Dynasty Prospect Rankings

Scouting the Stat Line: Top 50 SEC and Pac-12 Hitting Prospects since 2015

How much weight should one give to college performance?

In the past, I have not moved beyond browsing historical leaderboards at The Baseball Cube, looking for patterns in performance that appeared to translate to success at the MLB level. Dynasty leaguers are no doubt familiar with this crude but entertaining analytical process. In the MLB it works fairly well to isolate the game’s best players. Take a look at MLB WAR leaders in 2019, for example, and most of the top 10 hitters are surefire stars. In college, however, leaderboards are filled with hitters who never made it to the MLB–even in the top conferences. Until now, this had convinced me that college statistics were not worth much deeper explanation and that college analysis should be left mostly to scouting–although I have always delighted in trying to find statistical sleepers. I suppose I could only resist looking further into college statistics for so long, however. Seeing other good research on the topic has also been inspiring, particularly Phil Goyette’s major league equivalencies for 2019 NCAA performance.

In this article, I present peak MLB-equivalent wOBA for college performances in the SEC and Pac-12. (wOBA is an all-encompassing statistic for a player’s offensive value.) My methodology follows the methodology I used to develop peak MLB offensive performance projections for minor leaguers. Briefly, I capture performance change for all players who go from a particular college conference to a particular minor league, e.g., perhaps the average player maintains 90% of their wOBA (relative to the league average) when transitioning from the SEC to Class A. Performance change is weighted by plate appearances in the origin or destination league, whichever is smaller. Performance change is then chained together with performance changes when transitioning to other levels all the way up to the MLB (performance changes for all levels come from my previous work on major league equivalencies). For instance, SEC performance is converted to Class A performance, then to High-A, Double-A, Triple-A, and finally, to the MLB. In Phil’s MLB equivalencies for college hitters, he talked to experts and surveyed the literature to assign a comparable minor league level to each college conference. My work tests those assignments quantitatively and indeed finds similar results to his qualitative approach–a reassuring thing!  More precisely, I find the SEC is slightly harder than the Pac-12, and performance in both leagues is approximately equivalent to performance in the Appalachian and Pioneer short-season leagues.

In my peak MLB offensive performance projections for minor leagues, I only projected wOBA as it was a more efficient use of time to focus on projecting one encompassing statistic than to try and project each component statistic separately in a consistent way. I’ve always liked low hanging fruit and focusing on wOBA is a good way to project a player’s total offensive contribution without having to do the difficult extra work of separately projecting each relevant component statistic. In this article, I’ve also included crude peak K%, BB%, and isolated power projections. These were developed in a less rigorous way than the wOBA projection and should be interpreted merely as one plausible outcome of what these three statistics could look like if a player reaches their peak wOBA. For each player, the combination of K%, BB%, and isolated power shown below is consistent with the peak wOBA figure (I used regression to find sets of K%, BB%, and isolated power values consistent with a given wOBA at the MLB level in 2019). However, as the old expression may go–I’m not so good with expressions–there are many ways to bake a cake, many ways to achieve the same peak wOBA. Some of the top projected players with both very high projected K% and isolated power (see, e.g. Trevor Larnach and Hunter Bishop) would most plausibly end up trading some power in for contact at the MLB level–but I’ll re-iterate, these K%, BB%, and ISO projections are not to be taken too seriously.

My takeaway from this research is that college statistics are indeed worth exploring and informative of future ability, even if they are also quite noisy. The problem with noisy college statistics appears to be less that college statistics are uninformative of a player’s ability, and more that they only capture a very small sample of performance. The top college hitters tend to not exceed 300 plate appearances in a single season, a similar sample to what is found in the short-season minor leagues. Over a college career, usually three years before being drafted, players rarely exceed 900 plate appearances, typically falling in the 600-ish range. This is about as many plate appearances as a full-time major leaguer gets in a single season.

While both college and short-season minor league single-season performances suffer from small sample size issues, they become more indicative of true talent over a larger sample. If you look at a single season leaderboard of college performance from five years ago you will likely see a lot of unfamiliar names. Look at a leaderboard of performances across a college career, though, and the names at the top of the leaderboard tend to be the ones that teams draft early on, the ones that make the biggest impact at the MLB level. That’s not to say either college or short-season minor league performance is as informative of future potential as MLB performance, or even Double-A or Triple-A performance. Scouting rightfully plays a greater role at lower levels of competition, and will always have a greater analytical edge over statistical performance in smaller samples. Regardless of the level of performance though, it’s of course harder for an analyst to contain one’s own enthusiasm if a player checks all the boxes, both for scouting and for performance.

