to clear 20 WARP3.
Part II: A general look at draft slot probabilities for picks #1-100
These are actually just the slots whose players were picked in my recent strat league draft. It includes every 1st rd pick, a decent sampling of the supplemental and second rounds and a handful of third round picks.
I only have eight drafts in my dataset which is much too small of a sample to generate draft slot data based solely on the players drafted in each particular slot. With a sample of eight each successful player is going ot have much too big an impact on the final average or probabilities. For example, Bagwell was drafted #109 and has produced over 120 WARP3 in his career. I can’t say that there is a 12.5% chance to draft a Bagwell-like player at #109. or that the average WARP3 return is 15 (plus whatever contribution from the other seven players drafted at #109).
A simple way to boost the sample is to use a rolling average. I’m now defining the value of the 109th pick not by players drafted 109th exclusively, but by the group of players drafted just before and after slot #109. This process really smoothes out the slot to slot variability a great deal and because there is very little slot to slot differentiation in the draft I don’t think I’m loosing any significant granularity in the data.
So the next technical question is what size group should I use and should that group size change. For the vast majority of the draft there is so little differentiation that I could have picked very large group sizes for the rolling average. For example, I don’t think pick #50 is much different than pick #70. I do think that there’s enough differentiation at the very top of the draft that it’s important to choose as small a group as is reasonably possible.
I decided on a group size of seven for most of the first round. That means the probabilities for slot #10 are actually based on picks from slots #7-13 from the eight drafts in my study. That yields a fairly large sample of 56 players per draft slot. I considered using a group size of 5 for the first half of the draft, but I wanted to minimize the number of arbitrary sample size changes as much as possible and from looking at the players in these slots I don’t think I’m missing anything meaningful differences by using a slightly larger group size.
The first overall pick is so different from the rest of the first round that I couldn’t include it in any of the rolling averages. That means the sample size for slot #1 is just the eight #1 overall picks and slots #2-4 are groped together creating a sample size of 24. I also increased the group size twice after the first round. A full description of the group and sample sizes is in the next table.
Picks Group Size Sample 1 1 8 2-4 3 24 5-30 7 56 31-100 9 72 101-260 11 88
I’m not thrilled with these different sample sizes, but the reasoning behind the decisions is sound and it does improve the meaningfulness of the data.
Let’s take a quick look back at slot #109 to see how the number of drafts in a study and/or the methodology dramatically changes the expected probability of finding a Bagwell-like player.
#Drafts Source Probability 8 me-straight ave 12.5% 16 BP (2005) 6.3 ~25 James (early 80s) 4.0 41 all drafts 2.4 8 me-rolling ave 1.1
Obviously as you increase the number of drafts in a given study the probability of finding a Bagwell-like player goes down. I think the “true” probability of finding a Bagwell-like player at slot #109 should be very close to zero. By doing a rolling average I can get pretty close to zero with just eight drafts of data.
With that way too long pre-amble let’s move onto the data.
Pick Player WARP3 Ave %<20 %20 %40 %60+ 1 Upton 57.9 25 25 13 38 2 Gordon 19.4 54 33 13 0 3 Clement 19.4 54 33 13 0 4 Zimmerman 19.4 54 33 13 0 5 Braun 16.2 66 20 11 4 6 Romero 16.3 68 16 11 5 7 Tulowitzki 16.8 70 13 11 7 8 Townsend 14.6 75 11 7 7 9 Pelfrey 17.4 70 13 9 9 10 Maybin 19.4 64 18 9 9 11 McCutchen 20.5 63 16 13 9 12 Bruce 20.5 64 14 13 9 13 Snyder 20.1 64 14 13 9 14 Crowe 19.6 64 14 14 7 15 Broadway 19.6 64 14 14 7 16 Volstad 17.4 68 14 13 5 17 Henry 16.6 70 13 13 5 18 Carillo 12.7 75 14 7 4 19 Mayberry 14.5 71 18 5 5 20 Pawelek 16.2 70 16 9 5 21 Pennington 14.3 73 14 7 5 22 Thompson 15.5 71 14 7 7 23 Ellsbury 15.1 73 14 5 7 24 Bogusevic 14.0 77 11 7 5 25 Garza 15.4 71 14 9 5 26 Hansen 12.3 79 9 9 4 27 Devine 11.2 79 11 7 4 28 Rasmus 10.4 80 11 5 4 29 Marceaux 9.3 82 11 5 2 30 Greene 8.9 82 9 7 2
I’ve never liked draft studies that state simple draft slot averages like these WARP3 averages because the average at every slot after #1 is a barely useful player. In fact, these data show only three slots (#11-13) with WARP3 averages over 20. That means a player like Rico Brogna with a 22.0 WARP3 career represents an above average return on every draft slot. That’s technically true, but it doesn’t really tell us anything useful. Teams don’t pay their 1st rd picks seven figure bonuses hoping to get someone nearly as good as Brogna.
