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.
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.
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.
