Batted-Ball Rates vs. Velocity Changes

Last year, I revisited Mike Fast’s “Lose a Tick, Gain a Tick” article and found how much a pitcher should expect to see his ERA, FIP and xFIP change with a velocity decline. Additionally, I found the rate of decline of strikeouts and walks. An interesting finding from the work was that FIP and ERA change by the same amount with a velocity decline while xFIP doesn’t follow the other two. I decided to examine some batted-ball stats to see which ones change when a pitcher’s velocity changes.

The theory I brought up at the time was that number of home runs increases as velocity declines. Well, the obvious answer is the correct one: the slower the fastball, the more home runs. Here are the curves for HR/9, HR/FB and my new favorite home run metric, HR/Batted Ball (link).

No real surprises. As fastball velocity increases, the number of home runs allowed drops. The key graph is the HR/FB. xFIP assumes pitchers have a constant HR/FB rate. This is not the case. Each pitcher is going to start at some point above, at, or below the league-average HR/FB rate. As a pitcher’s velocity drops with age, his ERA and FIP will increase more than his xFIP. For example, here are the ERA, FIP and xFIP plots for Tim Lincecum and CC Sabathia:

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You can notice that during each pitcher’s career, there’s a point at which his xFIP was constantly higher than the other two metrics. For Lincecum, the transition for xFIP being lower than the other two occurred when his velocity dropped to 90 mph in 2012. For Sabathia, the transition also happened in 2012, when his velocity dropped to 92.3 mph.

There’s no reason to throw out xFIP as a metric. It’s especially useful as a stat to help show which pitcher may be a bit unlucky giving up home runs in a single season or less. As years’ worth of data become available on a pitcher, however, they can exhibit a skill at preventing (or not preventing) home runs.

Now, I’ll move onto BABIP, for which metric the results are less predictable.

From 94 mph and lower, there is effectively no change in BABIP. If the large drop at 97 mph is removed, BABIP only varies a little over 10 points. So as a pitcher’s velocity drops, he should not expect to give up more non-homer hits.

Moving on, here are the fly-ball, line-drive and ground-ball velocity curves in a single graph.

Well, that’s interesting. Line-drive rates stay relatively constant as velocity changes. This coincides with the above BABIP graph. Since line drives are the leading force behind larger BABIPs, these values support each other.

The other piece of information is the move from ground balls to fly balls as velocity declines. So as velocity declines, pitchers give up both fly balls and home runs per fly balls. No wonder the overall home-run numbers are up.

Now, time for the curve with the Hard-Med-Soft hit data and how it changes as average fastball velocity changes.

What a mess. After trying to make sense of it, I have the notion to just ignore all of it. I lumped the same data into 3-mph groups to look for any overall trends. The Med data makes the same low mph jump, but otherwise the data is relatively constant with Hard Hit data bouncing up and down within a 1.5% band.

When a pitcher’s velocity drops, historically, the hard hit, line-drive, and BABIP data doesn’t increase. The noticeable increase exists in the number of home runs. The increase in home runs along with a decrease in strikeouts caused by a velocity decline hurt pitchers in two ways simultaneously. Some pitchers can make the adjustment with less velocity by throwing more breaking pitches, while others continue to throw the same and struggle.





Jeff, one of the authors of the fantasy baseball guide,The Process, writes for RotoGraphs, The Hardball Times, Rotowire, Baseball America, and BaseballHQ. He has been nominated for two SABR Analytics Research Award for Contemporary Analysis and won it in 2013 in tandem with Bill Petti. He has won four FSWA Awards including on for his Mining the News series. He's won Tout Wars three times, LABR twice, and got his first NFBC Main Event win in 2021. Follow him on Twitter @jeffwzimmerman.

18 Comments
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Brian Cartwright
11 years ago

try separating BABIP into components – for gb, for (ld+fb) and for pu.

pitchers show little variance on gb babip, but have fairly predictable hit rate on balls in the air to the outfield

Detroit Michael
11 years ago

I don’t understand that second graph (but wish I did). Aren’t HR/FB rates about 10% or .10?

