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:

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.

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Brian Cartwright
8 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