I think it’s much more useful to establish some reasonable levels of production and then try to determine the probability that a draft slot will produce those types of careers. That draft slot #10 has an average return of 19.4 WARP3 isn’t meaningful to me. I’d much rather know that draft slot #10 has a 64% chance of not producing anything “useful”, an 18% chance of producing a “useful” player, a 9% chance of producing a “good” player and a 9% chance of producing a very good to great player. That may average down to 19.4 WARP3, but it’s a fuller more meaningful picture of what draft slot #10 can produce.
The first thing that jumps out from this table is the huge difference between the first pick and the second pick. At least in this specific time period, the first overall pick includes some once in a generation talents (Griffey, ARod) and the 2nd pick is really just another pick in the top half of the first round.
There are two interesting observations from the %<20 column. One, even premium top 5 picks are less than 50/50 bets to reach 20 WARP3 in their careers. And I think this is a nice example of why having specific names attached to these slots provides some very useful perspective. Alex Gordon and Ryan Zimmerman are already consensus top 20 prospects in the game. Clement and Tulowitski have also received considerable hype in their first prospect season as professionals. Based on their draft pedigree they are perhaps much riskier than the new prospect buzz they’ve received. Of course, that assumes prospects with longer track records have higher success rates. They may not. The second thing to note is how quickly the percentage rises. In just 29 slots from #2 to #30 the percentage of “failures” rises from 54% to 82% and as we’ll see we’re not far from 90+%.
The %20 (“useful” players) column isn’t very revealing. The top 5 slots are at least 20%, but the next 20 or so spots are fairly consistent bouncing around 15%. There is a second drop to ~10% for the last five picks of the first round. All of these outcomes represent positive return on investment, but there isn’t much differentiation.
The %40 (“good” players) column is pretty consistent at 13% right down to pick #17. There’s a little blip at #8-10, but that’s somewhat offset by an increase in %60+ and just in general with these low success rates all of the numbers are going to be prone to bouncing around a bit. Obviously, there’s no good reason for their to be such a significant difference between pick #17 and pick #18, but the drop that happens to occur there is substantial and it persists for the rest of the draft. This is the beginning of a steady decline in the percentage of “good” players.
There are a couple significant blips in the %60+ column. I don’t think these changes are very meaningful. It just happens that in this eight year period there weren’t any very good players drafted in the #2-4 slots. There happened to have been a slight concentration of very good players in the #7-15 range. I think you can mentally smooth out the blips and reasonably think about it as a ~7% rate for the first half of the round and ~5% rate for most of the back half of the round. The difference between the first and second halves of the draft is actually bigger in the %40 column (11.5 vs 7.7%) than in the %60+ column (5.9 vs 4.6%).
These probabilities are generated from whatever types of player happened to be drafted in the appropriate slots during this eight-year period. However, we wouldn’t really expect a HS pitcher drafted 17th to have the same success probabilities as a C hitter drafted 17th. I don’t have a big enough sample to break things down quite that finely, but I can look at the overall first round success rates for HS pitchers and hitters and C pitchers and hitters. Please note that this table assumes that each of these classes are evenly distributed in the first round, but that may not be the case.
Type #players %20 %40 %60+ (%40+) HS pos 58 17 14 12 26 C pos 47 19 9 13 21 C pit 67 12 10 2 12 HS pit 34 12 3 0 3
The two groups of position players were much more successful than the two groups of pitchers. The group of HS position players produced the highest percentage of “good” players. C pitchers were the most frequent type of player drafted, but they succeeded at a rate far less than that of both groups of position players. HS pitchers barely registered any 40+ WARP3 successes.
