How a Player's Minor League Numbers Translate to his Future MLB Success

When scouting, it is generally better to scout based off what you see (bat speed, approach, swing path, etc.) vs. what the numbers say. But to minor league numbers hold any relevance? Is there a correlation between a player’s minor league stats and his future major league stats? I took a sample of 40 players, who have debuted since 2000, played with the Rangers at some point in their career, and have at least 200 major league at-bats. I took their statistics at the Low-A, High-A, AA, and AAA levels and ran tests to find both the average difference between the stats at each level compared to the player’s MLB stats and the strength of correlation between the numbers for AVG, OBP, and SLG.

Here are the means, or averages, the standard deviations, or average difference from the mean, and r values, or strength of correlation (0-.5 is a weak correlation, .5 to .8 is a moderate correlation, and .8 to 1 is a strong correlation) for each of the levels for batting average, on-base-percentage, and slugging percentage. If the r value is at .5 or higher, that shows that the numbers have some relevance. I have also listed the current slash lines for the players on each roster that have 200+ PAs at any minor league level, and then translated that line to their projected major league career slash line based on the averages. Remember that there are tons of players who are well better or well worse than the average, so the projected stats are more of a fun tool than something to read anything into.

Keep in mind that the numbers for High-A are biased and the average drop should not be so extreme. Much of the sample that I used were hitters that went through the hitter-friendly California League, back when the Rangers High-A team was out in Bakersfield, whereas now these players are playing in the pitcher-friendly Carolina League. The drop shouldn’t be so big for these hitters, so take that into consideration.

Low-A Batting Average:

mean=-.027

Standard deviation=.037

r=.334 (weak)

 

Low-A OBP:

mean=-.034

Standard deviation=.039

r=.4626 (weak)

 

Low-A Slugging Percentage: mean=-.025

Standard deviation=.077

r=.508 (moderate)

 

In Hickory (-.027/-.034/-.025):

C Kellin Deglan (.231/.331/.393) to (.194/.284/.340) using High-A stats

C Kevin Torres (.241/.279/.324) to (.214/.245/.299)

1B Ronald Guzman (.282/.331/.398) to (.255/.297/.373)

SS Luis Marte (.222/.248/.280) to (.195/.214/.255)

2B Nick Urbanus (.198/.268/.234) to (.171/.234/.209)

1B Nick Vickerson (.237/.377/.360) to (.210/.343/.335)

OF Lewis Brinson (.246/.325/.429) to (.219/.291/.404)

OF Nomar Mazara (.227/.304/.362) to (.200/.270/.337)

 

High-A Batting Average:

mean=-.037

Standard deviation=.032

r=.4208 (weak)

 

High-A OBP:

mean=-.047

Standard deviation=.035

r=.5069 (moderate)

 

High-A Slugging Percentage: mean=-.053

Standard deviation=.06

r=.771 (moderate)

 

In Myrtle Beach (-.037/-.047/-.053):

C Jorge Alfaro (.259/.331/.443) to (.232/.297/.418) using Low-A stats

C David Lyon (.239/.321/.416) to (.212/.287/.391) using Low-A stats

SS Hanser Alberto (.213/.253/.287) to (.179/.214/.241) using AA stats

1B/OF Preston Beck (.249/.347/.352) to (.212/.300/.299)

2B Chris Bostick (.282/.354/.452) to (.255/.320/.427) using Low-A stats

3B Joey Gallo (.245/.334/.610) to (.218/.300/.585) using Low-A stats

OF Royce Bolinger (.285/.330/.426) to (.248/.283/.373)

OF Zach Cone (.262/.326/.461) to (.235/.292/.436) using Low-A stats

OF Odubel Herrera (.257/.289/.339) to (.223/.250/.293) using AA stats

OF Nick Williams (.293/.337/.543) to (.266/.303/.518) using Low-A stats

 

AA Batting Average:

mean=-.034

Stardard deviation=.03

r=.3556 (weak)

 

AA OBP:

mean=-.039

Stardard deviation=.038

r=.4054 (weak)

 

AA Slugging Percentage: mean=-.046

Stardard deviation=.07

r=.523 (moderate)

 

In Frisco (-.034/-.039/-.046):

C Pat Cantwell (.253/.318/.316) to (.216/.271/.263) using High-A stats

C Tomas Telis (.263/.292/.348) to (.229/.253/.302)

C Zach Zaneski (.237/.310/.364) to (.203/.271/.318)

1B Trever Adams (.257/.334/.386) to (.220/.287/.333) using High-A stats

2B Edwin Garcia (.252/.308/.315) to (.215/.261/.262) using High-A stats

2B Rougned Odor (.281/.326/.465) to (.247/.287/.419)

3B Ryan Rua (.291/.371/.480) to (.257/.332/.434)

1B/OF Jordan Brown (.302/.348/.451) to (.274/.313/.413) using AAA stats

OF Chris Grayson (.192/.299/.295) to (.155/.252/.242) using High-A stats

3B/OF Drew Robinson (.257/.367/.405) to (.220/.320/.352) using High-A stats

OF Jake Skole (.201/.319/.286) to (.164/.272/.233) using High-A stats

OF Jake Smolinski (.258/.345/.401) to (.230/.310/.363) using AAA stats

 

AAA Batting Average:

mean=-.028

Standard deviation=.023

r=.4686 (weak)

 

AAA OBP:

mean=-.035

Standard deviation=.027

r=.6687 (moderate)

 

AAA Slugging Percentage: mean=-.038

Standard deviation=.06

r=.631 (moderate)

 

In Round Rock (-.028/-.035/-.038):

C/1B Brett Nicholas (.289/.357/.474) to (.255/.318/.428) using AA stats

3B Alex Buchholz (.267/.328/.398) to (.239/.293/.360)

2B Kensuke Tanaka (.323/.403/.404) to (.295/.368/.366)

INF Guilder Rodriguez (.272/.360/.291) to (.244/.325/.253)

OF Jared Hoying (.249/.284/.479) to (.221/.249/.441)

OF Brad Snyder (.288/.352/.504) to (.260/.317/.466)

OF Ryan Strausborger (.232/.297/.360) to (.198/.258/.314) using AA stats

 

What I was expecting to see was a very low strength or correlation for the lower levels, and higher ones at the upper levels. Low-A went as expected, with the lowest strength of correlation for both batting average and slugging percentage. However, Double-A had very weak correlations, with the weakest in OBP and second weakest in average and slugging. Triple-A unsurprisingly had fairly strong correlations, and High-A very surprisingly, at least to me, had pretty strong correlations, including the strongest correlation of any stat at any level with an r value of .771 in slugging percentage.

What these numbers showed is that there definitely is a correlation between a player’s minor league stats and his future major league stats, but not enough of one to overlook a player’s raw tools, or to overvalue a player’s minor league numbers. In other words, high-performing minor league players such as Nate Gold (who never made the majors) don’t even come close to consistently translating that success to the major league level, but they do have a higher tendency to do so than a lesser performer at the minor league levels, which makes sense.