Statistical Rankings of Draft Eligible College Players-Off the Radar

Jun 4, 2011; Atlanta, GA, USA; Georgia Tech Yellow Jackets designated hitter Daniel Palka (32) hits a home run against the Southern Miss Golden Eagles during the second inning in the Atlanta regional of the 2011 NCAA baseball tournament at Russ Chandler Stadium. Mandatory Credit: Dale Zanine-USA TODAY Sports

In March, on this site, I introduced “Tool WAA“, which was an attempt to use college baseball statistics to predict MLB success. With the MLB draft coming up and the college regular season over, I wanted to use the statistic to rank draft eligible players. So I only looked at ones that were Redshirt Sophomores or older (i.e., at least 3 years in college so they are eligible for the draft once going to a NCAA school), and only ones that had at least 100 plate appearances in 2013 (for most of the players, when it was possible, I used their career college numbers for the stats). I also only looked at players from what you might call the 5 major conferences, the ACC, SEC, Big 12, Big 10, and Pac 12. There are prospects in the non-Major conferences of course, including some really high rated ones like Michael Lorenzen or Kris Bryant, but I only tested the Tool WAA in a Major conference, and it isn’t fair to compare Big Sky statistics to SEC statistics because the strength of competition is different. If you recall in the initial article, things like OPS and K-BB did not test well, meaning they weren’t very predictive. HR % and Speed Score were the things that tested the best. So in this version of Tool WAA, I am only using those two statistics. I am also subtracting the simple Speed Score by 5 and the HR % by 2.6 %, just like I did in the initial post, which is why most players will have negatives. So we are measuring power and speed. It is a simple way to evaluate prospects statistically that I think can be a helpful way when used with scouting and not used in a way that is too binary. What I found when doing this is that there are very few college players that hit for both power and have speed. According to this measure, there were just 12 that had at least the average threshold of power and the average threshold of speed. There are 276 players on the list, too many to put in this post, so what I have done below is posted the top 20 in Tool WAA:

Name    College    Conference    Speed Score %    HR %    Simple Tool WAA
Daniel Palka    Georgia Tech    ACC    2.24    3.31                    5.55
Max Rossiter    Arizona State    Pac 12    -3    -1.53                   4.53
Aaron Cornell    Oklahoma State    Big 12    4.84    -1.55         3.29
Hunter Renfroe    Miss ST    SEC    -0.37    3.4                           3.03
Tyler Horan    Virginia Tech    ACC    -0.69    3.48                     2.79
Austin Wilson    Stanford    Pac 12    1.55    1.2                          2.75
Brett Williams    NC State    ACC    3.22    -0.56                         2.66
Pat Blair    Wake Forest    ACC    3.32    -0.76                             2.56
Matt Conway    Wake Forest    ACC    2.29    0.18                     2.47
Cody Stubbs    North Carolina    ACC    1.4    1.03                   2.43
Matt Oberste    Oklahoma     Big 12    0.33    2.04                     2.37
Justin Ringo    Stanford    Pac 12    2    0.25                               2.25
Michael O’Neill    Michigan    Big 10    2.58    -0.45                2.13
Andrew Rash    Virginia Tech    ACC    -1.125    3.21              2.08
Stephen Talbott    Purdue    Big 10    3.52    -1.52                      2
Josh Scheffert    Nebraska    Big 10    1.44    0.42                   1.86
Chris Marconcini    Duke    ACC    1.07    0.43                         1.5
Michael Camporeale    Washington    Pac 12    0.75    0.67    1.42
Thomas Brittle    Clemson    ACC    2.82    -1.42                      1.4
Shane Kennedy    Clemson    ACC    2.12    -0.9                      1.22

If you want to view the whole list, you can by clicking here.