Also note that position players have much greater career production upside than pitchers. There were a total of 101 1st round pitchers and only one (Mike Mussina) exceeded 60 WARP3. In contrast, roughly the same number of drafted position players (105) produced thirteen 60+ WARP3 players. (This is true looking at the entire draft as well. There are six 60+ WARP3 pitchers from this period and thirty-five 60+ WARP3 position players.)
With that information in the background we can look more closely at some of the specific players from the last June draft. There were three HS pitchers selected – Volstad, Pawelek and Thompson. It’s likely that their specific success probabilities are lower then their generic rates. We can also make a reasonable mental adjustment to reduce the %60+ probabilities for all pitchers. And conversely, we can give slight adjustments up for the position players. I don’t think you can quantify these little adjustments, but it’s good to keep them in the back of your head.
A closer look at the Sox two first round picks brings up some interesting points. Prior to the draft every perceived college oriented team was rumored to be interested in Trevor Crowe and Jacoby Ellsbury. Both are polished college centerfielders that project to be top of the order hitters. BA ranked them very similarly at #25 and #29. We know that the Indians valued Crowe over Ellsbury, but it’s quite possible that the Sox or some other team would have taken Ellsbury over Crowe if given the opportunity. If we had access to all 30 team’s draft board I doubt we’d see much of a consensus opinion about these two similar players. And yet the historical record will show that Crowe was taken a seemingly significant nine spots ahead of Ellsbury. That fairly large gap – even in the middle of the 1st round where we might expect slot to slot differentiation - doesn’t really reflect the relative merit of the two players. (And it’s examples like this that convince me that large rolling averages even in the first round don’t cover up meaningful distinctions.)
However, because Crowe was picked ahead of the #17/18 breakpoint and Ellsbury was picked after it, it appears that Crowe has a much greater probability of becoming a good player. I don’t think it makes sense to think that this type of draft slot analysis should outweigh the known pre-draft similarity of two players. Don’t take the generally small differences in these numbers too seriously. These two players are closer than the following numbers suggest. In fact, based on Boston hype and a couple hundred AB many people probably think of Ellsbury as the better prospect.
Pick Player WARP3 Ave %<20 %20 %40 %60+ 14 Crowe 19.6 64 14 14 7 23 Ellsbury 15.1 73 14 5 7
Hansen is interesting because his signability dramatically changed his draft position. I can establish reasonable success probabilities based on the guaranteed money he received (4th in the draft), by his pre-draft ranking (8th by BA excluding Drew and Weaver) or by his actual draft slot. For 99% of the players in the draft we can use the player’s actual draft slot because it is a good proxy for the other two methods. Unfortunately, players that slip for signability reasons are a major pain because it is reasonable to look at them in all three ways. For Hansen that looks like this:
Pick Method WARP3 Ave %<20 %20 %40 %60+ 4 cash 21.9 54 33 13 0 8 pre-draft 14.6 75 11 7 7 26 slot 12.3 79 9 9 4
Each of those lines is obviously quite a bit different. The cash method suggests a much higher probability to at least exceed the 20 WARP3 threshold and the slot method suggests a much lower probability to become a %60+ player (note: I’m treating the 0% for pick #4 as random noise). For large studies I think you have to just leave these players in their slots and note when you can that they may not really be representative of that slot. When you try to look at an individual player though, you have to consider all of the possibilities and subjectively squeeze that player somewhere into that range.
As it turns out the pick 4/cash line is a pretty reasonable way to look at a college reliever like Hansen. The Sox are banking on a very high probability (and in the draft that means ~50%) that he will be at least “useful”, but the chances of any college reliever exceeding 60 WARP3 are pretty slim. That line actually does a nice job representing the pluses and minuses of drafting Hansen.
First round – supplemental
It was a large supplemental round last year with eighteen picks and my league drafted a little more than half including all three Sox players.
Pick Player WARP3 Ave %<20 %20 %40 %60+ 31 Torra 8.9 82 10 6 3 33 Drennen 6.2 88 7 3 3 36 Buck 4.5 90 7 1 1 38 Iorg 3.5 93 6 0 1 39 Sanchez 3.1 93 6 0 1 41 Jones 2.4 96 3 1 0 42 Buchholz 3.6 94 3 1 1 43 McCormick 4.8 93 3 1 3 45 Lowrie 5.7 92 4 1 3 47 Bowden 5.4 92 4 1 3
The WARP3 average continues to plummet into the low single digits. For the rest of the draft it’ll bounce around between 0-5 WARP3 with the baseline steadily decreasing. On average, no pick after the first round is worth much of anything in terms of career production. The %<20 jumps over 90% by the mid-30s and it gradually increases over time. In terms of “good” players there’s a trough between #36 and #42 (happens to be Buchholz), but that’s just randomness. If a player isn’t picked in the 1st rd his chances of being a “useful” or “good” player are never higher than 10% and 5% respectively.
