Using High-A Stats to Predict Future Performance

Last week, I looked into how a player’s low-A stats — along with his age and prospect status at the time — can be used to predict whether he’ll ever play in the majors. I used a methodology that I named KATOH (after Yankees prospect Gosuke Katoh), which consists of running a probit regression analysis. In a nutshell, a probit regression tells us how a variety of inputs can predict the probability of an event that has two possible outcomes — such as whether or not a player will make it to the majors. While KATOH technically predicts the likelihood that a player will reach the majors, I’d argue it can also serve as a decent proxy for major league success. If something makes a player more likely to make the majors, there’s a good chance it also makes him more likely to succeed there.

Things that were predictive for players in low-A included: age, strikeout rate, ISO, BABIP, and whether or not he was deemed a top 100 prospect by Baseball America in the pre-season. However, a player’s walk rate was not significant in predicting a player’s ascension to the majors. Today, I’ll analyze what KATOH has to say about players in class-A-advanced leagues. Here’s the R output based on all players with at least 400 plate appearances in a season in high-A from 1995-2009:

This looks very similar to what I found for low-A players: Walk rate isn’t significant, and everything else has very similar effects on the final probability. However, the coefficients from this model are all a tad bigger than those from the low-A version, implying that high-A stats might be a bit more telling of a player’s future. Intuitively, this makes sense: The closer a player is to the big leagues, the more his stats start to reflect his future potential.

By clicking here, you can see what KATOH spits out for all current prospects who logged at least 250 PA’s in high-A as of July 7th. I also included a few notable players who fell short of the threshold, namely Joey Gallo (who checks in at a remarkable 99.8%), Peter O’Brien, and Jesse Winker. Here’s an excerpt of the top-ranking players:

Player	Organization	Age	MLB Probability
Joey Gallo	TEX	20	100%
Corey Seager	LAD	20	99%
Carlos Correa	HOU	19	99%
Albert Almora	CHC	20	93%
Nick Williams	TEX	20	93%
D.J. Peterson	SEA	22	93%
Jesse Winker	CIN	20	91%
Orlando Arcia	MIL	19	88%
Jose Peraza	ATL	20	87%
Colin Moran	MIA	21	87%
Renato Nunez	OAK	20	86%
Tyrone Taylor	MIL	20	85%
Hunter Renfroe	SDP	22	84%
Josh Bell	PIT	21	84%
Raul Mondesi	KCR	18	83%
Daniel Robertson	OAK	20	83%
Jorge Polanco	MIN	20	81%
Dilson Herrera	NYM	20	77%
Breyvic Valera	STL	21	77%
Peter O’Brien	NYY	23	76%
Matt Olson	OAK	20	75%
Jorge Alfaro	TEX	21	75%
Patrick Leonard	TBR	21	75%
Dalton Pompey	TOR	21	73%
Billy McKinney	OAK	19	73%
Teoscar Hernandez	HOU	21	73%
Brandon Nimmo	NYM	21	72%
Jose Rondon	LAA	20	70%
Rio Ruiz	HOU	20	70%
Brandon Drury	ARI	21	70%

Next up will be double-A. Unlike A-ball, double-A tends to be a random mishmash of prospects and minor-league lifers, so it will be interesting to see how KATOH handles this wide array of players. And perhaps double-A is where a player’s walk rate finally starts to tell us something about his future success.

Statistics courtesy of Fangraphs, Baseball-Reference, and The Baseball Cube; Pre-season prospect lists courtesy of Baseball America.