Archive for Statcast

I recently wrote about Statcast measures that were highly correlated with power metrics, such as HR/FB and ISO. The research confirmed that several Statcast measures could be useful tools for identifying undervaled power sources. One factor I ignored in that analysis was pull rate, but in taking a belated look at it, I found that one of the apparently strong relationships gets notably weaker when we control for a hitter’s pull tendencies.

In general, exit velocity on flyballs and line drives turned out to be strongly correlated with ISO for hitters with at least 150 batted ball events in 2018. However, when you isolate the top 10 percent of the sample in terms of pull rate, the relationship is still meaningful, but it’s not quite as strong. That could have implications for how we view two of last season’s top power hitters, Jose Ramirez and Alex Bregman.
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I’ve always been a week or two behind evaluating hitters so I continue trying to find ways to gain an edge. Today’s stab in the dark is trying to see if StatCast data can determine if a player is hurt. A recent study of mine found no correlation between playing through an injury and exceeding their projection in the next season. Instead of looking at preseason projections, I’m going to go a little more in-season today and determine how much of an impact an injury has on a hitter’s in-season production.

As Al showed, there are a ton of metric available StatCast batted ball metrics to use. I started down the path of using several of them but quickly found there is more to injuries than just power. A hitter’s plate discipline and speed results (e.g. turning singles into doubles) also matter. Instead of incorporation all of them in, I decided to use BaseballSavant’s xwOBA metric to measure a hitter’s production since it combines all of these factors.

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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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I heard in passing from a credible source:

“The effectiveness of a pitcher’s slider relies on it having the same spin as his fastball.”

I figured it would be an easy test and could help to immediately identify top-rated sliders. After looking at the data every conceivable way and came up with the following conclusion: publicly available spin information has near ZERO correlation to a slider’s effectiveness. But while rooting around, I did find two factors which do matter, fastball velocity and the difference in slider and fastball velocity.

The theory behind the quote is that a hitter has a tougher time differentiating a fastball and slider if they are spinning at the same rate. So, the closer the difference, a higher chance for a swing-and-miss.

I compared 2018 pitchers with at least 200 sliders and 200 four-seamers thrown. Then, I compared just the difference, the absolute value of the difference, square of difference. Nothing tangible. Nothing matched.

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One of the keys to fantasy success involves finding when projections can be systematically off. The hard part for fantasy owners is that most of these findings, like pitch velocity, get quickly incorporated into projections. Since it’s difficult to find these discrepancies, I was intrigued when I saw this quote by Mitchel Lichtman (MGL) in an article he wrote:

So, the substantial under-projections seem to occur when a player gains speed but his wOBA remains about the same.

And by substantial, it was a 22 point difference is wOBA. This is a major difference and could point owners to some nice upside plays. I decided to go ahead and dive in.

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