Yesterday, I asked you to vote on which group of pitchers you expect to post a better ERA over the rest of the season. One group was composed of the 10 biggest SIERA overperformers, while the other included the underperformers. For the first time, I’m going to take the same polling idea and use it for hitters. So let’s follow the same concept and compare two groups of hitters based on xwOBA overperformance and underperformance. We know that xwOBA isn’t perfect. Neither is SIERA. In fact, no estimated/expected/forecasted equation is going to be perfect because there will always be a player or multiples that figure out how to do something we have a difficult time quantifying or there’s simply bound to be players each year that fall into either end of the extremes for no reason at all except for randomness. So let’s keep that in mind when reviewing these two groups.

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7/13/21

The latest episode of “The Sleeper and the Bust” is live. Support the show by subscribing to our Patreon!!

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Last week, I quickly introduced my updated hitter xK% equation thanks to commenter suggestions. Let’s now put the new equation into action and update my potential hitter K% regressor list. The original list used the earlier version of this equation and can be found here. As you might have expected, many of the same names made this new list. The xK% equation is updated, but the result isn’t dramatically different than it had been. So I won’t be discussing the names I did last week, just the new ones.

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Last week, I quickly introduced my updated hitter xK% equation thanks to commenter suggestions. Let’s now put the new equation into action and update my potential hitter K% improver list. The original list used the earlier version of this equation and can be found here. As you might have expected, many of the same names made this new list. The xK% equation is updated, but the result isn’t dramatically different than it had been. So I won’t be discussing the names I did last week, just the new ones.

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Yesterday, I used my newly unmasked hitter xK% equation to identify and discuss the hitters who have most underperformed the metric. Today, let’s now look into the biggest overperformers, or hitters who may be deserving of a higher strikeout rate right now.

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Last week, I unveiled the latest version of my hitter xK% metric. I was reminded of the need to do so based on some of the comments to my xwOBA articles. The gist of the comments were that Statcast’s xwOBA isn’t a fully expected mark if it takes strikeout and walk rates at face value. Even those rates should technically be adjusted to their own expected marks and then be used in xwOBA, rather than using the actuals. Adjusting strikeout and walk rates in xwOBA wouldn’t be nearly as actionable as adjusting the results of batted balls, but they could affect both counting stats like batting average and home runs (more or fewer expected balls in play), plus runs scored and stolen bases (more or fewer times on base), etc. So it’s still useful to be aware of.

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A whopping eight years ago, I shared the hitter xK% metric I developed using a couple of our plate discipline metrics. It was quite good, using only three variables, but still had a strong R-squared of 0.81. Since then, I haven’t discussed it all that much, but still use it to help formulate my Pod Projections. However, I have actually been using an updated version that I had never shared and it’s even better. The comments on my recent xwOBA articles inspired me to finally reveal the latest and greatest version of the hitter xK% metric.

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Yesterday, I listed and discussed the hitters with at least 200 PA who have most underperformed their xwOBA marks. Today, let’s now flip to the overperformers, those who have posted wOBA marks most above their xwOBA marks.

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It’s been nearly two months since I reviewed xwOBA and listed and discussed both the underperformers and overperformers. So let’s get back to it as we near the halfway point in the season. Obviously, xwOBA isn’t perfect. No expected metrics are and they never will be. However, it does a better job of predicting future wOBA than wOBA itself, even though it’s not even built as a predictive stat (it’s backward looking). So with that in mind, let’s investigate the hitters who have most underperformed their xwOBA marks so far.

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Baseball collects a lot of data. It’s awesome. FanGraphs is a fun place for data. Exit velocity, spin rate, launch angle; these are fun data points. But, the vast majority of data that is floating around in lakes and clouds is generally not as exciting. Take some kind of machinery for example. Right now there are gears whirling, sensors sensing, detectors detecting, you get the point. This type of data is typically referenced in discussions about the internet of things (IoT). Baseball analytics has always benefited from what is learned in industry and in this post, I’ll be investigating whether a common industry technique, a Principal Component Analysis (PCA), can be useful in baseball analytics. 

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