Swinging strike rate, or the percentage of pitches that result in a swing and a miss, is one of the most powerful tools for any fantasy player to utilize when attempting to evaluate starting pitcher performances. The calculation of the metric is incredibly simple once you understand what it's measuring:
SwStr% = Swing and misses / Total pitches
Sports Info Solutions and PitchFX both track and provide the inputs for this metric and the stat has been publicly accessible for nearly a decade. The advent of swinging strike rate was a breakthrough for fantasy baseball analysts and those who project player performances because it was the first time we could begin quantifying how dominant a pitcher was relevant to others in the league. As such, swinging strike rate became the best metric we had for predicting and correlating a pitcher's raw strikeout rate. Case in point, the correlation between swinging strike rate and strikeout rate for all qualified starting pitchers in 2018 was an impressive 0.75, one of the highest correlations you will see between any two metrics in baseball!
So, now that we've established the importance of swinging strike rate, we can begin delving into ways to apply it to your fantasy baseball drafts. Given the accessibility of the stat and the strong correlation with strikeout rate, pretty much anyone can target "high strikeout pitchers" and luck into a pitcher who also has a high swinging strike rate. So how do we get an edge?
The answer is looking at the data in a different way than everyone else and by diving a layer deeper than most. Here are the three steps I took:
1) Analyze the data in a way that that's not commonly shown for SwStr% - first half and second half differences. By identifying the players that saw a surge (or a decrease) in swinging strike rate in the second half, I surmised that I will be able to better pinpoint the starters who might carry over that change into 2019.
2) Find the metrics that are strong leading indicators for swinging strike rate. Those leading indicators are four-seam fastball velocity and average spin rate, which showed to have a correlation of 0.52 with swinging strike rate for all qualified starting pitchers who threw more than 25% four-seam fastballs in 2018. If you're curious why I only included 4-seam fastball and their associated spin rates, check out these two great articles on MLB.com explaining spin rate (here and here).
3) Does the change in swinging strike rate translate to lower in-zone contact (z-contact%)? While z-contact% is more of a descriptive stat rather than an indicator stat, it's an important one to measure since it's explaining if the increase in swing and misses is translating to a change in the batted ball result from the at-bat and distinguishes if the increase swing and misses are due to better stuff (i.e. missed bats inside the strikezone) or simply due to players chasing pitches outside of the strikezone because of better movement, etc. The analysis of chase rate (o-swing%) out-of-strikezone contact (o-contact%) each could partially accomplish this goal, as well.
Here's a look at the leaderboard to see both aspects of my research:
Please note: When looking at the leaderboard above, be sure to consider the percentage of time the starting pitcher deployed his four-seam fastball before drawing any meaningful conclusions on a change in his four-seam fastball velocity. This is because the less the fastball is thrown, the less of an impact it would have had on his overall SwStr%.
The biggest limitation of my study is the sample size. Since the Statcast data is still relatively new, I wasn't able to extract it in an aggregate sample beyond the 2018 season. With that said, I have more confidence in my conclusions since the 1st half correlation and the 2nd half correlation between SwStr% and 4-seam fastball velocity + spin rate were 0.50 and 0.52, respectively, which is well within a reasonable expected variance of the full-season 0.52 correlation.