RADIO
In Part 12 of his fantasy baseball strategy series to make sabermetrics accessible to fantasy owners, Rick Lucks breaks down how to use Pitch Info data to identify pitching sleepers and busts.
One of the most fundamental questions in fantasy sports is if a player's current performance is sustainable. More than any other sport, baseball has a slew of statistical measures that can be dissected numerous ways to analyze player performance.
Pitch Info is a publicly available pitch tracking system that provides a lot of different data to help fantasy owners make this determination for mound breakouts and busts alike.
In this article, we'll look at how to effectively use this data to give you an edge in your fantasy baseball league throughout the season.
How to Interpret Pitch Info Data
The first data point, and the easiest to understand, is velocity. Generally speaking, a pitcher that loses fastball velocity is losing something to either an undisclosed injury or the aging process. Pitchers that gain velocity can expect to increase their production. For example, Mike Minor shifted to a relief role and increased his average fastball velocity from 91.8 mph over his career to 95 mph last season, striking out more batters (21.5% career K% to 28.7% last year) as a result. His overall effectiveness benefited immensely (2.55 ERA vs. 3.93 career).
The average major league heater was 92.8 mph in 2017, though of course a pitcher's established baseline is a better indicator of future performance. Other variables like movement and location also matter, but velocity is a good introduction to using Pitch Info data.
Slightly more advanced is pitch mix, or what pitches a pitcher throws and how often he throws them. A pitcher may improve his production by abandoning a poor pitch or developing a new, effective one. This is a good stat to consult if a pitcher sees a sharp change in his K% or BABIP, as a change in pitch mix could represent the change in approach that supports the new number. If the change does not have a corresponding pitch mix shift, it may be less sustainable.
For example, consider Robbie Ray. His K% increased last year relative to 2016, 28.1% to 32.8%. His BABIP declined in the same time frame, from .352 in 2016 to .267 last season. Are these numbers the result of random fluctuation, or did Ray change his pitch selection to bring them about?
Pitch Info tracks each pitch's individual results, so any change in pitch selection can be evaluated by comparing an offering's usage percentage and its performance, in this case SwStr% and triple slash line against.
The biggest change in Ray's pitch selection was that he threw fewer sinkers (from 19.4% to 3.6%) in favor of curves (5.5% to 20.5%) relative to 2016. Ray's sinker had a SwStr% of just 6.7% in 2016, so it wasn't generating many whiffs at all. Ray's curve posted an excellent 18.4% SwStr% last season, providing plenty of evidence that his K% surge was real.
Ray's curve also outperformed his slider when put into play. Ray's sinker was crushed in 2016 (.382/.437/.581), likely serving as the primary culprit for his elevated overall BABIP. By contrast, opposing batters could do virtually nothing with Ray's curve last year (.188/.259/.267). Ray's change in pitch mix seems to support his BABIP improvement too.
That said, there is a price to pay for everything. Ray's sinker was a strike more than half of the time in 2016, posting a Zone% of 52.6%. Ray's curve is almost never a strike (36.2% Zone%), relying instead on hitters chasing it out of the zone (38.7% chase rate). The result was fewer strikes and a higher BB% (10.7% vs. 9.2% in 2016). Still, the change was a net benefit for Ray's fantasy value.
The same type of analysis may be performed for a number of other stats, including FB%, LD%, GB%, and HR/FB. There is no point in looking at a league average pitch mix, as every pitcher owns a different arsenal. All of these variables may be considered over a pitcher's complete repertoire to determine how good he is (or should be) without relying on any conventional metrics. This can be good for identifying sleepers, as pitchers that have one or two standout pitches could break out by simply using them more often. Let's have some fun with our example and look at Clayton Kershaw's arsenal.
Kershaw threw five different pitches in 2017: a fastball 46.6% of the time, a slider 34.3% of the time, a curve 16.7% of the time, a sinker 1.2% of the time, and a change 1.2% of the time. The sinker and change were thrown 29 times each over the entire season, so they were probably recording errors or pitches that accidentally slipped out of Kershaw's hand. Regardless, the sample size is too small to consider them in this discussion, leaving three offerings for our analysis.
His fastball registered a Zone% of 55.6% last season, slightly better than average. It recorded a solid 6.6% SwStr% despite living in the zone, allowing batters to hit .255/.287/.455 against it. It was a good pitch, but not enough to make Kershaw the icon he is.
That is what the slider is for. It was only a strike 33.7% of the time, but compensated by making hitters chase it at a whopping 47.6% clip. That helped give it a SwStr% of 24.4%, absolutely obliterating the league's 10.5% SwStr% rate and explaining how Kershaw compiles so many Ks.
Kershaw also has a curveball. It was a strike slightly more often than the slider at 37%, but posted a lower O-Swing% of 38.7%. This gave it a SwStr% of 14.3%--very good, but inferior to Kershaw's slider. Why throw it?
Sometimes, hitters actually put the ball in play. Batters managed a triple slash line of only .149/.155/.327 against Kershaw's curveball in 2017, compared to .207/.258/.277 against the slider and .255/.287/.455 against the heater. All three are well above average, and Kershaw's arsenal is an embarrassment of riches if there ever was one. He's fun to look at, but he can't be a baseline.
What is the baseline for this type of analysis? It depends on the observer, as there are almost as many ways to interpret this data as there are data points to consider. The league average O-Swing% was 29.9% in 2017, and most good wipeout-type pitches need to beat this number substantially. The overall Zone% was 45%, including pitches like splitters in the dirt and high fastballs that were never intended as strikes.
The fastball will always be inferior in results to pitches that do not need to live in the strike zone, like Kershaw's slider, as pitches hit outside of the zone offer better results than offerings in the hitting zone when they are put into play. However, getting ahead in the count is necessary to make those pitches work as intended, making mediocre fastball results a necessity.
It is dangerous to generalize, but 2-seam fastballs and sinkers tend to stink for fantasy purposes. They're usually in the strike zone, but get hit harder than fastballs. They may post strong GB% rates, but also have high BABIPs and scary triple slash lines. Any sinker hit in the air was probably a mistake, so the HR/FB rate is usually high for the limited number of fly balls hit against them. Their SwStr% rates also tend to be poor. Overall, fantasy owners prefer a fastball or cutter to be the strike zone pitch in a pitcher's repertoire.
Personally, I like a fastball with a SwStr% of around 9% and a Zone% of at least 53%. Many pitchers succeed with a lower Zone%, but I can't stand watching walks. I then look for a wipeout pitch that offers a SwStr% of at least 15% and an O-Swing% of 40%. Ideally, there is a secondary K pitch, like Kershaw's curve, that prevents the 0-2 pitch from being too predictable. Only aces really fulfill all of these criteria, but I can dream, right?
Conclusion
To conclude, Pitch Info tracks a lot of data of interest to fantasy owners, including average velocity, pitch mix, and individual pitch results. All of this data may be used to predict who will break out or which breakouts can sustain their current performance. The next entry in this series will discuss another variable to consider when determining the potential of a pitcher's repertoire: spin rate.
