The other day I began pulling together Savant data to determine whether there was an ideal zone percentage for different types of curveballs (CUs) and sliders (SLs). I haven’t found much on that front yet. However, I did realize that I don’t really know what makes curveballs effective, both from a results standpoint (extra whiffs, weaker contact, etc.) or a trait standpoint (vertical break, horizontal break, velo). I took a look at all of these factors for the curveballs in the 2019 baseball season to see if anything stuck out.

I analyzed a sample of 214 pitchers, representing everyone from 2019 who threw at least 20 innings, a curveball at least 10% of the time, and qualified for Savant’s pitch movement leaderboard. From this sample I pulled info on every pitcher’s spin profile, wOBA, xwOBACON, zone percentage, SwStr %, and RHB/LHB splits. I even noted all that same info for the rest of their arsenal as well as just to have a full view. Then they were bucketed in every way imaginable with averages and standard deviations to see which ones stood out. I do want to preface all my findings by saying that the sample size is not ideal, as the buckets were mostly of decent size (roughly 100-plus players), but I did get granular at times (the smallest group was 48).

I am most focused on the following metrics: CU wOBA, CU xwOBACON, CU SwStr %, CU Drop & Tail (as a % difference vs. the average pitcher at similar velocity). Here are the averages across the entire sample: Read the rest of this entry »

With the 2019 season firmly in the books and the expanded offering of spin-related pitching data now readily available across the internet, I decided it was time to take a hard look at every team’s pitching staff. The hope in doing so was to identify a trend, if any, within the spin metrics of the best clubs. Do any staffs have a noticeable tendency to use pitchers with a specific spin profile?

To answer this, I pulled together every pitcher and their average spin metrics for each pitch type that they threw a qualified amount of times (30-plus in most cases) in 2019. This meant ignoring splitters because of sample size considerations. I was also tempted to use Bauer Units — a proxy for spin rate divided by velocity, as well as a nod to Trevor Bauer — to control for velocity in this study, but I decided to keep this post more straightforward. The study instead uses raw spin rate, horizontal and vertical movement, and spin efficiency as reported by Baseball Savant. I then aggregated the players’ data by the team they finished the season with to create an average spin profile for every team. This team profile weighs all of their qualified pitchers equally.

Once I was able to establish what the normal team looks like across those categories, I wanted to identify any clear outliers to possibly show where organizations consciously emphasized certain metrics. To do that, I produced league rankings and standard deviations for each category based on the team averages. Read the rest of this entry »