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Rick Lucks breaks down how to use Pitch Info data to identify pitching sleepers and busts as his series on making sabermetrics more accessible to fantasy owners continues.
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 in 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. Unlike many of the other metrics we've looked at in this series, Pitch Info data stabilizes (or becomes predictive) very quickly. As such, our examples will lean on 2020 data as opposed to 2019.
Let's look at how to effectively use this data to give you an edge in your fantasy baseball league throughout the season.
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Interpreting Pitch Info Data: Velocity
The first data point 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, Shane Bieber saw his average fastball velocity spike to 94.5 mph last season (93.4 mph in 2019). His K% soared as a result (41.1 K% vs. 30.2% in 2019), making him the best pitcher in the league (1.63 ERA, 2.04 xFIP) as opposed to a mere stud (3.28 ERA, 3.23 xFIP in 2019).
When evaluating a pitcher's velocity, you should always look at his baseline velocity as opposed to an arbitrary league average. Bieber's 94.5 mph isn't all that impressive by modern standards, but it clearly allowed him to take his game to a new level. Other variables like movement and location also matter, but velocity is a good introduction to using Pitch Info data.
Interpreting Pitch Info Data: Pitch Mix
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%, 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.
Let's return to Bieber as our example. He made two substantial repertoire changes as part of his huge year, and Pitch Info allows us to track the performance of each pitch. First, Bieber threw fewer fastballs (45.6% in 2019, 37.4% last year) and sliders (26.5% to 11.6%) in favor of more curves (20.4% to 26.3%). Bieber's velocity spike helped increase his fastball's SwStr% from 5 in 2019 to 8.4% last season, but his curve's 24 SwStr% still dwarves it. The switch would be expected to increase his strikeout totals.
Reducing his slider usage is more of a lateral move, as it beats the curve in SwStr% (27.8%) but loses in chase rate (40% vs. the curve's 47.4). Both are elite offerings, and featuring them in any combination will make Bieber a tremendous fantasy asset.
Astute readers may have noticed that Bieber's fastball and slider usage declined by more than his curve usage increased. The difference is made up by a brand new cutter thrown 16.2% of the time that dominated hitters in 2020 with a 17 SwStr% and 61.5 Zone%. It generates whiffs like a secondary pitch but gets Bieber ahead in the count like a fastball, making it the perfect weapon for nearly any situation.
The same type of analysis may be performed for a number of other stats, including BABIP, 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.
Interpreting Pitch Info Data: Pitch Results
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 30.6 in 2020, and most good wipeout pitches need to beat this number substantially. The overall Zone% was 41.2, including pitches like splitters in the dirt that were never intended as strikes.
The fastball will generally be inferior in results to pitches that do not need to live in the strike zone, 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 (sometimes) 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 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 straight four-seamer or a cutter to be the "zone pitch" in a pitcher's repertoire.
Personally, I look for fastballs with a SwStr% of around 9% and a Zone% of at least 53%. I then look for a wipeout pitch that offers a SwStr% of at least 17 and an O-Swing% of 40. Ideally, there is a secondary K pitch that prevents the 0-2 offering 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 managers, 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. If you would like more analytical tools to help you dominate your leagues in 2021, check out more articles here.
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