Minors to the Majors: Pitcher Metric Correlations

Yesterday, I identified and discussed 11 starting pitchers significantly outperforming their SIERA marks, suggesting potentially major impending regression. One such pitcher on the list, ranking second in SIERA outperformance, was Mike Soroka. Through 10 starts and 65.1 innings, Soroka has allowed an amazing microscopic HR/FB rate of just 2.9%, which easily leads all qualified pitchers in baseball.

A commenter pointed out that Soroka’s minor league numbers suggest some ability to suppress home runs. Indeed, his performance down on the farm was quite magnificent, as he posted a minuscule 4.6% HR/FB rate throughout his minor league career spanning 370.2 innings. So because he displayed such home run suppression skills in the minors, and so far they have been maintained in the Majors, we can now confidently anoint Soroka as a HR/FB rate king, right? Not so fast.

In my response to the commenter, I began thusly:

Pretty confident that minor league HR/FB rate is meaningless.

Of course, I had no data or studies to link to backing up my assumption. When pressed for a justification of my confidence, I replied:

Sample size, wildly varied level of competition, significant ballpark effects, and what worked in the minors might not work in the Majors.

You Aren't a FanGraphs Member
It looks like you aren't yet a FanGraphs Member (or aren't logged in). We aren't mad, just disappointed. We get it. You want to read this article. But before we let you get back to it, we'd like to point out a few of the good reasons why you should become a Member. 1. Ad Free viewing! We won't bug you with this ad, or any other. 2. Unlimited articles! Non-Members only get to read 10 free articles a month. Members never get cut off. 3. Dark mode and Classic mode! 4. Custom player page dashboards! Choose the player cards you want, in the order you want them. 5. One-click data exports! Export our projections and leaderboards for your personal projects. 6. Remove the photos on the home page! (Honestly, this doesn't sound so great to us, but some people wanted it, and we like to give our Members what they want.) 7. Even more Steamer projections! We have handedness, percentile, and context neutral projections available for Members only. 8. Get FanGraphs Walk-Off, a customized year end review! Find out exactly how you used FanGraphs this year, and how that compares to other Members. Don't be a victim of FOMO. 9. A weekly mailbag column, exclusively for Members. 10. Help support FanGraphs and our entire staff! Our Members provide us with critical resources to improve the site and deliver new features! We hope you'll consider a Membership today, for yourself or as a gift! And we realize this has been an awfully long sales pitch, so we've also removed all the other ads in this article. We didn't want to overdo it. In case you didn't read all that, here's the short version. Membership includes:
✓Ad-free browsing
✓Unlimited FanGraphs & RotoGraphs articles
✓Historical & platoon projections
✓Leaderboard & player-page stat heatmaps
✓One-click data exports
✓Customizable player-page dashboards
✓Dark & Classic site modes
✓Weekly Members-only mailbag
✓FanGraphs Walk-Off, your year in review
✓Optional homepage photo removal
✓Gift articles to non-members

I hate to make assertions without the data to back it up. So as a result, I was inspired to expand my research to all pitcher metrics to see if I am right and what metrics, if any, correlate upon transitioning from the minors to the Majors.

I exported the career minor league stats of every pitcher since 2006 and removed those who recorded fewer than 50 innings. That left me with a whopping 8,733 total pitchers.

I then exported the career Major League stats of every pitcher since 2002 with a minimum of 50 innings pitched.

Last, I performed a VLOOKUP using the Player ID to pull the minor league stats worksheet over to the Major league stats worksheet. I then removed all players missing minor league stats.

Finally, my player population totaled 1,160, which is a reasonable sample size.

What follows are correlations and observations of most of the important metrics you care about from the minors to the Majors.

Minors to Majors: Batted Ball Type Correlations
LD% GB% FB% IFFB%
0.07 0.75 0.74 0.34

Not surprisingly, minor league line drive rate allowed is pretty meaningless. On the other hand, ground ball and fly ball rates mean a whole lot. Generally, a ground ball pitcher in the minors is going to remain one in the Majors, and vice versa. Pop-up rate is nowhere near as correlated, but still meaningful enough to suggest that pitchers do carry over some of those skills. Interestingly, these correlations almost perfectly match the Major League Year-to-Year correlations Matt Klaassen calculated back in 2013. Every metric in the above table has a slightly lower correlation than on Matt’s table, but the order is the same and the gaps similar.