Before proceeding to the leaderboards, a few caveats are in order. Major league equivalencies suffer from numerous selection biases since only certain players from one league are selected for “promotion” to another league. These biases work in opposite directions. First, lucky players tend to be promoted or drafted only to regress, while unlucky players are often never drafted or promoted at all, and thus never given the chance to regress positively and offset the negative regression folk–this understates origin league difficulty, in our case college, relative to destination league difficulty, in our case the minor leagues. On the other hand, teams only draft players with a skill set they think will translate to MLB success, a skill set that is likelier to translate to strong minor league performance as well–this overstates origin league difficulty relative to the destination league. I don’t think there is any clean solution to these sampling issues beyond simply testing the major league equivalencies for predictiveness. I’ve done this for my top 3030 propsects wOBA projections and found they work sufficiently, holding their own with typical wOBA projections for MLB players. I haven’t done this with the college translations, but since they follow a very similar process to the minor leagues they should do alright.

In any case, simply skimming the leaderboards offers both confidence and caution. At the top of the list, you’ll notice there are more successes and fewer busts. Further, most players at the top of the leaderboards end up getting a shot in the MLB. As you move down the list, the names start becoming unfamiliar quickly–unless you are a huge Pac-12 or SEC fan. There are plenty of busts throughout, and also plenty who end up far outperforming their college projection in the majors (looking at you, Mr. Bregman!) and vice-versa. It does not need to be said but I shall say it anyway: college statistics are no panacea, no Grays Sports Almanac from the future.

Table notes: All college data from Baseball-Reference. Only included players with a minimum of 500 plate appearances across their college career. Player must play successive years (it was easier for data collection). All figures on a 2019 MLB scale. MLB averages in 2019: 325 wOBA; 22.4% K, 8.4% BB, .187 ISO. The college plus (+) stats are relative to the college conference average in a given year. For instance, Spencer Torkelson posted a 1.83 wOBA+ in 2020, 83% above the Pac-12 league average in 2020. A value of 1 is average. Seems pretty good to me.

