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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One of the best things about being a FanGraphs author is the ability to receive feedback and immediately act on suggestions from readers smarter than me to improve my analysis. I’m no math or data wiz, but have learned so much from just trying to develop equations over all these years that I could actually play one decently on the Internet now. A week ago, I shared a long overdue update of my hitter xK% equation. It incorporated metrics from Baseball-Reference.com, and performed darn well, clocking in with a 0.941 adjusted R-squared. Yet, it was still ripe for improvement.

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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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A former Mets farmhand who amassed 68 innings in the Majors before pitching for a season in KBO (Korea) last season, Chris Flexen 플렉센 signed with the Mariners this past offseason as a real mystery man. Incredibly, he actually entered the 2021 season with more walks allowed than strikeouts during his short MLB career, but a 28% strikeout rate versus just a 6.4% walk rate in Korea suggested that maybe something had changed. So suddenly he was an interesting flyer in fantasy leagues with the possibility he learned something overseas that would translate back to the Majors. That certainly didn’t happen over his first eight starts, but things might be clicking now, as we’ll see over his last five starts.

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Yesterday, I reviewed the starting pitchers leading the league in SIERA over the last 30 days. Now let’s flip to the other end — those starting pitchers who have posted the worst SIERA marks in the league over the last 30 days. Is a decline in velocity to blame? Sudden control issues? Let’s find out.

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Pitchers can change rapidly. Whether it’s changing their mix pitch, gaining or losing velocity, adjusting their mechanics, or something else, a pitcher we see today isn’t necessarily the same version we were expected during draft season. So it’s important to always monitor their underlying skills to see if the results suggest a new pitcher has arrived, whether better or worse. With that in mind, let’s dive into the last 30 day SIERA leaders. Are there any surprises among the crowd?

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