Minors to Majors: Batted Ball Direction Correlations
Pull% Cent% Oppo%
0.56 -0.06 0.47

This surprises me. I would have never guessed the correlations for Pull% and Oppo% would be this high. Perhaps this is a proxy for pitch location or the pitcher’s pitch mix, both of which won’t change drastically in the Majors. It’s weird that Cent% is so low and actually negative!

Minors to Majors: Pitch Outcome Correlations
K% BB% SwStr% Str%
0.68 0.61 0.55 0.51

No surprises here as all four of these metrics carry over to some degree to the Majors. I find two things interesting here — the SwStk% correlation is meaningfully below K%, while Str% (strike percentage) is meaningfully below BB%. So both per pitch metrics are below the related plate appearance outcome metric. Odd.

Minors to Majors: Luck Metric Correlations
BABIP HR/FB
0.14 0.02

Finally, we get to the meat and the entire reason I did this research to begin with.

While BABIP’s correlation is quite low, it’s not completely meaningless. This does make sense, but it’s unlikely related to the pitcher’s skill or lack of skill in suppressing hits on balls in play. Instead, it’s probably more a function of the pitcher’s batted ball type profile, which as we learned above, carries over pretty well to the Majors.

Now, take a close look at that HR/FB rate correlation. Can you see it or shall I provide a magnifying glass? That correlation is essentially nada. There is absolutely no correlation between a pitcher’s minor league HR/FB rate and his Major League fly ball rate. This is exactly what Matt Klaassen found in his correlation calculations, though strangely, he found a negative number. Regardless of whether you look at the correlation of HR/FB rate from the minors to the Majors or the Majors Yr 1 to the Majors Yr 2, you will reach the same conclusion — if there is any skill difference between pitchers, it’s near impossible to capture and driven primarily by both luck and a myriad of other factors.

Now let’s summarize the metrics from highest to lowest correlation from the minors to the Majors.

Minors to Majors: Correlation Summary
Metric Correlation
GB% 0.75
FB% 0.74
K% 0.68
BB% 0.61
Pull% 0.56
SwStr% 0.55
Str% 0.51
Oppo% 0.47
IFFB% 0.34
BABIP 0.14
LD% 0.07
HR/FB 0.02
Cent% -0.06

This sorted list simply reiterates what we already know to focus on for Major Leaguers. If you’re evaluating a minor league pitcher, ignore his BABIP, LD%, and HR/FB rate. Focus on his batted ball profile (ground ball, fly ball, or neutral), his strikeout rate (and to a lesser extent SwStk%), and walk rate (and to a lesser extent Str%). That’s really it. Obviously, there’s no reason to concern yourself with ERA, although it might influence the organization’s decision on whether or not to promote the pitcher.





Mike Podhorzer is the founder of ProjectingX IQ, an advanced fantasy baseball analytics platform that transforms projection data and in-season performance signals into actionable intelligence. He is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year and three-time Tout Wars champion. He is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. Follow Mike on X@MikePodhorzer and contact him via email.

22 Comments
Oldest
Newest Most Voted
hebrewMember since 2016
7 years ago

this is awesome work for those of us in dynasty or keeper leagues! thanks, Pod.

ccovilleMember since 2020
7 years ago

Good stuff. Always like seeing you guys try to figure out how to translate numbers from the minors.

thecodygriffin
7 years ago

My understanding of what you are using as your correlation value may be leading me astray here, so feel free to correct me, but a truly “meaningless” correlation should be one that is exactly equal to 0, correct?

I don’t think your analysis is wrong. We should expect Soroka to regress back to the population norm. However, there must be some leeway, however small, at the individual level. If someone was so inclined to do an extremely detailed analysis, maybe his minor league performance, combined with pitch mix, combined with spin, etc could potentially suggest he is a 8%, not 10% HR/FB% guy?

I guess my issue is with your usage of the word “meaningless” within the context of an individual player. For a population, I agree, but for Soroka himself, I essentially interpret the 0.02 as “So you’re telling me there’s a chance?”

Please set me straight.

HappyFunBallMember since 2019
7 years ago
Reply to  thecodygriffin

Well it’s possible that Soroka is a unicorn.

One of the great big black boxes of pitching analysis is sequencing. Perhaps Soroka has some magical sequencing talent that leads to his FBs being inordinately poorly struck. It’s not impossible, it’s just not measurable.

shakeappealMember since 2020
7 years ago
Reply to  HappyFunBall

The Ben Clemens piece is good on this: https://blogs.fangraphs.com/how-does-mike-soroka-do-it/

One thing I’ve also idly wondered is whether guys like Soroka and Hendricks get a weird little edge in a context where batters are “tuned” to a heavy diet of sliders and overpowering stuff.

