Archive for exit velocity

Note: Many thanks to Alex Chamberlain, who provided the correlations cited in this column, as well as insights regarding some of the relationships.

As I have searched for ways to spot undervalued power sources over the last few seasons, I have relied heavily on several Statcast metrics that are available on Baseball Savant. I have leaned especially hard on average flyball distance. While the leaderboard typically includes several players who are proven power sources, it has also featured some players who appear to be undervalued. For example, Scott Schebler, Kendrys Morales, Trey Mancini, Tim Beckham and Mitch Moreland all finished in the top 20 percent in average flyball distance in 2017 (min. 50 flyballs), and that gave me a little extra confidence to give them a try in 2018. For stretches, Morales and Moreland paid some dividends, but it was far from a foolproof method.
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As we are not even two weeks into the season, it is ridiculously early to draw many conclusions from season-to-date stats. Still, most of us aren’t just standing pat with our opening day rosters, and it’s not just injuries and playing time trends that are guiding our add/drop moves.

It’s certainly not mere coincidence that Jakob Junis is coming off Monday night’s scoreless seven-inning performance against the Mariners and he is atop the most-added lists on ESPN and CBS for starting pitchers. Owners have not been scared off by Junis’ total of nine strikeouts over 14 innings, as they have been drawn in by his 0.00 ERA and 0.50 WHIP.
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This is a long one. I appreciate your patience in advance.

Mike Podhorzer, I and sporadic others have — but primarily Mike has — carried the torch on developing ‘expected’ metrics, such as xBABIP (expected batting average on balls in play), xHR/FB (expected home run-to-fly ball ratio) and xK% (expected strikeout rate), all and the rest of which can be found here. For the uninitiated, these xMetrics help describe how a hitter or pitcher should have performed based on various measurements of the events that unfolded and typically are more predictive of future performance than the original metric. They’re not perfect, but, like other advanced metrics, they give us a better understanding of player performance and ability.

Each metric — xHR/FB, xK%, etc. — has formulas for both hitters and pitchers, with the hitter metrics typically having stronger correlations than those for pitchers. Unfortunately, pitcher xBABIP has always eluded us. It’s inappropriate to repurpose hitter xBABIP for pitchers, but it’s because the model coefficients (weights) would be different, not because the theory underpinning the model is flawed.

That’s the problem, though: hard hits, line drives, infield fly balls — these all should affect a pitcher’s BABIP allowed. Our intuition begs it to be true. Yet there’s a resounding lack of evidence that suggest otherwise. The correlation between BABIP and hard-hit rate (Hard%), line drive rate (LD%) and infield fly ball rate (IFFB%), among others, borders on nonexistent:

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