The odds are very good that you’ve read too many analyses of last week’s draft already. The odds are also very good that you have no idea that Jamie D’Antona‘s OBP was .450 this year. I’d like to take a look at the season numbers for the Division I college players taken in the first two rounds last week, not as part of the questionable sport of prospect-watching, but as an exercise in learning how to interpret college numbers–putting them in context and separating the wheat from the chaff.

For the last hundred years or so, the professional thinking on how to evaluate players at the high school or college level was that it was a purely observational task. There was no real understanding of what college numbers meant, so deciding how good a player was needed to be done purely by sending a scout down to watch him and record his observations in the few games he saw. There’s still a lot of that in play, as the crowds of guys with clipboards and radar guns at any major conference game will attest, but major league clubs are starting to try to learn how to interpret college numbers and, as John Cougar once sang, “collate them all in their proper places.” Given that the notion that minor league numbers might actually mean something if discounted properly is only about 20 years old in most corners, the fact that the same transformation is only now taking place in the college scouting ranks is not surprising. The scouts aren’t going away, of course, and they shouldn’t, but most organizations are trying to add another set of tools to their preparation techniques (and quite a few of them seem to be trying to go back to gather old data for analysis as well).

First off, let’s look at the hitters. In addition to the trio of rate stats and raw homer totals, there are a couple of numbers here for each player to help translate their performance to a more neutral setting. First off, the SoS column represents the team’s strength of schedule. The scale is set to balance at 100; it makes a reasonably good straight multiplier for offensive stats. The second factor is the park factor (PF), which is also scaled around 100 and is an inverse multiplier. These park factors are for runs; the relationship is actually not linear for multiplying OBP, for example, and runs (something I’ll talk more about next week), but it’s close enough within the ranges that all of these teams are in. Those two factors get you pretty close to a translation to an average college player; the only thing missing now is an MLE (or a set of them) to translate into equivalent minor league performance (paging Clay Davenport).

None of these guys were chosen for their defense, so you can safely ignore that factor. Most of them are solid with the glove; Quintanilla and Maier might stick at their valuable defensive positions. That’s actually an interesting change; most years, there are one or two guys that are identifiable as defensive picks. This year, Javi Herrera is the only one who might be considered that way. It may be that, as teams move toward more statistically-based picks, the fact that we don’t have equally good defensive measures yet may steer teams away from those picks for a while.

Player           College           MLB Team    SoS   PF  AVG  OBP  SLG  HR

Rickie Weeks     Southern          Brewers      86   84 .500 .619 .987  16
Michael Aubrey   Tulane            Dodgers     105   79 .420 .505 .733  18
Aaron Hill       Louisiana State   Blue Jays   111   93 .366 .475 .607   9
Brian Anderson   Arizona           White Sox   111  109 .366 .425 .668  14
David Murphy     Baylor            Red Sox     114   88 .413 .487 .614  11
Brad Snyder      Ball State        Indians      96  139 .405 .522 .770  14
Conor Jackson    California        D'backs     113   78 .388 .538 .675  10
Brian Snyder     Stetson           A's         103   87 .396 .505 .670  11
Carlos Quentin   Stanford          D'backs     114   83 .398 .491 .631  10
Mitch Maier      Toledo            Royals       96  101 .448 .525 .691   9
Matt Murton      Georgia Tech      Red Sox     107   98 .301 .403 .524  13
Omar Quintanilla Texas             A's         113   92 .351 .420 .547   5
Anthony Gwynn    San Diego State   Brewers     109  101 .368 .439 .500   1
Shane Costa      Cal St. Fullerton Royals      113   94 .372 .435 .545   4
Vince Sinisi     Rice              Rangers     110   90 .355 .438 .523   9
Javi Herrera     Tennessee         Indians     106   85 .296 .403 .426   9
Todd Jennings    Long Beach State  Giants      114   74 .296 .343 .404   5
Andre Ethier     Arizona State     A's         109  103 .377 .488 .573  10
Jamie D'Antona   Wake Forest       Dbacks      103  110 .360 .450 .752  21

  • Rickie Weeks is an extremely risky pick, but he has a huge upside. No matter how much you adjust a 1.606 OPS downward, you still get an extremely high number. That’s a very weak schedule, so it’s possible that he’ll wilt under better competition. On the other hand, you can make a case that the bonus necessary to sign the second pick requires that you pick someone who has more uncertainty in order to give yourself a chance of breaking even, so it’s hard to criticize the Brewers for this one.

  • Michael Aubrey and Jamie D’Antona are opposite sides of the same coin; both have good power numbers against respectable but not top-notch schedules, but Aubrey plays in a big defensive park, while D’Antona plays in a mild hitter’s haven.

  • Aaron Hill and Conor Jackson are a more matchable pair. Both are the kind of players who are unlikely to explode on the scene but are quite likely to have solid major league careers. Both played top-notch competition and showed great plate control and acceptable power. Hill’s a shortstop but will slide down the defensive spectrum as he rises, while Jackson’s already on the shallow end.

  • There could always be something that the scouts see to justify it, but numerically Brad Snyder looks like a bad pick. He played against a below-average schedule in a severe hitter’s park, and those numbers just kind of fade away when you start adjusting them.