Lowrie is an interesting player because there’s a bit of a perception that he was underdrafted and that perhaps he has already answered some of the questions about him. He reportedly played a surprisingly solid shortstop and even if he still projects as a 2B that represents a nice improvement in his toolbox. And more importantly, he hit well with wooden bats perhaps quieting some of those “aluminum bat hitter” concerns. How much should 200 AB in the NYPL change our perceptions about a very recent draft pick? In my opinion not very much at all, but it’s possible that with an improved defensive profile and some solid wood bat production under his belt he would have been a late 1st rd pick. Just for the hell of it how would his success probability profile change if he went at #25 instead of #45?
Pick Player WARP3 Ave %<20 %20 %40 %60+ 25 Garza 15.4 71 14 9 5 45 Lowrie 5.7 92 4 1 3
The dropoff in this range is so steep that a twenty spot improvement makes a huge difference. I don’t think you can let 200 good NYPL ABs change your perception of a player that much, but I suppose you can start to build a case that a player is better (or worse) than his draft position suggests with each professional game.
Ideally it would be possible to find a large group of college position players whose draft status gives them very similar probabilities of long-term success. That group would include some high and low performers in their first abbreviated professional season. Would the probability of success for the two groups differ from each other and/or from their baseline draft slot status? Beats me. I would guess that the signal would be so small that it would be quite difficult to pick up a meaningful difference. If you could though, it would probably be more likely for college position players. I think that the unpredictability for pitchers (both HS and C) is so great that it would be very unlikely that a half season of performance data would make any difference at all. That’s even more true now that teams like the Sox severely limit the innings for most high round pitchers. I know of enough anecdotal examples of very good HS position players that stunk in the GCL that I doubt GCL performance of a teenage position player would change their success probabilities very much either.
From eyeballing my data I consider the 1st supplemental round through the 3rd round to be the next tier of draft picks. Here’s a table with the success rates of the various types of players from these rounds. I added the %40+ data from the first round table just to make the steep decline clear.
Type #players %20 %40 %60+ (%40+) (1st rd %40+) C pos 101 6 1 5 6 21 HS pos 131 2 5 0 5 26 C pit 163 4 0 2 2 12 HS pit 92 7 0 0 0 3
Again, the two groups of position players are at the top of the list although in these rounds the C hitters edge the HS hitters. HS pitchers are again the least successful group although I expect a couple of the %20 pitchers to clear 40 WARP3 and that will let them catch up a bit to the C pitcher group. Nevertheless, a HS pitcher like Bowden has some very long odds to overcome. There aren’t enough JC picks at the top of the draft to say much of anything about the historical chances of a player like Buchholz.
We drafted about a third of the 2nd round though not Egan who slots in at #57. Reimold and Escobar received the most buzz from this group of players, but most of these names will be recognizable to people who follow the minors or the draft closely.
Pick Player WARP3 Ave %<20 %20 %40 %60+ 50 Bianchi 4.7 93 4 0 3 52 Carte 2.8 94 6 0 0 53 Italiano 2.4 94 6 0 0 56 Mason 2.9 96 3 0 1 60 Wood 4.6 92 6 1 1 61 Reimold 4.4 93 4 1 1 62 Head 4.4 93 4 1 1 65 Costanzo 4.0 93 4 1 1 66 Headley 3.8 94 4 0 1 67 Whittleman 4.3 94 4 0 1 75 Escobar 6.1 92 3 1 4
The most obvious characteristic of this round to me is its flatness. The WARP3 averages are between 2.5-4.5, the %<20 bounces between 92-94, the %20 is around 4% and the combined %40+ is around 2%. There aren’t many data points that fall outside of those boundaries. Slot #75 is an exception, but a lot of that is because John Olerud, a top of the draft talent that fell to the 3rd rd due to health/signability issues, happens to be included in the slot 75 range. If I dropped him the slot 75 data would fall right in line with the rest of the second round.