Is it then harder to pick up quirky sinkerballers with elite command/tunneling when you face them one or two days out of the month?

docgooden85Member since 2018
7 years ago
Reply to  thecodygriffin

A 0.02 correlation coefficient might as well be, and for practical purposes IS, zero. Even 0.5 is on the weak side and hard to know how much signal vs. noise you have there. There is always a chance data with a weak linear correlation better fits to a non-linear function but that’s a math class for another day.

Jimmy von AlbadeMember since 2019
7 years ago
Reply to  thecodygriffin

No, you’re not interpreting that right. A correlation of exactly zero will pretty much never happen. A correlation close to zero is meaningless from a statistical point of view.

It is of course possible that Soroka is a low HR/FB% guy, but the point is that looking at his minor league numbers as evidence of that is not a fruitful endeavor.

thecodygriffin
7 years ago
Reply to  thecodygriffin

After thinking about this more, it is definitely Mike’s use of the word “meaningless” that is throwing me off (not your fault Mike, it is mine). I should be interpreting it as “insignificant”, “extremely insignificant” in the case of HR/FB%, but not the true definition of “meaningless” or that of absolutely ZERO significance.

Semantics and, admittedly a subtle difference, but to me “meaningless” is an outright dismissal of the possibility where as “insignificance” means that it is extremely unlikely based on the data we have.

docgooden85Member since 2018
7 years ago
Reply to  thecodygriffin

Meaningless is the correct interpretation. Insignificant (i.e., weak or even very weak correlation) is not in evidence with 0.02. Regression using real world data isn’t so precise that you should be focusing on +/- a couple of hundredths. Your takeaway should be there is no evidence of any correlation for HR/FB.

DDD
7 years ago

Great read, great insight, thanks Mike! I wonder if there would be much difference if you limited your scope to only those that were ranked as a top 100 prospect just prior to their ML call up. I ask this because it seems there is much more volatility in outcomes with those that are not quite as highly regarded as prospects.

DDD
7 years ago
Reply to  Mike Podhorzer

I understand. I was actually thinking the same thing as I typed. But, figured I’d send it anyway as something to ponder. And, after pondering a bit longer, I thought maybe there’s actually an opposite effect since more highly regarded prospects tend to get to the majors quicker, creating a smaller sample size in the minors. Thanks for the response.

dezreMember since 2020
7 years ago

Great read, and awesome follow up from yesterday. Always cool to see author-reader interactions drive further analysis. And vlookups…oh my!

Can anyone point me to similar MiLB to MLB metrics correlation analysis for hitters?! Or, uh, Mike – will you set aside frivolous activities such as sleeping and eating to indulge my curiosity and perform this analysis for us (me)?!

Awesome read. Thanks again.

dezreMember since 2020
7 years ago
Reply to  Mike Podhorzer

Yeah, I’ve only found older stuff after a cursory online search. Good luck! I hope whatever analysis you perform ends up fruitful! I’m sure it’ll be a cool read. Thanks again for your work.

Urban Shocker
7 years ago

maybe I’m missing something, but given the high Flyball correlation, a more measured response might be “normally pitchers HR rate would be expected to regress unless said pitchers show an ability to suppress balls in the air.”

jlebeck66
7 years ago

Depending on the exact execution of your VLOOKUP you may want look into the INDEX-MATCH combo. I use to use VLOOKUP a lot, but once I got introduced to INDEX-MATCH I haven’t used VLOOKUP at all. Used properly it is more efficient than VLOOKUP which is a lifesaver when dealing with bigger data sets.

jlebeck66
7 years ago
Reply to  Mike Podhorzer

Fair enough!

Toffer Peak
7 years ago

This is so awesome thanks for running the numbers. It’s great to have a theory and then run the numbers to find that they match your expectations.

evo34Member since 2023
7 years ago

HR/FB rate is not predicted by past HR/FB rate. This much was already known.

What is not included in this article is what statistics actually do predict HR/FB rate. They are out there. Presented in articles on this site, even.

Also, your study suffers from a huge selection bias because it only considers those minor leaguers who had major league careers (by your numbers, appox. 1 in 8 qualify). The most extreme HR pitchers will never make it to the majors, leaving a population of pitchers who were selected, in part, for their presumed ability to have normal or better HR rate.