Analyzing the MLB draft using WAR

There has been plenty of work published regarding the value of draft picks, most recently the work done by Victor Wang. Wang’s previous studies (part 1 and part 2 ) on the draft were a major help for me in conducting this study. He was recently honored by Beyond the Boxscore for the Best Novel Research Article/Project. The basic idea was to test his hypothesis and findings using WAR and more recent draft results.

Using the first 100 picks from the 1992-1999 drafts I came up with a sample of 388 players who reached the major leagues. Of these players 212 were drafted out of college, 167 out of high school and nine from Junior College or Community College. I used Wins Above Replacement as my benchmark for evaluating players. The WAR data was obtained from’s historical database . I took the average of the player’s WAR over their first six seasons. I chose six seasons as the cutoff because a team retains control of a player for this amount of time before they are eligible for free agency. For a small sample of players I used their 2010 CHONE projections in instances where they did not yet complete six years of service.

Similar to Wang’s study I broke the data down into first round picks (1-30), second round (31-70), and third round selection (71-100). I have broken down the data based on school below. Please note junior college/community college players are excluded due to a small sample size.

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First Round
College hitters– 1.336 WAR/year
High School hitters– 1.204 WAR/year
College pitchers– .649 WAR/year
High School pitchers– .878 WAR/year

As noted in prior studies college hitters hold the edge in terms of production, although were are only talking about .1 WAR per season over high school batters. One thing I found surprising is that my study shows that high school pitchers have actually outperformed college pitchers. Previous studies focusing on earlier time frames found an advantage for college pitchers although as one knowledgable poster from the Sons of Sam Horn message board points out, this trend seems to have balanced out in more recent seasons. This may explain the difference in results. I found the standard deviations for college pitchers (1.049) to be slightly lower than the SD for high school pitchers (1.261) in this round.

Second Round
College hitters– .773 WAR/year
High School hitters– .672 WAR/year
College pitchers– .087 WAR/year
High School pitchers– .084 WAR/year

Here we see a noticeable drop of in pitcher’s WAR/year for both college and high school players. College hitters are still the most productive in this group. The SDs for pitchers in this round was much lower than in the first round (.617 for college and .497 for HS).

Third Round
College hitters– .115 WAR/year
High School hitters– .424 WAR/year
College pitchers– (.023) WAR/year
High School pitchers– .058 WAR/year

In this round we see high school batters hold the edge in terms of production. College pitchers actually had a negative WAR in this range, which means that on average they were below replacement level during their first six seasons.

We can see clearly that hitters are the safest picks, particularly college batters within the first two rounds which goes along with Victor’s previous studies. Victor Wang hypothesized that the best strategy would be to draft hitters early and then stock up on pitchers in the later rounds. I won’t argue that hitters are much more productive draft picks on average than pitchers although it would seem to me that taking a pitcher in the first round is not such a bad idea especially if an organization is thin on pitching prospects. You can see the noticeable decline in WAR from pitchers taken outside of the top 30. Obviously pitchers have more risk and variation due to injury and such than hitters which makes them a riskier selection.

Focusing on the First Round

Here is the WAR/year based on draft position within the first round:
1-10– 1.417 WAR/year
11-20– 1.115 WAR/year
21-30– .353 WAR/year

There is a drastic drop in performance between picks 11-20 and 21-30. The difference between the top 10 and 11-20 is not nearly as large. What does this mean? There is likely a general consensus of the top 20 or so prospects each year. After this however, the talent quickly becomes more diluted and it becomes increasingly tougher to find players who might contribute in the major leagues.

Position by Position
Here is the WAR/year broken down by position. I only used the positions listed in the Baseball-Reference draft database although many players surely switched positions. This is something I did not take into account so please keep that in mind. I also did not include two-way players Brooks Kieschnick and Rick Ankiel. For ease I combined first baseman and third baseman into a group of corner infielders and second baseman and shortstop into middle infielders. In parenthesis is the amount of players in each position.

outfielders (64)– .977 WAR/year
middle infielders (48)– .561 WAR/year
corner infielders (41)– 1.046 WAR/year
catchers (29)– .829 WAR/year
right-handed pitchers (151)– .314 WAR/year
left-handed pitchers (55)– .404 WAR/year

A comparative study on an unwritten rule of baseball.

I think the most interesting note from this is that lefties outperformed righties. This could obviously be due to the fact that there are far fewer lefties than rightist which makes them that much more valuable to a team and there is also greater variation among southpaws. We should also note the low WAR/year amount for middle infielders compared to other position players.

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Christopher Taylor
Christopher Taylor

It would be nice to know the SD for all categories and not just hitters…