  • We’re in the stage of the revolution where everyone’s trying to figure out what to copy from the A’s. There’s an interesting theory of thought that there are still some weaknesses in their analytical abilities; it may be that what they’re actually doing to succeed is simply something that Rob Neyer has talked about–just focusing more intensity on a smaller pool of prospects. The Omar Quintanilla selection may play into that theory. His numbers, while quite good, don’t scream out as great unless you make a big adjustment for the strength of Texas’ schedule and for the national title last year. He’s probably the college shortstop from this year who’s most likely to play shortstop well in the majors, but this was a tools selection as much as it was a stats selection. If you assume that the A’s have identified their pool as “players from top college conferences” in much the same way that the Braves have identified theirs as “players from Georgia and nearby,” this may make sense.

  • A .939 OPS for an outfielder against a good but not stellar schedule in a neutral park? If his name was Anthony Smith instead of Anthony Gwynn, or if the Brewers were smarter, he would have been a 10th-rounder.

Next, the pitchers. They have the same two factors listed as the hitters, but to provide better insight into competition levels, I’ve provided RBOA (Runs Below Opponent Average) numbers for the starters. I’ve also listed PAP (Pitcher Abuse Points) numbers. It appears that, as with most things, some teams are paying attention to workload and some aren’t. The number of relievers on this list is interesting; one recent trend towards dealing with the excessive workloads many college pitchers carry is to look for guys that the scouts like but who haven’t been used as staff aces in college; some of them will be converted to starters if all goes according to plan.

Player       College        SoS   PF   ERA     IP  HR    K  BB  RBOA   PAP

K. Sleeth    Wake Forest    103  110  2.81   96.0   4  102  29  35.8  326K
T. Stauffer  Richmond        98   96  1.97  114.0   5  146  19  39.6  262K
P. Maholm    Miss State     107   87  2.76  107.2   3  101  39  34.4  147K
R. Wagner    Houston        111   86  1.93   79.1   1  148  21   -     -
C. Cordero   CSU Fullerton  113   94  1.42   50.2   2   63   7   -     -
D. Aardsma   Rice           110   90  3.25   52.2   3   44  19   -     -
B. Sullivan  Houston        111   90  2.91  123.2  12  154  44  34.1  151K
D. Moore     North Carolina 105   75  3.56   93.2   7   65  41  19.0   21K
B. Finch     Texas A&M      111   91  5.40   65.0   2   57  25   3.0  104K
S. Beerer    Texas A&M      111   91  1.82   49.1   4   58  12   -     -
A. Alvarez   CSU Long Beach 114   74  2.35  122.2   7  102  31  56.5   20K
J. Banks     FIU            102   71  3.50  105.1  12  114  25  38.8   98K
T. Pauly     Princeton       98   82  1.46   55.1   1   74  25   -     -
L. Kensing   Texas A&M      111   91  3.83   89.1   4   58  22  16.0   65K
S. Baker     OK State       108  105  3.79  111.2   9   97  29  19.5   53K

  • Kyle Sleeth, Tim Stauffer, and Paul Maholm all pitched really well this year (RBOAs in that range are worth around three wins each for their teams above an average pitcher), and they each threw a very large number of pitches. It may be that this is where the whole issue of organizational health for pitching prospects starts, so that’s worth tracking as these guys and others like them develop over the next few years. Stauffer’s K/BB ratio is especially noteworthy.

  • Statistically, Brad Sullivan fits right in with the top-10 pick guys; what caused him to fall may actually be that his performance was off a bit this year from past seasons, both in college and with Team USA.

  • Daniel Moore is a strange pick. There’s absolutely nothing in his record that calls out for a second-round pick, so I’m guessing the scouts saw something they liked.

  • Brian Finch is an even stranger pick, unless you believe he fits the underutilized profile I discussed earlier or you believe the Orioles looked at A&M’s overall schedule without taking his role into account. He split time between the bullpen and a spot as a midweek starter this year–the fact that his starts came against the likes of Texas-San Antonio and Texas A&M-Corpus Christi rather than against Big 12 competition shows up in his miniscule RBOA number.

  • Abe Alvarez may have been the best pick of the draft. He compiled a great record against one of the toughest schedules in the nation, his PAP score is remarkably low for the ace on a top team, and he was available in the second round. As a possibly unrelated note (although it may not be, given the conformity of baseball culture), Alvarez has hair that will have Barry Zito calling for tips.

Overall, the first round for both groups looks to be fairly strong from an analytical standpoint, at least among the college guys. The second round features quite a few questionable choices, so it may be that the lesson is starting to get through but didn’t make it all the way through the system yet this year. As always, we could be just one big flameout away from the end of the fad, or we could be seeing the start of something new and long-lasting, so it’ll be interesting to see where the trends go next year.

Boyd Nation is the sole author and Webmaster of Boyd’s World, a Web site devoted to college baseball rankings, analysis, and opinions. In real life, he’s an information security analyst with an energy company. He’s writing a series of articles for BP on the college game and the College World Series. He can be reached at

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