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 firstname.lastname@example.org