NameRankPeak MLB wOBA projectionPeak MLB K%Peak MLB BB%Peak MLB ISOFinal college yearAge in final yearCareer PAJunior year PAJunior year K%+Junior year BB%+Junior ISO+Junior year wOBA+Sophomore year PASophomore year K%+Sophomore year BB%+Sophomore ISO+Sophomore year wOBA+Freshman year PAFreshman year K%+Freshman year BB%+Freshman ISO+Freshman year wOBA+Conference
Spencer Torkelson10.43223%15%0.370202020628820.893.254.111.832890.811.272.341.372570.951.463.161.44Pac-12
Andrew Vaughn20.40716%15%0.2952019217452450.72.192.171.522560.391.73.111.662440.580.81.751.25Pac-12
Adley Rutschman30.39520%17%0.2772019218212660.742.562.241.623080.721.71.641.432470.881.080.540.82Pac-12
Michael Conforto40.39225%16%0.2952014218342720.872.362.21.4830411.61.981.312580.931.12.521.38Pac-12
Brent Rooker50.39028%11%0.3262017226213091.011.523.071.622291.180.71.971.19831.180.490.950.9SEC
Andrew Benintendi60.38516%13%0.2612015205560#N/A#N/A#N/A#N/A2880.641.782.661.512680.50.960.590.97SEC
JJ Bleday70.36722%13%0.2492019217113470.851.562.271.411660.731.70.931.261980.710.930.620.96SEC
Austin Martin80.36113%10%0.217202021665690.151.161.691.353230.541.11.361.352730.891.170.491.1SEC
Jonathan India90.36026%12%0.2552018218153000.981.832.381.472510.90.91.1212640.920.831.061.03SEC
Pete Alonso100.35920%10%0.2402016216512560.681.212.211.421710.741.081.581.152241.040.911.170.98SEC
Hunter Bishop110.35934%12%0.2832019216172801.131.62.671.461651.681.081.170.971721.550.891.541.09Pac-12
Christin Stewart120.35728%10%0.2662015216362210.991.32.521.352411.160.672.21.231741.151.271.421.17SEC
Heston Kjerstad130.35624%5%0.268202021691780.610.722.051.483001.10.621.591.193130.930.761.441.19SEC
Evan White140.35420%6%0.2442017217392480.670.991.911.382490.950.561.231.212420.790.630.721.01SEC
Cameron Cannon150.35214%10%0.2032019216442780.541.131.671.372630.461.391.71.231030.520.90.60.99Pac-12
Trevor Larnach160.35032%13%0.2532018216223151.161.572.271.392481.261.611.071.13591.521.210.170.64Pac-12
Anfernee Grier170.34930%9%0.2622016207362781.121.151.631.312871.230.780.951.081710.930.470.770.9SEC
Nick Senzel180.34916%12%0.1952016217042590.461.541.891.342280.911.031.331.142170.761.491.091.15SEC
Dansby Swanson190.34925%11%0.2342015216963360.931.312.251.313360.971.181.481.2241.351.080.610.98SEC
Bryan Reynolds200.34731%12%0.2482016219292841.151.722.121.353261.190.971.131.093191.020.771.481.18SEC
Michael Toglia210.34633%10%0.2632019207742781.210.972.041.212831.181.671.881.32131.361.591.881.12Pac-12
Nick Quintana220.34628%11%0.2402019217812761.011.461.871.322541.031.242.081.252511.271.351.511.13Pac-12
Scott Kingery230.34514%6%0.2042015216512590.420.371.511.32490.681.541.231.311430.91.410.961.01Pac-12
Tyler Keenan240.34523%9%0.228202021599800.930.92.321.443170.821.341.421.152021.020.681.421.11SEC
Kole Cottam250.34525%8%0.2402018215742600.991.092.051.332150.830.681.351.13991.030.610.780.92SEC
Tristan Pompey260.34029%11%0.2322018217222241.111.341.441.243200.941.411.31.291781.520.631.60.97SEC
Kyle Martin270.33917%11%0.1842015226192540.611.572.231.382720.680.871.111.13930.761.130.981.02SEC
Mitchell Tolman280.33825%12%0.2082015217762910.831.611.281.262781.211.31.681.282070.781.030.851.08Pac-12
Gage Canning290.33632%6%0.2542018217232631.140.92.081.3224310.511.751.152171.820.611.190.96Pac-12
Jeremy Martinez300.3369%8%0.1592016217602560.280.761.631.362790.371.220.631.042250.391.040.771.07Pac-12
Anthony Servideo310.33427%14%0.198202021425870.982.21.831.482671.051.60.651.04711.340.450.520.78SEC
Max Schrock320.33315%11%0.1652015206452310.451.421.341.181440.970.821.81.122700.551.411.341.07SEC
Alex Bregman330.33312%9%0.1632015219093120.411.181.661.22830.491.031.451.153140.510.811.741.27SEC
J.J. Matijevic340.33323%6%0.2142017217242750.820.862.121.372880.980.61.10.991610.680.991.50.96Pac-12
Thomas Dillard350.33224%13%0.1882019217613090.841.751.481.232850.931.571.641.231671.650.811.030.88SEC
KJ Harrison360.33227%9%0.2182017207602490.971.071.571.142440.931.212.261.192671.21.191.951.23Pac-12
Nick Madrigal370.3318%6%0.1502018217072010.190.791.071.192820.340.981.291.282240.370.681.071.11Pac-12
Logan Ice380.33019%13%0.1652016216072240.671.682.21.311560.851.161.381.062270.722.050.320.99Pac-12
Mikey White390.33027%9%0.2142015218202681.011.181.551.262861.021.011.491.142661.090.750.811SEC
DaShawn Keirsey400.32922%5%0.2072018217042250.790.661.661.312420.830.81.141.112370.930.690.570.96Pac-12
Alfonso Rivas410.32822%10%0.1862018217462660.791.041.41.212650.81.511.361.342151.030.810.750.91Pac-12
Jeren Kendall420.32635%6%0.2492017218122951.350.81.81.172881.210.871.831.212291.510.941.951.17SEC
J.B. Woodman430.32629%10%0.2092016217302770.981.191.981.252601.311.541.211.051931.170.721.351.03SEC
Harrison Bader440.32526%9%0.2002015217543051.021.112.11.212020.950.80.991.172470.60.770.631.01SEC
Kameron Misner450.32329%14%0.1842019216712661.111.81.251.151600.861.831.41.332451.210.921.191.03SEC
Rhett Wiseman460.32332%9%0.2192015217573441.231.191.951.242741.040.861.150.991391.160.991.461.06SEC
Dallas Carroll470.32213%11%0.1362017237312430.461.481.881.372510.551.11.411.162370.561.260.441.03Pac-12
Michael Curry480.32228%8%0.2072018207272631.040.971.361.132500.911.131.361.112141.610.511.550.97SEC
Will Toffey490.32121%13%0.1552017228272620.621.791.81.382640.941.090.30.883011.251.160.981.03SEC
Kevin Kramer500.32021%10%0.1672015216113060.751.251.371.210000030511.170.981.04Pac-12
Garrett MitchellBonus0.31917%6%0.164202021543730.20.711.211.222930.730.831.431.191771.250.610.380.87Pac-12
Tyler GentryBonus0.35424%8%0.254202021312740.721.081.921.52381.090.711.551.130#N/A#N/A#N/A#N/ASEC
Alerick SoularieBonus0.34819%10%0.2142020203110#N/A#N/A#N/A#N/A740.570.861.591.062370.841.391.571.32SEC

The Author

Jordan Rosenblum

Jordan Rosenblum

Jordan is an American living in Finland. In addition to writing for The Dynasty Guru, he's a doctoral candidate at Åbo Akademi researching explanations of income inequality, and a Workforce Strategist at OnWork Oy. His favorite baseball area is quantitative analysis of prospects.

Fun fact about Finland: they play pesäpallo here, which is like a soft-toss version of American baseball, except home runs are somehow outs.

2 Comments

  1. Alek Brugman
    July 22, 2020 at 1:06 pm — Reply

    Wow so crazy, didn’t realize the depth of the Pac12/ SEC. Great insight

    • July 23, 2020 at 12:25 pm — Reply

      Thank you Alek Brugman. I am glad you so vastly exceeded your projection!

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