By the time we get to the 2nd rd the chance of any player becoming a “good” 40+ WARP3 player is roughly 1 in 50.
BA just released its Top 100 list and it includes a pull quote for each player. The quote for Reimold is quite timely.
"He's got five plus tools and he has a little bit more ability to use them than maybe we knew. He was a good first-round talent that we got in the second round. We're fortunate--really fortunate."
--Orioles scouting director Joe Jordan
He’s making the case for Reimold that I suggested might be possible for Lowrie. While Reimold may have profiled as a solid second round pick on draft day, now that Jordan has seen him play professionally for a few months he believes that Reimold’s tools play more like a good 1st round pick. A comparison of Reimold’s draft slot and what Jordan now thinks he profiles as would look something like the following set of numbers.
Pick Player WARP3 Ave %<20 %20 %40 %60+ 20 Pawelek 16.2 70 16 9 5 61 Reimold 4.4 93 4 1 1
In Jordan’s opinion, Reimold may have been a player with a 2% chance to exceed 40 WARP3 in June, but he’s now better characterized as a player with a 14% chance to exceed that level of productivity. And it seems like BA, which ranked him above ~40 players that were drafted above him, agrees.
A brief BA Top 100 sidebar smack dab in the middle of the post
I’ve never looked at June draft picks in the BA Top 100, but since the list came out while I was writing this I thought it might be interesting to take a quick look at the June draft picks that are considered Top 100 prospects prior to their first full professional season.
Player BA Slot %40 %60+ %40+ Upton 2 1 13 38 51 Gordon 13 2 13 0 13 Zimmerman 15 4 13 0 13 Tulowitski 25 7 11 7 18 Maybin 31 10 9 9 18 Clement 33 3 13 0 13 Pelfrey 36 9 9 9 18 Braun 49 5 11 4 15 McCutchen 50 11 13 9 22 Hansen 54 “8” 7 7 14 Bruce 76 12 13 9 22 Pennington 83 21 7 5 12 Pawelek 85 20 9 5 14 Romero 87 6 11 5 16 Carillo 88 18 7 4 11 Volstad 97 16 13 5 18 Reimold 99 61 1 1 2
There are seventeen June picks on the list. I have no idea if that’s high, low or average in comparison to other years. The players that made it match pretty well with their draft position. Sixteen of the players were taken within the first 21 picks (I listed Hansen as slot “8” to better reflect his pre-draft status). The lone notable exception is the previously mentioned Reimold.
This suggests both that the brief on field performances didn’t change their status very much and that BA believes that the top prospects in the June draft have very comparable success probabilities to more experienced minor leaguers with similar rankings. For example, Upton with zero professional experience ranks right between Delmon Young and Brandon Wood with two plus years of professional experience. Alex Gordon (no professional experience outside of the AFL) and Ryan Zimmerman (308 pro AB including a great MLB trial) are ranked alongside fellow 3B Andy Marte with 4+ years of professional experience. My probabilities for Gordon’s and Zimmerman’s draft slots are unfortunately low, but if we give them a more realistic 20% chance of being 40+ WARP3 players based solely on their draft status (and for Gordon that’s all we have), then that suggests that Marte’s chance to be that good should be about the same if BA did a good job of making their list. I’ve never looked closely enough out old BA lists to have a feel for how accurate that might be, but it would be an interesting way to judge how successful BA (or any group with a list) is at merging inexperienced players in with established prospects with professional track records.
Also note that BA seemed to do a pretty good job reflecting the greater risk for recently drafted pitchers. There are ten June draft picks within the top 54, but just two are pitchers. And they were the highest rated C pitchers to sign. The other four June pitchers (two HS, two C) that made the list are clustered between #85-97.
Back to the Third Round to wrap this part up
We only drafted a handful of players from the third round.
Pick Player WARP3 Ave %<20 %20 %40 %60+ 81 Neighborgall 4.9 92 6 1 1 83 Owings 5.5 90 7 1 1 85 Inman 3.6 93 6 1 0 93 Erbe 2.3 99 0 0 1 99 Teagarden 1.3 97 3 0 0
The probabilities in the 3rd round are pretty similar to the second round with a slight downward trend. The jump to 97-99% in the %<20 column for slots #93 and 97 is just a random fluctuation. That percentage mostly stays in the 93-94% range until around pick #225 when it jumps into the high 90s for good.