Yesterday, I discussed six starting pitchers who had seen their strikeout rates surge the most over the last 30 days compared to the rest of the season to date. Today, we’re going to review the starting pitchers on the opposite end — those whose strikeout rates have declined the most over the last 30 days.
Pitchers sometimes change rapidly. Whether it’s gaining or losing velocity, altering their pitch mix, changing their delivery, moving on the rubber, or something else, it’s important to pay attention to trends in their underlying skills as it could be very telling. So let’s review the strikeout rate surgers over the last 30 days compared to what these pitchers posted for the season heading into this period.
The top prospects just keep on coming! On Friday when baseball returned after the all-star break, the Red Sox recalled their third best prospect and 55th overall ranked prospect, Jarren Duran. Then on Sunday, the Angels recalled their top prospect and ninth overall ranked prospect, Brandon Marsh. Let’s dive into each of their statistical records and investigate their chances of fantasy success this year.
With five of the top 50 preseason top prospects now in the Majors, let’s review their performances and discuss their rest of season outlooks.
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.
Since 2013, I have polled you dashingly attractive readers on which group of pitchers you think will post the better aggregate ERA post all-star break. The two groups were determined based on ERA-SIERA disparity, pitting the overperformers versus the underperformers during the pre-all-star break period.
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.
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.
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.
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.