Jetsy Extrano
11 years ago

These graphs have each line independently zeroed to whatever its max/min value was? I think? I get that you are interested in the variation, but it makes it hard for me to get the variation in context.

I’d find it easier to follow with just the actual value (HR%, etc.) plotted.

Jetsy Extrano
11 years ago
Reply to  Jeff Zimmerman

Fair point. It would be slightly untrue to plot it all off league average as a base rate, but might be worth it…

Just labeling the axis ?HR% would help some.

One Mississippi
11 years ago

First of all, I love this article and all articles like it. However, my question is this; since fip-type ERA estimators use home runs as a component, and since older, slower pitchers allow more home runs, shouldn’t fip-type estimators indicate higher than actual ERA?
Lincecum’s and Sabathia’s are doing the opposite. I would think that as more of your runs come from HR, fip and xfip would punish you even more heavily.

jimmy
11 years ago

xFIP doesn’t punish you for home runs, it punishes you for fly balls

One Mississippi
11 years ago
Reply to  jimmy

Ok. So xFip would miss the boat, but fip should be even higher than ERA, given that it uses HR. Also, since lower velocities allow more fly balls, xFip should be up, too.

jimmy
11 years ago

The way you’re saying it makes it sound like you’re showing the relationship between the change in these metrics and the change in fastball velocity. But these graphs are by velocity, not change in velocity. Are they labeled wrong or am I misunderstanding what you’re saying?

Squirrel
11 years ago

Jeff,

This might be a dumb question, and it might be slightly (slightly) off topic, but I’ve been looking for an appropriate post to ask this question and I think this one is close enough: I was looking at when LD%, GB%, and FB% stabilize and on the Fangraphs info page, it says GB and FB rates both stabilize at 80 BIP, yet LD rates don’t stabilize until 600 BIP… how can LD rates not be stable if the other two options are? Wouldn’t any further changes in LD rates after 80 BIP also change either GB or FB rates?

Here’s the link for quick reference:
http://www.fangraphs.com/library/principles/sample-size/

Again, I might’ve missed something painfully obvious, but it’s been bugging me.

Squirrel
11 years ago
Reply to  Squirrel

*These are rates for offensive statistics, BTW.

Jetsy Extrano
11 years ago
Reply to  Squirrel

A lot of what ‘stabilization’ numbers tell you is about how wide the variation of underlying rate is in the MLB population.

I bet what you see here is that we have heavy GB hitters, and heavy FB hitters, but the spread of LD% talent is tighter. So for a given observed LD%, you have to regress more.

Frank
11 years ago

This might be a little obvious, but I actually find the Hard-Med-Soft data interesting in that it says something about the baseball adage “let the pitcher supply the power.” The idea is that harder the pitcher throws, the further the ball will go if you make contact, but it seems to not be the case. It looks like there are sweet spots for medium and hard hit balls from around 89-91. I assume its because the harder throwers are more difficult to time, leading to time and square up, leading to a lot of softer contact. Meanwhile, when you start getting to the low 80s, the pitches are easier to time and hit well, but because the incoming pitches are relatively slow, the ball doesn’t get hit as hard, meaning these two combined effects lead to more medium hit balls (or maybe the pitchers in this velo range are just trickier in general to compensate for a lack of velocity and can better limit hard contact?). Leaving the 89-91 range as the sweet-spot where the hitters can make solid contact with the pitch, and the pitch has enough incoming velocity to be hit hard. At least that’s what I take from that plot. Like I said, maybe it was obvious, but I didn’t anyone mention it so I figured I’d throw it out there. It might be interesting to look at the distribution of batted ball distances vs incoming pitch velocity. It may need to be grouped by launch angle to make sense of it though.

Frank
11 years ago
Reply to  Frank

That 4th sentence should read: harder throwers are more difficult to time and square up, leading to…